CHAPTER the relative brutality of heat-related problems.

CHAPTER 1
INTRODUCTION
1.1Introduction
This project is designed for probable visibility in foggy conditions. The main purpose of this project is to reduce accidents in fog and save human life. They are important because they help you to see through of the fog. It can be help pilots and drivers steer their vehicles at night and in fog it can be used in heavy rain to see the way clearly. Shown in (fig 1.1)
An infrared camera is a non-contact device that detects infrared energy(heat) and converts it into an electronic signal, which is then processed to produce a thermal image on a video monitor and perform temperature calculation.

Allow you to not only screen thermal performance, but also recognize and evaluate the relative brutality of heat-related problems. new innovations particularly detector technology, the incorporation of built-in visual imaging and automatic functionality.

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One of the innovations that were showcased at the 2005 Detroit auto show was the night vision systems in high end cars intended to improve driver safety for the duration of night time where there is insufficient illumination. This high technology safety option is probable to be available in the 2006 models of the Mercedes Benz S Class and BMW’s 7 series. The night vision systems use infrared sensors to let drivers see as much as three or four times farther ahead and help them quickly distinguish among objects. While night vision systems help reduce the chances of accidents, it is ultimately the driver’s responsibility to recognize obstacles in the road.1
In order to reduce traffic accidents involving animals, which is a major concern in worldwide traffic, Autoliv has developed a state-of-the-art vehicle mounted night vision animal detection system. The system is currently used by Audi, BMW and Daimler. The main contributions of this paper include: world’s first vehicular animal detection system to reach the customer market, an efficient classification approach based on a cascade boosting concept which is robust to occlusion, pose and scale variations, a large database of thousands of hours of far infrared (FIR) video data recorded worldwide including several hundred thousand example images of animals in traffic situations, a tracking approach to handle animal movement and estimate animal states, a validation approach to efficiently reduce the number of false detections and human-machine-interface (HMI) and warning concepts to highlight animals at risk of collision. The presented system detects animals up to 200 meters away from the car while generating very few false warnings. For animals that are considered a potential danger, advanced HMIs such as marking lights which actively illuminates the animals are applied, giving the driver the quick and accurate information he or she requires. The Autoliv night vision animal detection system is complementary to currently used methods for preventing accidents with animals. By using it, the driver is given all opportunities to react to dangerous situations and to avoid potential accidents.2
A night vision system must increase visibility in situations where only low beam head lights used today. As pedestrians and animals have highest risk increase in night time traffic due to darkness, the ability of detecting those objects should be the main performance criteria, and the system must remain effective facing the headlights of the oncoming vehicle. Thus BMW has applied the Night vision system in its vehicles to avoid accidents which can cause the deaths. In the paper, the performance of night vision camera (NVC) is evaluated. The night vision camera is produced by Autoliv for BMW. The paper results not only depended knowledge on performing NVC, but also the understanding the complex product that involves knowledge about engineering involved in it. An Automotive night vision system is system to increase the vehicle driver’s perception and seeing distance in darkness or poor weather beyond the reach of vehicles head light. They are currently offered as optional equipment on certain premium vehicles the camera is connected to the front grill of automotive. It senses the images in the form of electronic signal and then sends it via cable to the LCD screen which helps the driver for his convenience.13
Traffic accidents are the major causes of accidental death of pedestrians in the country. we intend to develop a visual system which detects pedestrians at night to reduce the risk of driving. Infrared imaging is more desirable in detecting human individual under dark conditions. 14

Fig 1.1: road view in camera
1.2Fog
A thick cloud of tiny water droplet suspended in the air at or near the earth surface which obscure or restrict visibility (to a greater extent than mist strictly, reducing visibility to below 300 meter.

1.2.1Fog classification
Fog is a visible collective of minute water droplets suspended in the air at or near the surface of the earth. When air is almost flooded with water vapor, this means that the relative humidity is close to 100%, and that fog can form in the existence of a sufficient number of reduction nucleus, which can be smoke or dust particle. There are different types of fog. Advection fog is formed through the mixing of two air sufficient with different temperatures and humidity. Another form is radiative fog. This is formed in a process of radiative cooling of the air at temperatures close to the dew peak. Some fogbanks are denser than others because the water droplets have grown bigger through increase. In fog conditions droplets can take up more water and grow greatly in size. The question whether scattering is less in the IR waveband compare to the visible range depends on the size distribution of the droplets. There are different ways to classify fog. An often-used classification is the one used by the International Civil Aviation Organization (ICAO). According to this system, fog can be classified in 4 categories.

visual range 1220 meters
visual range 610 meters
visual range 305 meters
visual range 92 meters.

1.3Low visibility weather condition
Due to fog and heavy rain, visibility space is reduced which results in increased speed clash that enhance the risks of crash. In US every year, more than 38,700 vehicle crashes occur due to fog and around 600 people are killed and more than 16,300 people are injured in such crashes every year 3.

According to the report of Federal Highway Administration, from 2002 to 2012, around 1.3 million vehicle crashes every year due to weather conditions within the U.S. Focusing on fog related accidents only, the annual average is 31,385 crashes, 511 deaths and 11, 812 people were injured in over 500 road accidents in U.S. 4.

As analyzed by Booz Allen Hamilton and based on NHTSA data, from 2005 to 2014, around 28,533 crashes occurred due to fog, in which, 10,448 persons were injured and 495 persons were
Killed 5.

1.4Problem of Statement
People are facing problems in everywhere to heavy fog and low visibility level.

Another problem is due to fog lots of accidents occur in winter.
1.5ObjectiveEnable to see the road in fog or smog.

To reduce the accidents.
1.6Scope of Project
In todays time, fog on roads are definitely dangerous as they block visibility for driver and the drivers coming your way, Fog is formed at the surface of the earth, which is made of irrelevant water droplets suspended in air. The bigger problem with fog is that it will reduce visibility for up to ¼ km depending on the weather conditions, especially in the winter season. On average you are more possible accident when you’re driving in fog compare to any other weather condition with much worse injuries caused to the driver and passengers.
To decrease accidents in a foggy weather, we have created a device that can positively decrease the chances of having an accident on road. The device can fully recognize vehicles coming its way and inform the driver by screen. More importantly the device can be bought online open market for an economical price.
The device will clearly recognize any cars. Even in the worst weather the device is able to record the view up ahead, stored in SD card.

1.7Project expectations
Superior fog and smoke resistance performance
More visibility
Reduce accident ratio

1.8Methodology
The proposed approach for the escorting project is presented as follows.

In this project we use raspberry pi to connect all devices.

Camera connects with raspberry pi via ribbon.

LCD is also connecting with raspberry pi Its use as an output device. LCD gives us the display and gives us the camera out video.

Camera fixed on the front grill or front glass.

IR lights are connecting with fog lights connection. We can fixed it on car headlights. When we on the fog lights the IR lights is also on. IR LEDs allow for cheap, efficient production of infrared light, which is electromagnetic radiation in the 700 nm to 1mm range.

IR camera catch the electromagnetic radiation and show the way clearly.

With the help of LCD we see the way view in screen.

Similarly, we can avoid some accidents.

1.9LITERATURE REVIEWLiterature review describes some overview of fog, visibility in fog, anti fog, thermal image, night vision. Many researchers have done several studies regarding these issues. In fact, this issue will be discussed in detail in the following sub topics.

1.9.1Literature survey
This paper describes the development and validation of an automatic defogging system control. This system consists of an auto defog sensor, an independent actuated defrost door flap toward windshield glass and a control head. The sensor signal is calibrated within 2%RH tolerance according to the windshield glass temperature. A fog probability (FP) value is suggested to indicate the likelihood of fogging for more sophisticated actions. Anti-fog control strategies are established on some practical requirements to be applied in actual vehicles.6
Modern vehicles are equipped with many cameras and their use in many practical applications is extensive. Detecting the presence of fog from images of a camera mounted in vehicles is a very challenging task with the potential to be used in many practical applications. Approaches introduced until now analyze properties of local objects in the image like lane markings, traffic signs, back lights of vehicles in front or head lights of approaching vehicles. By contrast to all these related works we propose to use image descriptors and a classification procedure in order to distinguish images with fog present from those free of fog. These image descriptors are global and describe the entire image using Gabor filters at different frequencies, scales and orientations. Our experiments demonstrated height potential of the proposed method for fog detection on daytime images.7
In this paper, a rescue methodology is proposed using a Hexarotor UAV. The aerial vehicle is used for scanning the roads in foggy weathers by using thermal sensing camera fixed on it. The drone will escort the police cars and/or car-ambulances by scanning the road ahead of them, and navigating them to reach their destinations safely and quickly despite the poor visibility caused by the fog. The main purpose of this work is to reduce the number of accident fatalities caused in foggy weather days by helping the rescue services to arrive quickly to the accident location. The project is implemented and flight tests are conducted and presented in this paper.8

CHAPTER 1
INTRODUCTION
1.1 Preamble
India is one of the agriculture based fastest growing country in the world. some of major crops grown in India are maize, wheat and rice etc. Maize is considered a promising option for diversity agriculture in upland areas of India. It now ranks as third most important growing crop in India. It is important to detect and manage nitrogen level in plants; Nitrogen is one of the nutrients which play an important role in yield of crops. Leaf normally contains 1% to 5% nitrogen level by weight. So we propose an approach for identification of nitrogen content in maize leaf using image processing technique.
The present way to find nitrogen content in leaves is Kjeldahl method. This method is also known as Nitrate test (chemical test method). This consumes time of about 96 hours, manpower, and is also costlier. To overcome these shortcomings the proposed system will find out the nitrogen content in the leaves using image processing technique in lesser time and lower cost.
Estimation of nitrogen content in maize leaf using image processing technique is the aim of this system. We use regression modelling to find the correlation between various image features and nitrogen content of leaves got from laboratory test results. Regression model is a statistical technique to determine the relationship between two or more variables. Regression is primarily used for prediction and causal inference. It is important to recognize that regression analysis is fundamentally different from ascertaining the correlations among different variables. Correlation determines the strength of the relationship between variables, while regression attempts to describe that relationship between these variables in more detail.
1.2 Overview of Chemical Analysis Method (Kjeldahl)
The Kjeldahl strategy is utilized for quantitative determination of nitrogen in synthetic substances and was created by Johan Kjeldahl. At first a test leaf of 0.2 g was broiler dried for 72 hours and after that legitimately squashed 2. The specimen was then blended with 5 ml H2SO4 (Hydrochloric Sulphuric Acid) in the nearness of K2SO4 (Potassium Sulphate) and CUSO4 (Copper Sulphate) and afterward warmed in the processing jar on the radiator for 4 hrs. Warming the substance with sulphuric acid separates the characteristic nitrogen to ammonium sulphate. In this step, potassium sulphate was added to increase the boiling point of the medium (from 337? to 373?). The arrangement was refined with sodium hydroxide (included little amounts around 10 ml) to change over the ammonium salt into ammonia. The measure of alkali present (henceforth the measure of nitrogen present in the example) was controlled by back titration. The end of the condenser was plunged into a solution of hydrochloric destructive or sulphuric acid.The ammonia reacts with the acid and the excess acid was then titrated with a sodium carbonate arrangement with a methyl orange pH pointer. Kjeldahl nitrogen results are calculated using this formula:
N% Sample Concentration-Blank concentrationSample WeightX Volume made (1) 1.3 NDVI (Normalized Difference Vegetation Index)
It is a numerical marker that uses near infrared region of the electromagnetic spectrum to observe whether the target contains live green vegetation or not. The light that plants absorb or not can in roundabout way quantify the measure of nitrogen the plants have. NDVI can be gained from satellite picture which may have high cost and not sensible for choosing nitrogen in little range. Contains various examples, it is to a great degree dreary.

1.4 SPAD Meter
It is chlorophyll measurement device. It is used by clipping the meter head on the leaf, the meter measures transmission of 650 nm red light which chlorophyll absorb it and transmission of 940 nm infrared light which chlorophyll does not absorb.The meter gives the relative value that has no unit but since the nitrogen quantity will vary with the quantity of chlorophyll, there are many research studies on converting SPAD value to nitrogen quantity in plant so the meter can be used to measure nitrogen quantity in plant too. This method suitable for medium area because of measuring nitrogen in 1 leaf, the sample leaf must be measured by SPAD meter at least 5 times and then find the average value. For a large area which contains many samples, it is very time-consuming.
Leaf Color Chart
It is a chart that’s consists of many shades of leaf color from light green to dark green. It is used by comparing the leaf colour with the colour in the chart. The chart will give a range of nitrogen quantity possible in that leaf. Different crop species requires different chart. This is the easiest but also the least accurate method and it is suitable for medium area.
The chemical test method takes too much time, NDVI method costs too much money from satellite image, leaf color chart are not accurate enough and SPAD meter is hard to use and takes too much time when using with sugarcane due to the height of sugarcane and crowded leaves as it grow.
1.6 Motivation for the project
The motivation of the project is:
Its various practical application and need to the farmer and agriculture society.

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Estimation of nitrogen content in leaves based on color and texture.

1.7 Problem Statement
To design and implement an efficient method for “Estimation of Nitrogen Content in Maize Leaves using Image Processing Technique”. Here, the input is an image of a maize leaf and output is percentage of nitrogen in the leaf. Estimation of nitrogen content in leaves by extracting the features like entropy, mean, average energy and variance using image processing techniques and regression modelling.

1.8 Scope of the project
The proposed approach can be used in several situations:
Automatic and faster nitrogen content estimation method in a plant based on leaf colour and it would be helpful for farmers.

Explore the possibility of determining the quality and quantity of crop yield by using texture and colour features.

1.9 Objectives of the project
The main objectives of the proposed system are :Estimation of nitrogen content based on colour and texture.

Proposing a method that is efficient and gives fast result.

1.10 Review of Literature
Vasudev B. Sunagar et al have proposed image processing technique for estimation of nitrogen content in leaves based on color and texture. The image is acquired using digital camera having high resolution. The camera is located at a position normal to the object. After getting the image, pre-processing technique is applied to remove the noise of the source image. The color and texture characters of maize leaves are extracted. Color characters analysed using RGB and HSI model. Texture features are entropy, energy, contrast and homogeneity. A relationship between extracted features and nitrogen content is developed.

V.K.Tewari et al 2 have proposed image processing technique using Regression Model for estimation of nitrogen content in paddy crop based on color. They have taken various color features such as R, G and B normalized ‘r’ and normalized ‘g’. Regression models were developed and evaluated between various image features; they have observed minimum and average accuracy. Actual and predicted values of nitrogen percent were linearly correlated with R2 value; this showed that the plant nitrogen content can be successfully estimated by its color image feature.
Piti Auearunyawat et al have proposed image processing technique using Regression Model for estimation of nitrogen content in sugarcane leaf based on color. Here, sugarcane leaf images are captured by a portable camera and then relationships between nitrogen content and leaf colors in red (R), green (G), blue (B) and near infrared (IR) are examined. They found that of the terms R, G, B, G/B, G/R, R/B and ((IR-R) / (IR+R)) had the significant relationship with nitrogen concentration in the sugarcane leaves.

M. M. Ali et al 4 have proposed image processing technique using Dark Green Color Index (DGCI) for estimation of plant chlorophyll and nitrogen contents in tomato leaf based on color. In this paper they have developed a non-destructive approach to detect plant Ch (Chlorophyll) and N levels using an image processing technique using the RGB (Red, Green and Blue) colour model. The experiment was conducted on tomato (Tommy Toy) in field with three N treatments (nitrogen application), where images leaf were collected using a camera. The algorithm achieves better correlation with the value of Ch and N that are-, measured in laboratory, compared with the existing non-destructive methods of SPAD 502 and Dark Green Colour Index (DGCI).

Asaram Pandurang Janwale et al 5 have proposed image processing technique using color model for estimation of nitrogen contents in cotton (Gossypium Hirsutum L) based on color. This paper reviews these different techniques used to detect Nitrogen deficiency in cotton plant and conclude image processing techniques using color models is the best technique to detect deficiency in cotton plant easily, inexpensively and more accurately.

Santosh S.Lomte et al 5 have proposed shading analysis; this study was used to develop a non-destructive procedure for checking cotton advancement and N status using an electronic camera. Electronic pictures were taken of the cotton covers amongst advancement and full grow. The result showed that the best relationship between safe house spread and over-the-ground hard and fast N content had a 2 estimation of 0.926 and a RMSE estimation of 1.631 gm. In this study 2 distinctive segments of picture of leaf were deleted, for instance, the condition of leaf, scope of leaf, condition of crevices present on the leaf, disease spots et cetera. These segments were removed using differing picture planning strategies. The segment extraction was the key motivation behind this work. These isolated components were used to choose the occasion of the particular deficiency related to key supplements of cotton plant. Nitrogen deficiency perceived by two preliminary strides, Histogram examination and estimation of leaf locale.
1.11 Organization of Report
The project report is organized as follows:
Chapter 2- System Requirements Specification
This chapter presents a brief description of the Matlab software used in project along with other software, hardware requirements.

Chapter 3- High Level Design
This chapter presents a brief discussion about the high level design of proposed system and description about texture features like entropy, mean, average energy and variance.

Chapter 4–Detailed Design
This chapter presents the Flow Chart for estimation of nitrogen content in maize leaves.
Chapter 5–Implementation
This chapter contains pseudo code of the proposed work.
Chapter 6–System Testing
This chapter deals with software test environment, test procedure and test cases for each module.

Chapter 7–Results and Discussion
This chapter gives the experimental results and analysis of the proposed system and process.

Chapter 8–Conclusion and Future work
This chapter gives the conclusion of the project and the future enhancement.

1.12 Summary
This chapter introduces overview of estimation of nitrogen content in section 1.1. Overview of chemical analysis method (Kjeldahl) is given in section 1.2. NDVI (Normalized Difference Vegetation Index) is explained in section 1.3. Section 1.4 briefly explains about SPAD meter. Explanation of the leaf color chart is given in section 1.5. Motivation of our project is mentioned in section 1.6. Problem statement of our project given in section 1.7. Scope of the project is listed in section 1.8. The objectives are clearly mentioned in section 1.9. The literature survey is described in section 1.10 and organization of the report is given in section 1.11.

CHAPTER 2
SOFTWARE REQUIREMENT
SPECIFICATION (SRS)
This chapter describes an overview and specification of Matlab programming language which is used for implementation of proposed work. It also represents other related requirements for normal operation of Matlab and the project. The SRS contains functional and non-functional requirements for solving the problem. A best specification gives complete statement of what the system is to do and is used as the basis for system design.
2.1 Specific Requirements
Matlab Tool
MATLAB, which initially stood for MATrix LABoratory, has developed into a state-of-the-art mathematical software package, which is used extensively in both academia and industry. It is an interactive program for numerical computation and data visualization, which along with its programming capabilities provides a very useful tool for almost all areas of science and engineering. It is one of the leading software packages for numerical computation. MATLAB program and script files have filenames ending with “.m” and also supporting “.p” but our project developing based on “.m” file10. The programming language is exceptionally straightforward since almost every data object is assumed to be an array. Graphical output is available to supplement numerical results.
Optimization Toolbox Product Description
Optimization Toolbox provides functions for finding parameters that minimize or maximize objectives while satisfying constraints. The toolbox includes several solvers for linear programming, mixed-integer linear programming, quadratic programming, nonlinear optimization, and nonlinear least squares (Curve fitting), graphical user interface 10. One can use these solvers to find optimal solutions to continuous and discrete problems, perform trade-off analyses, and incorporate optimization methods into algorithms and applications.

