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Convergence of Cloud with AI for Big Data Analytics

Foundations and Innovation
Edited by Danda B. Rawat, Lalit K Awasthi, Valentina Emilia Balas, Mohit Kumar and Jitendra Kumar Samriya
Series: Advances in Learning Analytics for Intelligent Cloud-IoT Systems
Copyright: 2023   |   Status: Published
ISBN: 9781119904885  |  Hardcover  |  
442 pages
Price: $225 USD
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One Line Description
This book covers the foundations and applications of cloud computing, AI, and Big Data and analyses their convergence for improved development and services.

Audience
Researchers and post-graduate students in computing as well as engineers and practitioners in software engineering, electrical engineers, data analysts, and cyber security professionals.

Description
The 17 chapters of the book masterfully and comprehensively cover the intertwining concepts of artificial intelligence, cloud computing, and big data, all of which have recently emerged as the next-generation paradigms. There has been rigorous growth in their applications and the hybrid blend of AI Cloud and IoT (Ambient-intelligence technology) also relies on input from wireless devices. Despite the multitude of applications and advancements, there are still some limitations and challenges to overcome, such as security, latency, energy consumption, service allocation, healthcare services, network lifetime, etc. Convergence of Cloud with AI for Big Data Analytics: Foundations and Innovation details all these technologies and how they are related to state-of-the-art applications, and provides a comprehensive overview for readers interested in advanced technologies, identifying the challenges, proposed solutions, as well as how to enhance the framework.

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Author / Editor Details
Prof. Danda B Rawat is the Associate Dean for Research & Graduate Studies, a Full Professor in the Department of Electrical Engineering & Computer Science (EECS), Founder and Director of the Howard University Data Science and Cybersecurity Center, Director of DoD Center of Excellence in Artificial Intelligence & Machine Learning (CoE-AIML), Director of Cyber-security and Wireless Networking Innovations (CWiNs) Research Lab, Graduate Program Director of Howard CS Graduate Programs and Director of Graduate Cybersecurity Certificate Program at Howard University, Washington, DC, USA. Dr. Rawat is the recipient of NSF CAREER Award and Department of Homeland Security (DHS) Scientific Leadership Award. Dr. Rawat has published over 250 scientific/technical articles and 11 books. He has been serving as an Editor/Guest Editor for over 70 international journals including the Associate Editor of IEEE Transactions of Service Computing, Editor of IEEE Internet of Things Journal, Associate Editor of IEEE Transactions of Network Science and Engineering and Technical Editors of IEEE Network.

Prof. Lalit K Awasthi is Director, Dr B. R. Ambedkar National Institute of Technology Jalandhar (INDIA). Prior to this, he was working as Director/Principal, Government Engineering College, Pragati Nagar, Shimla. He has also served as Professor and Head in Department of Computer Science and Engineering, National Institute of Technology Hamirpur. He received his Ph.D. degree from Indian Institute of Technology Roorkee in Computer Science and Engineering His research interests are distributed fault tolerant computing, mobile computing, wireless sensor networks, mobile ad hoc networks. He has published more than 150 research papers in various journals and conferences of international repute and guided many PhDs in these areas.

Prof. Valentina E Ballas is a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a Ph.D. in Applied Electronics and Telecommunications from Polytechnic University of Timisoara. Dr. Balas is author of more than 280 research papers in refereed journals and International Conferences. Her research interests are in Intelligent Systems, Fuzzy Control, Soft Computing, Smart Sensors, Information Fusion, Modelling and Simulation. She is the Editor-in Chief to International Journal of Advanced Intelligence Paradigms (IJAIP) and to International Journal of Computational Systems Engineering (IJCSysE), member in Editorial Board member of several national and international journals and is evaluator expert for national, international projects and PhD Thesis. Dr. Balas is the director of Intelligent Systems Research Centre in Aurel Vlaicu University of Arad and Director of the Department of International Relations, Programs and Projects in the same university. She served as General Chair of the International Workshop Soft Computing and Applications (SOFA) in ninth editions 2005-2020 held in Romania and Hungary. She is a member of EUSFLAT, SIAM and a Senior Member IEEE, member in TC – Fuzzy Systems (IEEE CIS), member in TC - Emergent Technologies (IEEE CIS), member in TC – Soft Computing (IEEE SMCS).

Dr Mohit Kumar is an Assistant Professor in the Department of Information Technology at Dr. B R Ambedkar National Institute of Technology, Jalandhar, India. He received his Ph.D. degree from Indian Institute of Technology Roorkee in the field of Cloud Computing, 2018. He has received M.Tech degree in Computer Science and Engineering from ABV-Indian Institute of Information Technology Gwalior, India in 2013. He has received his B. Tech degree in Computer Science and Engineering from MJP Rohilkhand University Bareilly, 2009. His research topics cover the areas of Cloud computing, Fog computing, Edge Computing, Internet of Things, Soft Computing and Blockchain. He has published more than 25 research articles in reputed transactions, SCI/SCIE/Scopus journals and international conferences. He has been Session chair and keynotes Speaker of many International conferences, webinars, FDP, STC in India. He is currently a leading guest editor in Frontiers of Computer Science. He has guided two M. Tech Thesis and guiding 4 Ph.D. Scholar. He has also served as reviewer in many reputed journals, transactions and international conferences. He is a member of the IEEE.

