book

Computational Intelligence in Sustainable Computing and Optimization

Name: Computational Intelligence in Sustainable Computing and Optimization
ISBN: 9780443237256

by Balamurugan Balusamy, Vinayakumar Ravi, Rajesh Kumar Dhanaraj, Sudha Senthilkumar, Brindha K

October 2024

Intermediate to advanced

250 pages

12h 59m

English

Morgan Kaufmann

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Title of Book
Cover image
Title page
Table of Contents
Copyright
Contributors
Chapter 1. Journey of computational intelligence in sustainable computing and optimization techniques: An introduction
1 Introduction to computational intelligence2 Goals of computational intelligence2.1 Fuzzy logic sets2.1.1 Evolution of fuzzy logic2.1.2 Understanding the concept of fuzzy logic2.2 Fuzzy logic set and decision trees2.2.1 Fuzzy control2.2.2 Merits of fuzzy logic system2.2.3 Demerits of fuzzy logic systems2.2.4 Application of fuzzy logic control system2.3 Fuzzy semantics in artificial intelligence2.3.1 Examples of fuzzy logic2.4 Applications of fuzzy logic and fuzzy semantics2.5 Advantages of fuzzy semantic and fuzzy logic systems2.6 Disadvantages of fuzzy semantic and fuzzy logic systems2.7 AI in immunology—machine learning algorithms for immunological data analysis3 Artificial neural networks3.1 Structure of artificial neural network3.1.1 Merits of artificial intelligence neural network3.1.2 Demerits of artificial intelligence neural network3.2 Working principle of artificial intelligence neural networks3.2.1 Types of artificial intelligence neural networks3.2.2 Uses of artificial intelligence neural networks3.3 Advantages and disadvantages of artificial neural network3.4 Neural network architecture types3.4.1 Perceptron model3.4.2 Radial basis function3.4.3 Multilayer perceptron3.4.4 Recurrent3.4.5 Long short-term memory neural network3.4.6 Hopfield network3.4.7 Boltzmann machine neural network3.4.8 Convolutional neural network3.4.9 Modular neural network3.4.10 Physical neural network3.5 Applications of neural networks3.5.1 Facial recognition3.5.2 Stock market prediction3.5.3 Social media3.5.4 Aerospace3.5.5 Defense3.5.6 Healthcare3.5.7 Signature verification and handwriting analysis3.5.8 Weather forecasting4 Evolutionary intelligence computing4.1 Evolutionary computational intelligence algorithms4.1.1 Evolutionary computational intelligence algorithms and biological-based neural network4.2 Working principle of evolutionary computation4.2.1 Evolutionary algorithms function4.3 Types of evolutionary algorithms4.3.1 Genetic algorithms4.3.2 Genetic programming4.3.3 Evolutionary programming4.3.4 Evolutionary strategies4.4 Real-time applications of evolutionary algorithms5 Swarm intelligence and unit swarm mathematical optimization5.1 Principles of swarm intelligence5.1.1 Applications of swarm intelligence5.1.2 Particle or element swarm optimization5.2 Uses of swarm intelligence5.3 Clustering behavior of ants5.4 Nest building behavior of wasps and termites5.5 Flocking and schooling in birds and fish5.6 Ant colony optimization5.7 Particle swarm optimization5.8 Swarm-based network management5.9 Cooperative behavior in swarms of robots6 Artificial intelligence immune system6.1 History of artificial intelligence immune systems6.1.1 Applications of artificial intelligence immune system6.2 Significance of artificial immune systems6.3 Advantages and disadvantages of the artificial immune system6.4 Survey on artificial intelligence immune system6.5 Techniques involved in artificial immune system6.5.1 Clonal selection algorithm6.5.2 Negative selection algorithm6.5.3 Immune network algorithms6.5.4 Dendritic cell algorithms7 Foundations of sustainable computing7.1 Green computing7.1.1 Important of green computing7.2 History of green computing7.3 Applications of green computing7.4 Advantages and challenges of green computing7.4.1 Advantages7.5 Challenges7.6 Origins and motivations of computational sustainable7.7 Biodiversity and conservation of computational sustainable7.8 Sustainable artificial intelligence7.9 Current and future use cases of sustainable AI7.10 Optimization techniques7.10.1 Optimization techniques application7.11 Optimizing software7.12 Intelligent transportation systems7.13 Electrical grid7.14 Power management7.15 Material recycling7.16 Cloud, edge, and parallel computing7.17 Telecommuting7.18 Real-life applications of optimization7.18.1 Benefits of optimization8 Computational learning theory8.1 Importance of computational learning theory8.2 Computational learning theory in machine learning9 Probabilistic models9.1 Categories of probabilistic models9.1.1 Generative models9.1.2 Discriminative models9.1.3 Graphical models9.2 Importance of probabilistic models9.3 Advantages of probabilistic models9.4 Disadvantages of probabilistic models
Chapter 2. Designing computational intelligence techniques based smart framework for sustainable computing
1 Introduction2 The prospects of high-tech smart cities3 IoT and information amassment4 AI and data analysis5 Blockchain solutions6 Concrete applications of smart cities6.1 Challenges of establishing smart cities6.2 The potential of blockchain-enabled smart cities7 The road ahead for smart cities7.1 Free and available urban technology7.2 Blockchain technology to facilitate ongoing evolution7.3 Blockchain to organize urban chaos7.4 Blockchain's potential to improve urban life7.5 Personal belongings in the information age8 Smart city peer-to-peer system8.1 Blockchain city planning: A tactic for the future8.2 Blockchain, IoT, and AI together to make cities smarter and greener9 AI, IOT, blockchain-based cities uses cases9.1 AI, IoT with blockchain-controlled smart lock10 Conclusion
Chapter 3. Multiple parameter optimization methods based on computational intelligence techniques in context of sustainable computing
1 Introduction1.1 Machine learning formulated as optimization2 Multiple parameter optimization methods2.1 Genetic algorithm2.1.1 Main components of genetic algorithm3 Particle swarm optimization4 Conclusion
Chapter 4. IoT-based vulnerability assessment for sustainable computing: Threats, current solutions, and open challenges
1 Introduction2 Proposed techniques2.1 Stage-1: Chest X-ray analysis2.2 Stage-2: Diagnosis engine2.3 Training the neural network2.4 Working of IoT spirometer2.4.1 Analyzing the sample3 Experimental analysis of proposed method and traditional method3.1 Performance evaluation of the proposed system3.2 Different detection mechanisms4 Conclusion and future work
Chapter 5. Amalgamation of optimization techniques in big data analytics through granular computing: A roadmap to smart industry framework
1 Introduction2 Importance of sustainable computing in big data analytics2.1 Environmental and ethical considerations2.2 Integration of optimization techniques and granular computing3 Applications and case studies4 Green data centers4.1 Real-world scenario showing the effectiveness of the integration5 Potential future developments in computational intelligence6 Ethical and social implications of these technologies6.1 Directions for future research in sustainable computing and smart industries7 Conclusion