Curve Fitting Tool Box
Curve Fitting Toolbox provides functions for fitting curves and surfaces to data. The toolbox lets you perform exploratory data analysis, pre-process and post-process data, compare candidate models, and remove outliers. One can conduct regression analysis using the library of linear and nonlinear models provided or specify your own custom equations.  The library provides optimized solver parameters and starting conditions to improve the quality of your fits. The toolbox also supports nonparametric modelling techniques, such as splines, interpolation, and smoothing.

Image Processing Tool Box
Image Processing Toolbox provides a comprehensive set of reference-standard algorithms, functions, and apps for image processing, analysis, visualization, and algorithm development. One can perform image analysis, image segmentation, image enhancement, noise reduction, geometric transformations, and image registration.

2.2 Minimum Hardware Requirements
Processor : Minimum Dual core Pentium 4
RAM : Minimum 2GB
Hard disk size : 40GB
2.3 Software Requirements
Operating System: Windows 7 and above
Programming Language: MATLAB 2014b 64bit
2.4 Summary
This chapter describes about the particular prerequisites in section 2.1. Least equipment necessities for the project is given in section 2.2. Programming prerequisites are given in section 2.3.

CHAPTER 3
HIGH LEVEL DESIGN
High level design provides an overview of the system flow. It gives more information for the user to understand the logic. Here we see the basic knowledge about the system design in flowchart. Following are the issues that are the primary modules for the design.
3.1 Proposed System
The proposed system framework contains the procedures to collect the sample images of maize leaf using camera under different illuminations (i.e., natural day light, fluorescent light ; incandescent light), image pre-processing, prediction of plant nitrogen content using regression model.

Sample Collection
Collect the sample of maize leaves. Capture the image of collected samples under the different uniform illumination (Day Natural light, Fluorescent light and Incandescent light) with a dark black background. To capture images a 13 mega pixel Sony digital camera is used. Nitrogen content of collected leaves is calculated in laboratory using Kjeldahl method which becomes the reference for development of proposed system.
Apply Pre-processing Techniques
Outside interference will cause a variety of noise in the process of image acquisition, which will significantly affect the quality of the image 1. So it is needed to pre-process the image, such as removing noise and enhancing image.

The noise of captured image often shows as the mutation of the isolated pixel in the image which is called as grain noise. Grain noise show high frequency characteristics and it has great gray difference. Also the spaces are not interrelated 5. The commonly used methods of smoothing were the median filter, neighbourhood mean, spatial low pass filter and frequency low pass filter. In this study, we use the median filter.

Gray Level Co-occurrence Matrix
A GLCM (Gray Level Co-occurrence Matrix) is a square matrix which contains of the same number of rows and columns as the number of gray levels in an image 1. Each matrix element represents the relative frequency with which two pixels, separated by a pixel distance (?x, ?y) occur within a given neighbourhood for the reliability of the statistical estimate 1, the matrix must contain a reasonably high level. To achieve this either the number of gray level values is reduced or a larger window is used. A compromise of the two approaches is generally used. Properties of GLCM are used for texture feature extraction.
3.2 Texture Analysis
Texture features like Entropy, Mean, Average energy and Variance are extracted from the leaf image along with color features.
Entropy
E, a scalar value representing the entropy of gray scale image I. Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image.
E=ip*(log2(p)) (1)
Where p is a pixel gray scale value.Mean
The mean is the average of the numbers: a calculated “central” value of a set of numbers.
x=xN (2)
Where:
x is the mean of the image.

x is the sum of the pixel values of an image.

N is the total number of pixel in the image.
Variance
It is an estimation of the spread between numbers in a data set. It measures how far each number in the set is from the mean. Variance is computed by taking the difference between each number in the set and the mean, squaring the differences and dividing the sum of the squares by the number of values in the set.
S2 = i=1n Xi- Xavg2n-1 (3)
Average Energy
Need to find the average energy, of pixels in an image or a window.
AE=i.jp(i,j)2 (4)
Returns the sum of squared elements.
Correlation
Correlation is a statistical measure that indicates the extent to which two or more variables fluctuate together. A positive correlation indicates the extent to which those variables increase or decrease in parallel; a negative correlation indicates the extent to which one variable increases as the other decreases.

Correlation is calculated using the formula below:
C=i,j(i-?i)(j-?j)p(i,j)?i?j (5)
Where:
? is the mean
? is the variance
p is the pixel
i,j is the pixel co-ordinate values
Returns a measure of how correlated a pixel is to its neighbour over the whole image. Range = -1 1, Correlation is 1 or -1 for a perfectly positively or negatively correlated image.
3.3 Color Features
Color features such as RGB and HSV are extracted from a leaf image.
RGB (Red Green Blue)
RGB refers to a system for representing the colors to be used on a computer display. Red, green, and blue can be combined in various proportions to obtain most colors in the visible spectrum.

HSI (Hue Saturation Intensity)
HSI color system directly describes the light colour by brightness (or lightness), tonality and saturation, which are suitable for color description by humans 1. It is easy for human visual system to distinguish different tonalities, but it is difficult to distinguish different color through brightness and saturation. In this system, H is defined as hue or tonality; I and S are defined as light intensity and saturation respectively. For the above characteristics correlation model is developed to analyse the dependency of nitrogen over the characteristics.
3.4 Normalization
Normalization is the process that changes to the range of pixel intensity values. NormalizedRed= RR2+G2+B2(6)
NormalizedGreen= GR2+G2+B2(7)
Estimation of Nitrogen
The nitrogen content of an image can be estimated by using the following steps
Linear fit using straight line equation based on regression model
Estimate the nitrogen content in maize leaves using regression model
Summary
This chapter explained about proposed system in section 3.1. Explained about texture analysis in section 3.2. Review of the color features is given in section 3.3. Normalization is depicted in section 3.4 and steps for estimation of nitrogen are given in section 3.5.

CHAPTER 4
DETAIL DESIGN
Figure 4.1 shows the flow chart for estimation of nitrogen content, the steps of the estimation process is explained later in this chapter.
4.1 Flow Chart of the Proposed System for estimation of nitrogen content
Start
Read input image from source
Pre-Processing image
Convert the RGB image into R, G and B three planes
Extract R, G and B components
Segment R, G and B component using thresholdingStop
Estimate the N Content using Regression model

Figure 4.1: Flow Chart of the Proposed System for estimation of nitrogen content.
Figure 4.1 shows the flow chart for the estimation of nitrogen content. The flow chart depicts following procedure:
The image is obtained through digital camera using different light sources such as day natural light, fluorescent light and incandescent light. The camera is located at a position normal to the object. Subsequent to getting the image, pre-processing techniques are used to remove the noise in source image. Then convert the RGB image into R, G and B images considering a segment R, G and B part utilizing thresholding techniques 2.Then apply regression model to estimate the plant nitrogen content.
4.2 Estimation of leaves nitrogen content using Regression model.

Start
Read input image from source
Pre-Processing image
Convert the RGB image into Gray
Obtained normalized ‘r’ ‘g’ ; ‘b’ from gray image
Extract the image features
Stop
Correlate between each features ; actual nitrogen content
Select the best Correlated feature
Estimate ‘N’ content using straight line equation

Fig 4.2 Estimation of leaves nitrogen content using Regression model.

Regression mels were developed between image feature and the plant nitrogen content, estimated by chemical analysis with Kjeldahl method. After pre-processing of the image, they were converted into the R, G, B, normalized ‘r’ and normalized ‘g’ image. Various image features were calculated such as mean, variance, average energy and entropy. After features calculation, they were compared with the actual lab values to develop a regression model to get a straight line equation. Then we take feature value and multiply it with slope value (m) of the regression line then add the y intercept constant value to get the estimated nitrogen content of that sample.
4.3 Summary
This chapter explains about the flow chart of the proposed system for estimation of nitrogen content in section 4.1. Explains about estimation of leaves nitrogen content using regression model in section 4.2.CHAPTER 5
IMPLEMENTATION
Implementation is the phase where the system goes for actual functioning.
5.1 Programming Language Selection.
5.1.1 Key Features of Matlab are:
Functions for integrating MATLAB based algorithms with external applications and languages such as C, Java, .NET, and Microsoft Excel.
Tools for building applications with custom graphical interfaces.

Development tools for improving code quality and maintainability and maximizing performance.

Built-in graphics for visualizing data and tools for creating custom plots.

Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, numerical integration, and solving ordinary differential equations.

High-level language for numerical computation, visualization and application development.

Interactive environment for iterative exploration, design, and problem solving.

5.1.2 MAT LAB GUI (Graphical User Interface)
A graphical user interface (GUI) is a graphical display in one or more windows containing controls, called components that enable a user to perform interactive tasks. The user of the GUI does not have to create a script or type commands at the command line to accomplish the tasks. Unlike coding programs to accomplish tasks, the user of a GUI need not understand the details of how the tasks are performed. GUI components can include menus, toolbars, push buttons, radio buttons, list boxes, and sliders just to name a few. GUIs created using MATLAB. Tools can also perform any type of computation, read and write data files, communicate with other GUIs, and display data as tables or as plots.
5.2 Pseudo codes for pre-processing procedures
Pseudo code for picture obtaining and showing
Input image obtained from source or assumption database and shows the original image.
imread(‘URL’);//Read the image from source URL
figure(); //display the window
imshow();//shows the original image
Pseudo code for smoothing a given information picture
Smoothing, likewise called obscuring, is a straightforward picture preparing operation. In this we will concentrate on smoothing keeping in mind the end goal to diminish noise.
for()//Looping
sm(:,:,i)=medfilt2(a(:,:,i),);//Applying median filter for //smoothing purpose
end//Loop end
Pseudo code for smoothing of leaf boundary
In this section we are utilizing morphological operation in light of the fact that dialet and erosion because noise removing from smoothing image.
imMask=imerode();//Morphological operation
imMask=imdilate();//Dilate the image
5.3 Pseudo code for Image Feature Extraction
Pseudo code for Color Components
To acquired color component like Red, Green and Blue from given input image
R=im1(:,:,1);//Extracting R component
G=im1(:,:,2);//Extracting G component
B=im1(:,:,3);//Extracting B component
Pseudo code for Normalization
Normalization means RGB image converted into three plane like R,G and B.

Normalized Red, Normalized Green, Normalized Blue=normal () //Normalization
Image_rgb1 = double();//Precision value setting
Image_rgb = imresize(, ); //find the image resize
Image_red = Image_rgb1(:,:,1);//Red plane
Image_green = Image_rgb1(:,:,2);//Green plane
Image_blue = Image_rgb1(:,:,3);//Blue plane
for y = 1:row //number of rows in image
for x = 1:col //number of columns in the image
Red = Image_red(y,x);//x and y values of Red component
Green = Image_green(y,x);// x and y values of Green //component
Blue = Image_blue(y,x); //x and y values of Blue component
NormalizedRed = Red/sqrt(Red^2 + Green^2 + Blue^2);//Computing the normalization red.

NormalizedGreen = Green/sqrt(Red^2 + Green^2 + Blue^2); // Computing the normalization Green.

NormalizedBlue = Blue/sqrt(Red^2 + Green^2 + Blue^2);
// Computing the normalization Blue.

Image_red(y,x) = NormalizedRed;
Image_green(y,x) = NormalizedGreen;
Image_blue(y,x) = NormalizedBlue;
End//loop is end
End//loop is end
Image_rgb1(:,:,1) = Image_red;
Image_rgb1(:,:,2) = Image_green;
Image_rgb1(:,:,3) = Image_blue;
Pseudo code for Converting RGB to Gray
In this section we are converting RGB to Gray image because easy to find image feature values like average energy, entropy, mean and variance.

gI = rgb2gray();//Converting RGB to Gray
Pseudo code for Average Energy
averageenergy = graycomatrix(gI);// co-occurrence matrix //syntax finding a normal from dark image
stats = graycoprops(averageenergy,{‘Energy’}) //calculates the statistics //specified in properties from the gray-level co-occurence matrix glcm.
Pseudo code for Entropy
E = entropy(I) returns E, a scalar value representing the entropy of gray scale image gI. Entropy is a statistical measure of randomness that can be used to characterize the texture of the input image.
e=entropy(gI);//entropy sentence structure
Pseudo code for Mean
The mean is the average of the numbers
m = mean2(gI); //Mean syntax Matlab
Pseudo code for Variance
An estimation of the spread between numbers in a data set. It measures how far each number in the set is from the mean. Variance is computed by taking the difference between each number in the set and the mean, squaring the qualifications and dividing the sum of the squares by the number of values in the set.
variance = var(double(im2(:)))// Variance syntax in matlab
Pseudo code for Nitrogen Calculation Part using Regression model
X = ; //Where ‘x’ indicates feature value like normalised ‘r’.
m;// m is a slope value
c;// c is a constant value
n= (x.*m) + c;//Estimate the ‘n’ content
disp(n); // display the n value
y= ; //Where ‘y’ indicates actual nitrogen content
error = y-n; //Subtract between actual nitrogen content and //estimating ‘n’ content. After subtract we got error //on each sample.
Pseudo code for Nitrogen Calculation Part using HSB (Hue Saturation Brightness)
R=double(im3(:,:,1));
G=double(im3(:,:,2));
B=double(im3(:,:,3));
R_av=mean(mean(R))/255; //Average value of R
G_av=mean(mean(G))/255;// Average value of G
B_av=mean(mean(B))/255; //Average value of B
maxi = max(max(R_av,G_av),B_av); // extract the maximum //average value
mini = min(min(R_av,G_av),B_av); //extract the minimum average value
if maxi == R_av H=((G_av-B_av)/(maxi-mini))*60;
end if maxi == G_av H=(((B_av-R_av)/(maxi-mini))+2)*60;
end if maxi == B_av H=(((R_av-G_av)/(maxi-mini))+4)*60;
end S=(maxi-mini)/maxi;
B=maxi;
N1 = ((H – 60)/60 + (1 – S) + (1 – B))/3;// Nitrogen Content in the Image
fprintf(1,’Nitrogen percentage =%f
‘,N1)
NP=mean(mean(N1))//mean of nitrogen content
5.4 Pseudo code for Computing R2 from Polynomial Fits
X = ;// X stores the feature values
Y = ; // Y stores the actual nitrogen content values
p = polyfit(X,Y,1)// Use polyfit to compute a linear regression //that predicts y from x
result yfit://Result of yfit Yfit = p(1) * X + p(2);// Using polyval saves you from typing the fit //equation
Yresid = Y – Yfit;// Compute the residual values.

SSresid = sum(Yresid.^2);// Square the residuals and total them //to obtain the residual sum of squares
SStotal = (length(Y)-1) * var(Y);// Compute the total sum of //squares of y by multiplying the variance of y by the number of observations minus 1
rsq = 1 – SSresid/SStotal// Compute R2 using the formula
5.5 Summary
This chapter presents pseudo codes for estimation of nitrogen content and image features extraction. Here briefly explains about programming language in section 5.1. The pseudo codes for pre-processing procedures are depicted in section 5.2. Pseudo code for image feature extraction is outlined in section 5.3. And pseudo code for computing R2 from polynomial fits given in section 5.4.

CHAPTER 6
SYSTEM TESTING
Software testing is essential for correcting errors and improving the quality of the software system. The software testing process starts once the program is written and the documentation and related data structures are designed. Without proper testing or with incomplete testing, the program or the project is said to be incomplete.

Testing is an important phase in the development life cycle of the product; this is the phase where the error remaining from all the previous phases will be detected. Hence testing plays a very critical role for quality assurance and ensuring the reliability of the software. During the testing, the program will be executed with a set of test cases and the output of the program for the test cases will be evaluated to determine whether the program is performing as expected. Errors which may be found during the testing will be corrected by using the following testing steps and correction will be recorded for future references. The testing stage has the following goals:
To maintain the quality of the project.

To find and eliminate any residual errors from previous stages.

To validate the software as a solution to the original problem.

To provide operational reliability of the system.

During testing the major activities are concentrated on the examination and modification of the source code.

6.1 Software test environment
In this section, the programs that made up the system were tested. This is considered as functional testing. During the development process itself all the syntax errors got rooted out. For this we developed test case that results in executing every instruction in the program or module i.e. every path through the program was tested.

Preparation of test data plays a vital role in the system testing. In this section we present some test images which we used to examine the system. We consider test images of three illuminations for testing as shown in table 6.1

Table 6.1: Different illumination of sample images is considered for testing.

SI no. Illumination types Number of images
1 Day natural light 100 images
2 Fluorescent light 100 images
3 Incandescent light 100 images
6.2 Test procedures
Test cases are the key factors to the success of any system. Performance of a system is analysed based on the cases written for functional performance of a system. Test cases are critical for the successful performance of the system. Test procedures will be command procedures which in general will do the following:
Compile ; link reference programs.

Execute reference program with test input data.

6.3 Functional test cases
6.3.1 Test cases for grade based on illumination.

Testing focuses on functional task of the system. Here the results obtained manually from Agriculture University that the results we are compared with the computed results obtained from the program. Some test results are mentioned in table 6.1 and all other test results are represented in results and analysis section. Here we compared three illumination images, and then we are deciding which illumination is correctly matched to actual results.

Sl.noIllumination image Test status
1
Day Natural Light
Accepted
2
Fluorescent Light Accepted
3
Incandescent Light Rejected
6.3.2 Test cases for prediction results and manual results on leaves.

Test case Illumination Type Actual results or Chemical test result Estimation of N content using regression model Errors in %
Sample 1
a) 0.43425 0.486 11
b) 0.43425 0.388 10
c) 0.43425 0.488 -12
Sample 2
a) 0.45763 0.485
6
b) 0.45763 0.364
20
c) 0.45763 0.488
-7
Sample 3
a) 0.44957 0.502
11
b) 0.44957 0.363
18
c) 0.44957 0.489
-8
Sample 4
a) 0.47420 0.486
2
b)
0.47420 0.396
16
c)
0.47420 0.489
-3
Sample 5
a)
0.47431 0.480
1
b) 0.47431 0.389
17
c) 0.47431 0.487
-2
Sample 6
a) 0.49552 0.479
3
b) 0.49552 0.404
18
c) 0.49552 0.489
1
Sample 7
a) 0.50485 0.481
4
b) 0.50485 0.423
15
c) 0.50485 0.488
2
Sample 8
a) 0.53902 0.490
8
b)
0.53902 0.401
25
c)
0.53902 0.487
9
Sample 9
a) 0.52987 0.495
6
b) 0.52987 0.386
26
c) 0.52987 0.490
7
Sample10
a) 0.53351 0.501
5
b)
0.53351 0.421
20
c) 0.53351 0.488
8
The goal is to find the how much sample has matched to chemical test results or experimental results. Test results are represented in table 6.2.

6.4 Summary
This chapter presents software test environment in section 6.1. Test procedure is given in section 6.2. Functional test cases are shown in section 6.3.

