Soft Computing for Problem Solving

1. Front Matter

   Pages i-xvii

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2. Power Distribution Network Reconfiguration Using an Improved Sine–Cosine Algorithm-Based Meta-Heuristic Search

   • Usharani Raut, Sivkumar Mishra

   Pages 1-13

3. Artificial Neural Network for Strength Prediction of Fibers’ Self-compacting Concrete

   • L. V. Prasad Meesaraganda, Prasenjit Saha, Nilanjan Tarafder

   Pages 15-24

4. Using Chaos in Grey Wolf Optimizer and Application to Prime Factorization

   • Harshit Mehrotra, Saibal K. Pal

   Pages 25-43

5. On the Consecutive Customer Loss Probabilities in a Finite-Buffer Renewal Batch Input Queue with Different Batch Acceptance/Rejection Strategies Under Non-renewal Service

   • A. D. Banik, Souvik Ghosh, M. L. Chaudhry

   Pages 45-62

6. Fuzzy Enhancement for Efficient Emotion Detection from Facial Images

   • Payal Bhattacherjee, M. M. Ramya

   Pages 63-77

7. Multi-objective Linear Fractional Programming Problem with Fuzzy Parameters

   • Suvasis Nayak, Akshay Kumar Ojha

   Pages 79-90

8. Hindi Speech Synthesis Using Paralinguistic Content Expression

   • T. V. Prasad

   Pages 91-105

9. An Effective Parameter Tuning for a Bi-objective Genetic Algorithm to Solve the Sum Coloring Problem

   • Olfa Harrabi, Jouhaina Chaouachi Siala

   Pages 107-119

10. Optimal Combined Overcurrent and Distance Relay Coordination Using TLBO Algorithm

    • Saptarshi Roy, P. Suresh Babu, N. V. Phanendra Babu

    Pages 121-135

11. CORO-LABs: Complexity Reduction of Layered Approach in Codifying Business Solutions Using Tuxedo

    • Ankit Shrivastava, Ashish Kumar, Pradeep Kumar Tiwari

    Pages 137-147

12. Chaotic Spider Monkey Optimization Algorithm with Enhanced Learning

    • Nirmala Sharma, Avinash Kaur, Harish Sharma, Ajay Sharma, Jagdish Chand Bansal

    Pages 149-161

13. Analysis of Liver Cancer Using Data Mining SVM Algorithm in MATLAB

    • Srinivas Vadali, G. V. S. R. Deekshitulu, J. V. R. Murthy

    Pages 163-175

14. ACOPF-Based Transmission Network Expansion Planning Using Grey Wolf Optimization Algorithm

    • Ashish Khandelwal, Annapurna Bhargava, Ajay Sharma, Harish Sharma

    Pages 177-184

15. Use of Improved Gravitational Search Algorithm for 3D Reconstruction of Space Curves Using NURBS

    • Amarjeet Singh, Kusum Deep

    Pages 185-197

16. Approaches to Question Answering Using LSTM and Memory Networks

    • G. Rohit, Ekta Gautam Dharamshi, Natarajan Subramanyam

    Pages 199-209

17. Data Extraction from Traffic Videos Using Machine Learning Approach

    • Anshul Mittal, Mridul Gupta, Indrajit Ghosh

    Pages 211-221

18. Stability Analysis and Controller Design for Unstable Systems Using Relay Feedback Approach

    • D. Kishore, K. Anand Kishore, R. C. Panda

    Pages 223-233

19. Ranking Alternatives Using QUALIFLEX Method by Computing All Spanning Trees from Pairwise Judgements

    • Debasmita Banerjee, Debashree Guha, Fateme Kouchakinejad

    Pages 235-247

20. Association Rule Hiding Using Chemical Reaction Optimization

    • N. P. Gopalan, T. Satyanarayana Murthy

    Pages 249-255

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.

• Artificial Neural Networks
• Particle Swarm Optimization
• Fuzzy Systems
• Data Mining and Clustering
• Supply Chain Management
• Signal and Image Processing
• Socpros 2017 Proceedings
• Socpros 2017
• Socpros

• Department of Mathematics, South Asian University New Delhi , New Delhi, India

  Jagdish Chand Bansal

• Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, India

  Kedar Nath Das

• Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK

  Atulya Nagar

• Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, India

  Kusum Deep

• School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, India

  Akshay Kumar Ojha

Dr. Jagdish Chand Bansal is an Assistant Professor at the South Asian University, New Delhi, India and visiting research fellow at Liverpool Hope University, Liverpool UK. He has an excellent academic record and is a leading researcher in the field of swarm intelligence and has published numerous research papers in respected international and national journals.

Dr. Kedar Nath Das is an Assistant Professor at the Department of Mathematics, National Institute of Technology, Silchar, Assam, India. Over the past 10 years, he has made substantial contributions to research on ‘soft computing’. He has published several research papers in prominent national and international journals. His chief area of interest is in evolutionary and bio-inspired algorithms for optimization.

Prof. Atulya Nagar holds the Foundation Chair as Professor of Mathematical Sciences and is Dean of the Faculty of Science at Liverpool Hope University, UK. Prof. Nagar is an internationally respected scholar working at the cutting edge of theoretical computer science, applied mathematical analysis, operations research, and systems engineering.

Prof. Kusum Deep is a Professor at the Department of Mathematics, Indian Institute of Technology Roorkee, India. Over the past 25 years, her research has made her a central international figure in the area of nature-inspired optimization techniques, genetic algorithms and particle swarm optimization.

Dr. Akshay Kumar Ojha is Associate Professor at the School of Basic Sciences, Indian Institute of Technology, Bhubaneswar, Odisha, India. He completed his B.Sc., M.Sc., and Ph.D. at Utkal University in 1978, 1980, and 1997, respectively. His research interest areas are geometric programming, artificial neural networks, genetic algorithms, particle swarm optimization, fractional programming, nonlinear optimization, data analysis and optimization, and portfolio optimization. Dr. Ojha has 34 years of experience and has published over 30 journal articles and 6 books.

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