An Overview on Neural Network and Its Application

A STUDY, 2018

First step towards AI is taken by Warren McCulloch a neurophysist and a mathematician Walter Pitts. They modelled a simple neural network with electrical circuits and got the results very accurate and derived a remarkable ability of neurons to perceive information from complicated and imprecise data. During the present study it was observed that trained neural network expert in analyzing the information has been provided with other advantages as Adaptive learning, Real Time operation, self-organization and Fault tolerance as well. Apart from convectional computing, neural networking use different processing units (Neurons) in parallel with each other. These need not to be programmed. They function just like human brain. We need to give it examples to solve different problems and these examples must be selected carefully so that it would not be waste of time.we use combination of neural networking and computational programming to achieve maximal efficiency right now but neural networking will eventually take over in future. We introduced artificial neural networking in which electronic models where used as neural structure of brain. Computers can store data as ledgers etc. but have difficulty in recognizing patterns but brain stores information as patterns. Further as artificial neural networking was introduced which has artificial neurons who act as real neurons and do functions as they do. They are used for speech, hearing, reorganization, storing information as patterns and many other functions which a human brain can do. These neural networks were combined and dynamically self-combined which is not true for any artificial networking. These neurons work as groups and sub divide the problem to resolve it. These are grouped in layers and it is art of engineering to make them solve real world problems. The most important thing is the connections between the neurons, it is glue to system as it is excitation inhibition process as the input remains constant one neuron excites while other inhibits as in subtraction addition process. Basically, all ANN have same network that is input, feedback or hidden and output.

Information & Management, 1994

Artificial neural networks are increasingly popular in today's business fields. They have been hailed as the greatest technological advance since the invention of transistors. The purpose of this paper is to answer hvo of the inost frequently asked questions: "What are neural networks?" " Why are they so popular in today's business fields?" The paper reviews the common characteristics of neural networks and discusses the feasibility of neural-net applications in business fields. It then presents four actual application cases and identifies the limitations of the current neural-net technology.

International Journal for Research in Engineering Application & Management , 2018

Improvements in compute power over the years has helped mathematicians and computer scientist to mainstream complex mathematical models and prediction for business. One such method is using Artificial Neural Networks (ANN) which is helping business community to augment their abilities to predict business outcomes. Current researches in ANN has led to significant progress in understanding customer behaviour, financial risk, prevent fraud among many others. A significant role has also been played by cloud computing companies like Amazon, IBM, Microsoft together with open source initiatives of organizations like Apache Foundation, Google etc., has helped the cause of the erstwhile business manager who were otherwise were having limited tools to predict, say, sales for the next quarter.. In this paper, we describe the basics of an artificial neural networks and its usage in businesses.

In this paper, we are expounding Artificial Neural Network or ANN, its different qualities and business applications. In this paper we additionally demonstrate that "what are neural systems" and "Why they are so essential in today's Artificial knowledge?" Because various advances have been made in creating Intelligent framework, some roused by natural neural systems. ANN gives an exceptionally energizing choices and other application which can assume imperative part in today's software, Computer engineering field. There are a few Limitations likewise which are said. An Artificial Neural Network (ANN) is a data handling worldview that is motivated by the way natural sensory systems, for example, the mind, prepare data. The key component of this worldview is the novel structure of the data preparing framework. It is made out of an extensive number of exceptionally interconnected handling components (neurons) working as one to take care of particular issues. ANNs, similar to individuals, learn by illustration. An ANN is designed for a particular application, for example, design acknowledgment or information arrangement, through a learning procedure. Learning in natural frameworks includes conformity to the synaptic associations that exist between the neurons. This is valid for ANNs too. This paper gives outline of Artificial Neural Network, working and preparing of ANN. It additionally clarifies the application and points of interest of ANN.

Deep neural networks (DNNs) are rapidly being used in safety-critical areas such as drone and aircraft control, supporting techniques for analyzing the safety of actions. Unfortunately, DNN analysis is NP-hard and existing algorithms become slower as the number of nodes in the DNN increases. Neural networks, a subset of artificial intelligence, have rapidly evolved, transforming the landscape of machine learning. Inspired by the structure and function of the human brain, these computational models have demonstrated exceptional capabilities in various applications. This research paper provides a comprehensive analysis of neural networks, encompassing their historical development, architectural components, training methodologies, real-world applications, existing challenges, and future directions.