CHAPTER 7
RESULTS AND ANALYSIS
Table 7.1.1 Some sample images on Different Illumination
Samples Day Natural Light Fluorescent Light Incandescent Light
Sample 01
Sample 02
Sample 03
Sample 04
Sample 05
Sample 06
Sample 07
Sample 08
Sample 09
Sample 10

7.1 Results of Maize Leaves on Different Illumination
Fig7.1 shows initially get the image from source. Then applied pre-processing techniques for noise removal and smoothing the image. After pre-processing image features are extracted from gray image and then analysis the nitrogen content of leaf part is done. Initially we have taken different illumination images.
Results on day natural light.

0264795
(a) Input image(b) Smooth image
Nitrogen (%) = 0.495

(c) Gray image
Results on fluorescent light.

(b) Input image(c) Smooth image
Nitrogen (%) = 0.386

(d) Gray image
Results on incandescent light.

(e)Input image (f) Smooth image
Nitrogen (%) = 0.490

(g) Gray image Fig 7.1 Estimation of Nitrogen Content in Maize Leaves on Different Illuminations
The above figure 7.1 shows the calculation of nitrogen content for day natural light, fluorescent light and incandescent light.
We have collected ten samples and each sample contains ten images on different illumination and angle.
7.2 Actual Nitrogen content of leaves and corresponding image features under fluorescent light
Table 7.2 lists various features extracted from the leaf images under fluorescent light. The actual nitrogen content of the sample leaves are plotted against the image features corresponding to the leaves images. Fig 7.2 shows the various plots of the actual nitrogen content vs. the image features extracted from the corresponding leaf images. Each plot contains the parameters m (slope) and c (y – intercept) of the regression line along with the coefficient of determination (R2) of the linear regression.

To further analyse the data, polynomial curve (quadratic and cubic) were used for curve fitting for the computed data, but the results were not satisfactory, hence only linear line fitting was retained for all the other data.

Image Features v/s Actual Nitrogen Content got by Kjeldahl method are plotted and a line is drawn using linear regression as can be seen in Fig 7.2.

Table 7.2 Image Features v/s Actual Nitrogen Content
Samples Actual nitrogen (%) Image Features
Average Energy Mean Entropy Variance Normalized R Normalized G
Sample_01 0.434 0.297 51.979 5.231 62.468 0.580 0.630
Sample_02 0.457 0.402 53.539 5.297 41.799 0.559 0.663
Sample_03 0.449 0.509 37.036 5.275 55.736 0.558 0.625
Sample_04 0.474 0.464 115.477 4.956 57.901 0.588 0.625
Sample_05 0.474 0.508 37.060 5.263 35.437 0.581 0.642
Sample_06 0.495 0.617 35.423 5.139 21.099 0.595 0.593
Sample_07 0.504 0.330 75.556 5.507 33.653 0.612 0.645
Sample_08 0.539 0.298 110.597 5.409 22.893 0.591 0.655
Sample_09 0.529 0.376 39.239 5.303 11.805 0.579 0.626
Sample_10 0.533 0.453 66.176 5.167 23.509 0.610 0.688

(a) Average Energy v/s Nitrogen Content (b) Entropy v/s Nitrogen Content
(c) Mean v/s Nitrogen Content (d) Variance v/s Nitrogen Content

(e) Normalized R v/s Nitrogen Content (f) Normalized G v/s Nitrogen Content
Fig 7.2. Image Features v/s Actual Nitrogen content got by Kjeldahl method are plotted and along with linear regression line.

7.3 Actual Nitrogen content of leaves and corresponding image features under day natural light.

Image Features v/s Actual Nitrogen Content got by Kjeldahl method are plotted and a line is drawn using linear regression as can be seen in Fig 7.3.

Table 7.3 Image Features v/s Actual Nitrogen Content
Samples Actual nitrogen (%) Image Features
Average Energy Mean Entropy Variance Normalized R Normalized G
Sample_01 0.434 0.175 102.961 5.954 13.269 0.592 0.553
Sample_02 0.457 0.262 160.670 5.639 12.353 0.592 0.542
Sample_03 0.449 0.252 171.783 6.025 28.960 0.618 0.554
Sample_04 0.474 0.430 135.445 4.862 35.589 0.594 0.559
Sample_05 0.474 0.322 120.713 5.195 12.822 0.583 0.562
Sample_06 0.495 0.326 145.529 5.390 4.156 0.581 0.551
Sample_07 0.504 0.287 146.310 5.426 17.022 0.586 0.552
Sample_08 0.539 0.254 118.437 5.988 15.957 0.599 0.548
Sample_09 0.529 0.223 151.557 6.001 6.786 0.607 0.545
Sample_10 0.533 0.434 92.377 3.921 9.274 0.616 0.539

(a) Average Energy v/s Nitrogen Content (b) Entropy v/s Nitrogen Content

(c) Mean v/s Nitrogen Content (d) Variance v/s Nitrogen Content

(e) Normalized R v/s Nitrogen Content (f) Normalized G v/s Nitrogen Content
Fig 7.3. Image Features v/s Actual Nitrogen Content got by Kjeldahl method are plotted and a line is drawn using linear regression.

7.4 Actual Nitrogen content of leaves and corresponding image features under incandescent light.

Image Features v/s Actual Nitrogen Content got by Kjeldahl method are plotted and a line is drawn using linear regression as can be seen in Fig 7.4.

Table 7.4 Image Features v/s Actual Nitrogen Content
Samples Actual nitrogen (%) Image Features
Average Energy Mean Entropy Variance Normalized R Normalized G
Sample_01 0.434 0.351 84.872 5.087 141.68 0.820 0.452
Sample_02 0.457 0.328 89.093 5.166 214.750 0.801 0.429
Sample_03 0.449 0.244 84.950 5.464 151.981 0.839 0.211
Sample_04 0.474 0.313 93.663 5.432 293.433 0.838 0.411
Sample_05 0.474 0.222 98.902 5.903 199.617 0.790 0.474
Sample_06 0.495 0.248 85.147 5.399 114.754 0.834 0.377
Sample_07 0.504 0.282 73.471 5.883 152.359 0.804 0.446
Sample_08 0.539 0.306 76.801 5.981 190.872 0.797 0.459
Sample_09 0.529 0.244 63.632 5.226 96.369 0.853 0.397
Sample_10 0.533 0.410 53.321 3.839 78.173 0.822 0.464

(a) Average Energy v/s Nitrogen Content (b) Entropy v/s Nitrogen Content

(c) Mean v/s Nitrogen Content (d) Variance v/s Nitrogen Content

(e) Normalized R v/s Nitrogen Content (f) Normalized G v/s Nitrogen Content
Fig 7.4. Image Features v/s Actual Nitrogen Content got by Kjeldahl method are plotted and a line is drawn using linear regression.

7.5 Comparison between Nitrogen content computed using Regression model and Actual Nitrogen content under Fluorescent light From the plots of various features under different light sources in previous figures, it can be seen that the Normalised R and G features have minimum R2 value. Using the regression line parameters m (slope) and c (y-intercept) and the image features, nitrogen content was computed. Table 7.5 gives the actual nitrogen content got by using Kjeldahl method and the computed nitrogen content using the regression line, along with the errors.

Table 7.5 (a) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized R Nitrogen content (%) computed using Regression line Error Error in (%)
Sample_01 0.434 0.580 0.388 0.045 10
Sample_02 0.457 0.559 0.364 0.092 20
Sample_03 0.449 0.558 0.363 0.085 18
Sample_04 0.474 0.588 0.396 0.077 16
Sample_05 0.474 0.581 0.389 0.084 17
Sample_06 0.495 0.595 0.404 0.090 18
Sample_07 0.504 0.612 0.423 0.080 15
Sample_08 0.539 0.591 0.401 0.138 25
Sample_09 0.529 0.579 0.386 0.142 26
Sample_10 0.533 0.610 0.421 0.111 20
Average 18.5%
Table 7.5 (b) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized G Nitrogen content (%) using Regression Error Error in (%)
Sample_01 0.434 0.630 0.484 -0.050 -11
Sample_02 0.457 0.663 0.499 -0.042 -9
Sample_03 0.449 0.625 0.482 -0.033 -7
Sample_04 0.474 0.625 0.482 -0.008 -1
Sample_05 0.474 0.642 0.489 -0.015 -3
Sample_06 0.495 0.593 0.467 0.027 5
Sample_07 0.504 0.645 0.491 0.012 2
Sample_08 0.539 0.655 0.495 0.043 7
Sample_09 0.529 0.626 0.482 0.046 8
Sample_10 0.533 0.688 0.510 0.022 4
Average -0.5
7.6 Comparison between Nitrogen Content (Regression model) and Actual Nitrogen Content on Day natural light.

Finding errors between actual nitrogen content and nitrogen content using regression.
Table 7.6 (a) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized R Nitrogen content (%) using Regression Errors Error in (%)
Sample_01 0.434 0.592 0.486 0.051 11
Sample_02 0.457 0.592 0.485 0.028 6
Sample_03 0.449 0.618 0.502 0.052 11
Sample_04 0.474 0.594 0.486 0.012 2
Sample_05 0.474 0.583 0.800 0.005 1
Sample_06 0.495 0.581 0.479 0.015 3
Sample_07 0.504 0.586 0.481 0.022 4
Sample_08 0.539 0.599 0.490 0.048 8
Sample_09 0.529 0.607 0.495 0.033 6
Sample_10 0.533 0.616 0.501 0.031 5
Average 6
Table 7.6 (b) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized G Nitrogen content (%) using Regression Errors Error in (%)
Sample_01 0.434 0.553 0.483 -0.049 -11
Sample_02 0.457 0.542 0.509 -0.052 -11
Sample_03 0.449 0.554 0.481 -0.032 -7
Sample_04 0.474 0.559 0.469 0.004 1
Sample_05 0.474 0.562 0.462 0.011 2
Sample_06 0.495 0.551 0.488 0.006 1
Sample_07 0.504 0.552 0.486 0.018 3
Sample_08 0.539 0.548 0.495 0.043 7
Sample_09 0.529 0.545 0.502 0.026 4
Sample_10 0.533 0.539 0.517 0.016 3
Average -0.8
7.7 Comparison between Nitrogen Content (Regression model) and Actual Nitrogen Content on Incandescent light.

Finding errors between actual nitrogen content and nitrogen content using regression.

Table 7.7 (a) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized R Nitrogen content (%) using Regression Errors Error in (%)
Sample_01 0.434 0.820 0.488 -0.054 -12
Sample_02 0.457 0.801 0.488 -0.034 -7
Sample_03 0.449 0.839 0.489 -0.040 -8
Sample_04 0.474 0.838 0.489 -0.015 -3
Sample_05 0.474 0.790 0.487 -0.013 -2
Sample_06 0.495 0.834 0.489 0.005 1
Sample_07 0.504 0.804 0.488 0.015 2
Sample_08 0.539 0.797 0.487 0.051 9
Sample_09 0.529 0.853 0.490 0.038 7
Sample_10 0.533 0.822 0.488 0.044 8
Average -0.5
Table 7.7 (b) Nitrogen content (Regression) v/s Actual nitrogen content with errors.

Samples Actual nitrogen (%) Normalized G Nitrogen content (%) using Regression Errors Error in (%)
Sample_01 0.434 0.452 0.489 -0.055 -12
Sample_02 0.457 0.429 0.488 -0.031 -6
Sample_03 0.449 0.211 0.479 -0.030 -6
Sample_04 0.474 0.411 0.487 -0.013 -2
Sample_05 0.474 0.474 0.490 -0.016 -3
Sample_06 0.495 0.377 0.486 0.008 1
Sample_07 0.504 0.446 0.489 0.014 2
Sample_08 0.539 0.459 0.489 0.049 9
Sample_09 0.529 0.397 0.487 0.041 7
Sample_10 0.533 0.464 0.490 0.042 7
Average -0.3
7.8 Maximum R2 value on features in under each light condition.

Table 7.8 Maximum R2 value on features in under each light condition
Illumination types
Day natural light-R2 value on
Normalized R ; G Fluorescent light-R2 value on
Normalized R ; G Incandescent light-R2value on
Normalized R ; G
R G R G R G
0.0496 0.2166 0.3957 0.0966 0.0005 0.0014
Table 7.8 shows maximum R2 values on features like normalized R ;G. After estimating maximum R2 values under each light condition we got maximum R2 values on normalized R in fluorescent light. In this proposed work we conclude fluorescent light is better for estimating maximum R2 values
7.10 Summary
This chapter explains about samples images collected on different illumination, Results of maize leaves on different illumination in section 7.1. Actual nitrogen content of leaves and corresponding image features under fluorescent light is given in section 7.2. Section 7.3 depicts the actual nitrogen content of leaves and corresponding image features under day natural light. Actual nitrogen content of leaves and corresponding image features under incandescent light is given in section 7.4. Review about the comparison between nitrogen content computed using the regression model and actual nitrogen content under fluorescent light is given in section 7.5. Comparison between nitrogen content (regression model) and actual nitrogen content on day natural light with errors in section 7.6. Explain table for nitrogen content (regression model) and actual nitrogen content on incandescent light with errors in section 7.7. Estimate the maximum r2 value on features in under each light condition in section 7.8. Results for estimation of nitrogen content on different illumination in section 7.9.

CHAPTER 8
CONCLUSION AND FUTURE WORK
8.1 Conclusion
In this work, estimation of nitrogen content in a maize leaf is done based on color and texture features like entropy, mean, average energy, and variance.
The literature review outlines several methods of estimation of nitrogen content in the leaf. The two estimation methods like Kjeldahl (chemical test) and SPAD meter, these are costlier and time consuming. The image processing methods like regression techniques may work well with commonly speedier for estimating nitrogen content in leaves.

This work explored the possibility of using image processing techniques to estimate the nitrogen content in maize leaves. To test the proposed method, 10 sample leaves were taken from different maize plants and their images were captured under various illumination sources. Then they were analysed at the laboratory in the agriculture university get the actual nitrogen content in the leaves. Ten images were captures under 3 different illumination sources (natural day light, incandescent light and fluorescent light) for each of the10 leaf samples to result in 300 digital images.

We carried out experiments by extracting various features from the images and correlated the features with the actual nitrogen content data using regression approach. The features that have maximum correlation will have higher value of R2. From the experiments, we can say that the images captured under fluorescent light perform better. Further, the image features like normalized ‘r’ and normalized ‘g’ give the better correlation result with highest value of R2(coefficient of determination). The regression model was developed between various image features and the plant nitrogen content shows that that the plant nitrogen content can be successfully estimated using its color image features.

8.2 Future work
Expansion of this work may target estimation of nitrogen content based on different illumination like infrared and ultra violet light. Also the use of other surface texture and color features can be explored for comparison between manual results.
Acknowledgement
My special thanks to Prof. Dr. HEMLA NAIK and Dr. GURUMURTHY, department of plant analysis, Agriculture University Shivamogga, for providing necessary laboratory results means chemical test results using Kjeldahl method on my project samples.

REFERENCES
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2 V.K.Tewari, Ashok Kumar Arudra, Satya Prakash Kumar, Vishal Pandey, Narendra Singh Chandel, “Estimation of plant nitrogen content using digital image processing”, proceeding Agri Eng. Int: CIGR Journal-july-2013.

3Piti Auearunyawat, Teerasit Kasetkasem, Audthasit Wongmaneeroj, Akinori Nishihara and Rachaporn Keinprasit. An Automatic Nitrogen Estimation Method in Sugarcane Leaves Using Image Processing Techniques”, International Conference on Agricultural, Environment and Biological Sciences (ICAEBS’2012) May 26-27, 2012 Phuket.4 M. M. Ali, Ahmed Al-Ani, Derek Eamus and Daniel K. Y. Tan. “An Algorithm Based on the RGB Colour Model to Estimate Plant Chlorophyll and Nitrogen Contents”, 2013 International Conference on Sustainable Environment and Agriculture IPCBEE vol.57 (2013) © (2013) IACSIT Press, Singapore DOI: 10.7763/IPCBEE. 2013. V57. 10.

5 Asaram Pandurang Janwale and Santosh S. Lomte, “Automatic Estimation of Nitrogen content in Cotton (Gossypium Hirsutum L) Plant by using Image Processing Techniques: A Review”. International Journal of Computer Applications (0975 – 8887) Volume 120 – No.20, June 2015.
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10 www.mathworks.in.

Chapter 1
Introduction
1.1 Child Health-care
It is very difficult to identify those determinants which are most closely related to the decision of health care utilization. In many developing economies socio-economic factors like poverty, safe drinking water, sanitation facilities and gender discrimination are important determinants of health outcomes. A good understanding of the determinants of child health care will assist policy maker to reduce mortality and immunization problems. United nation’s children fund categories child nutrition determinants into three groups: (a) inadequate food intake and incidence of diseases, (b) insufficient food available in the household, lack of health services, poor sanitary condition and inadequate child healthcare at household level, (c) inadequate resources available at household and society level. So child nutrition and healthcare determinants are categories into individual, household level and society level.
In childhood under nutrition is closely related to incidence of diseases, inappropriate child care and food deficiency (Allen and Gillespie, 2001). Women cannot take timely independent decision regarding provision of treatment to child and man is busy in outside home activities, in this spare child health care is badly affected.
According to the empirical findings of 36 countries women’s status has significant positive impact on child’s nutritional status. Women with higher status are better cared for themselves and are able to provide higher quality care to their children. Among developing countries south Asian children has high malnutrition rate. In less developed countries child health is a significant marker for testing the quality of life (Wang et.al, 2003)
In many developing countries, population growth rate is declining that has shifted away the attention of the population and development communities from fertility reduction toward maternal and child healthcare goals. In many countries women at same time faces the need to combine their economic activities with child care activities and the burden falls mostly on low income group women.

Investment in child health is determines by intra household resources allocation decision and depends on gender discrimination in household. House hold where women play primary role in decision making the proportion of resources allocated to child care are higher than the families where women had less decision maaking power ( Thomas 1990; Duraisamy and Malathy 1991)
Health is a state of complete physical, mental and social well-being and not merely the absence of diseases of infirmity (WHO 1992). While care is defined as the provision of attention and support to meet physical, mental and social need of growing children. So healthcare is defined as the provision of attention and support to protect child physical, mental and social well-being and also from diseases to boost up child growth. Illness reduces child’s appetite and depresses the absorption of nutrients. Health is studies as the function of income and wealth status, educational status, medical care, age, gender, race, marital status, environmental pollution and also on personal behavior and habits. For child care mostly mothers make choices about child’s diet, prevent measures for diseases and when child receive medical care. However women have limited options regarding decision making. Kumar, (1985).

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1.2 Women Empowerment
Women’s empowerment is a vital process that has been quantified, calculated, and described in different ways. “Having increased life options and choices, gaining greater control over one’s life, and generally attaining the capability to live the life one wishes to live called empowerment”. The on top definitions mean that empowerment is a dynamic process of change whereby “those who have been refused the ability to make choices achieve such ability” Kabeer, (1999).

