Cognitive Big Data Intelligence with a Metaheuristic Approach (Cognitive Data Science in Sustainable Computing)

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Cognitive Big Data Intelligence with a Metaheuristic Approach
Cognitive Big Data Intelligence with a Metaheuristic Approach
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Contents
Contributors
Preface
1 - A discourse on metaheuristics techniques for solving clustering and semisupervised learning models
	1. Introduction
	2. Overview of clustering
		2.1 K-means clustering
		2.2 Hierarchical clustering
		2.3 Fuzzy C-means
		2.4 Model-based clustering
		2.5 Particle swarm optimization
		2.6 Clustering using PSO
		2.7 Ant colony optimization
			2.7.1 Constructing the ant probability solution
			2.7.2 Updating the pheromone amount
		2.8 Clustering using ACO
		2.9 Genetic algorithm
			2.9.1 Selection
			2.9.2 Crossover
			2.9.3 Mutation
			2.9.4 Clustering using genetic algorithm
			2.9.5 Computing the fitness function
		2.10 Differential evolution
			2.10.1 Initialization
			2.10.2 Mutation
			2.10.3 Recombination
			2.10.4 Selection
		2.11 Clustering using differential evolution
		2.12 Semisupervised learning algorithms
			2.12.1 Overview
			2.12.2 Self-Training
			2.12.3 Co-Training
		2.13 PSO-assisted semisupervised clustering
		2.14 Semisupervised clustering using GA
			2.14.1 GINI index
	3. Conclusion
	References
2 - Metaheuristics in classification, clustering, and frequent pattern mining
	1. Introduction
		1.1 Introduction to metaheuristics
		1.2 Classification of metaheuristic techniques
		1.3 Working of some metaheuristic algorithms
			1.3.1 Genetic algorithm
			1.3.2 Particle swarm optimization algorithm
			1.3.3 Ant colony optimization algorithm
	2. Metaheuristics in classification
		2.1 Use of ant colony optimization in classification
		2.2 Use of genetic algorithms in classification
		2.3 Use of particle swarm optimization in classification
	3. Metaheuristics in clustering
		3.1 Use of ant colony optimization in clustering
		3.2 Use of genetic algorithms in clustering
		3.3 Use of particle swarm optimization in clustering
	4. Metaheuristics in frequent pattern mining
		4.1 Use of ant colony optimization in frequent pattern mining
		4.2 Use of genetic algorithms in frequent pattern mining
		4.3 Use of particle swarm optimization in frequent pattern mining
	5. Conclusion
	References
3 - Impacts of metaheuristic and swarm intelligence approach in optimization
	1. Introduction
		1.1 Introduction of metaheuristic
		1.2 Introduction of swarm intelligence
	2. Concepts of Metaheuristic
		2.1 Optimization problems
		2.2 Classification of metaheuristic techniques
			2.2.1 Local search versus global search
			2.2.2 Single-solution versus population based
				2.2.2.1 Hybridization and memetic algorithms
			2.2.3 Parallel metaheuristics
			2.2.4 Nature-inspired and metaphor-based metaheuristics
		2.3 A generic metaheuristic framework
	3. Metaheuristic techniques
		3.1 Simulated annealing
		3.2 Genetic algorithms
		3.3 Ant colony optimization
		3.4 Bee Algorithms
		3.5 Particle swarm optimization
		3.6 Harmony search
		3.7 Tabu search
	4. Swarm intelligence techniques
		4.1 Bat Algorithm
		4.2 Firefly algorithm
		4.3 Lion Optimization Algorithm
		4.4 Chicken swarm optimization algorithm
		4.5 Social Spider Algorithm
		4.6 Spider monkey optimization algorithm
		4.7 African buffalo optimization algorithm
		4.8 Flower pollination algorithm
	5. Impacts of metaheuristic and swarm intelligence approach in optimization
		5.1 Implication of the metaheuristic techniques in optimization
		5.2 Implication of the swarm intelligence techniques in optimization
	6. Conclusion
	References
	Further reading
4 - A perspective depiction of heuristics in virtual reality