A neural network is a collection of neurons that are interconnected and interactive through signal processing operations. The traditional term "neural network" refers to a biological neural network, i.e., a network of biological neurons. The modern meaning of this term also includes artificial neural networks, built of artificial neurons or nodes. Machine learning includes adaptive mechanisms that allow computers to learn from experience, learn by example and by analogy. Learning opportunities can improve the performance of an intelligent system over time. One of the most popular approaches to machine learning is artificial neural networks. An artificial neural network consists of several very simple and interconnected processors, called neurons, which are based on modeling biological neurons in the brain. Neurons are connected by calculated connections that pass signals from one neuron to another. Each connection has a numerical weight associated with it. Weights are the basis of long-term memory in artificial neural networks. They express strength or importance for each neuron input. An artificial neural network "learns" through repeated adjustments of these weights.

2014

Artificial Neural Network (ANN) is an information processing paradigm that is inspired by the way biological nervous systems, such as the brain, process information. The key element of this paradigm is the novel structure of the information processing system. Neural networks, have remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques. A trained neural network can be thought of as an "expert" in the category of information it has been given to analyze. This expert can then be used to provide projections given new situations of interest and answer "what if" questions. so in this paper we tried to introduce a brief overview of ANN to help researchers in their way throw ANN.

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

Digital Systems, 2018

Due to the recent trend of intelligent systems and their ability to adapt with varying conditions, deep learning becomes very attractive for many researchers. In general, neural network is used to implement different stages of processing systems based on learning algorithms by controlling their weights and biases. This chapter introduces the neural network concepts, with a description of major elements consisting of the network. It also describes different types of learning algorithms and activation functions with the examples. These concepts are detailed in standard applications. The chapter will be useful for undergraduate students and even for postgraduate students who have simple background on neural networks.

In this research project, the features of biological and artificial neural networks were studied by reviewing the existing works of authorities in print and electronics on biological and artificial neural networks. The features were then assessed and evaluated and comparative analysis of the two networks was carried out. The metrics such as structures, layers, size and functional capabilities of neurons, learning capabilities, style of computation, processing elements, processing speed, connections, strength, information storage, information transmission, communication media selection, signal transduction and fault tolerance were used as basis for comparison. A major finding in the research showed that artificial neural networks served as the platform for neuro-computing technology and as such a major driver of the development of neuron-like computing system. It was also discovered that Information processing of the future computer systems will greatly be influenced by the adoption of artificial neural network model.

International Journal for Research in Applied Science & Engineering Technology (IJRASET), 2021

The purpose of this study is to familiarise the reader with the foundations of neural networks. Artificial Neural Networks (ANNs) are algorithm-based systems that are modelled after Biological Neural Networks (BNNs). Neural networks are an effort to use the human brain's information processing skills to address challenging real-world AI issues. The evolution of neural networks and their significance are briefly explored. ANNs and BNNs are contrasted, and their qualities, benefits, and disadvantages are discussed. The drawbacks of the perceptron model and their improvement by the sigmoid neuron and ReLU neuron are briefly discussed. In addition, we give a bird's-eye view of the different Neural Network models. We study neural networks (NNs) and highlight the different learning approaches and algorithms used in Machine Learning and Deep Learning. We also discuss different types of NNs and their applications. A brief introduction to Neuro-Fuzzy and its applications with a comprehensive review of NN technological advances is provided.

ipcsit.com

Neural Network technology performs "intelligent" tasks similar to those performed by the human brain. Today, many researchers are investigating Neural Networks, the network holds great potential as the front-end of expert system that require massive amount of inputs from sensor as well as real-time response. Neural Networks has been successfully applied to broad spectrum of data-intensive applications, such as; Process modeling and control, Machine diagnosis, Medical diagnosis, Voice Recognition, Financial forecasting, Fraud detection. In this paper presentation, real-world applications of neural network was considered including "Traveling Salesman Problem Routes". Elements of an Artificial Neural System (ANS), Characteristics of (ANS), Historical Developments in (ANS) Technology, Applications of (ANS) Technology, Commercial Development in (ANS), Neural Networks versus conventional computers, etc was also given due consideration.