One definition of women’s empowerment is “An expansion in the range of capable choices available to women so that actual results reflect the particular set of choices which the women value” Kabeer, (2001) is another definition of women empowerment. Empowerment is also seen as the process by which the powerless get greater control upon their lives, gaining power not over others but to attain goals and ends Kishor et.al, (2004).
“Empowerment is a process, by which women gain greater control over material and intellectual resources which will assist them to increase their self-reliance and enhance them to assist independent rights and challenge the ideology of patriarchy and the gender based discrimination against women. This will also enable them to organize themselves to assert their autonomy to make decision and choices, and ultimately eliminate their own subordination in all the institutions ad structures of society “Batliwala, (1995); Malhotra, (2002); Yesudian, (2004).

Empowerment factors such as education, exposure to media and standard of living should positive relationship towards maternal health care utilization as well as full autonomy and decision makings such as staying with siblings or parents, self-health care and buying important household items had significant impact on maternal health care utilization Yesudian,
2004).

If the women empowered than the maternal health care and child health care are greater and they pay attention on the female children more because when women have empowerment than they make decisions about health care so the maternal and child health care rate is high.

1.3 Measures of Women Empowerment
There are a variety of socio-cultural and demographic factors that have impact on women’s empowerment. Reviews regarding these factors are discussed in this section.

A. Economic Contribution
Women’s participation in paid job/business and other income creating activities are considered to lessen their economic dependency, have more control over resources and increased their involvement in decision making and their mobility. Women’s participation in paid jobs can be effective only if they have full control over their earning; otherwise, it is just an increment to their responsibilities without any meaningful benefit. In this connection Samarasinghe, (1993) also related economic independence of women with their empowerment. Human capital utilization lags and market inefficiencies exposed through gender-sensitive economic indicators. With women depicting one-half of human capital, limited the capacity of women to contribute prevents economic growth Hausmann, et.al, (2012) in local and regional markets and national levels Elson, et.al, (1997); Randriamaro, (2006). Make sure that women have equal access to financial and development contingency empowers women, expenditures progress toward gender equality and can interpret into improved national and international economic efficiency world bank, (2012). Some theories about economic development and gender equality accord that a “simultaneous relationship” exists among economic growth and women’s empowerment Slusser, (2009).

B. Education
In earliest studies education was used as the indirect measure of women’s empowerment. It was considered that educational attainment helps the women to be empowered by creating money earning capability between them, developing confidence to face challenges, increasing ability to make decisions regarding themselves and their partner. All these things lead women to empowerment. However, some studies concluded that educated women still have to face many problems that restrict their processes of empowerment. Informal education also has a potential for empowering women, in this regard Parveen, (2005) explained in her study in rural Bangladesh that both formal and informal education has positive significant impact on women’s empowerment. She argued that education and skill improve the socio-economic condition of women and make able them to demand and protect their rights more efficiently.

C. Political participation
Political participation assumed as one of the measurement of women empowerment because political participation provides women a space for increasing their strength and opportunities for choosing the leaders who have abilities to solve their problems and commitment to decrease gender disparity.
Oxaal and Baden (1997) concluded that political empowerment of women is imperative for women’s empowerment, because higher number of women in politics will support women problem at every level. The author assumed that elected women councillors at regional government level had little knowledge about the problems faced by the women. Therefore, there was a need to initiate capacity building programs for local councillors. In another study, Stromquist, (1995) assumed political participation as important measurement of empowerment as political participation develop capacity among women to analyzed, organize and mobilize resources for social change.

Although women’s empowerment increased with the performing of gender based policies, governance still rearrange behind other dimensions. As awareness increasing about the political importance of gender-based economic development and policy reform, decision makers are starting to give more attention to the women in politics. Moreover, mostly development agencies and international investors now grasp that good governance, which permit democratic reform and encourages transparency, fosters an efficient environment for getting policy objectives Arndt, et.al, (2006).

D. Women’s awareness about their rights
Women’s awareness about their rights and practices of these rights is assumed to have positive impact on women’s empowerment. In order to decline gender gap or gender inequality and to promote empowerment, it is important for women to recognized root causes of their problems as well as inherent structural and institutional discrimination. There is also a need of restructuring of women’s role that restricted their own growth. These views are also consistent with the radical feminists; Taylor, et.al, (1993) who wanted structural changes. All these changes have not possible without promotion of awareness rising campaigns. Oxaal and Baden, (1997) assumed that successful application of women’s reproductive and sexual rights were associated with economic independence and bargaining power of women. Similar point of view was also put forwarded by Bisnath, (2001) who stressed on political mobilization, consciousness increasing and education for attaining women’s empowerment. The author also suggested changes in laws, civil codes and system of property rights, social and legal institutions in order to get gender equality.

E. Household status
Household status is commonly considered as strong indicator of good education, provider of good media exposure and better awareness. The participation of household status in empowering women is reviewed there.

In a study based on Indian national family health survey conducted during1998-99; Yesudian (2004) assumed that status of household does not directly affect the women empowerment. However, it was argued that household status provides means of empowerment like education and mass media. Nawar et,al. (1995) reported that spousal bargaining power within household changes with the change in social status.

The educated working women in non-traditional environment have high bargaining power in the household. In another study argued by Parveen, (2007) in Bangladesh, it was concluded that landless and marginalized women have little power to identify gender based discrimination than the women of richer household. Furthermore, women of richer families gain better education, media exposure, and control over resources that provided them more confidence to challenge traditional and cultural attitude; while, women from poorer families have fewer chances to enhance knowledge, skill and resources that could provide them confidence and self-esteem.

1.4 Domestic Violence
Domestic abuse, spousal violence, family violence, is called domestic violence and closer partner violence, is briefly defined as a pattern of abusive behaviors by one or both partners in an intimate relationship. physical aggression (hitting, kicking, biting, shoving, restraining, slapping or throwing objects) as well as threats, sexual and emotional abuse, controlling or dominating behaviors, intimidation, stalking and passive or covert abuse (e.g., neglect or economic deprivation) all are forms of domestic violence.

“Any action of gender based violent behavior which causes bodily, sexual, emotional or psychosomatic injury, or yield pain to a women as well as threats of these acts, whether happening in communal or personal life United Nations, (1993) United Nations, (1995)”
“Any act between the intimate partners that cause physical, sexual, or emotional harm is known as intimate partner violence world health organizations, (2002)”
Marital violence is also recognized as spousal exploitation, family abusive behavior, or intimate partner violence. Domestic violence involves any type of abuse in any intimate relationship (dating, living together, cohabitation or marriage). It can takes place in homosexual or heterosexual relationship in any form i.e., physical, psychological, emotional, sexual, verbal and economical. Domestic violence ranges to domestic murders which include dowry deaths, bride burning and honor killing. Physical abuse involves bodily harms, pains, suffering and injuries. It is committed by pushing, chocking, throwing objects, hitting, slapping, kicking, pulling hair or dragging.
Due to its epidemic nature, intimate partner violence violence perpetrated by a man against his female partner is increasing being observed as a human development problem worldwide Kishor, (2004). Intimate partner violence is also considered domestic violence and spousal mistreat. Intimate partner violence is a multidimensional phenomenon that includes mortal, impulsive, and sexual violence and stalking. The prevalence of physical intimate partner violence in many countries of the world has been estimated to range between 13 and 61% Garcia-Moreno, (2006). Intimate partner violence has poured out negative effects on the economic wellbeing Renzetti, (2009) mortal health Matthew, (1996) and impulsive health DeJonghe, (2008) of person victims and on the direction of fall unintended pregnancy Pallitto, (2004).

1.5 Measurements of Domestic Violence
The measurement of violence according to the demographic and health survey project has been evolved in keeping with the research on how to increase the validity of prevent measurement, as well as in response to the raising of the bar for authentic standards in the collection of sensitive data Ellsberg, (2001); WHO, (2001). The steps which are taken in the demographic and health survey project to respond to these changing standards for research in domestic violence, and the extent to which the information in this report reflects these concerns are discuss in this section.

Generally different approaches used to calculate prevalence of domestic violence in the demographic and health survey regress into two categories. The first is a single question household approach and the second is which included in the demographic and health survey domestic violence measurement that combines the earlier approach with the use of a modified conflict tactics scale to measure domestic violence.

The single question household approach: The woman is asked one question to find out whether she has ever experienced violence. Women who give a positive answer are then asked further questions like who the partner was/is (including the husband), and the frequency of the violence. No more questions are asked of women who say no to the first question. Thus the woman is given just one chance to tell about any violence.

The modified conflict tactics scale approach as embodied in the domestic violence module:
In this approach a modified version of the conflict tactics scale to achieve information on domestic violence, and then a list of single questions to get at violence experienced by someone other than a husband or partner, and violence during pregnancy. The original conflict tactics scale made by sociologist Murray Straus in the 1970s consists of a variety of single person questions regarding specific step of violence such as slapping, punching, and kicking. The original scale had 19 items Straus, (1979; 1990). The modified list used by demographic and health survey consist only about 15 items of physical and sexual violence. If the respondent accepted that any one of the selective acts or findings has taken place, she is considered to have beard violence.

1.6 Research Question
A research question of this study is, to see what is the impact of women empowerment dimensions on child immunization status for South Asian countries collectively? Is the immunization status for male and female children are different? What is the impact of women empowerment dimensions on child health care for all countries separately?
1.7 Objectives of the studyThe objectives of our current study are:
To examine the dimensions of women empowerment and child health care: a pooled analysis of South Asian countries.
2) To make a comparison of the relating male and female children between dimensions of women empowerment and child health care for South Asian countries.

3) To examine the dimensions of women empowerment and child health care: a pooled analysis of South Asian countries individually.

1.8 Significance of the study
Vaccination contributes to public health. It eliminates the most childhood diseases, like polio from all over the world excepting some countries Duclos, et.al, (2009). Children immunization is considered a major indicator of healthy children because it provides the protection of children from childhood diseases, Bofrarraj, (2011). This may be considered significant in many ways:
1. Outcomes of this study may make the authorities realize the existing situation of the immunization of children in developing countries.

2. It was expected that this study will be helpful for government to adopt practical measures to promote the immunization level and making policies for awareness of dimensions of women empowerment in developing countries.

3. This study will be also being helpful for parents to understand the issues related to the immunization level of children.

1.9 Organization of the study
Chapter one: is about the introduction of dimensions of women empowerment and child health care; a pooled analysis for developing countries and provides an overview of the study. Objectives were set according to the nature of the problem. Bases on the objectives, few research questions were assumed. This chapter also describes the significance of this study in the relevant field.

Chapter two: explains the literature review of the study. It also discusses the issues related to the immunization level of children. It sheds light on the core dimensions and factors which becomes the reasons of immunization of children.

Chapter three: deals with the theoretical frame work of the study in which the relationships of different variables will be discussed which effect directly or indirectly to the immunization level of under five year old children.
Chapter four: explains the methodological approach that was used during this research. It also included the research design, source of data, sample size, research models, econometric functions of the model and description of variables.
Chapter five: includes analysis and interpretation of data with percentage evaluation. In this chapter result of immunization level of children under five year of age for all countries collectively discussed and the results of immunization level of children for all countries separately also discussed in this chapter.
Chapter six: explains the analysis and interpretation of data with percentage evaluation. In this chapter result of immunization level of children under five year of age for male and female children separately discussed.
Chapter seven: explains the conclusion and policy recommendations.

Chapter 2
Literature Review
Literature review provides knowledge and guideline to researchers regarding their research topic. It depicts the previous published work of scholars and researchers in the related field Sekaram, (1992). The present review is an attempt to encircle the child health-care and dimensions of women’s empowerment indicated in previous studies, conducted in various South Asian developing countries. Child health is the most important element for an economy’s growth, especially a female child’s health, as she becomes mother and gives birth to a child with almost the same health status as she has her own health. In the earlier literature a number of studies have focused on child health-care Bugvi, et.al, (2014) explained the factor associated with incomplete immunization among children aged 12-23 months in Pakistan. They concluded that despite governmental efforts to increase rates of immunization against childhood diseases, the proportion of incompletely immunized children in Pakistan is still high. Targeted interventions are needed to increase the immunization rates in Pakistan. These interventions need to concentrate on people with low socioeconomic and educational status in order to improve their knowledge to this topic.

Streatfie, et.al, (1990) have analyzed the maternal education and child immunization. In this study the child immunization was dependent variable and education and profession of both mother and her father, economic status, ages of mother and child, hamlet of residence, numbers of children ever born and still living, and knowledge of the purpose of the injections. Survey data was used in this study that has examined. The study was conducted into two villages in the sub district of Sukomulyo in KulonProgo, the western district of Yogyakarta, Indonesia by interviews of women’s who had at least one child under 5 year. Chi -square test and logistic regression model were used for bivariate and multivariate approaches respectively. It was concluded that maternal education is positively related to many aspects of knowledge of immunization that may be expected to influence the use of such a preventive health service. Effective health education schemes have the potential to improve immunization coverage among people to the same level as among those with substantial schooling. Child survival inventions, such as the expanded program of immunization, may also be implemented effectively among other non-literate populations in developing countries with village authority structures like those in Indonesia.
Becker, et.al, (1993) have analyzed the determinants of use of maternal and child health services in Metro Cebu, the Philippines. The control variable were family planning, prenatal care, childhood immunizations and oral rehydration salts and the explanatory variables were categorized as demographic, education, socioeconomic and access communication variable. Survey data was used in this study that has examined. Data was obtained by currently married mothers who were not pregnant at the time of the survey and had an eligible child at least six months of age (n=4629)8000 women from Metro Cebu, the Philippines. Polytomous logistic regression methods were used in this study. It was concluded that for all health-care outcomes, parents with higher education, and particularly maternal education, was associated with greater utilization in both urban and rural areas. The effect of women’s education on oral rehydration salts knowledge and ever use was less than for before delivery care and immunization.
Elo, (1993) has analyzed the Utilization of maternal health-care services in Peru: the role of women’s education. Prenatal care and source at delivery were control variables and explanatory variables were age, sex, household composition and size, ethnic group affiliation and education. Survey data was used in this study. Peruvian demographic and health survey data of reproductive-aged women carried out in September to December 1986 with the standard demographic and health survey questionnaire was used in that study. To obtain the relative effects of covariates of interest on maternal health-service use, logistic regression model was used in that study. It was concluded that , greater differentials were found in the utilization of maternal health-care services by place of residence, suggesting that much greater efforts on the part of the government are required if they want to reach modern maternal health-care services for women in rural areas.

Mahmood, et.al, (1994) examined the relationship between selected individual and household level factors and survival of children under five years of age based on the data in Pakistan demographic and health survey of 1990-91. Results of multivariate and logistic analysis has been shown that mother greater than 35 years of age enhance the survival of children, probably because of awareness, experience and with the increase in the age, whereas younger mothers less than 20 have less experience and age about the child survival. Mother’s education is secondary and higher has net effect on the child survival in urban areas while it has weak effect in rural areas. Immunization coverage for children has increased insignificantly in recent years, the Pakistan demographic and health survey (1990-91) data report that 12-23 months age of children are 35 percent fully vaccinated, whereas the children of five years indicate 43 percent of all children who had ever been immunized, 37% in rural and 49 in urban areas.

Pebley, et.al, (1996) have analyzed the prenatal and delivery care and childhood immunization in Guatemala: do family and community matter? In this study the child immunization was dependent variable and family characteristic, characteristics of the community, and characteristics of the individual pregnancy or child were independent variables. Survey data was used in this study that has examined. The data were collected from national survey of maternal and child health held in Guatemala in 1987 logistic regression model was used. It was concluded that estimates obtained from a multi dimensions logistic model indicate that use of basic (or “modern’) health services differs substantially by ethnicity, by social and economic factors, and by availability of health services. The results also show that family and community membership are vital determinants of the use of health care, even in the presence of controls for a large number of observed characteristics of individuals, families and communities.

David, et.al, (2001) probed children’s health care use a prospective investigation of factors related to care-seeking. In the study child health care visits obtained from a computerized database comprised the dependent variable. Independent variables were organized into a 5-component framework consisting: demographic characteristics; household characteristics; child health and prenatal health care use; child behavior and mental health; and mothers’ mental health and health care use. Families were followed prospectively for 2 years to accumulate health care use data and multivariate regression analysis was used to determine items related to volume of child health care use. They concluded that previous use is the best predictor of future health care use. Additionally, the study also suggested that maternal perceptions of child health and maternal emotional functioning have impact on the decision-making process involved in seeking health care on favor of children. Effective management of pediatric health care use needs to address broader needs of the child and family beyond only the child’s health, most notice maternal functioning.

Richardson, et.al, (2002) estimatted identifying domestic violence cross sectional study in primary care for measured the prevalence of domestic violence among women attending general practice; tested the association between experience of domestic violence and demographic factors; evaluated the extent of recording of domestic violence in records held by general practices; and assess acceptability to women of screening for domestic violence by general practitioners or practice nurses. They held a questionnaire survey for review of medical records.1207 women (; 15 years) participate selected practices in the survey. The authors concluded that with the high reduction of domestic violence, health professionals should develop a high level of awareness of the possibility of domestic violence, especially affecting pregnant women, but the case for screening is not yet convincing.

Joanna, et.al, (2003) estimated inequities among the very poor health care for children in rural southern Tanzania. In a baseline household survey in Tanzania early in the implementation phase of integrated management of childhood illness, they were included cluster samples of 2006 children under 5 years of age in four rural districts. Questions focused on the limits to which cares knowledge of illness, care-seeking outside the home, and care in health facilities were consistent with integrated management of childhood illness guidelines and messages. They used principal components analysis to compute a relative index of household socioeconomic status, with weighted scores of wealth index, education of the household head. They concluded that Care-seeking behavior is lowest in poorer as compare to rich families, even within a rural society that might easily be assumed to be uniformly poor.

Tanaka, (2005) has analyzed the prenatal leave and child health across OECD Countries. Infant mortality, prenatal mortality, neonatal mortality, post-neonatal mortality, child mortality and low birth weight were control variables and the explanatory variables for the study were weeks of job-protected paid leaves, weeks of other leave including unpaid leave, fertility rate, female employment-to-population ratio, gross domestic production per capital, public expenditures on health care as percentage of gross domestic production Share of population with health insurance coverage Number of dialysis patients, population Public expenditures on family cash benefits per child, Public expenditures on maternity and parental leave per child Public expenditures on family services per child. Survey data was used in this study that has examined. Data used on parental leave for 16 European countries, 16 the United State and Japan from 1969 to 2000. The data are obtained from the following publicly available resources: three sources from the international labor organization including international labor organizations yearbook of labor statistics, the 1984 global survey on Protection of working mothers, and ‘maternity protection at work, international labor organization (1999) from the United State social security administration since 1958, Social Security Programs Throughout the World the social expenditure database, health data 2002 and 2003 from the world health organization, European health for all database 2003; and the work life research Centre in the United Kingdom. Ordinary least squares regression was used for estimating the effect of maternity and parental leave on infant health outcomes. It was concluded that a significant relationship between paid leave and low birth weight, an important factor for infant health.

