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Intrusion detection system is a detection mechanism that detects unauthorized, malicious presents in the computer systems. The purpose;
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detection and prevention. It discusses tracing back mobile attackers, secure routing with intrusion prevention, anomaly detection, and AI-based;
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, medical image fusion based on pulse coupled neural networks, artificial neural networks for intrusion detection, recognition of Arabic sign;
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detection and prevention. It discusses tracing back mobile attackers, secure routing with intrusion prevention, anomaly detection, and AI-based;
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cluster; fuzzy-based controller for improvement of network performance; multi-layer perceptron artificial neural network; fault detection and;
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dragonfly key exchange protocol, intrusion detection & prevention system based on artificial neural network, and detection of sophisticated network;
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* Provides up-to-date coverage of neural network paradigms and applications* Presents the work of internationally renowned experts;
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of neural network paradigms, however, the newest of these have not appeared in book form-until now. Recent Advances in Artificial Neural Networks;
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disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and;
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disciplines like neural networking, deep learning, artificial intelligence, visualization, e-learning in higher education, e-healthcare, security and;
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State of the Art in Neural Networks and Their Applications presents the latest advances in artificial neural networks and their;
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This book gathers the most current research from across the globe in the study of artificial neural networks. Topics discussed include a;
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This book gathers the most current research from across the globe in the study of artificial neural networks. Topics discussed include;
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This book is an exploration of an artificial neural network. It has been created to suit even the complete beginners to artificial neural;
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and Machine Learning.This must-have compendium presents the theory and case studies of artificial neural networks. The volume, with 4 new;
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, and the analysis of artificial neural networks; and the application aspect dealing with applications of artificial neural networks in a broad;
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attacks. In this book a novel approach for intrusion detection in wireless sensor networks is developed. Common performance and network metrics;
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, auto encoders, recurrent neural networks, and modified/hybrid artificial neural networks. Includes applications of computer;
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intrusion detection network (IDN). Using overlay networks that allow an intrusion detection system (IDS) to exchange information, IDNs can;
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of learning by example. The field of artificial neural networks has evolved significantly in the last two decades, with active partici- tion from;
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Artificial Neural Networks for Engineering Applications presents current trends for the solution of complex engineering problems that;
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With respect to the ever-increasing developments in artificial intelligence and artificial neural network applications in different scopes;
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This book introduces readers to the fundamentals of artificial neural networks, with a special emphasis on evolutionary algorithms. At;
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for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The;
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for intrusion detection. It examines various designs and performance results, along with the strengths and weaknesses of the approaches. The;
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This book provides an in-depth overview of artificial intelligence and deep learning approaches with case studies to solve problems;
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, malware detection, intrusion detection and sentiment analysis. It explains how such classification problems can be solved using various;
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