	1. Introduction to virtual reality
	2. Heuristics in brief
	3. Virtual reality–enabled case studies
		3.1 Virtual reality in crime scene evaluation
		3.2 Virtual reality in assessing a chess game
		3.3 Virtual reality in client assignment problem
		3.4 Client assignment algorithms
			3.4.1 Virtual assignment algorithm
			3.4.2 Greedy assignment algorithm 1
			3.4.3 Greedy assignment algorithm 2
	4. Performance evaluation and discussion
	5. Conclusion
	References
5 - A heuristic approach of web users decision-making using deep learning models
	1. Introduction
	2. Analysis of user online behavior using deep learning models
		2.1 Classic neural networks
		2.2 Convolutional Neural Networks
		2.3 Recurrent neural networks
		2.4 Self-organizing maps
		2.5 Boltzmann machines
		2.6 Deep reinforcement learning
	3. Greedy algorithm as the heuristic
	4. Background study
	5. Description of the dataset
	6. Implementation and discussion
	7. Conclusion
	References
6 - Inertia weight strategies for task allocation using metaheuristic algorithm
	1. Introduction
	2. Related work
	3. Standard PSO
	4. Model of task allocation in VM
	5. Inertia weight strategy
	6. Performance evaluation
		6.1 Experiment setup
		6.2 Result and analysis
	7. Conclusion and future work
	References
7 - Big data classification with IoT-based application for e-health care
	1. Introduction
	2. State of the art
	3. Big data in health care
		3.1 Biomedical data mining
	4. Classification techniques
	5. IoT-based smart biomedical data acquisition and processing system
		5.1 IoT-based data communication framework
	6. Multiagent system for biomedical data processing
	7. Detection of cardiac abnormalities
		7.1 Classification algorithm
	8. Results and discussion
	9. Conclusion
	References
8 - Study of bio-inspired neural networks for the prediction of liquid flow in a process control system
	1. Introduction
	2. Related work
	3. Experimental setup
	4. Preliminary details of the algorithm
		4.1 Preliminary details of the neural network (NN)
		4.2 Preliminaries of the firefly algorithm
		4.3 Preliminaries of particle swarm optimization (PSO)
	5. Proposed model
		5.1 Modeling of the flow rate using a neural network
	6. Results and discussion
		6.1 Computational efficiency test
			6.1.1 Flowchart
		6.2 Convergence test
		6.3 Accuracy test
	7. Conclusions and future work
	References
9 - Affordable energy-intensive routing using metaheuristics
	1. Introduction
	2. Literature survey
	3. Problem description
	4. Routing
		4.1 Routers
		4.2 Router paths
		4.3 Router transmission
	5. Routing algorithms
	6. Routing table
	7. Metaheuristics
		7.1 Constructive metaheuristics
		7.2 Population-based metaheuristics
		7.3 Hybrid metaheuristics
	8. Metaheuristics for efficient routing
	9. Proposed solution using metaheuristics
		9.1 Probability estimation of congestion
		9.2 Memetic algorithms
	10. Conclusion
	References
10 - Semantic segmentation for self-driving cars using deep learning: a survey
	1. Introduction
	2. Semantic segmentation for autonomous driving
		2.1 Autonomous driving
		2.2 Semantic segmentation
	3. Deep learning
		3.1 Machine learning
		3.2 Artificial neural networks
		3.3 Deep learning
		3.4 Learning process of deep neural networks
			3.4.1 Forward propagation and activation functions
			3.4.2 Loss functions and optimization methods
			3.4.3 Back propagation
		3.5 Challenges
			3.5.1 Learning rate
			3.5.2 Underfitting and overfitting
			3.5.3 Dataset splitting
			3.5.4 Gradient vanishing and gradient exploding
		3.6 Convolutional neural networks
		3.7 Autoencoders
	4. Related work
	5. Experimental results
	6. Conclusion
	References
11 - Cognitive big data analysis for E-health and telemedicine using metaheuristic algorithms
