SMART RISK ANALYST

2019 ◽  
Author(s):  
Dr. SHILPA LADDHA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Engineering Students ◽  
Arbitrary System ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Practical Applications ◽  
Area Of Interest ◽  
Artificial Neural

The book is a special practical guide to all who want to learn the Artificial Neural Networks from a perspective with its practical applications. Having the deep knowledge of theoretical concept, one can apply it for creative decision making. The book is demonstration of a case study which can be implemented on range of topics. This is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (India). She is Doctorate in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

SMART RISK ANALYST

2019 ◽  
Author(s):  
Dr. SHILPA LADDHA
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Credit Risk ◽  
Engineering Students ◽  
Arbitrary System ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Practical Applications ◽  
Area Of Interest ◽  
Artificial Neural

The book is a special practical guide to all who want to learn the Artificial Neural Networks from a perspective with its practical applications. Having the deep knowledge of theoretical concept, one can apply it for creative decision making. The book is demonstration of a case study which can be implemented on range of topics. This is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (India). She is Doctorate in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

scholarly journals Relationship between isoseismal area and magnitude of historical earthquakes in Greece by a hybrid fuzzy neural network method

2012 ◽  
Vol 12 (1) ◽  
pp. 37-45 ◽  
Author(s):  
G-A. Tselentis ◽  
E. Sokos
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Target Function ◽  
Historical Earthquakes ◽  
Strong Nonlinearity ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Abstract. In this paper we suggest the use of diffusion-neural-networks, (neural networks with intrinsic fuzzy logic abilities) to assess the relationship between isoseismal area and earthquake magnitude for the region of Greece. It is of particular importance to study historical earthquakes for which we often have macroseismic information in the form of isoseisms but it is statistically incomplete to assess magnitudes from an isoseismal area or to train conventional artificial neural networks for magnitude estimation. Fuzzy relationships are developed and used to train a feed forward neural network with a back propagation algorithm to obtain the final relationships. Seismic intensity data from 24 earthquakes in Greece have been used. Special attention is being paid to the incompleteness and contradictory patterns in scanty historical earthquake records. The results show that the proposed processing model is very effective, better than applying classical artificial neural networks since the magnitude macroseismic intensity target function has a strong nonlinearity and in most cases the macroseismic datasets are very small.

Analysis on Pattern Classification using Artificial Neural Networks

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
S. Kumar ◽  
S. Singh ◽  
V K Mishra
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Pattern Classification ◽  
Character Recognition ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Artificial Neural

Artificial neural networks (ANN) is one of the most dynamic research and application areas for pattern classification. ANN is the branch of Artificial Intelligence (AI). The network is trained by 'n' number of algorithm like back propagation algorithm. The different combinations of performance functions are used for training the ANN. The back propagation neural network (BPNN) can be used as a highly successful algorithm for pattern classification with suitable combination of performance functions while training and learning ANN. When the maximum likelihood algorithm was compared with back propagation neural network method, the BPNN was more accurate than other algorithms. A Multilayer feed-forward neural network algorithm is also used for pattern classification. However BPNN gives more effective results than other pattern classification algorithms. Handwriting Recognition (or HWR) is the ability of a machine to receive and interpret handwritten input from different sources like paper documents, photographs, touch-screens and other input devices. Various performance functions is examined in this paper so as to get to a conclusion that which function would be better for usage in the network to produce an efficient and effective system. The training of back propagation neural network is done with the application of Offline Handwritten Character Recognition.

scholarly journals Mitigating Faults and Revenue Losses Using Fault Detectors at Trans Amadi Industrial Layout, Port Harcourt Rivers State

2021 ◽  
Vol 9 (12) ◽  
pp. 883-890
Author(s):  
Nwoke G. O.
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Detection ◽  
Transmission Line ◽  
Transmission Lines ◽  
Distribution Networks ◽  
Back Propagation Algorithm ◽  
Artificial Neural ◽  
Industrial Layout

