IMAGE CLASSIFICAION OF ALL MTU CHAPEL BOOKS USING ARTIFICIAL NEURAL NETWORK

CHAPTER ONE

INTRODUCTION

Background to the study: The deep neural network is also regarded as the feature that mimics the functions in the processing of data and of the human brain producing patterns for decision-making. A subset of artificial intelligence is deep learning in machine learning which has networks able to learn from unstructured or unlabeled knowledge. They are efficient recognizers and classifiers of patterns. They act as black-box, model-free, and adaptive instruments to capture and learn critical data structures. In the fields of prediction and estimation, pattern recognition and optimization, their computational skills have been proven. They are especially suitable for problems that are too complex for classical mathematics and conventional procedures to model and solve. (Mehrotra et al., 1997).

One of the reasons for popularity of the neural network is the development of the simple error backpropagation (BP) training algorithm which is based on a gradient-descent optimization technique Training a neural network with a supervised leaning algorithm such as BP implies using a series of training examples to find the weights of the links connecting the nodes. An error function in the form of the number of the squares belonging to the errors between the true ones which the output from the training set and the computed outputs is minimized iteratively. The law of learning or training determines how the weights are changed in each iteration.

In computer vision, classification of images relates to a phase that can identify an image by its visual content. An image classification algorithm, for example, it could be designed to say whether a picture includes a human figure or not. While for humans, there is the identification of an object that is trivial, in computer vision applications, a robust picture classification is still a big challenge. The most common neural network class used for image classification is Convolutional Neural Networks (CNNs). CNN consists of layers of input and output with multiple hidden layers in-between. It derives its name from the kind of hidden layers of which it consists. Usually, the hidden layers of a CNN consist of convolution layers, pooling layers, completely related layers and layers of normalization. CNN uses a kernel and slides it through the image to create the convolutional layer. (Amir Shahroudy et al., 2017)

1.2 Statement of the problem

the proposed system is set to eradicate the issue associated with the manual way, the chaplaincy unit check for students present with their Mountain Top University chapel books but by the introduction of Image Classification Model which will make the chaplaincy officials to stop one-by-one checking of chapel books or manual, which will take a lot of time especially in a situation where Mountain Top University grows to the extent of having over 10, 000 students

1.3 Aim and Objectives

The aim of this study is to use methods of deep learning to develop a model that can accurately classify images. The objectives are to;

1. Collect the data to be used to train and test the model.
2. Create several models and construct the layers of the models or use transfer learning.
3. Train different models using the data collected
4. Test the accuracy of the models to find the most accurate model.

1.4 Scope of the study

This research is limited to Mountain Top University as the model is made to classify the various books used by students in the student chapel.

1.5 Significance of the study

This research provides an application that serves as the framework for platform upon which applications can be built and operationalized for the classification of images worldwide. This model can be used to verify if students are with the valid chapel material depending on the service at hand.

1.6 Definition of Terms

Convolutional Neural network: Convolutional neural networks are a subset with profound neural networks that are most widely used for visual imagery analysis.

Deep Learning: Deep learning is part of a wider family of techniques for machine learning focused on artificial neural networks with teaching representation.

Artificial Neural network: An artificial neural network (ANN) is the piece of a computing system to simulate the human brain's way of analyzing and processing information.

Web Scraping: Web scraping is the scraping of data used for website data extraction.

Machine Learning: Machine learning is the study of algorithms in computers that are improved by experience automatically.