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3rd International Conference for Convergence in Technology (I2CT)

作者： Sahi, Geetanjali Lal Bahadur Shastri Inst Management Delhi India

ISBN: (纸本)9781538642733

In E-commerce, website is the bask point of contact between business and its customers, hence for online vendors, it is imperative that their B2C website be usable. The present study comprehensively explores the outstanding ability of Artificial Neural Networks (ANNs) to uncover knowledge hidden in data and thus discover relationships between input and output variables and entails questionnaire survey approach to assess usability of B2C websites. The performance of various ANN models has been evaluated by altering different parameters of a neural network. Three metrics viz. MSE, MAE and MAPE have been used to measure the performance of the neural network. The empirical results show that best result is obtained by using tansig purelin transfer function with trainlm training function having three nodes at hidden layer with 90 percent data for training. Further, in assessing the usability of E-commerce websites using ANN it was found that although all dimensions were important, System Quality followed by Trust were most significant.

关键词： Artificial Neural Networks back propagation algorithm transfer functions B2C E-commerce system quality trust extension quality propriety of content perceived usefulness

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Face Detection Through Neural Network 2

Face Detection Through Neural Network

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2nd International Conference on Current Trends In Engineering and Technology (ICCTET)

作者： Joseph, Sheeba Sowmiya, R. Thomas, Roshni Ann Sofia, X. Maharaja Engn Coll Dept Comp Sci Engn Coimbatore Tamil Nadu India

ISBN: (纸本)9781479979868

The aim of this paper is to implement an effective system to locate upright frontal faces on monochromatic images with use of a neural network-based classifier. In this paper, a new approach to reduce the computation time taken by fast neural nets for the searching process is presented. The principle of divide and conquer strategy is applied through image decomposition. Each image is divided into small size sub-images and then each one is tested separately using a fast neural network. Compared to conventional and fast neural networks, experimental results show that a speed up ratio is achieved when applying this technique to locate human faces in automatically in clustered scenes. Furthermore, faster face detection is obtained by using parallel processing techniques to test the resulted sub-images at the same time using the same number of fast neural networks. Moreover, the problem of sub-image centering and normalization in the Fourier space is solved.

关键词： Sobel masks feed forward network sliding window training set back propagation algorithm

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Temperature and Combustion Quality Control in Power Station Boilers using Artificial Neural Networks 10

Temperature and Combustion Quality Control in Power Station ...

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1st Amrita ACM-W Celebration of Women in Computing in India (A2CWiC)

作者： Sujatha, K. Pappa, N. MIT Dept Instrumentat Engn Madras 44 Tamil Nadu India

ISBN: (纸本)9781450301947

The classification is an important domain in boiler flame image processing and is a preliminary step toward detection, recognition and understanding of combustion condition. In this paper, back propagation algorithm (BPA) is introduced for boiler flame image classification. Firstly, we define a feature vector for each flame image including 7 feature elements, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the flame centroid respectively. The classification and recognition of the flame image is done to measure the temperature from the flame colour and the flue gas emissions from the flame colour. The samples including 102 flame images, parts of which are used to train and test the model. Finally, the entire samples are recognized and classified. Experiments prove this method is effective for classification of boiler flame images.

关键词： Combustion quality Temperature identification Image Processing back propagation algorithm Fisher's linear discriminant Analysis

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Intelligent Decision Support System for Breast Cancer

Intelligent Decision Support System for Breast Cancer

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1st International Conference on Swarm Intelligence

作者： Janghel, R. R. Shukla, Anupam Tiwari, Ritu Kala, Rahul ABV Indian Inst Informat Technol & Management Soft Comp & Expert Syst Lab Gwalior India

ISBN: (纸本)9783642134975

Breast cancer is the second leading cause of cancer deaths in women worldwide and occurs in nearly one out of eight women. Currently there are three techniques to diagnose breast cancer: mammography, FNA (Fine Needle Aspirate) and surgical biopsy. In this paper we develop an integrated expert system for diagnosis, prognosis and prediction for breast cancer using soft computing techniques. The basic aim is to compare the various neural network models from the recent literature. Breast cancer database used for this purpose is from the University of Wisconsin (UCI) Machine Learning Repository. Three different data sets have been used, each employing different diagnostic technique. It can use diagnosis, prognosis and survivability prediction of breast cancer patient in one intelligent system. We implement six models of neural networks namely back propagation algorithm, Radial Basis Function Networks, Learning vector Quantization, Probabilistic Neural Networks, Recurrent Neural Network, and Competitive Neural network. Experimental Results show that different models give optimal performance for different types of data sets. However, all the models are able to solve the problem to a reasonable extent.