	1. Introduction
		1.1 Why E-health care?
		1.2 Advantages of E-health care
	2. Cognitive computing technologies for E-health care
	3. Cognitive big data analytics for E-health care
		3.1 Role of Hadoop and Apache Spark in E-health care analytics
			3.1.1 Hadoop
			3.1.2 Apache Spark
	4. Need for cognitive big data analytics in E-health care
	5. Advantages of cognitive big data analytics in E-health care
	6. Challenges of cognitive big data analytics in E-health care
	7. Metaheuristic approach for optimization of cognitive big data healthcare
		7.1 Benefits of metaheuristic approach over classical optimization methods
		7.2 Applications of metaheuristics in cognitive big data–based healthcare
	8. Cognitive big data analytics use cases in E-health care
	9. Future of cognitive big data analytics in E-health care
	10. Market analysis of cognitive big data analytics in E-health care
	11. Cognitive big data players in E-health care
	References
12 - Multicriteria recommender system using different approaches
	1. Introduction
	2. Related work
	3. Working principle
		3.1 Modeling phase
		3.2 Prediction phase
		3.3 Recommendation phase
		3.4 Content-based approach
		3.5 Collaborative filtering approach
		3.6 Knowledge-based filtering approach
	4. Proposed approaches
		4.1 K-nearest neighbor (KNN)
		4.2 Support vector machine (SVM)
		4.3 Artificial neural networks (ANNs)
	5. Experimental data analysis
		5.1 Data set
		5.2 Confusion matrix
		5.3 Recall value
		5.4 Precision value
		5.5 F1 score
		5.6 Accuracy
	6. Result
	7. Conclusion
	References
13 - Optimization-based energy-efficient routing scheme for wireless body area network
	1. Introduction
		1.1 Three-tier wireless body area network (WBAN) architecture
			1.1.1 Ease of deployment
			1.1.2 System lifetime
			1.1.3 Latency
			1.1.4 Quality
		1.2 Motivation and application scenario
	2. Related work
	3. Case study on an energy-efficient hybrid C-means donkey-smuggler optimization-based routing technique for a wireless sensor ...
	4. Analysis of the previous approach
		4.1 Network configuration
		4.2 Protocol approach
			4.2.1 Analysed scheme
				4.2.1.1 Initialization phase
				4.2.1.2 Cluster head selection and cluster formation phase
					4.2.1.2.1 Cost factor computation for CH selection
						4.2.1.2.1.1 Estimation of energy loss of cluster members
						4.2.1.2.1.2 Estimation of energy loss for SN
						4.2.1.2.1.3 Calculation of sensor node energy
						4.2.1.2.1.4 Estimation of required transmission power
					4.2.1.2.2 Reply to sink node
					4.2.1.2.3 Cost factor computation
					4.2.1.2.4 Cluster formation and scheduling
				4.2.1.3 Data sensing phase
				4.2.1.4 Cooperative decision-based data transmission
				4.2.1.5 Process at the cluster head
	5. Conclusion
	References
14 - Livestock health monitoring using a smart IoT-enabled neural network recognition system
	1. Introduction
	2. System architecture
		2.1 Monitoring and controlling system
		2.2 Central monitoring unit
		2.3 Functions of the central monitoring unit
		2.4 Local monitoring unit
		2.5 Functions of the local monitoring unit
		2.6 Basic hardware requirements
		2.7 Wearable device platform
		2.8 Algorithm
		2.9 Data collection and transmission
	3. Recognition of a diseased bird by the central monitoring unit using Raspberry Pi
	4. Results and discussion
	5. Conclusion
	References
15 - Preserving healthcare data: from traditional encryption to cognitive deep learning perspective
	1. Introduction
	2. Related works
		2.1 PKE-based systems
		2.2 SKE-based systems
		2.3 ABE-based systems
		2.4 Cognitive HE-based systems
	3. Encryption algorithms
	4. Performance evaluation
	5. Future challenges of cognitive encryption models in healthcare
	6. Conclusion
	References
Index
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	Q
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