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Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 pp. 1001-1005 © Krishi Sanskriti Publications http://www.krishisanskriti.org/jbaer.html Advance Applications of Artificial Neural Network Sujata Saini1 and Preeti2 1,2 Department of Computer Science and Application, Govt. College for Women, Rohtak E-mail: [email protected], [email protected] Abstract—This paper explores the various applications of neural network. Neural network desire to produce artificial systems capable of sophisticated computations similar to the human brain. The focus of this paper is on the importance and the influence of different types of neural schemas which played a critical role in business and medical applications. Neural network is one of the most important components in Artificial Intelligence. It has been studied for many years in the hope of achieving human-like performances in many fields like speech reorganization and computer vision as well as information retrieval. To make the term 'neural network' used in this paper clear to expand considerably on its content, it is useful to analyze the general structure of a neural network and explore the advantages and the applications of neural network. Beginning with a primarily definition and typical structure of neural networks at different stages, Neural networks are studied with respect to their learning processes and architecture structures like Human beings in artificial intelligence. A case study on some specific networks and related algorithms is followed and developed with regarding neural objects. The applications of neural network models and related algorithms in information retrieve systems are analyzed as well as investigated. widely applicable to risk management and forecasting. Since the various neural-network systems now in use are implemented with mathematically sound principles, they hold out promise for future applications. 2. ARTIFICIAL NEURAL NETWORK An artificial neural network does not emulate the thought processes and if/ then logic of the human brain as done by an expert system. The basic model assumes that information processing takes place through the interaction of large number of highly interconnected processing elements called neurons. Researchers have developed a similar tool with same mechanism as that of human brain works, and are called as “Artificial Neural Networks” (ANN) An ANN consists of a large number of simple processing elements-thatinterconnected. 1. INTRODUCTION An artificial network is an interconnected group of nodes, akin to the vast network of neurons in a brain. Each circular node represents Neural Networks basically aim at mimicking the structure and functioning of the human brain, to create intelligent behavior. Researchers have attempted to build a silicon-based electronic network that is modeled on the working and form of the human brain. Neural networks can performed successfully where other methods can not, predicting system behavior, recognizing and matching complicated, vague, or incomplete data patterns. Apply Artificial Neural networks to pattern recognition, interpretation, prediction, diagnosis, planning, monitoring, debugging, instruction, repair, and control. Recently, applications of artificial neural net- works have been increasing in business and medical fields for last few years. More and more development tools are emerging on the market. Many neural-network systems have been shown to work well in identifying intricate patterns, learning from experience, reaching some conclusions, and making predictions. Neural-network systems have already been at work for over 10 years in the finance world. Now, they are Fig. 1.1: Artificial neural network (structure of neuron) 3. NEURAL NETWORK ARCHITECTURES There are three fundamental different classes of network architectures: 1) Single-layer feed forward Networks : The single layered neural network where neurons are organized in the form of layers(Fig. 1.2). In the simplest form of a Sujata Saini and Preeti 1002 layered network, an input layer of source nodes that projects onto an output layer of neurons, but not vice versa. This network is strictly a feed forward type network. In single-layered network, there are only one input and one output layer. Input layer is not counted as a layer whether no mathematical calculations take place at this layer. 2) Multilayer feed forward Networks : The second class of a feed forward neural network establishes itself by the presence of one or more hidden layers(Fig. 1.3). The function of hidden neuron is to intervened between the external input and the network output in some useful manner. The input signal applied to the neurons in the second layer. In Multilayer, The output signal of the second layer is used as inputs to the third layer, and so on for the rest of the network. 3) Recurrent networks: A recurrent neural network has at least one feedback loop(Fig. 1.4). A recurrent network consists of a single layer of neurons with each neuron feeding its output signal back to the inputs of all the other neurons. Self-feedback refer to a situation where the output of a neuron is fed back into its own input. The presence of feedback loops have a profound impact on the learning capability of the network and on its performance. ( Graphical Representation ) Fig. 1.2: Single-layer Feed-forward Network Fig. 1.4: Recurrent Network 4. APPLICATIONS OF ARTIFICIAL NEURAL NETWORK 1) Regression analysis Regression analysis is a statistical process used for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, Regression analysis is widely used for prediction and forecasting. where its use have substantial overlap with the field of machine learning. Regression analysis is usually used to understand which among the independent variables are related to the dependent variable, and to explore the forms of these relationships. 