Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications
- Length: 418 pages
- Edition: 1
- Language: English
- Publisher: Academic Press
- Publication Date: 2021-08-19
- ISBN-10: 012822844X
- ISBN-13: 9780128228449
- Sales Rank: #0 (See Top 100 Books)
The field of computational intelligence has grown tremendously over that past five years, thanks to evolving soft computing and artificial intelligent methodologies, tools and techniques for envisaging the essence of intelligence embedded in real life observations. Consequently, scientists have been able to explain and understand real life processes and practices which previously often remain unexplored by virtue of their underlying imprecision, uncertainties and redundancies, and the unavailability of appropriate methods for describing the incompleteness and vagueness of information represented. With the advent of the field of computational intelligence, researchers are now able to explore and unearth the intelligence, otherwise insurmountable, embedded in the systems under consideration. Computational Intelligence is now not limited to only specific computational fields, it has made inroads in signal processing, smart manufacturing, predictive control, robot navigation, smart cities, and sensor design to name a few.
Recent Trends in Computational Intelligence Enabled Research: Theoretical Foundations and Applications explores the use of this computational paradigm across a wide range of applied domains which handle meaningful information. Chapters investigate a broad spectrum of the applications of computational intelligence across different platforms and disciplines, expanding our knowledge base of various research initiatives in this direction. This volume aims to bring together researchers, engineers, developers and practitioners from academia and industry working in all major areas and interdisciplinary areas of computational intelligence, communication systems, computer networks, and soft computing.
Title-page_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Recent Trends in Computational Intelligence Enabled Research Copyright_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Copyright Dedication_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Dedication Contents_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Contents List-of-contributo_2021_Recent-Trends-in-Computational-Intelligence-Enabled- List of contributors Preface_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Preface Chapter-1---Optimization-in-the-sensor-c_2021_Recent-Trends-in-Computational 1 Optimization in the sensor cloud: Taxonomy, challenges, and survey 1.1 Introduction 1.2 Background and challenges in the sensor cloud 1.2.1 Key definitions 1.2.2 Challenges/issues 1.3 Taxonomy for optimization in the sensor cloud 1.3.1 Load balancing 1.3.2 Information classification 1.3.3 Information transmission 1.3.4 Information processing 1.3.5 Limitations of existing work 1.4 Discussion and future research 1.4.1 Discussion 1.4.2 Future research 1.4.2.1 Fault tolerance 1.4.2.2 Node routing 1.4.2.3 Node deployment 1.4.2.4 Security 1.4.2.5 Information maintenance 1.5 Conclusion References Chapter-2---Computational-intelligence-techniq_2021_Recent-Trends-in-Computa 2 Computational intelligence techniques for localization and clustering in wireless sensor networks 2.1 Introduction 2.2 Wireless sensor networks 2.2.1 Characteristics 2.2.2 Research issues/challenges 2.3 Localization and clustering in wireless sensor networks 2.3.1 Localization 2.3.2 Clustering 2.4 Computational intelligence techniques 2.4.1 Computational intelligence techniques for localization 2.4.1.1 Evolutionary algorithms 2.4.1.2 Swarm intelligence algorithms 2.4.1.3 Fuzzy logic 2.4.1.4 Learning systems 2.4.2 Computational intelligence techniques for clustering 2.4.2.1 Evolutionary algorithms 2.4.2.2 Swarm intelligence algorithms 2.4.2.3 Fuzzy logic 2.4.2.4 Learning systems 2.5 Future research directions References Chapter-3---Computational-intelligent-techniqu_2021_Recent-Trends-in-Computa 3 Computational intelligent techniques for resource management schemes in wireless sensor networks 3.1 Introduction 3.2 Wireless sensor networks 3.2.1 Characteristics 3.2.2 Applications 3.2.3 Issues/challenges 3.3 Resource management in wireless sensor networks 3.3.1 Computational intelligence