Holta, et.al, (2008) attempted to see the impact of exposure to domestic violence on children and young people: A review of the literature. Impact is explored across four different yet inter-related domains (“domestic violence exposure and child abuse; impact on parental capacity; impact on child and adolescent development; and exposure to additional adversities”), with potential outcomes and important messages concerning best practice responses to children’s needs highlighted. A variety of search of identified databases was conducted within an 11-year framework (1995–2006). This yielded a vast literature which was selectively organized and analyzed according to the four domains identified in the upper. The authors find that children and adolescents living with domestic violence were at increased risk of experiencing emotional, mortal and sexual mistreat, of developing emotional and behavioral issues and of greater exposure to the presence of other adversities in their lives. It also highlights a range of protective aspects that can lessen against this impact, in particular a strong relationship with and attachment to a caring adult, usually the mother. It concluded that children and young people may have significant affect by living with domestic violence and effect can endure even after pattern has been taken to secure their safety. It also concluded that there is rarely a direct causal pathway leading to a key outcome and that children are active in making their own social world. Implications for inventions suggest that timely, correct and individually tailored responses need to build on the resilient blocks in the child’s life.

Watts, et.al, (2008) estimated that how does economic empowerment affect women’s risk of intimate partner violence in low and middle income countries? a systematic review of published evidence. Published data from 41 websites were used in this study. The study years (data collection) ranged from 1992 to 2005. Data had been collected before 2000 in 17 websites and from 2000 in 24 websites. Mostly defined the age of the woman sample with the most common age range begin 15–49. Household wealth and women’s higher education were generally has significant impact. Evidence about women’s participation in income generation and experience of previous year violence was mixed, with five finding a protective association and six documenting a risk association. They were hypothesized that at a single person and household level, economic development and higher income may have protective impacts on intimate partner violence. Context specific factors effects whether financial autonomy is associated with higher risk. They also concluded we cannot guarantee that women’s empowerment will always low the risk.

Janssens, (2009) has examined the impact of women empowerment and the creation of social capital in Indian villages. That paper focused on trust and on cooperative behavior as a key source and appearance, respectively, of social capital. In that study social capital (community, infrastructure) were the dependent variables and age, education and household size were independent variables. In study the sample size of 2000 households was collected which based on focus group interviews with women’s groups during a earlier-survey study visit in 2002 and a post-survey visit in 2004. In the study participating households were significantly less well educated. The participants slightly had fewer household members but more children under the age of 14. Cluster sampling methodology and empirical strategy was used in this study. The paper was concluded that trust in community members is significantly higher among the richest part of the population and the better educated. It was also concluded that the program and control villages are highly compared both in population characteristics and in village characteristics. The findings suggest substantial slop on the larger community. The households who did not attend the program themselves but who live in a program village are significantly more trusting and more likely to engage in collective action than households in control villages.
The pattern and predictors of bacille calmette Guerin immunization delays in the first three months of life in a Sub Saharan African community where bacille calmette guerin is given at birth in order to facilitate compulsory changes in current policy and practices for improvement has been examined by the Olusanya, (2010). This cross-sectional study was based on a reconsidering analysis of a earlier reported prospective university of Iowa Scottish Highlanders program executed in an inner-city area of Lagos, southwest of Nigeria, with an approximated population of 250,000 Olusanya, et.al, (2009). A cross-sectional study in which vaccination delays between infants aged 0-3 months attending community- based bacille calmette guerin clinics in Lagos, Nigeria over a 2 year period from july 2005 to june 2007 were measured by survival analysis and related factors calculated by multivariable logistic regression. Vaccination is higher in those children who born at hospitals as compare to those born outside the hospitals.
Yount, et.al, (2011) analyzed the Impacts of domestic violence on child growth and nutrition. A conceptual framework for the analysis was proposed by which domestic violence against mothers may effect child growth and nutrition, before delivery and during the first 36 months of life. Literatures from various studies were synthesized and critically review the evidence for each pathway. That review exposed gaps in knowledge and opportunities for research. The findings from that conceptual review suggested that domestic violence may have impact on early childhood growth and nutrition through biological and behavioral pathways. The framework also found that interim strategies to mitigated the effects of children’s exposure to domestic violence exposure on child growth and nutrition. Given the global burden of child malnutrition and its long-term effects on human-capital formation enhance child growth and nutrition may be another reason to prevent domestic violence and its cascading after-effects.
Owais, et.al, (2011) has conducted this research to measure the impact of a low-literacy immunization promotion educational intervention for mothers living in low-income communities of Karachi on infant immunization completion rates. A poisson regression model has used to estimate effect of the intervention. The multivariable poisson regression model included mother education, paternal work status and household head. Cooking fuel used at home, place of residence, the children immunization status at enrolment and mother’s awareness about the effect of immunization on child health. At 4 month attendance, among 179 mother infant pairs in the intervention group, 129 has been received all 3 doses of diphtheria pertussis and tetanus/hepatitis B vaccine, whereas in the control group 92/178 had received all 3 doses. Multivariable analysis exposed a significant improvement of 39% in diphtheria pertussis and tetanus/hepatitis B completion rates in the obstruction group. A simple educational intervention designed for low-literate populations, improved diphtheria pertussis and tetanus hepatitis B vaccines achievement rates by 39%.

Khan and Sajid (2011) concluded in this study that patriarchy was very dominant in rural Punjab as compared to urban Punjab. For analysis 100 battered wives were selected from 10 villages of Gujarat having age between15-55. All belonged to rural area, living in middle class and were victims of violence. The consent was taken through questionnaire. The data was subjected SPSS and proportion test has been used to infer conclusions. Reasons of wife battering were joint family system, exploitation of religion, public norms, low level of male education, intrusion of in-laws in husband wife relation, family traditions and cultural norms. All such reasons minimized the role of women in domestic decision making, block equal opportunities, make women powerless ultimately degradation, humiliation as life partner, as a consequence, women have urge of living in a nuclear family and in some cases they want divorce. Domestic violence was dependent variable while age, education, occupation, family structure are independent variables. Binominal test has been used for the analysis of the association (good/bad) between respondent and in-laws that is significant at probability of level 0.05. the results reflected a positive liaison between male dominance, financial conditions of the household, intrusion of in-laws, inferior education of male with the severity of wife beating were of the opinion that nearly one-third of male respondents make out that violence is justified and usual for a male to hit his spouse. Choi, et.al, (1996). The results showed that high education, employment of women, were less likely to experience violence. Large household size and husband’s low level of education also positively related with domestic violence. It has also reported that mother in-law has exceptionally higher power of making decisions in a joint family, especially in South Eastern part of the country Asia. Kadir et.al, (2003). Similarly it was stated that the common reason of misunderstanding and clash between life partners is the interference of in-laws, ultimately lead to physical violence. The consequences of wife battering were not to live in a nuclear family and to put out marriage contract Fikree, et.al, (1999).

Monazza, et.al, (2012) investigated the relationship between parental schooling on the one hand and both child health consequences and health-seeking behavior of parents (child immunization status) on the other. Parent’s education may affect child health through higher household income, greater publicity to media, literacy, knowledge about better health, mother’s participation in the labor market, and the extent of maternal empowerment within her husband’s home. Latest data from two m provinces (Punjab and Khyber pakhtun khwan) of Pakistan were estimated. Child health score functions were estimated using ordinary least square model and community-fixed effects. Estimates were based on a sample of children aged under five years. Estimates revealed that father’s education is positively related with health-seeking behavior and education of mother is highly associated with child health outcomes.

Singh, (2013) has analyzed the trends in child immunization across geographical regions in India. Full immunization was dependent variable in this study and birth order & interval, status of the child, age of the woman at childbirth, mother’s education, mother’s working status, father’s education, religion, social group, mass media exposure, and household wealth were included as independent variables in the study. Three rounds of the national family health survey held during 1992–93, 1998–99 and2005–06 were analyzed. In that study bivariate analyses, urban-rural and gender inequality ratios, and the multivariate-pooled logistic regression model were applied to examine the trends and patterns of inequalities over time survey data was used in this study that has examined. It was concluded that change over one and half decades (1992–2006) shows major variations in child immunization coverage across six geographical regions in India. Despite a decrease in urban-rural and gender differences over time, children residing in rural areas and girls remained disadvantaged. It is essential to integrate strong immunization systems with broad health systems and coordinate with other primary health care delivery programs to achieve immunization coverage.

Mandal, (2013) analyzed the concept and types of women empowerment. Global empowerment of women is a new concept. Since the twentieth century second half, the issue of women’s empowerment has gained value among scholars of universities, and in national and international platforms. But the concept was not deeply inbred into the governments’ policies and programs until the declaration of the ‘women’s decade’ in 1975. The author addressed the challenges and conditions of this situation. It called for government, political decision makers and other actors to take step to ensure women’s all round development for making India a developed country. He concluded that Women are still a minority in the society though they appoint fifty percent of the total population. These strong fifty per cent population needed to be provided with equal opportunities in society, social, political, educational, religious and legal conditions. For getting complete equality in the society between two genders, it is necessary to remove all kinds of domination, oppression and discrimination against the fair gender by their male partner. It is hoped that India will prosper and find its Vision – 2020 with hand to hand co-operation and active participation of both male and female in all stages of developmental activities.

Oyefara, (2014) has analyzed Mothers’ Characteristics and Immunization Status of Under-Five Children in Ojo Local Government Area, Lagos State, Nigeria. In this study dependent variable was full immunization status of a child and while independent variables were education, religion, ethnic background, and wealth index, mother age, profession, marital status, and form of marriage. Cross section survey data was used in this study. Data was collected by questionnaires. For the survey a total of 265 respondents were randomly sampled using multistage random sampling methods. Bivariate analysis involved the use of chi-square to estimate the relationships between outcome variable and various independent variables; and multivariate analysis consisted of logistic regression model on dependent and explanatory variables. It was concluded governments (federal, state, and local government) need to encourage those factors that will enhance women’s autonomy in the study location. Women education is also important.

Dibaya, (2014) has analyzed the impact of routine immunization coverage in controlling measles and progressing in Africa. In this study the dependent variable was child immunization status and residence, household income, child’s sex, parents’ age, and education were independent variables. Primary data was used in this study that has examined. The data was collected from the Rwanda demographic and health survey and Uganda demographic and health survey. Sample was included women of reproductive age at interview ;the child immunization data was linked to individual women records. Logistic regression model was used in this study. The findings of the study was significant public health implications for Uganda, as it lags behind in introducing RCV (rubella-containing vaccine) in its national immunization schedule, and Rwanda in achieving and sustaining high coverage rates of RCV. It’s concluded that all three levels– intrapersonal (child’s gender and age), interpersonal (parents’ age and education, and household economic status), and community (residence type) – are important in explaining differences in immunization status among children in the study.

Javed, et.al, (2014) has investigated the mother’s related disparities, especially women empowerment and mother’s education on childhood immunization status in Pakistan by using the Pakistan demographic and health survey 2006-07. By using the last birth preceding 5 years to the survey is used to find the dependent variable whether the child is fully immunized or not. In urban areas 43.2 percent and in rural 34.2 percent children of age 12-59 months get all the 8 doses of recommended vaccination. The logistic regression results suggest that children of mothers which has no empowerment, Low level of education and working in agriculture sector have no enough amount of wealth and have female children with earlier birth order born in Punjab than in any other province are more vulnerable to under immunization. Father’s education has significant impact on immunization in urban area as compare to rural. Regional immunization coverage is also different in rural areas across the provinces. Overall results indicate that if the education of parents and wealth status is good then the fully immunization is confirmed.

L.Sherra, et.al, ( 2015) attempted to see exposure to violence predicts poor educational outcomes in young children in South Africa and Malawi. The study reported on data which collected from the child community care study, a longitudinal study of children affected by human immunodeficiency syndrome/ acquired immunodeficiency syndrome going to school at community-based organizations in South Africa and Malawi. Objective data were collected in 2011–2012 with follow up 12–15 months later.3-4 year old children were selected for the analysis. All types of violence were included in the regression models as dependent variables and child gender and age, whether the child had lost either or both biological parents, and caregiver education were used as independent variables. Logistic regressions were used for binary outcomes variables in the study. Linear regressions were used for scale outcomes variables. They concluded that Violence experiences were associated with a number of educational findings, which may have long-term consequences. Community-based organizations may be well placed to detect such violence, with a special emphasis on the challenges faced by children who are HIV positive.

Taswe, et.al, (2015) have analyzed the factor influencing the use of maternal health care services and childhood immunization in Swaziland. In this study bivariate and multivariate variables were used. Dependent variables were antenatal care, delivery care, postnatal care, and immunization and women age, parity, exposure to newspapers, exposure to the radio, exposure to the television, maternal education, wealth quintile, literacy, and residence were independent variables. Secondary data was used in this study obtained by Swaziland demographic and health survey from the 2006-2007. In this study chi-square test and logistic regression model were used. It was concluded that it is important to study factor related to maternal and child immunization uptake to inform relevant stakeholders about specific areas of improvement. To educate families about the importance of maternal and child health care services program should be implemented.

Research Gap:
The literature has focused on determinants of child health care. In previous researches child health care has been measured by different techniques and methods like upper middle are circumference, immunized record and vaccines etc. For South Asian developing countries a number of studies have attempted to see the determinants of child health care (up to 60 months age). Theoretically, dimensions of women empowerment may affect the child health care by using the latest data of demographic and health survey.
Women empowerment has different dimensions in the current study two dimensions of women empowerment decision making and domestic violence have been analyzed.

In the data set demographic and health survey 2013 the question on the domestic violence on women has been edited. Theoretically this aspect is not only affect the child health nutrition and growth but the maternal health as well. Maternal health is also related with child health. So, the significance of domestic violence, women empowerment becomes eminent in the analysis of child health care. The current study will attempt to analyze the effect of women empowerment, and domestic violence of child health care. From the policy perspective the analysis of child health care is more important in the case of child vaccines.

The majority of the earliest studies Biswas, et.al, (2001) Dibaya, (2014) Dutta, (2013) have analyzed the child healthcare separately for South Asian developing countries. The current study analyzed the child healthcare collectively for developing countries. To make a comparative study of female and male children will be another novelty of the study.

Chapter 3
Theoretical Framework
There are several behavioral models that could be applied to health care. For example,the Andersen and Newman framework of health utilization and Grossman’s theory of the demand for health care are the most appropriate theoretical frame work to examine how parents think about child health care. The Grossman model explain the determinants of human capital which individual possess. This model explains mainly the demand side of health care determinants. Anderson and Newman model covers the dimensions: type of health service system, individual and societal determinants of health care utilization behavior. Our study covers the health belief model and Grossman’s theory of demand for healthcare.

3.1Andersen and Newman Framework of Health Services UtilizationThe .purpose of .this framework is to discover conditions that either facilitate or hold up utilization. The goal is to develop a behavioral model which provides measures of access to medical care. Firstly the frame work was developed in the 1960s and has since gone through four phases. Developed in the 1990s, the framework below represents the fourth phase.

An individual’s access to and use of health services is considered to be a function of three characteristics:
“1) Predisposing Factors
The socio-cultural characteristics of individuals that exist prior to their illness.

? Social Structure: Education, occupation, civilization, social networks, social interactions, and culture
? Health Beliefs: Attitudes, values, and knowledge that people have concerning and towards the health care system
? Demographic: Age and Gender”
2) Enabling Factors
The logistical aspects of obtaining care.

? Personal/Family
The means and know how to access health services, income, health insurance, a regular source of care, travel, amount and quality of social relationships
? Community
Available health workers and facilities, and waiting time
? Possible additions
Genetic factors and psychological characteristics
3) Need Factors
The most instant cause of health service use, from functional and health problems that generate the need for health care services. Perceived need would better help to understand care-seeking and loyalty to a medical schedule, while evaluated need will be more closely related to the kind and amount of treatment that will be provided after a patient has presented to a medical care provider. Andersen, (1995)
? Perceived
How people view their own general health and functional state, as well as how they experience symptoms of illness, pain, and worries about their health and whether or not they judge their problems to be of sufficient importance and scale to seek professional help. Andersen, (1995)
? Evaluated: Represents professional judgment about people’s health status and their need for medical care. Andersen, (1995)
“3.2 Health Belief ModelHochbaum, (1958) has founded that the purpose of this model was to investigate why people did not participate in public health programs like tuberculosis or cervical cancer screening. The health belief model was originally developed in the 1950s by social psychologists working at the United States. Public health service to explain why many people did not participate in public health programs such as tuberculosis or cervical cancer screening. Subsequently, it was extended by Leventhal, Rosenstock, Becker and others to explain conflicting reactions to symptoms and to explain variations in devotion to treatment.
The health belief model was founded on attempts to integrate stimulus-response theory with cognitive theory in explaining behavior. The design of the health belief model was influenced by Kurt Lewin’s theories which state that perceptions of reality, rather than objective reality, influence behavior. Earlier stimulus-response theory had stressed the importance of the consequences of behavior in predicting actions, while cognitive theory modified this by stressing the significance of the person’s subjective valuations, and their judgment of the likelihood that an action would have desired consequences. This combined approach was termed value-expectancy theory: reinforcements and incentives do not influence action directly, but via influencing the person’s valuation of the action and their judgment of the likelihood that it will produced results. In this perspective, health behaviors are influenced by a person’s desire to avoid illness or to get well, and by their confidence that the recommended action will achieve this. This implied a phenomenological approach: it is not the actual world, but the person’s perceptions of it that influence their behavior. It extended the descriptive approach of associating health behaviors with demographic factors such as social class or society, and emphasized the role of personal characteristics and perceptions. ”
The health belief model breaks down health decisions into a series of stages and offers a catalogue of variables that influence health action; it does not provide a model of exactly how these operate. In the health belief model, the likelihood that a person will follow a defensive behavior is influenced by their subjective weighing of the costs and benefits of the action; the perception involves the following elements:
Perceived susceptibility: the person’s judgment of his or her risk of contracting the condition.

This might be measured by questions such as “Taking all factors into account, what do you think are your chances of getting the disease?”
Perceived seriousness of the condition
The severity of the condition (its clinical consequences, disability, pain or death) and its impact on life style (working ability, social relationships, etc.). Questions might include “If you got the disease, how serious would that be?” Or, more objective indicators might be used, such as the number of days off work or in bed.

The perceived threat has a cognitive component and is influenced by information. It creates a pressure to act, but does not determine how the person will act. That is influenced by the balance between the perceived effectiveness and cost of alternative courses of action:
Perceived benefits of an action
Perceived benefits of an action will the proposed action be effective in reducing the health risk? Does this course of action have other benefits? Again, it is the person’s beliefs, rather than factual evidence, that important. The beliefs will reflect social and cultural influences.

Perceived barriers to action
How do these benefits compare to the perceived costs of action?
Are there barriers to action? Will it involve expense, pain, or embarrassment? This can be
assessed via questions such as “What difficulties do you see in undertaking this action?” An 11-item barrier scale for adopting preventive behaviors for acquired immune deficiency syndrome was published by Maguenetal. Note that there may be difficulties in scoring such responses, and the barriers mentioned may be quite independent of each other.

The balance between benefits and costs may suggest the person’s likelihood of acting and their preferred course of action, but do not necessarily determine that they will act. Indeed, if benefits are closely balanced against costs the person may fluctuate, perhaps experiencing nervousness. The final element in the health belief model was therefore:
A stimulus or sign to action
When a person is motivated and can perceive a beneficial action to take, actual change often occurs when some external or internal cue (e.g., a change in health, the physician’s advice, or a friend’s death) triggers action.). The magnitude of the cue required to trigger action would depend on the motivation to change and the perceived benefit to cost ratio for the action. Although Hochbaum saw cues as being vital, they have rarely been studied empirically.