关键词： Artificial Neural Networks Breast Cancer Expert System back propagation algorithm Radial Basis Function Network Learning Vector Quantization Probabilistic Neural Networks Recurrent Neural Networks Competitive Neural Network

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Application of Neural Networks in Power Quality

Application of Neural Networks in Power Quality

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International Conference on Soft Computing Techniques and Implementations (ICSCTI)

作者： Pedapenki, Kishore Kumar Gupta, S. P. Pathak, Mukesh Kumar IIT Roorkee Dept Elect Engn Roorkee Uttar Pradesh India

ISBN: (纸本)9781467367929

Use of power electronic converters with nonlinear loads produces harmonic currents and reactive power. A shunt active power filter provides an elegant solution to reactive power compensation as well as harmonic mitigation leading to improvement in power quality. However, the shunt active power filter with PI type of controller is suitable only for a given load. If the load is varying, the proportional and integral gains are required to be fine tuned for each load setting. The present study deals with neural network based controller for shunt active power filter. The performance of neural network controller evaluated and compared with PI controller.

关键词： Active Power Filter Neural Networks back propagation algorithm Soft Computing

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Energy Forecasting For Grid Connected MW Range Solar PV System 7

Energy Forecasting For Grid Connected MW Range Solar PV Syst...

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7th IEEE India International Conference on Power Electronics (IICPE)

作者： Sahoo, Ashwin Kumar Sahoo, Sarat Kumar CV Raman Coll Engn Dept Elect Engn Bhubaneswar Odisha India VIT Univ Sch Elect Engn Vellore Tamil Nadu India

ISBN: (纸本)9781509045303

An accurate forecasting of PV output is essential to improve real-time control performance and to reduce possible negative impacts. For an energy management system (EMS) of distributed energy resources, accurate forecasting of solar irradiation and thus PV power output can reduce the impact of uncertainty for PV power generation, improve system reliability, and increase the penetration level of the PV power generation system. Solar power output can be predicted based on historical solar irradiance and weather data, using Artificial Neural Networks and then is converted to PV power output. Neural Networks is designed to train, visualize, and validate network models using feed forward network with back propagation algorithm. The model is trained;using data collected from a 10 kW PV system which includes beam solar irradiance, relative humidity, temperature and wind speed as input for the training set. The future DC and AC power outputs are predicted for any given day. Also the power forecasting is being extended for a 100 kW and 1 MW PV system.

关键词： grid connected PV system solar PV forecasting artificial neural network back propagation algorithm weather classification component

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Performance Study of Combined Artificial Neural Network algorithms for Image Steganalysis 1

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1st International Conference on Internet Computing and Information Communications (ICICIC)

作者： Sujatha, P. Purushothaman, S. Rajeswari, R. VELS Univ Dept Comp Sci Madras 600117 Tamil Nadu India PET Engn Coll Tirunelveli 627117 Tamil Nadu India Mother Teresa Womens Univ Kodaikanal India

ISBN: (纸本)9788132212980;9788132212997

Steganalaysis is a technique for detecting the presence of hidden information. Artificial neural network (ANN) is a widespread method for steganalysis. back propagation algorithm (BPA), radial basis function (RBF), and functional update back propagation algorithm (FUBPA) are some of the popular ANN algorithms for detecting hidden information. Training and testing performance is improved when two algorithms are combined instead of using them separately. This paper analyzes the performance of combined algorithms of BPARBF and FUBPARBF. Among the two combinations FUBPARBF provides promising results than BPARBF since FUBPA uses less number of iterations for the network to converge. But still organizing the retrieved information is a challenging task.