techniques 3.3.2 Literature survey 3.3.2.1 Resource management scheme in wireless sensor networks using computational intelligence: ongoing research works 3.3.2.1.1 Resource identification scheme 3.3.2.1.2 Resource scheduling 3.3.2.1.3 Resource allocation 3.3.2.1.4 Resource sharing 3.3.2.1.5 Resource provisioning 3.3.2.1.6 Resource utilization 3.3.2.1.7 Resource monitoring 3.3.2.2 Analytical study of ongoing research works in computational intelligence 3.3.2.3 Resource management scheme for wireless sensor network using an artificial neural network 3.4 Future research directions and conclusion References Chapter-4---Swarm-intelligence-based-MSMOPSO-f_2021_Recent-Trends-in-Computa 4 Swarm intelligence based MSMOPSO for optimization of resource provisioning in Internet of Things 4.1 Introduction 4.1.1 Related work 4.1.2 Our contributions 4.2 Proposed method 4.2.1 Network environment 4.2.1.1 Creation of swarms 4.2.1.2 Multi-swarm multi-objective particle swarm optimization 4.2.1.3 Best-fit approach 4.3 Agency 4.3.1 Device agency 4.3.2 Fog agency 4.3.3 Example scenario 4.4 Simulation 4.4.1 Simulation inputs 4.4.2 Simulation procedure 4.4.3 Performance measures 4.4.3.1 Cost 4.4.3.2 Resource utilization 4.4.3.3 Energy consumption 4.4.3.4 Execution time 4.4.4 Results 4.5 Conclusion Acknowledgment References Chapter-5---DNA-based-authentication-to-ac_2021_Recent-Trends-in-Computation 5 DNA-based authentication to access internet of things-based healthcare data 5.1 Introduction 5.2 Literature survey 5.2.1 Internet of things generic architecture 5.2.2 Challenges in the internet of things 5.2.3 Security challenges in internet of things layers 5.2.3.1 Challenges related to perception layer 5.2.3.2 Challenges related to the network layer security 5.2.3.3 Authentication schemes in the internet of things 5.2.4 Authentication schemes in the internet of things 5.2.5 DNA cryptography 5.3 Methodology 5.4 Security analysis 5.4.1 Password file compromise attack 5.4.2 Dictionary attack 5.4.3 Replay attack 5.5 Scyther analysis 5.6 Conclusion References Chapter-6---Computational-intelligence_2021_Recent-Trends-in-Computational-I 6 Computational intelligence techniques for cancer diagnosis 6.1 Introduction 6.2 Background 6.2.1 Cancer research data 6.2.2 Genomic data for cancers 6.2.3 Imaging data for cancers 6.3 Approaches to computational intelligence 6.3.1 Evolutionary computation 6.3.2 Learning theory 6.3.3 Artificial neural networks 6.3.4 Probabilistic methods 6.4 Computational intelligence techniques for feature selection in cancer diagnosis 6.4.1 Advantages of feature selection 6.4.2 Rough sets for feature selection 6.4.2.1 Fuzzy-rough set-based feature selection 6.4.3 Genetic algorithms for feature selection 6.4.4 Adaptive network fuzzy inference system 6.4.5 Deep learning for cancer diagnosis 6.4.5.1 Deep neural networks as feature extractors 6.4.6 Autoencoders for feature extraction 6.4.7 Particle swarm optimization for feature selection 6.5 Computational intelligence methods for cancer classification 6.5.1 Classification methods 6.5.1.1 Fuzzy multilayer perceptron 6.5.1.2 Artificial neural network classifier and deep neural networks 6.6 Conclusion Acknowledgment References Chapter-7---Security-and-privacy-in-the-intern_2021_Recent-Trends-in-Computa 7 Security and privacy in the internet of things: computational intelligent techniques-based approaches 7.1 Introduction 7.2 Internet of things 7.2.1 Architecture 7.2.1.1 Three-layer architecture 7.2.1.2 Four-layer architecture 7.2.1.3 Five-layer architecture 7.2.1.4 SoA-based architecture 7.3 Characteristics 7.4 Research issues/challenges 7.5 Applications 7.6 Security and privacy in the internet of things 7.6.1 Security 7.6.1.1 Issues 7.6.2 Privacy 7.6.2.1 Issues 7.7 Computational intelligent techniques 7.7.1 Artificial intelligence 7.7.2 Neural networks 7.7.3 Evolutionary computation 7.7.4 Artificial immune systems 7.7.5 Fuzzy system 7.7.6 Machine learning 7.7.7 Bio-inspired algorithm 7.8 Computational intelligent techniques to provide security and privacy for the internet of things 7.8.1 Confidentiality 7.8.2 Integrity 7.8.3 Authentication 7.8.4 Availability 7.9 Future research direction References Chapter-8---Automatic-enhancement-of-coronary-ar_2021_Recent-Trends-in-Compu 8 Automatic enhancement of coronary arteries using convolutional gray-level