Advanced Analytics and Deep Learning Models. Группа авторов
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href="#ulink_e2ddd03b-80e7-54e3-afc1-6dba213c4984">7.4 Denoising Autoencoder 7.5 Recursive Neural Network (RCNN) 7.6 Deep Reinforcement Learning 7.7 Deep Belief Networks (DBNS) 7.8 Conclusion References 8 A Proposed LSTM-Based Neuromarketing Model for Consumer Emotional State Evaluation Using EEG 8.1 Introduction 8.2 Background and Motivation 8.3 Related Work 8.4 Methodology of Proposed System 8.5 Results and Discussions 8.6 Conclusion References 9 An Extensive Survey of Applications of Advanced Deep Learning Algorithms on Detection of Neurodegenerative Diseases and the Tackling Procedure in Their Treatment Protocol 9.1 Introduction 9.2 Story of Alzheimer’s Disease 9.3 Datasets 9.4 Story of Parkinson’s Disease 9.5 A Review on Learning Algorithms 9.6 A Review on Methodologies 9.7 Results and Discussion 9.8 Conclusion References 10 Emerging Innovations in the Near Future Using Deep Learning Techniques 10.1 Introduction 10.2 Related Work 10.3 Motivation 10.4 Future With Deep Learning/Emerging Innovations in Near Future With Deep Learning 10.5 Open Issues and Future Research Directions 10.6 Deep Learning: Opportunities and Challenges 10.7 Argument with Machine Learning and Other Available Techniques 10.8 Conclusion With Future Work Acknowledgement References 11 Optimization Techniques in Deep Learning Scenarios: An Empirical Comparison 11.1 Introduction 11.2 Optimization and Role of Optimizer in DL 11.3 Various Optimizers in DL Practitioner Scenario 11.4 Recent Optimizers in the Pipeline 11.5 Experiment and Results 11.6 Discussion and Conclusion References
7 Part 3: Introduction to Advanced Analytics 12 Big Data Platforms 12.1 Visualization in Big Data 12.2 Security in Big Data 12.3 Conclusion References 13 Smart City Governance Using Big Data Technologies 13.1 Objective 13.2 Introduction 13.3 Literature Survey 13.4 Smart Governance Status 13.5 Methodology and Implementation Approach 13.6 Outcome of the Smart Governance 13.7 Conclusion References 14 Big Data Analytics With Cloud, Fog, and Edge Computing 14.1 Introduction to Cloud, Fog, and Edge Computing 14.2 Evolution of Computing Terms and Its Related Works 14.3 Motivation 14.4 Importance of Cloud, Fog, and Edge Computing in Various Applications 14.5 Requirement and Importance of Analytics (General) in Cloud, Fog, and Edge Computing 14.6 Existing Tools for Making a Reliable Communication and Discussion of a Use Case (with Respect to Cloud, Fog, and Edge Computing) 14.7 Tools Available for Advanced Analytics (for Big Data Stored in Cloud, Fog, and Edge Computing Environment) 14.8 Importance of Big Data Analytics for Cyber-Security and Privacy for Cloud-IoT Systems 14.9 An Use Case with Real World Applications (with Respect to Big Data Analytics) Related to Cloud, Fog, and Edge Computing 14.10 Issues and Challenges Faced by Big Data Analytics (in Cloud, Fog, and Edge Computing Environments) 14.11 Opportunities for the Future in Cloud, Fog, and Edge Computing Environments (or Research Gaps) 14.12 Conclusion