Abstract: Transmission line fault detection is an important aspect of monitoring the health of a power plant since it indicates when suspected faults could lead to catastrophic equipment failure. This research looks at how to detect generator and transmission line failures early and investigates fault detection methods using Artificial Neural Network approaches. Monitoring generator voltages and currents, as well as transmission line performance metrics, is a key monitoring criterion in big power systems. Failures result in system downtime, equipment damage, and a high danger to the power system's integrity, as well as a negative impact on the network's operability and dependability. As a result, from a simulation standpoint, this study looks at fault detection on the Trans Amadi Industrial Layout lines. In the proposed approach, one end's three phase currents and voltages are used as inputs. For the examination of each of the three stages involved in the process, a feed forward neural network with a back propagation algorithm has been used for defect detection and classification. To validate the neural network selection, a detailed analysis with varied numbers of hidden layers was carried out. Between transmission lines and power customers, electrical breakdowns have always been a source of contention. This dissertation discusses the use of Artificial Neural Networks to detect defects in transmission lines. The ANN is used to model and anticipate the occurrence of transmission line faults, as well as classify them based on their transient characteristics. The results revealed that, with proper issue setup and training, the ANN can properly discover and classify defects. The method's adaptability is tested by simulating various defects with various parameters. The proposed method can be applied to the power system's transmission and distribution networks. The MATLAB environment is used for numerous simulations and signal analysis. The study's main contribution is the use of artificial neural networks to detect transmission line faults. Keywords: Faults and Revenue Losses

Contributions to Predict the Malfunction Probability of the Human-Machine-Environment System, Using Artificial Neural Networks

2015 ◽  
Vol 760 ◽  
pp. 771-776
Author(s):  
Daniel Constantin Anghel ◽  
Nadia Belu
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Design Experiment ◽  
Manufacturing Processes ◽  
Feed Forward Neural Network ◽  
Feed Forward ◽  
Linear Relationships ◽  
The Neural Network ◽  
Artificial Neural

This paper presents the application of Artificial Neural Networks to predict the malfunction probability of the human-machine-environment system, in order to provide some guidance to designers of manufacturing processes. Artificial Neural Networks excel in gathering difficult non-linear relationships between the inputs and outputs of a system. We used, in this work, a feed forward neural network in order to predict the malfunction probability. The neural network is simulated with Matlab. The design experiment presented in this paper was realized at University of Pitesti, at the Faculty of Mechanics and Technology, Technology and Management Department.

scholarly journals Prediction of adsorption efficiencies of Ni (II) in aqueous solutions with perlite via artificial neural networks

2017 ◽  
Vol 43 (4) ◽  
pp. 26-32 ◽  
Author(s):  
Sinan Mehmet Turp
Keyword(s):  
Neural Network ◽  
Experimental Data ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Back Propagation ◽  
Data Sets ◽  
Adsorption Efficiency ◽  
Back Propagation Algorithm ◽  
Artificial Neural

AbstractThis study investigates the estimated adsorption efficiency of artificial Nickel (II) ions with perlite in an aqueous solution using artificial neural networks, based on 140 experimental data sets. Prediction using artificial neural networks is performed by enhancing the adsorption efficiency with the use of Nickel (II) ions, with the initial concentrations ranging from 0.1 mg/L to 10 mg/L, the adsorbent dosage ranging from 0.1 mg to 2 mg, and the varying time of effect ranging from 5 to 30 mins. This study presents an artificial neural network that predicts the adsorption efficiency of Nickel (II) ions with perlite. The best algorithm is determined as a quasi-Newton back-propagation algorithm. The performance of the artificial neural network is determined by coefficient determination (R2), and its architecture is 3-12-1. The prediction shows that there is an outstanding relationship between the experimental data and the predicted values.

scholarly journals Diagnosing Diabetes Using Artificial Neural Networks

2020 ◽  
Vol 5 (2) ◽  
pp. 221-224
Author(s):  
Joy Oyinye Orukwo ◽  
Ledisi Giok Kabari
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Success Rate ◽  
Medical Diagnosis ◽  
Learning Algorithm ◽  
Diabetes Diagnosis ◽  
Feed Forward Neural Network ◽  
The Neural Network ◽  
Artificial Neural

Diabetes has always been a silent killer and the number of people suffering from it has increased tremendously in the last few decades. More often than not, people continue with their normal lifestyle, unaware that their health is at severe risk and with each passing day diabetes goes undetected. Artificial Neural Networks have become extensively useful in medical diagnosis as it provides a powerful tool to help analyze, model and make sense of complex clinical data. This study developed a diabetes diagnosis system using feed-forward neural network with supervised learning algorithm. The neural network is systematically trained and tested and a success rate of 90% was achieved.