templates and path-based metaheuristics 8.1 Introduction 8.2 Background 8.2.1 Iterated local search 8.2.1.1 Implementation details 8.2.2 Tabú search 8.2.2.1 Implementation details 8.2.3 Simulated annealing 8.2.3.1 Implementation details 8.2.4 Univariate marginal distribution algorithm 8.3 Proposed method 8.3.1 Automatic generation of convolutional gray-level template 8.3.2 Binary classification of the gray-level filter response 8.3.3 Image postprocessing 8.4 Computational experiments 8.4.1 Results of vessel imaging enhancement 8.4.2 Postprocessing procedure 8.5 Concluding remarks Appendix 1 Matlab code of the tabú search for the traveler salesman problem References Chapter-9---Smart-city-development--Theft-hand_2021_Recent-Trends-in-Computa 9 Smart city development: Theft handling of public vehicles using image analysis and cloud network 9.1 Introduction 9.2 Motivation scenario 9.3 Issues and challenges of image authentication through Internet of Things-based cloud framework 9.3.1 Biometric system 9.3.1.1 Characteristics 9.3.1.2 Biometric techniques 9.3.1.3 Biometric system technology 9.3.1.4 Facial recognition system 9.3.1.5 Different application areas of facial recognition 9.3.1.6 Facial recognition process flow 9.3.2 Internet of Things 9.3.2.1 Internet of Things elements 9.3.2.2 Technologies involved in Internet of Things 9.3.2.2.1 Wireless sensor networks 9.3.2.2.2 Communication by radiofrequency identification 9.3.2.2.3 IP protocol 9.3.2.2.4 Internet of Things 9.3.2.3 Applications of the Internet of Things 9.3.3 Cloud computing 9.3.3.1 Features of the cloud computing model 9.3.3.1.1 On-demand self-service 9.3.3.1.2 Broad network access 9.3.3.1.3 Resource pooling 9.3.3.1.4 Rapid elasticity 9.3.3.1.5 Measured service 9.3.4 Different cloud management services 9.3.5 Cloud-enabled Internet of Things 9.4 Proposed facial recognition system implementation for theft handling 9.4.1 Algorithm 9.4.2 Flow chart 9.4.3 Simulation result 9.5 Conclusion References Chapter-10---Novel-detection-of-cancerous-cells_2021_Recent-Trends-in-Comput 10 Novel detection of cancerous cells through an image segmentation approach using principal component analysis 10.1 Introduction 10.1.1 Principal component analysis 10.1.2 Objective of the work 10.2 Algorithm for analysis 10.2.1 Binarized masked segmentation image 10.2.2 Confusion matrix 10.2.3 Image assessment using PCA 10.2.4 Selection of highest probability 10.3 Methodology 10.4 Results and discussions 10.4.1 Detection of cancerous cell from brain MRI 10.4.2 Detection of cancerous cells from a breast mammogram 10.5 Conclusion References Chapter-11---Classification-of-the-operating-spect_2021_Recent-Trends-in-Com 11 Classification of the operating spectrum for the RAMAN amplifier embedded optical communication system using soft comput... 11.1 Introduction 11.2 Soft computing approaches in the optimization procedure 11.3 Objective of the present problem 11.4 Result analysis 11.5 Practical implications 11.6 Conclusion 11.7 Limitations of research 11.8 Future research References Chapter-12---Random-walk-elephant-swarm-water-search-a_2021_Recent-Trends-in 12 Random walk elephant swarm water search algorithm for identifying order-preserving submatrices in gene expression data: ... 12.1 Introduction 12.2 Problem description 12.2.1 Order-preserving submatrices 12.2.2 Solution generation 12.2.3 Cost function 12.2.3.1 Transposed virtual error (VEt) 12.3 Method 12.3.1 Elephant swarm water search algorithm 12.3.2 Random walk elephant swarm water search algorithm 12.4 Numerical experiments 12.4.1 Parameter settings 12.4.2 Benchmark functions 12.4.3 Convergence analysis 12.4.4 Comparison with other metaheuristic algorithms 12.4.5 Performance of random walk elephant swarm water search algorithm with the change in the objective function dimension 12.4.6 Effectiveness of context switch probability 12.4.7 Impact of random inertia weight strategy 12.4.8 Success rate 12.4.9 Statistical analysis 12.4.9.1 Wilcoxon’s rank sum test between elephant swarm water search algorithm and random walk elephant swarm water search... 