2) Pattern and sequence recognition Pattern and sequence recognition is a branch of machine learning that focuses on the recognition of patterns and regularities in data, although it is in few cases considered to be nearly synonymous with machine learning. . In pattern recognition, there may be a higher interest to formalize, visualize and explain the pattern. The terms pattern recognition, machine learning, sequence recognition, data mining and knowledge discovery in databases. Pattern recognition has its origins in engineering and the term refer from computer vision. Pattern recognition is generally categorized according to type of learning procedure used to generate the output value. 3) Swarm Intelligence This is an approach to, as well as application of artificial intelligence is similar to neural network. Programmers study represent how intelligence emerges in natural systems like swarms of bees even though on an individual level, a bee just follows simple rules. They study relationships in nature like some prey-predator relationships that give an insight in to how intelligence emerges in a swarm or collection from simple rules at an individual level. 4) Machines learning Fig. 1.3: Multi-layer Feed-forward Network In Machine learning and cognitive science, artificial neural networks are a family of statistical learning algorithms inspired by biological neural networks (the central nervous Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 Advance Applications of Artificial Neural Network systems of animals as well as humans, in particular the brain) and are used to estimate or approximate functions in ANN. It has strong ties to statistical and mathematical optimization, which deliver methods, theory and application domains to the field. Machine learning is also described as a scientific discipline that explores the construction and study of algorithms that can learn from data. Such algorithms are operate by building a model from example inputs and using that to make predictions or decisions, rather than following strictly static program instructions. Machine learning is closely related to overlaps with computational statistics a discipline that also specializes in prediction-making. 5) Social computing As we know, Social problems vary form country to country. So, researchers in the field of social science implement the approaches in their native problems by their own ways. Huge amount of data analysis is involved in many problems of that kind. Whenever, Finding different patterns from this data, inexact or hierarchical matching of patterns and making future predictions for intelligent decision making are the real challenges. By analysis of huge amount of data, application of computational tools like Artificial Neural Network like Association rules, Decision trees, Cluster Analysis are widely used. Social computing use the approaches like Data processing, including filtering, Clustering, Blind signal separation and compression. 6) Stock market prediction Neural networks , as well as artificial intelligence methods, have become very important in making stock market predictions. Much research on the applications of NNs for solving business problems have proven their advantages over statistical and other methods that do not include Artificial intelligence, although there is no optimal methodology for a certain problem. In order to identify the main benefits and limitations of previous methods in NN applications and to find connections between methodology and data models, problem domains and results obtained a comparative analysis of selected applications is conducted. It can be concluded from analysis that Neural-nets are most implemented in forecasting stock prices, returns, and stock modeling, and the most frequent methodology is the Back-propagation algorithm. The importance of neural-net integration with other artificial intelligence methods is emphasized by numerous authors. Inspite of many benefits, there are some limitations that should be investigated, such as the relevance of the results, and the "best" topology for the certain problems. 7) Medical Diagnoses Evaluations of the key prognostic factors in different forms of cancer have shown that we must have more precise therapy guidelines and also more accurate prediction of the patients outcome. Statistical analysis should be very useful for the clinician as a tool which provide more clarity to the 1003 complicated classification systems, risk group categories or therapeutic options. The TNM system is a key tool in oncology, describing the anatomic extent of the different forms of cancer are helpful to the clinician in the process of therapeutic choice. The system has its own limitations, although it has specifications for every organ location that does not comprisemany newer markers or pathological findings, which are necessary for specific diagnosis and therapy. This is the main reason, why new prediction instruments are needed which would adjust to every specific clinical parameter, giving results of great accuracy. ANNs are a possible solution, permitting to discover nonlinear relationships between all the parameters (depend on each other or independent), being superior to the logistic regression, which need supplementary modeling in order to have a comparable flexibility. With the speed and power of the actual computer hardware and dedicated software, ANNs can easily correlate different prediction factors. Find the hidden interactions among variables, predict an outcome for a group of patients, stratify patients in risk groups, or approximate a function and complete a known pattern. Other possible applications of the ANNs in medicine include, but are not limited to the imaging, diagnosis, pathology and prognosis evaluation of appendicitis, back pain, dementia, myocardial infarction, arrhythmias, psychiatric disorders, acute pulmonary embolism or sexually transmitted diseases. 8) Heavy Industries and Space Robotics and cybernetics have taken a leap combined step with artificially intelligent expert systems. An entire manufacturing process is totally automated now, controlled and maintained by a computer system in car manufacture, machine tool production, computer chip production and almost every hightech process. 9) Aviation Generally, airlines use expert systems in planes to monitor atmospheric conditions and system status. The plane can be put on autopilot once any course is set for the fixed destination. 