A small number of the studies that have evaluated the health belief model provide results that can be summarized numerically. As early as 1974, Rosenstock reviewed seven studies that had evaluated the health belief model. Six lent support to the importance of several of the variables in the model as explanatory or predictive variables. However, a seventh major investigation conflicted in most respects with the findings of earlier studies. Rosenstock noted that the first six studies were undertaken following widespread public advertising alerting the public to particular health issues; the seventh study was undertaken in the absence of any operation. Rosenstock concluded that the health belief model can predict voluntary health behavior in people who are essentially healthy.

Janz and Becker’s review showed that future studies supported the predictive validity of the health belief model, as did several cross-sectional studies. Perceived barriers appear as the single best analyst of succeeding behavior. Janz, et.al, reported on a series of studies that evaluated the HBM in predicting uptake of mammography screening, and also reviewed several studies that used it in designing interventions to increase screening. They concluded that interventions that incorporated the health belief model precepts tended to produce superior results, but it is often not possible from the studies to isolate the effects of the health belief model from other characteristics of the interference.

.

Reactions to the health belief model suggested that it held merit, but explained rather little variance. Accordingly, many derivatives have been proposed. Examples include Langlie’s model that included perceived susceptibility, perceived benefits of changing health behavior, barriers & costs, health locus of control, socioeconomic status, and situational constraints.

Kats planned a model of defensive health behavior that built on the health belief model and included three classes of variables. ‘Predisposing motivation’ was influenced by the desire to avoid illness, to gain approved by others and to follow personal values. ‘Blockage variables’ included a lack of knowledge and resources. ‘Conditioning variables’ included factors that modify the above variables, such as perceived susceptibility and previous illness experience. In this theory, cognitive factors operate chiefly by modifying motivation. This approach differed from the health belief model in that it clearly included motivation and also replaced the linear associations between the factors and health behaviors by a threshold model.

David Mechanic proposed ten types of influence on behavior, most referring to the first stages of the health belief model: frequency and visibility of symptoms; their seriousness; the person’s tolerance threshold; their level of knowledge; the availability of treatment and constraints on action (such as job demands). Cummings, et.al, reviewed 14 models of health behaviors and asked eight experts judge the similarity of the variables they contained. From this, a form of multidimensional scaling produced six categories of variables: Accessibility of health services; Attitudes towards health care; Threat of illness; Knowledge about disease; Social network factors (e.g. social pressures, support, traditions); Demographic factors.

The health belief model is generally taken as marking the beginning of systematic and theory-based research on health behavior. It has been widely used and applied to explaining health behaviors as well as used to design intervention programs. It has been used in cross-cultural studies, although its constructs may need to be adapted to each cultural group. Tang, et.al, for example, studied cultural barriers in participation in cancer screening among Chinese and Asian peoples in the United States.

The perceived benefits of the health belief model look like Bandura’s concept of outcome expectations. Following Bandura’s development of social learning theory, Rosenstock, et.al, suggested that self-efficacy be added to the health belief model, to cover the person’s sense of confidence that they could successfully change behavior to produce the desired outcomes. As Janz et.al, note, however, this represents an addition of the original purpose of the health belief model, for it originally applied to single actions such as receiving an immunization, while self-effectiveness is most relevant to health actions that require sustained effort, such as smoking termination. Social pressure may also modify behaviors, and this lies beyond the scope of most models of health behavior.

Among limitations of the health belief model, Janz, et.al, noted that the concepts have been inconsistently and often poorly measured, making it difficult to draw comparisons across studies. But this criticism may be misplaced, because measures of concepts such as barriers to action cannot be measured generally, but have to be specific to particular behaviors. The health belief model describes constructs that predict behaviors, but it says nothing of how these are expected to interrelate.

“3.3 Grossman’s theory of the demand for health careGrossman’s theory understand the contribution of the Grossman model to health economics and also to access the application of consumer theory to the debate on the demand for health and health care. The model goes beyond traditional demand analysis and has been extremely powerful in health economics. It also introduces the idea of investing in human capital (health and education) to improve outcomes in both the market (work) and non-market (household) sectors.
Demand for health care is derived from a demand for health (few people want health care for its own sake). Demand for health is derived from the demand for utility (e.g. healthy days in which to participate in free time and work). Individuals are not inactive consumers of health but active producers who spend time and money on the production of health. Health can be seen as lasting over time periods. It depreciates (perhaps at a non-constant rate) and can therefore be analyzed as a capital good.
Health demand consists of two elements:
(1) Consumption effects: health yields direct utility i.e. you feel better when you are healthier.

(2) Investment effects: health increases the number of days available to participate in market and non-market activities –the novel bit of the model.
In investment decisions analogy with a firm using inputs to produce goods is to make decisions according to production functions – relationship between inputs and outputs. Education plays a vital role in determining the efficiency of health capital and also in other production functions, therefore influences consumption patterns of households.
Some implications of this model are to:
Raise education amongst the poorly educated
Reduce price of health care, especially to the poor
Increase wages of the low paid
Use policies to affect depreciation”
3.4 Child Healthcare
Vaccination is a major contributor to public health. It has removed some of the very dreaded childhood diseases, like polio, from all over the world excluding few countries. Childhood vaccination is considered to be a most important health indicator of a healthy child. It aware protection from common childhood diseases, is estimated to stop millions of deaths and cases of disability all over the world, and is therefore assumed beneficial by the scientific community. In 1974, the world health organization introduced its expanded program on immunization with the aim of controlling six childhood diseases: tuberculosis, diphtheria, pertussis (whooping cough), tetanus, polio and measles. Consistent with the world health organization recommendations, Pakistan launched expand program on immunization in 1978 under the guidance of the world health organization. Now, the national expand program on immunization aims to vaccinated all children under the ages of 0 and 23 months against eight vaccine-preventable diseases which consist, in addition to the above-mentioned diseases, hepatitis B (vaccine introduced in 2002) and haemophilus influenza type b (vaccine introduced in 2008).
The benefits of getting a child vaccinated are strictly questionable, yet studies have shown that, in some developing countries, a sizeable number of parents, usually belonging to low income wealth disadvantaged populations, reduced child immunization. The primary reason for parent’s not fully immunized children is that their children will not be infected with these diseases like polio, whooping cough and measles. Further, the parents are not aware about the side effects of immunization. Due to structural, cultural and economic factors, Pakistan has lower immunization level than other countries in the region. Bugvi, et.al, (2014)
Characteristics of Health Care Utilization
The major component of the framework for viewing health services defines the unit of health service utilization to be analyzed. It is an important dimension because the configuration of the other components of the framework very considerably, depending on special characteristics of the unit analyzed.

The characteristics of main importance consists type, purpose and unit of analysis with respect to type of health service gradually argue that societal determinants have resulted in very unique long-term trends for physician, hospital, and dental services. Further, the current individual determinants of hospital, doctors, and dental services are vary considerably. Utilization can also be characterized by purpose. Primary care has to do with eliminating illness before it start. Secondary care refers to the mechanism of treatment which returns a person to his past state of functioning. Tertiary care provides stabilization for long-term non reversible illnesses like heart disease or diabetes. Characteristics of health Services utilization oblige for the private needs of the patient but do not do any effort to treat his illness conditions. The determinants of every type of care different considerably. For example, factors related to use of stopping services like general checkups, immunization and vaccinations vary from those related to diagnosis and treatment National Center for Health Statistics, (1965).

Another method of looking at the illness feelings is way the episode concept. It is an attempt to delineate a main illness experience and all of the medical care associated with that experience. The episode approach is important if one is interested in studying main questions such as care associated with selective diagnoses, reasons for late in seeking care, continuity of care received, level of patient compliance, and patterns of referral. The importance of the episode concept was developed by Solon et.al, (1967).

Determinants of child Health-care
The utilization of health services can be considered as a kind of individual behavior. In general the behavioral sciences have trying to explain a person behavior as a function of abilities of the individual himself, abilities of the environment in which he lives, and some interaction of these individual and society forces Moore, (1969). To date most of the important studies and theories dealing with health services utilization have emphasized the single person characteristics while less attention has been given to the societal effect.
Societal Determinants of child Health-care
Definitions
The main societal determinants of health service utilization are technology and norms. It should be noted that the postulated causal relation between the societal factors and resulting utilization behavior discussed below, can only be inferred. The health care system structures the provision of formal health care goods and services in society. Formal health care goods and services include physician care, hospital care, dental care, drugs, health appliances and services provided by other health care practitioners.”
A national health care system consists of two major dimensions, resources and organization. Together they shape the provision of health care services to the individual.

“The resources of the system are the labor and capital devoted to health care. Included would be health personnel, structures in which health care and education are provided, and the equipment and materials used in providing health services. Organization describes simply what the system does with its resources. It refers to the manner in which medical personnel and facilities are coordinated and controlled in the process of providing medical services. Both resources and organization include two sub-components. The resource component includes total volume of resources relative to the population served and the way in which the resources are geographically distributed within a country. Volume includes personnel/ population ratios for various kinds of health related occupations (including physicians, nurses, dentists, etc.) actively providing medical care. Total amount of resources can also be measured by examining facilities which provide patient care. In this case, bed/population ratios for hospitals of various kinds, nursing homes, and other institutions providing inpatient care are common measures.”
Structure, the 2nd elements of organization, deals with characteristics of the system that determine what happens to the patient following entry to the system. Of interest here are: the nature of medical practices of the primary practitioners who first see the patient in the system, the utilization of ancillary personnel, processes of referral to other sources of care, means of admission into the hospital, characteristics of hospital care, and the disposition and care of patients following hospitalization. The structural component is the most difficult of the health services systems components to define as well as to relate to utilization patterns. The definitional problems result from the many facets of structure, only some of which are mentioned above. Also, the structure component is highly interrelated to the other components. Certainly, access as we have defined it depends in part on structure, and the structure of any system is dependent on the resources available to it.

The identified societal determinants in the literature of child healthcare are as:
Women Empowerment
Women empowerment is a main problem faced by the developing countries. The traditional system in families makes the women less empowered which devalued the behavior of women for child health and comparatively the child health is poor. Women’s economic empowerment is thinking to play a huge role in shaping maternal and child health findings. Financially findings women are assumed to invest more resources in there and their children’s health comparatively to their less empowered counterparts Carlson, (2015) Golla, (2011) Malhotra, (2005)
Gender Discrimination;
Gender discrimination is another social phenomenon in developing countries. A number of studies identified the existence of gender discrimination by the household. Child health is one of the most important issues where gender discrimination still exists. Van, et.al, (2008).The infant and under five years of age child mortality rates have reduced steadily in the previous decade, but they are till one of the largest in the world and are characterized on the bases of gender discriminations. The number of deaths of girl children aged 1–4 per 100 boy child deaths in 1970, 1985 and 1996 were, respectively, 108.7, 128.2 and 124.2. These figures illustrate the dangerous situation for girls as compare to boys during the 1970s and lack of progress thereafter world health organization, (2000).
Individual Determinants of Healthcare:
Definitions:
The underlying model assumes that a sequence of conditions contributes to the type of volume of health service a person uses. Use is dependent on: (1) the predisposition of the individual to use services; (2) his ability to secure services; (3) his illness level.

Predisposing Component
Some individuals have a chance to use services greater than other individuals, where chance toward use can be stopped by individual abilities which exist earlier to the onset of specific episodes of illness. People who have these characteristics are more conscious to use health services even though the abilities are not directly responsible for health service use. Such characteristics consists demographic, societal structural and attitudinal related variables. Age and sex, for example, between the demographic variables, are intimately related to health and illness.
Demographic Characteristics
Age of child
Children’s immunization status is also more affected to factors like feeding/weaning practices, care, and awareness about infection at specific ages. A cumulative sign of child healthcare is directly linked with age Anderson, (1995) as cited in Aschalew, (2000)
Birth order
It is assumed that parents give low attention to elder children when born to a new child who needs more attention and care. A study concluded that stunting is rare in birth order two-three Sommerfelt,(1994), and higher birth order greater than 5 has positive impact with child malnutrition Jeyaseelan,(1997).

Age of women
Women’s age and relationship are important component that affect maternal depletion, especially in greater fertility countries Zerihun,(1997), as cited in Winkvisit,(1992). Demographic and health surveys estimated in Burkina Faso, Ghana, Malawi, Namibia, Niger, Senegal, and Zambia show a higher proportion of mothers age 15-19 and 40-49 that eliminate chronic energy deficiencies. A regional study in Ethiopia also concluded that women with youngest age group (15-19) and women in the oldest age group surveyed (45-49) are the most affected by under nutrition Teller, et.al, (2000).

Household economic status
The economic standard of a household is an element of access to enough food supplies,
Use of health services, availability of clean water sources, and sanitation facilities, which are key components of children and maternal nutritional status. A study of most of the demographic and health surveys held in developing countries (Loaiza, 1997) and one study in the Southern Nations, Nationalities and Peoples Region of Ethiopia (Teller, 2000) shown that women from low income households were the most affected by immunization.

Enabling Component
Even though single persons may be ready to use health services, some means must be available for them to do so. A condition which allowed a family to act on a value or fulfill a need for health service use is described as enabling. Enabling situations make health service sources available to the individual. Enabling conditions can be calculated by family sources such as income, level of health insurance coverage or other resource of third-party payment, whether yes or not the individual has a continuous resource of care, the nature of that continuous resource of care and the availability of the resource.
Illness Level
Assuming the availability of predisposing and enabling terms, a person or his family must receive illness or the probability of its presence for the use of health services to take place. Illness level shows the most quickly reason of health service use. Measures of received illness consisted number of disability days that an individual faced. In these days the individual is not able to do what he usually does be that work, go to school, take care of the house, or play with other children.

3.5 Measurement of Child Healthcare
In this section we will discuss how we can measure the child health care by different methods.

Measurement of Quality for Children’s Health-care
Performance measurement is a strong tool to drive improvements in the quality of care that could be enacted at the provider, health plan, health system or state levels. However, a series of issues make the calculations of children’s health care quality vary and more challenging than calculating adult care. These consist: 1) the unique point of view childhood, such children’s quick growth and development, their higher likelihood of being in poverty, and dependence on their families; 2) the reliance on consensus suggestions because of the dearth of randomized dependent trials available to insure what constitutes quality child health care, 3) public and private sector demand for measurement is not good and 4) challenges in imposing measures that would be suitable and provide meaningful information. A measure which is most widely used to measures of child health care is depend on administrative data and track the number of well-child visits and immunizations received. However this is a feasible method, it limited the concepts of care that can be assessed.
Evidence for Quality
Except, guidelines for children’s health care tend to draw more on expert concord. Sege, et.al, describe the limited evidence from randomly controlled trials to guide child health care. The outcomes of child healthcare vary from the usual findings of medical care. Especially in relatively minor effects at great periods during childhood may result in large differences in comparatively development and adult function.
Challenges in Implementing Meaningful, Yet Feasible Indicators
Whether a huge number of measures are present for characterizing the quality of children’s health care, the most widely used measures focus on counting the number of healthy-child visits and vaccinations received. An environmental scan of child health quality measurement identified approximately 300 methods utilized across the spectrum of children’s health care. Sources for the measures compendium included the Child and Adolescent Health Measurement Initiative and the American Medical Association, sponsored physician consortium for performance improvement.
3.6 Conceptual Basis:
We follow the health belief model and Grossman’s theory of demand for healthcare to calculate our dependent variable child health care. According to demographic and health survey report we can measure the child health care status by the vaccination cards and verbal information that we collect by the mothers of child. The demographic and health survey report follow the world health organization according to this report we said the child is fully immunized if the child take three doses of diphtheria, pertussis, and tetanus vaccine, one dose of bacilli calmette guerin vaccine, three dose of oral polio vaccine and one dose of measles vaccine. If the child will miss any one dose of any vaccine we said the child is not fully immunized. The independent variables are household characteristics, parent’s characteristics community characteristics and child characteristics, socioeconomic characteristics, domestic violence and women empowerment which are affect our dependent variables child immunization. The data that was collect for demographic and health survey reported by questionnaires selected from Asian developing countries. The questionnaires filled by mothers that have at least one child. We use KR and IR file in STATA and collect the data of our all variables. In demographic and health survey report expending program on immunization was followed.

Chapter 4
Data and Methodology
This chapter describes the methodology of the study. There is a brief discussion on the research procedure adopted in this research, sample size, research design, research instrument (model, functions, management of the model, data analysis and summary). The data methodology mainly deals with the requirement of data, source of data and model specification. We examine a variety of empirical models where child health care is the dependent variable and women empowerment and domestic violence are main explanatory variables. In this research we would use the additive index technique for indexing of child health care, women empowerment and other variables.

4.1 Research Design
The purpose of the study is to investigate the socioeconomic determinants of preventive measures of child health care (vaccination) in South Asian developing countries. The research has been conducted to check the variables which can become the reason to affect the immunization level of children in the age of under five years. First of all the study will conclude the significance of eight vaccinations that whether the children of developing countries has been taken all the vaccinations or not or has completely immunized or not. Secondly, the research will find out the male and female children disparities with respect to explanatory variables.

The operational definitions of variables have been given in tables 4.1:
Table 4.1 Operational definitions of variables
Variables Definitions
Dependent variable
Received all vaccinations 1 if received all vaccinations; 0 for no vaccinations
Explanatory variables
Principal explanatory Variables
WEMPOWER( Women empowerment) 1 if women have empowered; 0 otherwise.
DVIOL (Domestic violence) 1 if violence does not exit; otherwise 0.
Individual characteristics
GOC ( Gender of child) 1 for male; 0 for female
NCUF (Number of children under five) 1for 0-2; 2 for 3-4; 3 for 5-6; 4 for greater than 6
BON (Birth order) 1for less than or equal to 2; 2 for3-4; 3 for 5-7; 4 for more than 7
Parents characteristics
MEL (Mother education level) 0 for illiterate; 1for primary; 2 for middle; 3 for higher
FEL ( Father education level) 0 for illiterate; 1for primary; 2 for middle; 3 for higher
Demographic characteristics
GHH (Gender of household head) 1 for male; 0 for female
Community characteristics
POR (Place of residence) 1 for urban; 0 for rural
Socioeconomic characteristics
WI ( wealth index) 1for Poorest; 2 for Poorer; 3 for Middle; 4 for Richer; 5 for Richest
4.2 Dependent VariableThe dependent variable in our current study is Child health care. We measured this variable by using the information that was given in demographic and health survey report. In current study immunization record take as proxy for child health care. In demographic and health survey data on four vaccinations are given in current analysis these vaccines combined to generate immunization status by the help of additive index methodology. We take this variable in binary form i.e., 1(fully immunized) and 0 (not fully immunized). We measure this variable by using the information that was given in demographic and health survey report. According to demographic and health survey report the child is fully immunized if the child take three doses of diphtheria-tetanus-pertussis vaccine, three doses of Polio vaccine, one dose of BCG vaccine and one dose of Measles vaccine. If the child missed any one dose of any vaccine the child is not fully immunized.
Vaccinations of children
Data in the demographic and health survey has been collected on each vaccination by a vaccination card on which there are some categories which have not any vaccination date on card, reported by mother and some vaccination marked on card. BCG not receive has been considered as if vaccination of BCG record is not found on card, reported by mother and vaccination marked on card are recorded as received BCG vaccination. Like this measles has been recorded as 0 for no vaccination of measles and vaccination date on card, reported by mother and vaccination marked on card are recorded as received measles vaccination. Diarrhea, pertussis and tetanus has three doses, in DPT1, DPT2, DPT3, 0 for no vaccination of diarrhea, pertussis and tetanus and vaccination date on card, reported by mother and vaccination marked on card are recorded as 1 which shows that how many children have received three doses of diarrhea, pertussis and tetanus vaccination. Polio vaccination has four doses including polio 0 at the time of birth. According to the vaccination card no makes as 0 for no doses of polio and other categories vaccination date on card, reported by mother and vaccination marked on card are recorded as 1 which shows that how many children have received three doses of polio vaccination. No and DK (don’t know) responses were recorded as 0 and considered as not received the vaccine.