Chapter 6. Computational intelligence for data analysis in pattern recognition and biomedical fields
1 Introduction1.1 Machine learning algorithms1.2 Deep learning1.3 Image processing techniques1.4 Signal processing1.5 Natural language processing (NLP)1.6 Genomic and sequencing data analysis1.7 Pattern matching and classification1.8 Time series analysis1.9 Data mining and feature selection1.10 Ensemble learning1.11 Anomaly detection2 GAIT-based pattern recognition in biomedical field2.1 Gait recognition and its applications in the medical field2.2 Pattern recognition is exceptionally helpful in abnormal gait recognition in the biomedical field for several key reasons2.3 Hidden Markov model and deep neural network based model2.4 LSTM-CNN based model2.5 RNN-LSTM based model2.6 RNN-based autoencoder model2.7 Gait-structural graph convolutional network (AGS-GCN) based model2.8 Sparse representation classifier based model2.9 DNN based model2.10 RNN based model2.11 Combination of CNN, LSTM, SVM and KNN based model2.12 CNN based model3 Gait-based recognition for Parkinson's disease3.1 PCA and LDA based model3.2 CNN based model3.3 RNN-LSTM based model3.4 LSTM-DNN based model3.5 SVM-ANN based model3.6 Different machine learning based techniques3.7 Different machine learning and ANN based techniques4 Gait-based recognition for cerebral palsy4.1 CNN based model4.2 Different machine learning based techniques4.3 Few deep learning (FCN, LSTM, CNN and transformer) based model5 Conclusion
Chapter 7. A block chain and artificial intelligence–enabled smart IoT framework for the development of sustainable city
1 Introduction1.1 Peer-to-peer energy trading system1.2 DITrust chain based on IoHT1.3 Cognitive edge architecture for shared economic service1.4 Vulnerabilities sustainability of secure edge computing1.5 RON-enhanced blockchain propagation system1.6 Learning via actions in a smart city context Observation-based security system1.7 New smart city development for minimal carbon emissions and environmental sustainability The development of a smart city building model1.8 Ideal IoT resource management1.9 Applications of artificial intelligence and machine learning in smart cities Applications of DRL-based UAVs in B5G and 5G connectivity Smart city health care and machine learning2 Conclusion
Chapter 8. Computational intelligence–based heuristic approach for maximizing energy efficiency in sustainable transportation and mobility
1 Introduction2 Literature review2.1 Recent trends in sustainable transportation and mobility2.2 Computational intelligence in sustainable transportation2.3 Electrification and sustainable transportation2.4 Future directions in sustainable transportation2.5 Eco-friendly mobility services2.6 Intelligent traffic management2.7 Electric vehicle routing and energy optimization2.8 Urban freight transportation and computational intelligence3 Proposed framework: Computational intelligence for energy efficiency in transportation3.1 Computational intelligence: A multifaceted paradigm3.2 Relevance to energy efficiency in transportation3.3 Synergy between computational intelligence and sustainable transportation3.4 Maximizing energy efficiency in sustainable transportation using computational intelligence4 Result analysis4.1 Energy consumption4.2 Emissions (CO2)4.3 Travel time
Chapter 9. Computational intelligence for sustainable computing in health care informatics
1 Introduction2 Privacy-preserving federated learning3 Predictive healthcare models3.1 Enhancing data quality in EHRs3.2 Energy-efficient healthcare infrastructure3.3 Sustainable mental health monitoring4 Sustainable laboratory practices5 Working on disease risk prediction using machine learning algorithms5.1 Algorithm 1: Mathematical model for disease risk prediction6 Results7 Conclusion
Chapter 10. Computational intelligence for sustainable computing in traditional medical system Ayurveda
1 Introduction2 Background2.1 Computational intelligence (CI)2.2 Sustainable development2.3 Sustainable computing2.4 Ayurveda3 Computational intelligence applications in the healthcare domain4 Ayurveda-based sustainable development initiatives in India5 Computational intelligence in Ayurveda5.1 Prakriti Identification5.2 Diet recommendation system5.3 Pulse diagnosis5.4 Medicinal plant identification6 Challenges7 Conclusion
