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Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems - Hardcover

 
9781032770871: Nature-inspired Metaheuristic Algorithms: Solving Real World Engineering Problems

Synopsis

This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities.

This book:

  • Discusses load balancing and demand response using nature-inspired optimization techniques
  • Presents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithms
  • Covers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniques
  • Explains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniques
  • Illustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniques

Nature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

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About the Author

Sulabh Bansal received the BE in Computer Science and Engineering from Shivaji University Kolhapur in 2000, and M.Tech and PhD degrees from Dayalbagh Educational Institute, Agra in the year 2012 and 2017 respectively. He is a senior member of IEEE, a member of ACM, and a permanent member of the Indian Society for Technical Education. He has a comprehensive experience of around 22 years in Information Technology, including working as an Industry professional, research fellow, and academician. His primary research areas include designing Algorithms for Optimization problems, Quantum Inspired Computing, High-Performance Computing, and Artificial Intelligence. He teaches several courses such as Artificial Intelligence, Machine Learning, Advanced Data Structures, Advanced Algorithms and Complexity, Design and Analysis of Algorithms, Soft Computing, Operating Systems, Pattern Recognition to both undergraduate and postgraduate students. He has published several research papers in high quality peer reviewed journals many of which are indexed in SCI and Scopus Q1. He has also presented his research work in several scopus indexed International Conferences and has been published by international publishers of repute. A couple of patents and copyrights have also been granted in his name. He is an active reviewer of various reputed International Journals in his research areas.

Shilpa Srivastava is an Associate Professor at the School of Sciences in Christ University, Delhi NCR. She possesses a degree in B.Sc(H) Mathematics from Delhi University, Post Graduate Degree MCA from MCRP University and PhD Computer Science & Engineering from Uttarakhand Technical University, Dehradun. She has also Qualified UGC NET exam in Computer Science Applications in 2015. She has a vast teaching and research experience of more than 20 years. She has publications in journals and conferences of national and international repute(SCI/Scopus/ESCI). She is a reviewer of many reputed journals. She has also worked as CO Principal investigator in a research project in collaboration with IIT Roorkee, which was funded by Liverpool Hope University UK. She has published one Patent also. Her areas of interest include Application of Soft Computing in medical domain, Theory of Computation, Algorithms, and e-health services.

Aprna Tripathi is an assistant professor in the department of data science and engineering, Manipal University Jaipur. She received her bachelor’s degree in sciences from Kanpur University (1998), master’s in computer applications from HBTI (2002), Kanpur, Master of Technology from Banasthali University (2007), Rajasthan and PhD from National Institute of Technology Allahabad, Prayagraj (2015). With over 15 years of teaching and research experience, she has established a strong foundation in academia. Her scholarly contributions can be found in prestigious national and international journals and conferences, including those recognized by SCI and Scopus. Furthermore, she actively contributes as a reviewer for prestigious academic journals. Her areas of specialization include Software Engineering, Software Testing, Data Visualization, and Data Structures & Algorithms. Notably, she has authored a book titled "Component-Based Systems: Estimating Efforts Using Soft Computing Techniques". She also holds membership in the Association for Computing Machinery (ACM).

Prem Prakash Vuppuluri is an Assistant Professor in the Department of Electrical Engineering and Coordinator, B.Voc. (Robotics and AI) Program at the Faculty of Engineering, Dayalbagh Educational Institute (Deemed to be University), Agra, India. He obtained his B.Tech. (Hons.) in Computer Science and Engineering from the Indian institute of Technology, Kharagpur, Masters in Engineering Systems from Dayalbagh Educational Institute (Deemed University), Agra and PhD in Electrical Engineering from D.E.I. in collaboration with Kiel University, Germany. He has eighteen years of work experience in academia and has taught undergraduate and graduate level courses such as Operating Systems, Systems Optimization using Evolutionary Algorithms, Introduction to Programming using C, Data Structures and Computer Architecture. His research interests include heuristic optimization, machine learning and nature-inspired computation. He has published several research papers in various reputed journals and conferences, and is recipient of several awards in various national and international avenues. He is a life member of the Systems Society of India, Indian Society of Technical Education and the Computer Society of India.

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Hardcover. Condition: new. Hardcover. This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities.This book:Discusses load balancing and demand response using nature-inspired optimization techniquesPresents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithmsCovers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniquesExplains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniquesIllustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniquesNature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. The text provides practical guidance to implement nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature-inspired metaheuristic algorithms have the potential to contribute to multiple UN SDG such as climate action, clean energy, and sustainable cities. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability. Seller Inventory # 9781032770871

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Bansal, Sulabh (Editor)/ Tripathi, Aprna (Editor)/ Srivastava, Shilpa (Editor)/ Vuppuluri, Prem Prakash (Editor)
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Hardcover. Condition: new. Hardcover. This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities.This book:Discusses load balancing and demand response using nature-inspired optimization techniquesPresents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithmsCovers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniquesExplains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniquesIllustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniquesNature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. The text provides practical guidance to implement nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature-inspired metaheuristic algorithms have the potential to contribute to multiple UN SDG such as climate action, clean energy, and sustainable cities. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781032770871

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Condition: New. Sulabh Bansal received the BE in Computer Science and Engineering from Shivaji University Kolhapur in 2000, and M.Tech and PhD degrees from Dayalbagh Educational Institute, Agra in the year 2012 and 2017 respectively. He is a senior member of IEEE, a mem. Seller Inventory # 1984361431

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Buch. Condition: Neu. Neuware - The text provides practical guidance to implement nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature-inspired metaheuristic algorithms have the potential to contribute to multiple UN SDG such as climate action, clean energy, and sustainable cities. Seller Inventory # 9781032770871

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Hardcover. Condition: new. Hardcover. This comprehensive text provides practical guidance for implementing nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature inspired metaheuristic algorithms have the potential to contribute to multiple United Nations sustainable development goals such as climate action, clean energy, and sustainable cities.This book:Discusses load balancing and demand response using nature-inspired optimization techniquesPresents energy-efficient routing and scheduling, energy management, and optimization using metaheuristic algorithmsCovers disease diagnosis, and prognosis using metaheuristic algorithms, drug discovery, and development using nature-inspired optimization techniquesExplains waste reduction and recycling, image processing, and computer vision using nature-inspired optimization techniquesIllustrates medical image analysis and segmentation using Ant Colony optimization, and Particle Swarm optimization techniquesNature-inspired Metaheuristic Algorithms is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology. The text provides practical guidance to implement nature-inspired algorithms and metaheuristics in real-life scenarios to solve complex optimization problems. It further demonstrates how nature-inspired metaheuristic algorithms have the potential to contribute to multiple UN SDG such as climate action, clean energy, and sustainable cities. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032770871

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