Nature-Inspired Optimization Algorithms : Recent Advances in Natural Computing and Biomedical Applications /
ed. by Aditya Khamparia, Ashish Khanna, Nhu Gia Nguyen, Bao Le Nguyen.
- 1 online resource (XIV, 154 p.)
- Intelligent Biomedical Data Analysis , 4 2629-7140 ; .
Frontmatter -- Preface -- Contents -- About the editors -- List of contributors -- 1 Selecting and assessing the importance of malware analysis methods for web-based biomedical services through fuzzy-based decision-making procedure -- 2 A medical intelligent system for diagnosis of chronic kidney disease using adaptive neuro-fuzzy inference system -- 3 Contrast enhancement approach for satellite images using hybrid fusion technique and artificial bee colony optimization -- 4 Role of intelligent IoT applications in fog computing -- 5 Energy-efficient routing employing neural networks along with vector-based pipeline in underwater wireless sensor networks -- 6 A review of global optimization problems using meta-heuristic algorithm -- 7 Secure indexing and storage of big data -- 8 Genetic algorithm and normalized text feature based document classification -- 9 Nature-inspired optimization techniques -- Index
restricted access http://purl.org/coar/access_right/c_16ec
This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations
Mode of access: Internet via World Wide Web.
In English.
9783110676112
10.1515/9783110676112 doi
Artificial intelligence--Medical applications.
Biomedical engineering.
Mathematical optimization.
Natural computation.
Nature-inspired algorithms.
Program transformation (Computer programming).
Algorithmus.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.
COMPUTERS / Intelligence (AI) & Semantics.
QA402.5 / .K43 2021
519.3
Frontmatter -- Preface -- Contents -- About the editors -- List of contributors -- 1 Selecting and assessing the importance of malware analysis methods for web-based biomedical services through fuzzy-based decision-making procedure -- 2 A medical intelligent system for diagnosis of chronic kidney disease using adaptive neuro-fuzzy inference system -- 3 Contrast enhancement approach for satellite images using hybrid fusion technique and artificial bee colony optimization -- 4 Role of intelligent IoT applications in fog computing -- 5 Energy-efficient routing employing neural networks along with vector-based pipeline in underwater wireless sensor networks -- 6 A review of global optimization problems using meta-heuristic algorithm -- 7 Secure indexing and storage of big data -- 8 Genetic algorithm and normalized text feature based document classification -- 9 Nature-inspired optimization techniques -- Index
restricted access http://purl.org/coar/access_right/c_16ec
This book will focus on the involvement of data mining and intelligent computing methods for recent advances in Biomedical applications and algorithms of nature-inspired computing for Biomedical systems. The proposed meta heuristic or nature-inspired techniques should be an enhanced, hybrid, adaptive or improved version of basic algorithms in terms of performance and convergence metrics. In this exciting and emerging interdisciplinary area a wide range of theory and methodologies are being investigated and developed to tackle complex and challenging problems. Today, analysis and processing of data is one of big focuses among researchers community and information society. Due to evolution and knowledge discovery of natural computing, related meta heuristic or bio-inspired algorithms have gained increasing popularity in the recent decade because of their significant potential to tackle computationally intractable optimization dilemma in medical, engineering, military, space and industry fields. The main reason behind the success rate of nature inspired algorithms is their capability to solve problems. The nature inspired optimization techniques provide adaptive computational tools for the complex optimization problems and diversified engineering applications. Tentative Table of Contents/Topic Coverage: - Neural Computation - Evolutionary Computing Methods - Neuroscience driven AI Inspired Algorithms - Biological System based algorithms - Hybrid and Intelligent Computing Algorithms - Application of Natural Computing - Review and State of art analysis of Optimization algorithms - Molecular and Quantum computing applications - Swarm Intelligence - Population based algorithm and other optimizations
Mode of access: Internet via World Wide Web.
In English.
9783110676112
10.1515/9783110676112 doi
Artificial intelligence--Medical applications.
Biomedical engineering.
Mathematical optimization.
Natural computation.
Nature-inspired algorithms.
Program transformation (Computer programming).
Algorithmus.
Big Data.
Künstliche Intelligenz.
Maschinelles Lernen.
COMPUTERS / Intelligence (AI) & Semantics.
QA402.5 / .K43 2021
519.3