10) Weather Forecast Basically, Neural networks are used for predicting weather conditions. Previous data is fto be fed for a neural network, which learns the pattern and uses that knowledge to predict weather patterns. 11) Gaming and Decision making Artificial Neural Networks(ANNs) have been in wide use since at least the 1980s for among other things, complex modeling algorithm and various recognition, prediction and filtering tasks. Their ability to learn and evolve has made them attractive as well as efficient to many different fields of research and innovation, including gaming. As an opponent AI has a huge impact on a game's enjoybility, the ability for such an opponent to learn and get better over Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 Sujata Saini and Preeti 1004 time is intriguing. The actions of non-player-characters (NPCs) in any games are either completely pre-determined or rule-based and dependent on different conditions being met . This can be done in such a manner that the NPC will still appear very intelligent and life-like to the player, but often there will be predictable and/or exploitable patterns which will make the game easier than intended or make remind the player too much that they are playing against a computer. This approach can ruin the game for the player looking for an immersive experience or a good challenge. This is why the idea of learning and evolving NPC by means of an ANN, is very interesting. 5. ADVANTAGES OF ARTIFICIAL NEURAL NETWORK 1. 2. 3. 4. 5. 6. 7. Adaptive learning: An ability to learn that how to do tasks based on the data given for training or initial experience. Self-Organization: An Artificial Neural Network can create its own organisation or representation of the information it receives during learning time. Real Time Operation: An ANN computations may be carried out in parallel and special hardware devices are being designed and manufactured which take advantage of this capability. Pattern recognition is a powerful technique used for harnessing the information in the data and generalizing about it. Neural nets learn to recognize the different patterns which existence is in the data set. Generally, the system is developed through learning rather than programming.. Neural nets teach themselves patterns in the data freeing the analyst for more interesting work. Neural networks are flexible in a particular changing environment. Neural networks may take some time to learn a sudden drastic change where they are excellent at adapting to constantly changing information. Neural networks can build informative models whenever the conventional approaches fail. Because neural networks can handle very complex interactions so they can easily model data which is too difficult to model with traditional approaches such as inferential statistics or programming. conventional approaches. Depending on the nature of the applications and the strength of the internal data patterns you can generally expect a network to train quite well. This phase applies to problems where the relationships may be quite dynamic or non-linear. Artificial Neural Network provide an analytical alternative to conventional techniques which are often limited by strict assumptions of normality, linearity, variable independence etc. 7. ACKNOWLEDGMENT The research thesis was finished under the instruction of Dr. Sudesh Lather. The student is grateful to her for her help in the whole process. Special thanks also to Professor Nisha Malik and Professor Suman for providing the reading list and helpful comments. This work was supported in part by a grant from the National Science Foundation. REFERENCES [1] [2] [3] [4] [5] [6] [7] [8] [9] P Venketesh, R Venkatesan, “A Survey on Applications of Neural Networks and Evolutionary Techniques in Web Caching”, IETE Tech Rev 2009;26:171-80. Rich,Knight and B Nair, “Artificial Intelligence”, TMH Publication. [Bradshaw, J.A., Carden, K.J., Riordan, D., 1991. Ecological ―Applications UsingNovel Expert System Shellǁ. Comp. Appl. Biosci. 7, 79–83. Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE Accost. Speech Signal Process. Mag., April: 4-22. N. Murata, S. Yoshizawa, and S. Amari, ―Learning curves, model selection and complexity of neural networks,ǁ in Advances in Neural Information Processing Systems 5, S. Jose Hanson, J. D. Cowan, and C. Lee Giles, ed. San Mateo, CA: Morgan Kaufmann, 1993, pp. 607-614. Kaushal Kumar & Abhishek, “Artificial Neural Networks for Diagnosis of Kidney stone Disease”, I.J. Information technology and computer science July-2012. Muhammad Akmal Sapon Khadijah Ismain & Suehazlyn Zainudin, “ Prediction of Diabetes by Using Artificial Neural Network”, International Conference on Circuits system and simulation 2011. Lawrence, W., Carter J., and Ahmadi, S. "A GCSE Maths Tutoring Game using Neural Networks". 2010 2nd International IEEE Consumer Electronics Society's Games Innovations Conference, 21-23 Dec. 2010. http://www.cogs.susx.ac.uk/users/davec/pe.html. 6. CONCLUSION In this paper, we discussed about the Artificial neural network, working of (ANN). Also training phases of an ANN, there are various advantages of Artificial Neural network over Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 Advance Applications of Artificial Neural Network About the Author Sujata saini was born in Rohtak, in 1993. Sujata saini currently studying M.S.C Computer Science From G.C.W,Rohtak . She received the B.S.C degree in Computer Science from Maharishi Dayanand University, Rohtak in 2013 and her research experience includes 2 years as Junior Researchist under Mrs. Anju Narwal (PHD Scholar) From Maharishi Dayanand. She works in a multi disciplinary environment involving Software Engineeering, Social Media and Communication, DBMS, AI, Neural Networks, Genetic Algorithm, Machine Learning and Robotics . Preeti was born in Delhi, in 1991. Preeti currently studying M.S.C Computer Science From G.C.W,Rohtak . She received the B.C.A degree in Computer Application from Maharishi Dayanand University, Rohtak in 2013 and she works in a multi disciplinary environment involving DBMS, Artificial Intelligence, Neural Networks and Software Engineering . Journal of Basic and Applied Engineering Research Print ISSN: 2350-0077; Online ISSN: 2350-0255; Volume 2, Number 11; April-June, 2015 1005