12.4.9.2 Wilcoxon’s rank sum test between random walk elephant swarm water search algorithm and other metaheuristics 12.4.10 Results on a real-life problem 12.4.11 Biological relevance 12.5 Conclusion References Chapter-13---Geopositioning-of-fog-nodes-based-on-_2021_Recent-Trends-in-Com 13 Geopositioning of fog nodes based on user device location and framework for game theoretic applications in an fog to clo... 13.1 Introduction 13.2 System model 13.3 Literature review 13.4 Problem formulation 13.5 Proposed method 13.5.1 Geopositioning of fog nodes 13.5.2 Applications of the proposed fog to cloud network 13.5.3 Allocation of device requests to the processing resources 13.5.4 User-to-user data transfer using fog nodes 13.5.5 Determining the cost of edges 13.5.6 Physical address of FNL2S 13.5.7 Packet flow inside the network 13.6 Simulation and discussion 13.6.1 Geopositioning of fog nodes 13.6.2 Request allocation to processing resources 13.6.3 User-to-user data transfer 13.7 Conclusions and future research References Chapter-14---A-wavelet-based-low-freque_2021_Recent-Trends-in-Computational- 14 A wavelet-based low frequency prior for single-image dehazing 14.1 Introduction 14.2 Literature survey 14.3 Motivation and contribution 14.4 Proposed method 14.4.1 Low-frequency prior 14.4.2 Noise removal in high frequency 14.4.3 Dehazing in low frequency 14.4.3.1 Dark channel prior 14.4.3.2 Atmospheric light and transmission map estimation 14.4.3.3 Transmission map refinement 14.4.3.4 Scene radiance restoration 14.4.4 Fuzzy contrast enhancement 14.4.4.1 Fuzzification 14.4.4.2 Implication relations 14.4.4.3 Inference procedures 14.4.4.4 Defuzzification 14.5 Analysis of results and discussion 14.5.1 Qualitative assessment 14.5.2 Quantitative assessment 14.5.3 Time complexity evaluation 14.6 Conclusions References Chapter-15---Segmentation-of-retinal-blood-vesse_2021_Recent-Trends-in-Compu 15 Segmentation of retinal blood vessel structure based on statistical distribution of the area of isolated objects 15.1 Introduction 15.2 Related works 15.2.1 Matched filter method 15.2.2 Technique related to the region growing after the scale-space analysis 15.2.3 Method related to the curvature estimation using mathematical morphology 15.2.4 B-COSFIRE method 15.2.5 Supervised approach 15.3 Basic morphological operations 15.4 Proposed algorithm 15.4.1 Preprocessing of the fundus image 15.4.2 Initial vessel-like structure determination 15.4.3 Locally adaptive line structuring element generation and blood vessel segmentation 15.4.4 Enhancement of vessel structure using difference of Gaussians 15.4.5 Binarization using local Otsu’s threshold 15.4.6 Elimination of noisy objects from a binary image 15.5 Experiment 15.5.1 Database 15.5.2 Experimental results 15.5.3 Performance measurement 15.6 Conclusions References Chapter-16---Energy-efficient-rendezvous-poin_2021_Recent-Trends-in-Computat 16 Energy efficient rendezvous point-based routing in wireless sensor network with mobile sink 16.1 Introduction 16.2 Problem statement 16.3 Literature survey 16.3.1 Cluster-based routing protocol with static sink 16.3.2 Cluster-based routing protocol with mobile sink 16.3.2.1 Rendezvous point-based routing protocol 16.4 System model 16.4.1 Network model 16.4.2 Energy model 16.5 General structure of a genetic algorithm 16.5.1 Encoding 16.5.2 Initial population 16.5.3 Fitness function 16.5.4 Selection 16.5.5 Crossover 16.5.6 Mutation 16.6 Proposed method 16.6.1 Cluster head selection 16.6.2 Rendezvous point selection 16.6.3 Tour formation for the mobile sink 16.7 Simulation environment and results analysis 16.7.1 Number of alive nodes 16.7.2 Cumulative energy consumption 16.7.3 Cumulative data packet received at base station 16.7.4 Changing the base station location 16.7.5 Packet drop ratio and packet delay 16.8 Statistical analysis 16.9 Conclusions and future work References Chapter-17---An-integration-of-handcrafted_2021_Recent-Trends-in-Computation 17 An integration of handcrafted features for violent event detection in videos 17.1 Introduction 17.2 Proposed method 17.2.1 Global histograms of oriented gradients feature descriptor 17.2.2 Histogram of optical flow orientation feature descriptor 17.2.3 GIST feature descriptor 17.2.4 Fusion feature descriptors 17.2.5 Classifier 17.2.6 Postprocessing 17.3 Experimental results and discussion 17.3.1 Datasets 17.3.1.1 Hockey Fight dataset 17.3.1.2 Violent-Flows dataset 17.3.2 Experimental setting 17.3.3 Evaluation parameter 17.3.3.1 Precision 17.3.3.2 Recall 17.3.3.3 F-measure 17.3.3.4 Accuracy 17.3.3.5 Area under the curve 