Chapter 11. Computational intelligence approach for anomaly detection and prediction in health care information
1 Introduction2 Motivation3 Machine learning3.1 Supervised machine learning3.2 Unsupervised learning3.3 Semi-supervised learning3.4 Reinforcement learning4 Deep learning4.1 Artificial neural network (ANN)4.2 Convolutional neural network (CNN)4.3 Recurrent neural networks (RNN)5 Anomaly detection5.1 Types of anomaly6 Anomaly detection methods6.1 Statistical methods6.2 Machine learning-based methods6.3 Time-series analysis6.4 Clustering-based methods6.5 Deep learning-based methods6.6 Ensemble methods7 Output of anomaly detection8 Machine learning for anomaly detection9 Machine learning algorithms for anomaly detection9.1 Isolation forest9.2 Random forests9.3 One-class support vector machines (SVM)9.4 Support vector data description (SVDD)9.5 Autoencoders9.6 Local outlier factor (LOF)9.7 Gaussian mixture models10 Anomaly detection in health care information11 Detection of anomalies in different diseases11.1 Cancer11.2 Diabetes11.3 Lung disease12 Comparison of popular anomaly detection algorithms13 Conclusion
Chapter 12. Artificial intelligence–based computational intelligence solutions for robotic automation
1 Introduction2 Role of AI in robotic automation3 The integration of CI techniques4 AI-based CI solutions4.1 Finance4.2 Energy4.3 Education4.4 Customer service4.5 Cyber security5 Challenges and future directions6 Conclusion
Chapter 13. Developing green computing awareness based on optimization techniques for environmental sustainability
1 Introduction1.1 Cloud computing1.2 Green cloud computing1.2.1 Green cloud computing features1.2.2 Green cloud computing advantages1.2.3 Green cloud computing limits1.3 Cloud computing and virtualization1.4 Green algorithms1.5 Optimization techniques2 Virtual machine placement2.1 Need for VMP2.2 Literature review2.2.1 ACO based VMP2.2.2 Other optimization2.2.3 PSO2.2.4 Hybrid ACO algorithm (HACOS)3 Virtual machine migration3.1 Important issues with VM migration3.2 ACO based VMM3.2.1 AIVMM algorithm3.2.2 AIVMM results4 Green cloud for sustainable development4.1 Green cloud attributes4.2 RUAEE algorithm5 Green scheduling algorithm in cloud computing data centers5.1 DVFS5.2 CACO5.3 Honeynet5.3.1 Calculation of energy consumption5.4 EEDPFSP6 Green data center optimization6.1 ABC based energy saving6.2 Server virtualization7 Conclusion
Chapter 14. Bio-inspired meta-heuristic algorithm for solving engineering optimization problems based on computational intelligence
1 Introduction to computational intelligence and engineering optimization1.1 Computational intelligence1.2 Bio-inspired algorithms in engineering optimization2 Bio-inspired approaches for solving optimization problems3 Application of metaheuristic algorithms in engineering optimization3.1 Structural design and engineering3.2 Scheduling and time management3.3 Energy systems and resource allocation3.4 Mechanical design and product development3.5 Network design and routing3.6 Manufacturing and process optimization3.7 Aircraft and aerospace design3.8 Environmental engineering3.9 Data mining and decision support3.10 Robotics and automation4 Evolutionary algorithms: Mimicking natural selection for optimization5 Swarm intelligence: Harnessing collective behavior for problem solving5.1 Ant colony optimization (ACO)5.1.1 Working of ACO algorithm5.2 Particle swarm optimization5.2.1 Algorithm5.3 Bee colony optimization (BCO)5.4 Firefly algorithm5.5 Cuckoo search algorithm6 Genetic algorithms: Evolutionary principles for problem solving6.1 Initialization6.2 Fitness evaluation6.3 Selection6.4 Crossover6.5 Mutation6.6 Replacement and termination7 Differential evolution: Efficient optimization through mutation and recombination7.1 Mutation in differential evolution7.2 Crossover7.3 Selection7.4 Population update8 Harmony search: Finding optimal solutions through musical harmony9 Hybrid metaheuristic algorithms: Combining multiple approaches for improved performance9.1 Combining strategies9.2 Advantage of hybridization10 Case studies of bio-inspired metaheuristic algorithms10.1 Optimizing structural design with bio-inspired algorithms10.2 Enhancing energy systems efficiency10.3 Revolutionizing data mining with ant colony optimization10.4 Solving complex scheduling tasks with swarm intelligence11 Challenges and future directions in engineering optimization with computational intelligence12 Conclusion: Harnessing the power of bio-inspired metaheuristic algorithms for engineering optimization
