| Description |
1 online resource (263 p.) |
| Contents |
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Acknowledgments -- Editors -- Contributors -- Chapter 1 Soft Computing Techniques Design and Analysis -- 1.1 Introduction -- 1.1.1 Soft Computing Techniques Design and Analysis -- 1.2 Introduction to Soft Computing -- 1.2.1 Motivation for Soft Computing -- 1.3 Soft Computing Techniques -- 1.3.1 Fuzzy Logic -- 1.3.2 Neural Networks -- 1.3.3 Evolutionary Computation -- 1.3.4 Probabilistic Reasoning -- 1.3.5 Machine Learning -- 1.3.6 Hybrid Approaches -- 1.4 Applications of Soft Computing |
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1.4.1 Healthcare and Medical Diagnosis -- 1.4.2 Finance and Investment Analysis -- 1.4.3 Transportation and Traffic Management -- 1.4.4 Environmental Modeling and Management -- 1.4.5 Robotics and Automation -- 1.4.6 Natural Language Processing and Sentiment Analysis -- 1.4.7 Engineering and Industrial Applications -- 1.5 Analysis of Soft Computing Techniques -- 1.5.1 Evaluation Metrics -- 1.5.1.1 Accuracy -- 1.5.1.2 Precision and Recall -- 1.5.1.3 F1 Score -- 1.5.1.4 Mean Squared Error (MSE) -- 1.5.1.5 AUC -- 1.6 Importance of Soft Computing in the Era of Big Data and Artificial Intelligence |
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1.6.1 Soft Computing Methodologies Benchmarking -- 1.6.2 Significance of Benchmarking -- 1.6.3 Key Considerations for Benchmarking -- 1.6.3.1 Best Practices for Effective Benchmarking -- 1.7 Sensitivity Analysis -- 1.8 Soft Computing Results Interpreting and Visualizing -- 1.9 Conclusion -- References -- Chapter 2 Trends in the Applications of Soft Computing Techniques for IoT -- 2.1 Introduction -- 2.2 Soft Computing and the Internet of Things (IoT) -- 2.2.1 Soft Computing and IoT for Data Analytics and Predictive Modeling -- 2.2.2 Soft Computing and IoT for Anomaly Detection and Fault Diagnosis |
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2.2.3 Soft Computing and IoT for Energy Optimization and Resource Management -- 2.2.4 Soft Computing and IoT for Decision-Making and Intelligent IoT Systems -- 2.3 Soft Computing Techniques in Healthcare and Medical Systems -- 2.3.1 Medical Image Analysis and Diagnosis -- 2.3.2 Predictive Analytics and Early Disease Detection -- 2.3.3 Clinical Decision Support Systems -- 2.3.4 Health Monitoring and Personalized Medicine -- 2.3.5 Healthcare Resource Management and Optimization -- 2.3.6 Intelligent Telemedicine and Remote Monitoring |
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2.4 Soft Computing Techniques in Natural Language Processing (NLP) -- 2.4.1 Sentiment Analysis and Opinion Mining -- 2.4.2 Named Entity Recognition and Entity Linking -- 2.4.3 Question Answering and Dialogue Systems -- 2.4.4 Text Generation and Language Modeling -- 2.5 Soft Computing Techniques in Robotics and Autonomous Systems -- 2.5.1 Perception and Sensing -- 2.5.2 Localization and Mapping -- 2.5.3 Motion Planning and Control -- 2.5.4 Human-Robot Interaction -- 2.5.5 Learning and Adaptation -- 2.5.6 Swarm Robotics and Collective Intelligence -- 2.6 Soft Computing Techniques in Smart Cities |
| Summary |
In recent years, soft computing techniques have emerged as a successful tool to understand and analyze the collective behavior of service- oriented computing software. Algorithms and mechanisms of self- organization of complex natural systems have been used to solve problems, particularly in complex systems, which are adaptive, ever- evolving, and distributed in nature across the globe. What fits more perfectly into this scenario other than the rapidly developing era of Fog, IoT, and Edge computing environment? Service- oriented computing can be enhanced with soft computing techniques embedded inside the Cloud, Fog, and IoT systems. Soft Computing Principles and Integration for Real-Time Service-Oriented Computing explores soft computing techniques that have wide application in interdisciplinary areas. These soft computing techniques provide an optimal solution to the optimization problem using single or multiple objectives.The book focuses on basic design principles and analysis of soft computing techniques. It discusses how soft computing techniques can be used to improve quality-of-service in serviceoriented architectures. The book also covers applications and integration of soft computing techniques with a service- oriented computing paradigm. Highlights of the book include: A general introduction to soft computing An extensive literature study of soft computing techniques and emerging trends Soft computing techniques based on the principles of artificial intelligence, fuzzy logic, and neural networks The implementation of SOC with a focus on service composition and orchestration, quality of service (QoS) considerations, security and privacy concerns, governance challenges, and the integration of legacy systems The applications of soft computing in adaptive service composition, intelligent service recommendation, fault detection and diagnosis, SLA management, and security Such principles underlying SOC as loose coupling, reusability, interoperability, and abstraction An IoT based framework for real time data collection and analysis using soft computing |
| Notes |
Description based upon print version of record |
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2.6.1 Intelligent Transportation Systems |
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Dr. Punit Gupta is currently working as associate professor in the department of computer and communication engineering at Manipal University Jaipur, India. Prof. Dinesh Kumar Saini is a full professor at the School of Computing and Information Technology, Manipal University. Dr. Kashif Zia is currently working as an associate professor in the faculty of computing and information technology, Sohar University, Oman |
| Subject |
Soft computing.
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COMPUTERS / Programming / Systems Analysis & Design
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| Form |
Electronic book
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| Author |
Gupta, Punit.
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Saini, Dinesh Kumar, 1974-
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Zia, Kashif
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| ISBN |
9781003859024 |
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100385902X |
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9781032716718 |
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1032716711 |
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9781003859031 |
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1003859038 |
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