17.3.4 Results and analysis 17.3.5 Space and time computation 17.4 Conclusion Acknowledgment References Chapter-18---Deep-learning-based-diabetic-r_2021_Recent-Trends-in-Computatio 18 Deep learning-based diabetic retinopathy detection for multiclass imbalanced data 18.1 Introduction 18.2 Related works 18.3 Data set and preprocessing 18.4 Methodology 18.4.1 Convolutional neural networks 18.4.2 Training (transfer learning) 18.4.3 Steps to train the proposed model 18.5 Experimental results and discussion 18.6 Conclusion and future work References Further reading Chapter-19---Internet-of-Things-e-health-revolut_2021_Recent-Trends-in-Compu 19 Internet of Things e-health revolution: secured transmission of homeopathic e-medicines through chaotic key formation 19.1 Introduction 19.2 Related works 19.3 Complication statements 19.4 Proposed frame of work 19.5 Work flow diagram of the proposed technique 19.6 Novelty of the proposed technique 19.7 Result section 19.7.1 Statistical key strength 19.7.2 Histogram and autocorrelation analysis 19.7.3 Chi-square comparison 19.7.4 Differential attacks 19.7.5 Security analysis 19.7.6 Analysis of the session key space 19.7.7 Analysis of the information entropy 19.7.8 Encryption–decryption process time 19.7.9 Time needed for an intrusion 19.7.10 Comparative study with earlier works 19.8 Conclusion Acknowledgments References Chapter-20---Smart-farming-and-water-saving-base_2021_Recent-Trends-in-Compu 20 Smart farming and water saving-based intelligent irrigation system implementation using the Internet of Things 20.1 Introduction 20.2 Related studies 20.3 System model 20.3.1 Hardware operation 20.3.2 Software operation 20.4 Application of machine learning model 20.5 Step-by-step procedure of the proposed methodology 20.6 Results and discussion 20.7 Comparative study among various Internet of Things based smart agriculture systems 20.8 Conclusion Acknowledgments References Further reading Chapter-21---Intelligent-and-smart-enabling-_2021_Recent-Trends-in-Computati 21 Intelligent and smart enabling technologies in advanced applications: recent trends 21.1 Introduction 21.2 Enabling intelligent technologies used in recent research problems 21.2.1 Internet of Things 21.2.2 Machine learning 21.2.3 Deep learning 21.2.4 Metaheuristics 21.2.5 Classification of various smart applications 21.2.5.1 Smart healthcare 21.2.5.2 Monitoring of patients 21.2.5.3 Disease detection and diagnosis 21.2.5.4 Outbreak prediction and prevention 21.2.5.5 Personalized treatments and recommendations 21.2.6 Smart home 21.2.7 Smart transport 21.2.8 Smart parking 21.2.9 Smart agriculture 21.3 Issues and challenges 21.4 Case study 21.5 Open research issues 21.6 Conclusion References Chapter-22---Leveraging-technology-for-healthcare_2021_Recent-Trends-in-Comp 22 Leveraging technology for healthcare and retaining access to personal health data to enhance personal health and well-being 22.1 Introduction 22.1.1 Blockchain technology: a brief overview 22.1.1 The work summary 22.2 Patient stories and identified challenges 22.2.1 Patient story 22.2.2 Patient story 22.2.3 Patient story 22.2.4 Patient story 22.3 Electronic health record, its security, and portability 22.3.1 Electronic health record 22.3.2 Electronic health record data-sharing challenges and opportunities 22.3.3 Blockchain and electronic health record 22.4 Discussion 22.4.1 Censorship resistance 22.4.2 Enhanced integrity and security 22.4.3 Data aggregation and identity basis 22.4.4 Ownership and access control 22.5 Conclusion Acknowledgments References Chapter-23---Enhancement-of-foveolar-archi_2021_Recent-Trends-in-Computation 23 Enhancement of foveolar architectural changes in gastric endoscopic biopsies 23.1 Introduction 23.1.1 Importance of gland and nuclei segmentation on clinical diagnosis 23.1.2 Traditional gland segmentation computational models 23.1.2.1 Region-growing method model 23.1.2.2 Watershed transform model 23.2 Current state of the art 23.3 Source of images and image processing 23.3.1 Description of the data set 23.3.2 Segmentation approach 23.3.3 Numerical definitions 23.4 Outcomes and discussion 23.5 Future possibilities and challenges 23.6 Conclusion Acknowledgment References Index_2021_Recent-Trends-in-Computational-Intelligence-Enabled-Research Index
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