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" Soft computing for problem solving : "
Jagdish Chand Bansal, [and 4 others], editors.
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BL
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| Record Number
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889953
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| Main Entry
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International Conference on Soft Computing for Problem Solving(2017 :, Bhubaneswar, India)
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| Title & Author
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Soft computing for problem solving : : SocProS 2017.\ Jagdish Chand Bansal, [and 4 others], editors.
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| Publication Statement
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Singapore :: Springer,, [2019]
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| Series Statement
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Advances in intelligent systems and computing ;; volume 816
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| Page. NO
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1 online resource
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| ISBN
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9789811315916
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: 9789811315923
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: 9789811315930
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: 9811315914
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: 9811315922
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: 9811315930
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9789811315916
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| Notes
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2 Materials and Methods
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| Contents
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Intro; Preface; About the Book; Contents; About the Editors; Power Distribution Network Reconfiguration Using an Improved Sine-Cosine Algorithm-Based Meta-Heuristic Search; 1 Introduction; 2 Improved Sine-Cosine Algorithm; 3 Load Flow Method for Implementing the PDNR; 3.1 Basic Load Flow Method; 3.2 Proposed Arrays; 3.3 Algorithm for Checking the Radial Configuration; 3.4 Algorithm for Checking the Connectivity of All Nodes to the Root Node; 3.5 Application of the Load Flow for PDNR Problem; 4 Results; 4.1 Reconfiguration Results of 33, 69, 84, 119 and 136 Bus RDNs; 5 Conclusion; References
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5.1 Comparative Study of Blocking Probabilities and Mean Waiting Times for the Case of Partial Batch Rejection and Total Batch Rejection Policy in a GI[X]/C-MSP/1/N Queueing System5.2 The Effect of the Correlation in the C-MSP on the CCL Probabilities in the GI[X]/C-MSP/1/N Queueing System; 5.3 Effect of the Batch Size, Service Time, and Batch Inter-Arrival Time Distributions on the CCL Probabilities in the GI[X]/C-MSP/1/N Queueing System; 6 Conclusion; References; Fuzzy Enhancement for Efficient Emotion Detection from Facial Images; 1 Introduction; 1.1 Fuzzy Enhancement
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Artificial Neural Network for Strength Prediction of Fibers' Self-compacting Concrete1 Introduction; 2 Scope of the Investigation; 3 Methodology and Materials; 3.1 Tests on Self-compacting Concrete; 3.2 Materials; 4 Network Development for ANN; 4.1 Artificial Neural Network model Development; 4.2 Feed-Forward Algorithm; 4.3 Back-Propagation Algorithm; 5 Predictive Model Development; 5.1 Artificial Neural Network Model; 5.2 Steps to be followed to Design an Artificial Neural Network; 6 Discussion of Test Results; 6.1 Artificial Neural Network Model; 7 Conclusion; References
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On the Consecutive Customer Loss Probabilities in a Finite-Buffer Renewal Batch Input Queue with Different Batch Acceptance/Rejection Strategies Under Non-renewal Service1 Introduction; 2 Description of the Model; 3 k-CCL Probabilities in the GI[X]/C-MSP/1/N Queueing System; 4 Some Useful Performance Measures of the GI[X]/C-MSP/1/N Queueing System; 4.1 Partial Batch Rejection Strategy; 4.2 Total Batch Rejection Strategy; 5 Numerical Illustrations
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Using Chaos in Grey Wolf Optimizer and Application to Prime Factorization1 Introduction; 2 The Grey Wolf Optimizer; 3 Chaos in Grey Wolf Optimizer; 3.1 Why Chaos?; 3.2 Chaotic Maps; 3.3 Adding Chaos to the GWO; 4 The Prime Factorization Problem; 4.1 Choosing an Objective Function; 5 Experiments and Results; 5.1 Results with Chaos Applied in a; 5.2 Results with Chaos Applied in C; 5.3 A Different Maximization Problem; 5.4 Results on the Prime Factorization Problem; 6 Conclusion and Future Research Challenges; References
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| Abstract
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This two-volume book presents outcomes of the 7th International Conference on Soft Computing for Problem Solving, SocProS 2017. This conference is a joint technical collaboration between the Soft Computing Research Society, Liverpool Hope University (UK), the Indian Institute of Technology Roorkee, the South Asian University New Delhi and the National Institute of Technology Silchar, and brings together researchers, engineers and practitioners to discuss thought-provoking developments and challenges in order to select potential future directions The book presents the latest advances and innovations in the interdisciplinary areas of soft computing, including original research papers in the areas including, but not limited to, algorithms (artificial immune systems, artificial neural networks, genetic algorithms, genetic programming, and particle swarm optimization) and applications (control systems, data mining and clustering, finance, weather forecasting, game theory, business and forecasting applications). It is a valuable resource for both young and experienced researchers dealing with complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.
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| Subject
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Soft computing, Congresses.
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| Subject
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Soft computing.
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| Dewey Classification
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006.3
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| LC Classification
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QA76.9.S63S64 2019
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| Added Entry
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Bansal, Jagdish Chand
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