Chapter 15. Secure sharing of health records stored in cloud using cryptographic secret sharing schemes through computational intelligence: A review
1 Introduction1.1 Cryptographic secret sharing schemes1.1.1 Shamir secret sharing (SSS)1.1.2 Blakely's secret sharing (BSS)1.1.3 Verifiable secret sharing (VSS)1.1.4 Quantum secret sharing (QSS)1.1.5 Visual cryptography1.1.6 Homomorphic secret sharing (HSS)1.1.7 Attribute based secret sharing (ABSS)1.1.8 Ramp secret sharing (RSS)1.1.9 Dynamic secret sharing (DSS)1.2 Computational intelligence1.3 Cryptographic secret sharing schemes with computational intelligence2 Literature review3 Comparative analysis4 Computational intelligence and secure sharing4.1 Computational intelligence techniques4.1.1 Fuzzy logic4.1.2 Neural networks4.1.3 Evolutionary computing algorithms4.1.4 Swarm intelligence4.1.5 Artificial immune system4.2 Enhancing EHRs security with computational intelligence5 Conclusion
Chapter 16. Private blockchain-based encryption framework using Computational Intelligence approach
1 Introduction2 Technologies of blockchain2.1 Block structure2.2 Algorithm of consensus2.3 Types of blockchain networks2.3.1 Coin: Ethereum3 Privacy regulations3.1 Blockchain and privacy3.1.1 Decentralized identity4 Cryptography related technologies4.1 Hash function4.2 Encryption on asymmetric5 Benefits of cryptography in blockchain6 Limitations of cryptography in blockchain7 Artificial Intelligence8 Computational Intelligence8.1 Artificial Intelligence vs. Computational Intelligence9 Interlinking of the blockchain technology and CI9.1 Advantages of combining CI with blockchain9.2 Challenges to the adoption of CI based blockchain solutions9.3 Trends for blockchain and CI combo10 A futuristic approach11 Conclusion
Index

Overview

Computational Intelligence in Sustainable Computing and Optimization: Trends and Applications focuses on developing and evolving advanced computational intelligence algorithms for the analysis of data involved in applications, such as agriculture, biomedical systems, bioinformatics, business intelligence, economics, disaster management, e-learning, education management, financial management, and environmental policies. The book presents research in sustainable computing and optimization, combining methods from engineering, mathematics, artificial intelligence, and computer science to optimize environmental resources

Computational intelligence in the field of sustainable computing combines computer science and engineering in applications ranging from Internet of Things (IoT), information security systems, smart storage, cloud computing, intelligent transport management, cognitive and bio-inspired computing, and management science. In addition, data intelligence techniques play a critical role in sustainable computing. Recent advances in data management, data modeling, data analysis, and artificial intelligence are finding applications in energy networks and thus making our environment more sustainable.

Presents computational, intelligence–based data analysis for sustainable computing applications such as pattern recognition, biomedical imaging, sustainable cities, sustainable transport, sustainable agriculture, and sustainable financial management
Develops research in sustainable computing and optimization, combining methods from engineering, mathematics, and computer science to optimize environmental resources
Includes three foundational chapters dedicated to providing an overview of computational intelligence and optimization techniques and their applications for sustainable computing

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