Artificial Neural Network Modelling for Waste

2011 ◽  
pp. 224-263 ◽  
Author(s):  
Eldon R. Rene ◽  
M. Estefanía López ◽  
María C. Veiga ◽  
Christian Kennes
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Wastewater Treatment ◽  
Back Propagation ◽  
Back Propagation Algorithm ◽  
Network Modelling ◽  
Waste Gas ◽  
Propagation Algorithm ◽  
Artificial Neural

Due to their inherent robustness, artificial neural network models have proven to be successful and have been used extensively in biological wastewater treatment applications. However, only recently, with the scientific advancements made in biological waste gas treatment systems, the application of neural networks have slowly gained the practical momentum for performance monitoring in this field. Simple neural models, after vigorous training and testing, are able to generalize the results of a wide range of operating conditions, with high prediction accuracy. This chapter gives a fundamental insight and overview of the process mechanism of different biological waste gas (biofilters, biotrickling filters, continuous stirred tank bioreactors and monolith bioreactors), and wastewater treatment systems (activated sludge process, trickling filter and sequencing batch reactors). The basic theory of artificial neural networks is explained with a clear understanding of the back propagation algorithm. A generalized neural network modelling procedure for waste treatment applications is outlined, and the role of back propagation algorithm network parameters is discussed. Anew, the application of neural networks for solving specific environmental problems is presented in the form of a literature review.

A Feed forward Neural Network MPPT Control Strategy Applied to a Modified Cuk Converter

2016 ◽  
Vol 6 (4) ◽  
pp. 1421
Author(s):  
Mohamed Tahar Makhloufi ◽  
Yassine Abdessemed ◽  
Mohamed Salah Khireddine
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Control Strategy ◽  
Back Propagation ◽  
Network Control ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Cuk Converter ◽  
Neural Network Controller ◽  
Artificial Neural

<p class="References">This paper presents an intelligent control strategy that uses a feedforward artificial neural network in order to improve the performance of the MPPT (Maximum Power Point Tracker) MPPT photovoltaic (PV) power system based on a modified Cuk converter. The proposed neural network control (NNC) strategy is designed to produce regulated variable DC output voltage. The mathematical model of Cuk converter and artificial neural network algorithm is derived. Cuk converter has some advantages compared to other type of converters. However the nonlinearity characteristic of the Cuk converter due to the switching technique is difficult to be handled by conventional controller. To overcome this problem, a neural network controller with online learning back propagation algorithm is developed. The NNC designed tracked the converter voltage output and improve the dynamic performance regardless load disturbances and supply variations. The proposed controller effectiveness during dynamic transient response is then analyze and verified using MATLAB-Simulink. Simulation results confirm the excellent performance of the proposed NNC.</p>

SMART RISK ANALYST

2019 ◽  
Author(s):  
Dr. Shilpa Laddha-Kabra
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Credit Risk ◽  
Radial Basis Function ◽  
Basis Function ◽  
Back Propagation ◽  
Arbitrary System ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

This book is an expert system for analyzing credit risk in consumer loan using Artificial Neural Network (ANN). When an individual needs to borrow money, the lender will not only expect repayment but will also want to have confidence that the amount lent can be repaid on time. The effort by the borrower to provide the lender with this confidence level will depend on the amount lent. For lending millions of dollars, the lender may want to take a security interest in assets that have a value in excess of the amount lent to cover fluctuations in the values of those assets during the time the loan is being repaid. When time and foresight permit advance arrangement of loans, the act of borrowing can be made much simpler. When time is short and the need for the loan was not anticipated, the act of going through the process of borrowing may be so time-consuming that obtaining the loan may not be possible at all. Radial Basis Function (RBF), Recurrent Neural Network (RNN), and Back propagation or Multilayer Perceptron (MLP) are the three most popular Artificial Neural Network (ANN) tool for the prediction task. Here the author used both feed forward neural network and radial basis function neural network, back propagation algorithm to make the credit risk prediction. The network can be trained with available data to model an arbitrary system. The trained network is then used to predict the risk in granting the loan. ABOUT THE AUTHOR Dr. Shilpa Laddha-Kabra is Assistant Professor in the Department of Information Technology at Government College of Engineering, Aurangabad (Maharashtra). She is Doctorate (Ph.D.) in Computer Science and Engineering. Her area of interest includes Neural Networks, Information Retrieval, Semantic Web Mining & Ontology and many more. She has a profound expertise in taking the full depth training of engineering students. She has Two Copyrights to her credit & her many research papers are published in prominent international journals.

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