We required 1 if the children received all the doses as received vaccination and 0 for they didn’t received any dose from the eight vaccinations. So as the earlier it has been explained that if the children didn’t receive vaccination like BCG, diarrhea, pertussis and tetanus, measles and polio make as 0 and if the children received all the doses of vaccination then recorded it as 1. So for our estimation finally we got the one variable vaccine, 1 for if the children received all vaccination and 0 for if these didn’t receive any vaccination in the under five year of age.

4.3 Independent variablesFor estimating child health care status, there are some variables which affect the completely immunization of children in the under five year of age. The following variables have been taken as independent variables:
Main Explanatory Variables
To estimate the child health care, the main explanatory variables are dimensions of women empowerment, it have many dimensions but we discuss about two dimensions of women empowerment in detail which are “women empowerment” and “domestic violence”.

Women Empowerment
One definition of women’s empowerment is “an expansion in the range of potential choices available to women so that actual outcomes reflect the particular set of choices which the women value.” Kabeer, (2001). Empowerment is also seen as the process by which the powerless gain greater control over their lives, gaining power not over others but to achieve goals and ends Kishor & Gupta, (2004).
“Empowerment is a process, by which women gain greater control over material and intellectual resources which will assist them to increase their self-reliance and enhance them to assist independent rights and challenge the ideology of patriarchy and the gender based discrimination against women. This will also enable them to organize themselves to assert their autonomy to make decision and choices, and ultimately eliminate their own subordination in all the institutions ad structures of society “Batliwala, (1995), Malhotra, (2002), Yesudian, (2004).

Empowerment factors such as education, exposure to media and standard of living should positive relationship towards maternal health care utilization as well as full autonomy and decision makings such as staying with siblings or parents, self -health care and buying important household items had significant impact on maternal health care utilization Princy, et.al,
(2004).

Women Empowerment Index
The 2012-13 Pakistan demographic and health survey collected data on the status of women including information on gender differences in access to and control over cash earnings, ownership of assets, relative earnings of husbands and wives, participation in household decision making, and women’s attitudes toward wife beating. On the basis of available data, to measure the effects of women empowerment on child health care, an additive index of women empowerment has been conducted. Index will be taken from demographic and health survey which depends upon the following questions:
Person who usually decides how to spend the money?
Person who usually decides on respondent healthcare?
Person who usually decides on large household purchases?
Person who usually decides visit to family?
Person who usually decides on money husband earn?
Responses for these questions were coded as respondent=1, husband=2, respondent and husband jointly=3, family elders=4, someone else=6 while in this study we have coded 0=women have no empowered, and 1= women have empowerment. All these variables taken in binary form and then add them after that the main variable of women empowerment converted into binary form instead of index 0 for women have not empowerment and 1 for women have empowerment. It is hypothesized that women empowerment has positive relationship with child health care………
Domestic Violence
“Domestic violence refers to the physical harm by one family member to another.”
“Any action of gender based violent behavior which causes bodily, sexual, emotional or psychosomatic injury, or yield pain to a women as well as threats of these acts, whether happening in communal or personal life United Nations, (1993) United Nations, (1995)”
“Any act between the intimate partners that cause physical, sexual, or emotional harm is known as intimate partner violence world health organizations, (2002)”
Marital violence is also recognized as spousal exploitation, family abusive behavior, or intimate partner violence. Domestic violence involves any type of abuse in any intimate relationship (dating, living together, cohabitation, or marriage). It can takes place in homosexual or heterosexual relationship in any form i.e., physical, psychological, emotional, sexual, verbal and economical. Domestic violence ranges to domestic murders which include dowry deaths, bride burning and honor killing. Physical abuse involves bodily harms, pains, suffering and injuries. It is committed by pushing, chocking, throwing objects, hitting, slapping, kicking, pulling hair or dragging.
Intimate partner violence occurs in cohabitation or between the couples who are living together. In intimate partnership victim is often a woman. Women in developed countries are more empowered and free to report to authorities about violence as compared to women in developing countries.

Domestic Violence Index
According to demographic and health survey, thirty-two percent of ever-married women age 15-49 have experienced physical violence at least once since age 15, and 19 percent experienced physical violence within the 12 months prior to the survey.
The index will be taken from demographic and health survey which depends upon the following questions
Does (did) your (last) husband ever?
(a) Push you, shake you, or throw something at you?
(b) Slap you?
(c) Twist your arm or pull your hair?
(d) Punch you with his fist or with something that could hurt you?
(e) Kick you, drag you, or beat you up?
(f) Try to choke you or burn you on purpose?
(g) Threaten or attack you with a knife, gun, or any other weapon?
Responses for these questions were coded as never=0, often=1, sometimes=2, yes, but not in last twelve months=3, no response=7 while in this study we have coded 0=no violence, often, sometimes=1 and no response given=7 (missing values not include). All these variables taken in binary form and after the construction of index we also convert it into binary form 0 for no violence and 1 for violence exits. It is hypothesized that domestic violence has inverse relationship with the child health care if the violence does not exit then the immunization level of children is better as compare to those children which lives with those mothers who bears violence.
Other supporting Variables
Gender of Household Head
Gender of household head means who is the head of house. If the head of household is a woman then we will give the 0 number and if the head of household is a male then we’ll give it 1 number. It is expected that if the head of the household is female then the female children are more completely immunized as compare to male children
Number of Children under Five
Numbers of children under five shows how many children live in a house who’s age are less than five. We’ll assign 1for 0-2; 2 for 3-4; 3 for 5-6; 4 for greater than 6. It is expected that if the number of children in a house is 2 then the immunization status is good as compare to those household which have more than 2 children.

Wealth Index
Wealth index means what’s the status of household income. It is the composite measure of a household’s cumulative living standards. The purpose to establish this index is to measure and test the inequalities prevail in household incomes, utilization of health services and health outcomes (Filmer and Pritchett, 2001: Gwatkin et.al, 2000). .The demographic and health survey wealth index categorizes households into five health quintiles (poorest, poorer, middle, richer, richest) by using these categories we define the household status, which are coded by, 1, 2, 3, 4 and 5 respectively. It is expected that if the economic status of the household is good then the immunization level is more as compare to low level economic status households.

Place of Residence
Place of residence is an independent variable in this analysis. We categorize into number form 1 for urban resident and 0 for rural resident. It is hypothesized that the vaccination status of urban resident children are good as compare to rural resident children.

Mother Education Level
Mother education level means what is the education of child mother. In Pakistan demographic and health survey file mother’s education categories into different parts 0 “illiterate” 1 “primary” 2 “middle” 3 “higher”. Expected results are that if the mothers are highly educated then the children are completely immunized as compare to those children who born at with less educated mothers.
Father’s Education Level
Father education level means what is the education of child father. In Pakistan demographic and health survey file father’s education categories into different parts 0 “illiterate” 1 “primary” 2 “middle” 3 “higher”. It is expected that if the fathers are highly educated then the male children are completely immunized as compare to female children.

Gender of Child
Gender of child will tell us what is the gender of child? We give 0(female child) and 1 (for male child). Expected results are that the male children are completely immunized as compare to female children.

Birth Order
Birth order will tell us what is the number of child? We categorized this variable into different types (e.g.,) 1for “less than or equal to 2” 2 for “3-4” 3 for “5-7” 4 for “more than 7”. We’ll assign numbers in continuous form. It is hypothesized that the vaccination status of first child is good as compare to the other children.
4.4 Data Source
For estimating the determinants of quality of completely immunization of children, the cross section data (micro data) has been taken from demographic and health surveys of South Asian developing countries (Pakistan, India, Nepal and Bangladesh). The main objective of demographic and health survey is to collect the complete data on fertility, family planning, infant, child and adult mortality, maternal and child health, nutrition and knowledge of human immunodeficiency virus/ acquired immunodeficiency syndrome and other sexually transmitted infections.

4.5 Sample Size
In research literature we come across a number of ways to select sample. In this study researchers selected the micro data. As it has been discussed the source of data is demographic and health survey of different years for different countries. Demographic and health survey provide all the basic socioeconomic indicators of the households. The total number of observation in our data is 15,063. The data on the children having age under five year is required to check the immunization level of children. In the second and third model we have to check the male and female comparison of complete immunization level of children. So the number of observations for male children is 7787 and for female children is 7276. In the other models we have to check the immunization level for all countries separately. The children of age under five years was considered fully vaccinated if he or she had received all of the vaccinations- BCG (which usually given at the time of birth), three doses of diarrhea, pertussis and tautness three doses of polio (which usually start from the one and half months of age and polio 0 at the time of birth) and measles which starts from 9 months of the age at the time of survey Biswas, et.al, (2001).
4.6 Econometric Model
The economic model provides the framework to identify the relationship and use of resulting information. The aim of this is to explain the socioeconomic determinants of preventive measures (vaccinations) of child health care in South Asian developing countries. In this analysis the Binary logistic model has been applied. The regressed which is the immunization, can take only two values, say, 0 and 1. Where 1 is used for the completely immunized children with eight vaccinations and 0 for not fully immunized. The percentage analysis has been analyzed as well as marginal effects. The other researcher also have used the binary logistic regression for the analysis Biswas, et.al, (2001), Mahmood, et.al, (1994), Khan and Raza (2013).

4.7 Binary Logistic Regression
Binary (or binominal) Logistic Regression is a type of predictive model that can be used when the target variable is a categorical variable with two categories – for example live/die, has disease/doesn’t have disease, purchases product/doesn’t purchase, wins race/doesn’t win, etc. A logistic regression model does not involve decision trees and is more akin to nonlinear regression such as fitting a polynomial to a set of data values. Logistic regression can be used only with two types of target variables:
A categorical target variable that has exactly two categories (i.e., a binary or dichotomous variable).

A continuous target variable that has values in the range 0.0 to 1.0 representing probability proportions.

As an example of logistic regression, consider a study whose goal is to model the response to a drug as a function of the dose of the drug administered. The target (dependent) variable, Response, has a value 1 if the patient is successfully treated by the drug and 0 if the treatment is not successful.

Ordered logistic model can also be used if the dependent variable is categorical variable but in this study the dependent variables is in binary form like 0 and 1 that’s why the binary logistic model has been used, Which is best for the estimation of the immunization of children in South Asian developing countries.

4.8 Assumptions of Binary Logistic Model
Logistic regression does not assume linearity of relationship between the independent variables and the dependent.

Does not require normally distributed variables.

Does not assume homoscedasticity, and in general has less stringent requirements.

4.9 Functional form of the model
The level of immunization is affected by many explanatory variables. These variables have been classified into different categories, so the general model of the immunization level of children is given as:
Immunization of children= f (Dimensional Empowerment, Child’s individual characteristics, parent’s characteristics, demographic characteristics, Socioeconomic characteristic)……..4.1
The functional form of the complete immunization level of children is expressed as below:
IMMUN= f (WEMPOWER, DVIOL, GOC, BON, NCUF, MEL, FEL, GHH, WI, POR)…….4.2
IMMUN = ?0 + ?1WEMPOWER + ?2DVIOL + ?3GOC + ?4BON + ?5NCUF + ?6MEL + ?7FEL + ?8GHH + ?9WI + ?10POR……….4.3
The second function represents the complete immunization level for male children in the South Asian developing countries, which is expressed as below:
IMMUNm =?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR…………4.4
The third function represents the complete immunization level for female children in the South Asian developing countries, which is expressed as below:
IMMUNf = ?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR………..4.5
IMMUNP = ?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR + ?10GOC………..4.6
IMMUNI = ?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR + ?10GOC………..4.7
IMMUNN = ?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR + ?10GOC………..4.8
IMMUNB = ?0 + ?1WEMPOWER + ?2DVIOL + ?3BON + ?4NCUF + ?5MEL + ?6FEL + ?7GHH + ?8WI + ?9POR + ?10GOC………..4.9
Chapter 5

Results and Discussions
World health organization has recommended the guidelines for immunization which are followed by the Pakistan expanded program on immunization. According to this guidelines all the children must receive a BCG vaccination against tuberculosis, three doses of DPT vaccine for the prevention of diphtheria, pertussis (whooping cough) and tatnus, three doses of polio vaccine and a vaccination against the measles in the first five year of the children age. 5.1Discussion and Findings
This chapter contains a detail description about the findings of the study. Results are calculated by running binary logistic regression through STATA 12.0. This presents the qualitative and quantitative analysis for the study. The factors which are responsible for the poor and good healthcare status of children are going to be presented and discussed here.

In the first section 5.1 there is the analysis of vaccination for South Asian developing selective countries.

5.2Vaccinations for South Asian countries
Binary logistic regression has been used for all regressions. The total number of children in the age of under five year for selective countries are 15,063 which are taken from demographic and health surveys of all countries. We will see whether the children have been fully immunized or not. Table 5.1 represents the estimates results of regression analysis for all variables of vaccinations and table 5.2 represents the percentage analysis for vaccination of children under five year of age.

5.1Econometric Analysis of vaccinations for south Asian countries collectively
Variable Coefficient Marginal Effect p- Value
Women Empowerment(no empowerment as reference)
Women empowered 0.1587 0.0359 0.015**
Domestic Violence(violence exist as reference)
Violence not exist -0.5111 -0.1158 0.000*
Wealth Index(poorest as reference)
Poorer 0.1223 0.0282 0.022**
Middle 0.2305 0.0531 0.000*
Richer 0.2867 0.0660 0.000*
Richest 0.4012 0.0918 0.000*
Mother Education Level(illiterate as reference)
Primary 0.3515 0.0839 0.000*
Middle 0.5500 0.1307 0.000*
Higher 0.8929 0.2080 0.000*
Father Education Level(illiterate as reference)
Primary 0.4796 0.1114 0.000*
Middle 0.3502 0.0816 0.000*
Higher 0.4893 0.1136 0.000*
Number of Children Under Five Year Age(continuous)
Less than equal to 2 -0.3019 -0.0684 0.000*
Gender of Child(female as reference)
Male 0.0522 0.0118 0.127
Gender of Household Head(female as reference)
Male -0.4069 -0.0921 0.000*
Birth Order(continuous)
Less than equal to 2 -0.1315 -0.0297 0.000*
Place of Residence(rural as reference)
Urban -0.0866 -0.0196 0.002*
No. of observation= 15,063 Prob>chi2=0.0000
LR chi2(21)= 1433.37 Pseudo R2=0.0690
(*shows significance at 1 % level, & **shows significance at 5% level,)
Table5.1 represents the econometric analysis and marginal effect for all the variables which tell us how immunization status affected by different variables for selective South Asian countries.

5.2Percentage estimations of vaccinations for South Asian countries collectively
Variables
Women empowerment
Empowered 91.48
Not empowered 8.52
Domestic violence
Exit 27.53
not exit 72.47
Wealth index
Poorest 17.26
Poorer 19.13
Middle 21.11
Richer 22.69
Richest 19.81
Mother education level
Illiterate 21.24
Primary 18.42
Middle 42.14
Higher 18.20
Father education level
Illiterate 12.06
Primary 21.09
Middle 44.99
Higher 21.86
Number of children under five year
Less than equal to 2 83.85
Greater than 2 15.36
Gender of household head
Male 84.54
Female 15.46
Birth order
Less than equal to 2 54.75
Greater than 2 29.05
Place of residence
Urban 43.26
Rural 56.74
Gender of child
Male 52.41
Female 47.59
Women Empowerment and Child Immunization
The table 5.2 represent the impact of women empowerment on children (upto five year age) immunization status in selected developing South Asian countries (Pakistan, Nepal, India and Bangladesh) which shows that 91.48% children live with those mothers which have empowerment and fully immunized while 8.52% children live with those mothers which have no empowerment but fully immunized. This shows that the empowered mothers are able to complete the vaccinations of their children as compare to those mothers which have not empowerment. According to our final results women empowerment has positive impact on child health care with 0.1587 probability. The results of women empowerment have the same impact on child immunization status for Nepal and India like collective countries analysis but the separate results of women empowerment for Pakistan and Bangladesh didn’t match with the other results.

Domestic Violence and Child Immunization
The table 5.2represent the impact of domestic violence on children (upto five year age) immunization status in selected developing South Asian countries (Pakistan, Nepal, India and Bangladesh) which shows that 72.47% children live with those mothers who are not bear domestic violence and fully immunized while 27.53% children live with those mothers who are bear the violence but fully immunized this shows that the immunization status of those children is high where no any violence exit and it has negative impact on child healthcare. Intimate partner violence is also known as domestic violence and spousal abuse. Intimate partner violence is a multidimensional phenomenon that includes physical, emotional, and sexual violence, as well as stalking. The prevalence of physical intimate partner violence in different parts of the world has been estimated to range between 13 and 61% Garcia-Moreno, (2006). Intimate partner violence has cascading negative effects on the economic wellbeing Renzetti, (2009), physical health Matthew, (1996) and mental health DeJonghe, (2008) of individual victims, as well as on the incidence of unintended pregnancy Pallitto, (2004). According to our final results domestic violence has negative impact on child health care with -0.5111 probabities.

The results of domestic violence have the same impact on child immunization status for Nepal and India like collective countries analysis but the separate results of domestic violence for Pakistan and Bangladesh didn’t match with the other results.

Gender of Child and Child Immunization
The table 5.3 represent the vaccinations status in selecting developing Asian countries of children (upto five years age) for male and female which shows that the children who are female 47.59% are fully immunized while the male children 52.41% are fully immunized the figures represent clear gender disparity in healthcare. Such type of disparities is also evidence by Chatterjee, (2014).
Birth Order and Child Immunization
The table 5.2 shows the relationship among the birth order of children and child immunization status the children whose birth order is less than equal to two 54.75% and fully immunized while the children whose birth order is greater than two 29.05% and fully immunized. This values shows that as well as the birth order of child being greater the vaccination status being low or the immunization status of children decreases. It negatively affects the child immunization status Khan and Raza, (2013) our final results are same to the Khan and Raza theory with -0.1315 probabities.

Gender of household head and Child Immunization
Table 5.2 shows that the children who living with female household head, 15.46% and fully immunized while the children who are live with male house hold head 84.54% and fully immunized. Our final results show negative impact on child immunization status according to table 5.1 with -0.4069.