关键词： back propagation algorithm Functional update back propagation algorithm Radial basis function Carrier image Information image

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Neural network based path planning plan design of autonomous mobile robot

Neural network based path planning plan design of autonomous...

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SICE-ICASE International Joint Conference

作者： Park, Kyung-Seok Choi, Han-Soo Chosun Univ Dept Control & Instrumentat Engn Kwangju South Korea

ISBN: (纸本)9788995003848

The part of manipulators is normally studied with regularized environmental conditions. however, it is the most difficult that the part of AMR must be studied with uncertainty in the environmental conditions. The part of AMR has skelton, sensor fusion, path planning etc. This paper is the research of the local pass planning that gathers information about external environment using neural network from each sensors and designs the algorithm which can determine which correct direction the robot can find. As the result of the research, AMR has been able to drive similarly as if the expert does and has been able to observe it acting without any control.

关键词： autonomous mobile robot neural network back propagation algorithm

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Artificial Neural Network Based Model for Forecasting of Inflation in India

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FUZZY INFORMATION AND ENGINEERING 2016年第1期8卷 87-100页

作者： Thakur, Gour Sundar Mitra Bhattacharyya, Rupak Mondal, Seema Sarkar Dr BC Roy Engn Coll Dept Comp Sci & Engn Durgapur W Bengal India Bijoy Krishna Girls Coll Dept Math Howrah W Bengal India Natl Inst Technol Dept Math Durgapur W Bengal India

Inflation can be attributed to both microeconomic and macroeconomic factors which influence the stability of the economy of any nation. With the raising of recession at the end of the year 2008, world communities started paying much contemplation on inflation and put enormous hard work to predict it accurately. Prediction of inflation is not a simple task. Moreover, the behavior of inflation is so complex and uncertain that both economists and statisticians have been striving to model and forecast inflation in an accurate way. As a result, many researchers have proposed inflation forecasting models based on different methods;however the accuracy is always being a major constraint. In this paper, we have analyzed the historical monthly economic data of India between January 2000 and December 2012 and constructed an inflation forecasting model based on feed forward back propagation neural network. Initially some critical factors that can considerably influence the inflation of India have been identified, then an efficient artificial neural network (ANN) model has been proposed to forecast the inflation. Accuracy of the model is proved to be satisfactory when compared with the forecasting of some well-known agencies.

关键词： Inflation forecasting Artificial neural network back propagation algorithm

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Data-Based Model of PEM Fuel Cell Using Neural Network 6th

Data-Based Model of PEM Fuel Cell Using Neural Network

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6th International conference on communication and computational technologies, ICCCT 2024

作者： Aruna, R. Manjula, M. Muthuselvi, R. Pradheeba, A. Vidhya, S. Department of Electrical and Electronics Engineering P. S. R. Engineering College Tamil Nadu Sivakasi India

ISBN: (纸本)9789819774227

A proton exchange membrane (PEM) fuel cell is an alternative energy source. Generally, the fuel cell is an electrochemical device in which the chemical energy is converted into electric energy, and the by-product of the fuel cell is water. Apart from the electrochemical reaction, multiple physical processes, such as heat and mass transfer, water formation, and vaporizations, occur during the function of the fuel cell. In the literature, various models based on multi-physics along with electrochemical reactions were developed to predict the performance of the fuel cell. In recent years, the machine learning and data-driven approach has been implemented in many applications to improve the study of the system. This paper presents the data-based model for the PEM fuel cells using an Artificial Neural Network (ANN) to anticipate the cell voltage for various operating conditions. The prediction of the fuel cell is done by collecting the data from the fuel cell using a data acquisition system. The backpropagation ANN technique is employed to predict the performance of the fuel cell by considering the three input parameters: temperatures, current density, and hydrogen gas pressure. The predicted response based on the ANN technique is obtained with a regression value > 0.99 for all the variables. A comparison of the actual output of the fuel cell and data-based model shows that the data-based model is more accurate and also reduces the need for wide experimentation in the physics-based models. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： ANN back propagation algorithm Data-based model Machine learning PEM fuel cell

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