No. of children under five year age and Child Immunization
Table 5.2 shows that the children who are under five year age in a house and strength of children are 2 or 1 83.85% fully immunized while if the strength of children under five year age is greater than two 15.36% are fully immunized. This shows that as well as the strength of children under five year age increase the immunization status will being decrease. Our final result in table 5.1 shows negative impact of number of children under five year age on child immunization status with -0.3019 probabilities.

Wealth Index and Child Immunization
The table 5.2 shows the relationship among the wealth index and immunization status the children who belong to the poorest socioeconomic status family 17.26% fully immunized, the children who belong to poorer socioeconomic status families 19.13% are fully immunized, the children who belong to middle socioeconomic status families 21.11% fully immunized, the children who belong to richer socioeconomic status families 22.69% fully immunized while the children who belong to richest socioeconomic status families 19.18% fully immunized. We have used wealth index socioeconomic variable to check the affect of socioeconomic status of family on immunization level of children. If the family has rich socioeconomic status then they will be able to get the more immunization for their children as compare to those parents who have poor socioeconomic status. Our results shown that the middle all categories of wealth index show positive and significant association with child immunization with 0.2305 and 0.2867 probabilities. We also have analyzed the wealth index for all countries separately the results of wealth index have same impact on child immunization level like the collective analysis.

Place of Residence and Child Immunization
Table 5.2 shows that the children living in rural area, 56.74% fully immunized, while 43.26% are residents of urban area who fully immunized. The econometric results of our current study show that place of residence has significant but negative impact on the status of child immunization with -0.0866 probabilities.
Mother’s Education Level and Child Immunization
The table 5.2 shows the impact of mother’s education level on child healthcare and we categorize it into different parts. The children who fully immunized according to their mother’s education level are illiterate mother’s children (21.24%), primary educated mother’s children (18.42%), middle educated mother’s children(42.14%), and higher educated mother’s children(18.20%). Mother’s education has been included to check the impact of the educated mothers on the level of immunization in children under five year of age. We will see the analysis to check the impact of different level of education of mothers and its impact on immunization level of children. Educated women have more awareness about the proper immunization schedule streatfield, et.al, (1990). Immunization status is low in uneducated parents as compare to educated families Naee, et,al. (2012).

Mother’s with education levels of primary, middle, and higher shows positively and significantly impact on child immunization. The educated mothers are also less likely to discriminate against girls over health care practices. The complete immunization is in the favor of boys among the illiterate mothers while it is in the favor of girls where the mothers have some sort of literacy. Our results are in agreement that “education enhances knowledge about effective ways to prevent, recognize, and treat childhood illnesses” Cleland, (1989) cited in Streatfield, et.al, 1990).

Father’s Education Level and Child Immunization
The table 5.2 shows the impact of father’s education level on child healthcare status and we categorize it into different parts. The children who are fully immunized according to their father’s education level are illiterate father’s children (12.06%), primary educated father’s children (21.90%), middle educated father’s children (44.99%), and higher educated father’s children (21.86%). Father’s education has been included to check the impact of the educated fathers on the level of immunization in children under five year of age. We will see the analysis to check the impact of different level of education of fathers and its impact on immunization level of children. Father’s with education levels of primary, middle, and higher shows positively and significantly impact on child immunization. Marginal effects also show that with the increase one unit of father education the immunization of children is more.

Chapter 6
In the previous chapter we analyze the impact of dimension of women empowerment (Empowerment and domestic violence) on child immunization status. There is assumed that all the two principle variables which are related with mother autonomy may affect the child immunization status of male and female children differently. It is based on the evidence that mother education propagate girls education more than the boys (Cleland, 1989,) cited in Streatfield et al, 1990). It has been found in the literature that female household head are more probable to go to school. There are some evidences related with child health as well (e,g.) the children from female household are comparatively less probable to be malnourished. The female education also has the same kind of effect Chatterjee, (2014).

In the coming section we’ll see the effect of women empowerment and women domestic violence on male and female children separately. The dimension of women empowerment as an explanatory variable has been included in the analysis to see whether it effect the immunization of male and female children
6.1 Male and female children under five years of age comparison of immunization in Selected South Asian countries
Male Children Female Children
Variable Coefficient Marginal Effect p- Value Variable Coefficient Marginal Effect p- Value
Women Empowerment(no empowerment as reference) WEMPOWER
Empowered 0.1934 0.0435 0.031** 0.5718 0.1298 0.005*
Domestic Violence(violence exist as reference) DVIOL
Violence exist -0.5374 -0.1210 0.000* -0.4929 -0.1119 0.000*
Wealth Index(poorest as reference) WI
Poorer 0.1438 0.0332 0.055*** 0.0905 0.0208 0.239
Middle 0.2280 0.0526 0.003* 0.2375 0.0545 0.003*
Richer 0.3242 0.0745 0.000* 0.2518 0.0578 0.004*
Richest 0.5304 0.1205 0.000* 0.2708 0.0621 0.009*
Mother Education Level(illiterate as reference) MEL
Primary 0.2936 0.0694 0.000* 0.4060 0.0973 0.000*
Middle 0.4784 0.1125 0.000* 0.6282 0.1502 0.000*
Higher 0.7544 0.1744 0.000* 1.0269 0.2401 0.000*
Father Education Level(illiterate as reference) 5.FEL
Primary 0.5258 0.1216 0.000* 0.4234 0.0983 0.000*
Middle 0.4076 0.0946 0.000* 0.2851 0.0664 0.000*
Higher 0.5014 0.1161 0.000* 0.4648 0.1078 0.000*
Number of children under five(continuous) 6.NCUF
Less than equal to 2 -0.2965 -0.0667 0.000* -0.3097 -0.0703 0.000*
Gender of Household Head(female as reference) 8.GHH
Male -0.4223 -0.0951 0.000* -0.3822 -0.0868 0.000*
Birth Order(continuous) 9.BON
Less than equal to 2 -0.1648 -0.0371 0.000* -0.0937 -0.0212 0.002*
Place of Residence(rural as reference) 10.POR
Urban -0.1284 -0.0289 0.019** -0.0441 -0.0100 0.434
No. of observation
= 7787 Prob>chi2=0.0000 No. of observation
= 7276 Prob>chi2=0.0000 Prob>chi2=0.0000
LR chi2(16)= 724.30 Pseudo R2=0.0676 LR chi2(16)= 654.60 Pseudo R2=0.0651 Pseudo R2=0.0690
(*shows significance at 1 % level, & **shows significance at 5% level,)
Table6.1 represents the econometric analysis and marginal effect for all the variables which tell us how immunization status affected by different variables for male and female children.

6.2Percentage estimates of vaccination for male and female children separately
Male Children Female Children
Variables
Women Empowerment WEMPOWER
Empowered 8.96 2.29
Not empowered 91.04 97.71
Domestic Violence DVIOL
Exit 30.42 27.30
Not Exit 69.58 72.70
Wealth Index WI
Poorest 17.31 17.21
Poorer 19.32 18.96
Middle 21.11 21.11
Richer 23.09 22.31 Richest 19.27 20.30 Mother Education level MEL
Illiterate 21.74 20.72
Primary 18.31 18.53
Middle 41.71 42.64
Higher 18.42 18.15
Father Education level 5.FEL
Illiterate 11.07 12.45 Primary 21.15 21.02 Middle 46.00 43.89
Higher 21.15 22.64
Number of children under five 6.NCUF
Less than equal to 2 84.28 83.38 Greater than 2 15.02 15.73 Gender of household head 8.GHH
Male 83.94 85.22
Female 16.06 14.78
Birth order number 9.BON
Less than
equal to 2 54.86 54.83
Greater than 2 29.84 28.18
Place of residence 10.POR
Urban 42.89 43.67 Rural 57.11 56.33 No. of observation
= 7787 Prob>chi2=0.0000 No. of observation
= 7276 Prob>chi2=0.0000 Prob>chi2=0.0000
LR chi2(16)= 724.30 Pseudo R2=0.0676 LR chi2(16)= 654.60 Pseudo R2=0.0651 Pseudo R2=0.0690
(*shows significance at 1 % level, & **shows significance at 5% level,)
Table6.2 represents the percentage results for all the variables which tell us how immunization status effect by different variables for male and female children.

Women Empowerment
The econometric results of current study show that Women empowerment has positive and significant impact on child immunization. However if women have empowerment then they are indifferent among the immunization status of their children on the bases of gender Ali et.al, (2011). The results shows that the female children are more completely immunized if the women are empowered as compare to male children. The female children are more immunized 0.5718 probability with the significant p value but male children are less immunized with 0.1934 probability with significant p. value and the percentage results also shows that the children who live with empowered women 91.04% male and 97.71% female which fully immunized while 8.96% male children live with those mothers who have no empowerment and 2.29% female children which fully immunized.

Domestic Violence
According to empirical results the domestic violence has negative and significant impact on child immunization status. The impact of domestic violence for male children is greater than as compare to female children which means that when mothers do not bear any violence they do more attention for baby female as compare to male. The immunization status for male children is less and -0.5347 probability with the significant p. value as compare to female children (Stephanie Holta, et al. (2008). Marginal effects also have the same results as the econometric analysis. The results are supported for male children by the percentage analysis. Our percentage estimations show that 30.42% male children live with those mother who bear violence and 27.30% girl and fully immunized while 69.58% male children live with those mother who not bear any violence and fully vaccinated and 72.70% female children live with those mother who not bear any violence and fully vaccinated.

Wealth index
It is found that wealth index as a substitution socioeconomic status of household positively impact on the immunization level of male children. Conceptully, it may be believed that children from good socioeconomic status household’s peroxide by household wealth index are more likely to be immunized. The higher the value of wealth index as a proxy of wealth increases the financial statuss of the households, so richer households should have healtherier children while other things remain equal. If the family is rich and they have more wealth then they will be able to get the more immunization for their children as compare to those parents who are poor. Our results shown that the middle all categories of wealth index show positive and significant association with child immunization but the immunization status for male children are higher as compare of female children. The male children are more immunized if the household is richer with the second position and the probability of 0.3242 with significant p value but female children are less immunized with the probability of 0.2518 with the significant p. value. A household is an important determinant of child health utilization. Since the poor households have fewer resources, therefore they usually tend to spend more on boys as compared to girls implying that poorer households have traditional attitudes and they consider that sons will give return to them in old age while daughters would move to husband’s home after marriage.(Jatrana, 2003; Pande, 2003). Mothers who are employed in government sector are much more likely to immunize their children, because they have much more income with government job (Naee, et.al. 2012). The table 6.2 shows the relationship among the wealth index and immunization status the male children who belong to the poorest socioeconomic status family 17.31% fully immunized, the male children who belong to poorer socioeconomic status families 19.32% fully immunized, the male children who belong to middle socioeconomic status families 21.11% fully immunized, the male children who belong to richer socioeconomic status families 23.09% fully immunized and the male children who belong to richest socioeconomic status families 19.27% fully immunized while among them the female children who belong to the poorest socioeconomic status family 17.21% fully immunized, the female children who belong to poorer socioeconomic status families 18.96% fully immunized, the female children who belong to middle socioeconomic status families 21.11% fully immunized, the children who belong to richer socioeconomic status families 22.31% fully immunized while the female children who belong to richest socioeconomic status families 20.30% fully immunized.

Mother Education level
Education of mothers plays an important role in the immunization of children. Educated women have more awareness about the proper immunization schedule streatfield, et.al.(1990) immunization status is low in uneducated parents as compare to educated families Naee, et.a, (2012). Role of educated mothers has been found to be more marked and importance for immunization in the poor families than in the non-poor families Arif, (2004). Estimates expose that education of father is positively associated with health seeking behaviour and mother’s education is highly related with child health consequences Monaza, et.al, ( 2012).

The results show that mother’s with education levels of primary, middle, and higher shows positively and significantly impact on child immunization. The educated mothers are also less likely to discriminate against girls over health care practices. The mothers with primary education are mostly prefer to immunized the female children (0.4060 probability) as compare to male children with the probability of 0.2960 and both have significant p. values. Middle educated mothers are mostly prefer to immunized the female children (0.6282 probability) as compare to male children with the probability of 0.4784 and both have significant p. The mothers with higher education are mostly prefer to immunized the female children (1.0269 probability) as compare to male children with the probability of 0.7544 and both have significant p. values. The complete immunization is in the favor of boys among the illiterate mothers while it is in the favor of girls where the mothers have some sort of literacy. The table 6.2 shows the impact of mother’s education level on child healthcare and we categorize it into different parts. The male children who fully immunized according to their mother’s education level are illiterate mother’s male children (21.74%), primary educated mother’s male children (18.31%), middle educated mother’s male children (41.71%), and higher educated mother’s male children (18.42%) while among them the female children who fully immunized according to their mother’s education level are illiterate mother’s female children (20.72%), primary educated mother’s female children (18.53%), middle educated mother’s female children (42.64%), and higher educated mother’s female children (18.15%). Our results are in agreement that “education enhances knowledge about effective ways to prevent, recognize, and treat childhood illnesses” Cleland, (1989) Streatfield, et.al, (1990).

Father Education level
Like mother education father’s education also play an important role to immunize the children. Father’s education has been included to check the impact of the educated fathers on the level of immunization in children under five year of age. We will see the analysis to check the impact of different level of education of fathers and its impact on immunization level of children. Father’s with education levels of primary, middle, and higher shows higher and significantly impact on male children immunization status with the (0.5258, 0.4076, 0.5014 probabilities) as compare to female children with the (0.4238, 0.2851,0.4246 probabilities) both have significant p. values. Marginal effects also show that with the increase one unit of father education the immunization of male children is more as compare to female children. The table 6.2 shows the impact of father’s education level on child healthcare status and we categorize it into different parts. The male children who are fully immunized according to their father’s education level are illiterate father’s male children (11.07%), primary educated father’s male children (21.15%), middle educated father’s male children (46.00%), and higher educated father’s male children (21.15%) while among them the female children who are fully immunized according to their father’s education level are illiterate father’s female children (12.45%), primary educated father’s female children (21.02%), middle educated father’s female children (43.89%), and higher educated father’s female children (22.64%).

Gender of Household Head
Gender of house hold head shows that if the head of household is male than the immunization level is lower for female children (0.3822 probability) as compare to male children which have greater level of immunization level with the (-0.4223probability) both have significant p. values. Gender of household head has negative but significant impact on child immunization status. South Asian developing countries are traditional countries that’s why there is found a male dominant society. Most of the families are considered their males as the head of the family. Table 6.2 shows that the male children who living with female household head, 16.06% and fully immunized and 83.94% fully immunized male children live with male household head while the female children who are live with male house hold head 85.22% fully immunized and 14.78% female fully immunized children live with female household head.

Place of Residence
The results of our current study show that place of residence have significant results for male children but the immunization level for the female children is high which lives with urban residence household with the (-0.0441 probability) as compare to male children with the (-0.1284 probability). Our percentage results show that the male children living in rural area, 57.11% fully immunized and 42.89% fully immunized male children live in urban areas, while 43.67% fully immunized female children residents of urban area and 56.33% female children live in urban areas who fully immunized.

Number of Children Under five
Our results show child immunization is negatively associated with the number of children under age five year for both male and female children in a household. If a child is from a household with more no. of children under five year age, there will be less probability of full immunization as compare to those children which from a house where number of children under five year age is less. The male children are more immunized with the (-0.2965 probability) with those household which have two or less than two children under five year age as compare to female children with (-0.3097 probability) both have significant p. values. Table 6.2 shows that the male children who are under five year age in a house and strength of children are 2 or 1 84.28% fully immunized and if the strength of male children under five year age is greater than two 15.02% are fully immunized while the female children who are under five year age in a house and strength of children are 2 or 1 83.38% fully immunized and if the strength of children under five year age is greater than two 15.73% are fully immunized. This shows that as well as the strength of children under five year age increase the immunization status will being decrease.

Child Birth order Number
Birth-order of the child has shown significant results. It negatively affects the immunization level of children. Such type of impact is supported by a number of studies (Habibov, 2011). The possible explanation may be that children with less birth order number have higher level of immunization as compare to greater birth order number children. Econometrics analysis has shown that if the birth order number is greater than 2 then the immunization level of male children is less as compare to female children with the probability of -0.1648 and -0.0212 respectively. Both have significant impact on the immunization level. Marginal effects also have the same results as the econometrics analysis. The table 6.2 shows the relationship among the birth order of children and child immunization status the male children whose birth order is less than equal to two 54.86% and fully immunized and the male children whose birth order is greater than two 29.84% fully immunized while the female children whose birth order is less than equal to two 54.83% and fully immunized and the female children whose birth order is greater than two 28.18% fully immunized..

Chapter 7
Conclusion and Policy Recommendations
This chapter deals with the conclusion drawn from the analysis of the data. In the following chapter the briefly discussion will be on the all final results and final conclusion of all the research major objective and achievements.

7.1Summary
The present study aimed at investigating Dimensions of women empowerment and child healthcare: pooled analysis for South Asian developing countries. Major objective of the study were a), to identify the collectively immunization status for children of selective South Asian countries (Pakistan, Nepal, India and Philippines) under five years of age children; b), to make a comparison of immunization status for male and female children under five years of age. Some research questions are formulated to achieve objectives. Those were 1). Is there all children under five years of age have completely immunized or not? 2). Is the immunization level of male children is higher as compare to female children?
This study follow the binary logistic technique and the data set have been taken from DHS by different South Asian developing countries for different years on more effective variables to check the immunization in children. Total sample of the children were 15,165, 7,787 for male children and 7,276 for female children. The logistic results have been found as well as the percentage analysis and the results are described in two sections. Findings of the study indicate that there is need for improvement in facilities provision regarding to immunization of children.
In the next part of this chapter detail conclusion were drawn.

7.2Conclusions
For enhancing the immunization level of children government must take time, careful planning, resources and environment must be extended. Government also made such policies by which awareness about the women empowerment and domestic violence may create in females. In some areas of developing countries some positive steps have not been taken for this purposes. This study finds that:
Women empowerment has positive and significant impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children.

Domestic violence has negative but significant impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children.

Birth order number, number of children under five years of age has negative but significant impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children. Male and female children are more immunized if the birth order number and number of children under five years of age are less than equal to 2.

Mothers and fathers education level has positive and significant impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children. Overall results can be concluded that the complete immunization increases as the education of parents increases from primary of higher for all the children.

Head of household is female than the immunization of female girls is higher as compare to male children. Place of residence has significant impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children.

Wealth index plays an important and positive impact on the probability of child immunization status, for collectively analysis of all countries and also for separately analysis of male and female children. With the increase in family income the immunization of children will also increase. Male children are more immunized as compare to female children.

7.3Recommendations
From the results and discussion from chapter 5, following recommendations may be found:
Awareness programs must be held by government about the dimensions of women empowerment so, that why women have made decisions about herself, her children and raise voice against the domestic violence.

Gender biasness must be immunized by giving awareness about the importance of male and female children.

Parent’s education must be the part of long run policy so that they can be able to understand the level of immunization.

Household size must be small and people must properly plan about the size of family. So, that they can easily manage the resources for children and their health.

In backward areas the immunization must be provided to enhance the immunization level, especially in rural areas.

Vaccination facilities must be in equal bases for rural and urban areas.

The vaccinators must be aware about the different languages of the country so, that they can communicate the different regions of people properly and easily.

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