Dr Jitendra Kumar Samriya is a faculty member in the Department of IT, Dr. B.R. Ambedkar National Institute of Technology, Jalandhar. He has completed his Master of Technology (M.Tech) and Ph.D. from BBA University (A Central University), Lucknow. His research interest is Cloud computing, Artificial Intelligence and Multi-objective Evolutionary optimization Techniques. He has published 15 research articles in reputed Journals, Conferences and Published five Indian and International patents. He is also a member of IEEE, SCRS.

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Table of Contents
Preface
1. Integration of Artificial Intelligence, Big Data, and Cloud Computing with Internet of Things
Jaydip Kumar
1.1 Introduction
1.2 Roll of Artificial Intelligence, Big Data and Cloud Computing in IoT
1.3 Integration of Artificial Intelligence with the Internet of Things Devices
1.4 Integration of Big Data with the Internet of Things
1.5 Integration of Cloud Computing with the Internet of Things
1.6 Security of Internet of Things
1.7 Conclusion
References
2. Cloud Computing and Virtualization
Sudheer Mangalampalli, Pokkuluri Kiran Sree, Sangram K. Swain and Ganesh Reddy Karri
2.1 Introduction to Cloud Computing
2.1.1 Need of Cloud Computing
2.1.2 History of Cloud Computing
2.1.3 Definition of Cloud Computing
2.1.4 Different Architectures of Cloud Computing
2.1.4.1 Generic Architecture of Cloud Computing
2.1.4.2 Market Oriented Architecture of Cloud Computing
2.1.5 Applications of Cloud Computing in Different Domains
2.1.5.1 Cloud Computing in Healthcare
2.5.1.2 Cloud Computing in Education
2.5.1.3 Cloud Computing in Entertainment Services
2.5.1.4 Cloud Computing in Government Services
2.1.6 Service Models in Cloud Computing
2.1.7 Deployment Models in Cloud Computing
2.2 Virtualization
2.2.1 Need of Virtualization in Cloud Computing
2.2.2 Architecture of a Virtual Machine
2.2.3 Advantages of Virtualization
2.2.4 Different Implementation Levels of Virtualization
2.2.4.1 Instruction Set Architecture Level
2.2.4.2 Hardware Level
2.2.4.3 Operating System Level
2.2.4.4 Library Level
2.2.4.5 Application Level
2.2.5 Server Consolidation Using Virtualization
2.2.6 Task Scheduling in Cloud Computing
2.2.7 Proposed System Architecture
2.2.8 Mathematical Modeling of Proposed Task Scheduling Algorithm
2.2.9 Multi Objective Optimization
2.2.10 Chaotic Social Spider Algorithm
2.2.11 Proposed Task Scheduling Algorithm
2.2.12 Simulation and Results
2.2.12.1 Calculation of Makespan
2.2.12.2 Calculation of Energy Consumption
2.3 Conclusion
References
3. Time and Cost-Effective Multi-Objective Scheduling Technique for Cloud Computing Environment
Aida A. Nasr, Kalka Dubey, Nirmeen El-Bahnasawy, Gamal Attiya and Ayman El-Sayed
3.1 Introduction
3.2 Literature Survey
3.3 Cloud Computing and Cloudlet Scheduling Problem
3.4 Problem Formulation
3.5 Cloudlet Scheduling Techniques
3.5.1 Heuristic Methods
3.5.2 Meta-Heuristic Methods
3.6 Cloudlet Scheduling Approach (CSA)
3.6.1 Proposed CSA
3.6.2 Time Complexity
3.6.3 Case Study
3.7 Simulation Results
3.7.1 Simulation Environment
3.7.2 Evaluation Metrics
3.7.2.1 Performance Evaluation with Small Number of Cloudlets
3.7.2.2 Performance Evaluation with Large Number of Cloudlets
3.8 Conclusion
References
4. Cloud-Based Architecture for Effective Surveillance and Diagnosis of COVID-19
Shweta Singh, Aditya Bhardwaj, Ishan Budhiraja, Umesh Gupta and Indrajeet Gupta
4.1 Introduction
4.2 Related Work
4.2.1 Proposed Cloud-Based Network for Management of COVID-19
4.3 Research Methodology
4.3.1 Sample Size and Target
4.3.1.1 Sampling Procedures
4.3.1.2 Response Rate
4.3.1.3 Instrument and Measures
4.3.2 Reliability and Validity Test
4.3.3 Exploratory Factor Analysis
4.4 Survey Findings
4.4.1 Outcomes of the Proposed Scenario
4.4.1.1 Online Monitoring
4.4.1.2 Location Tracking
4.4.1.3 Alarm Linkage
4.4.1.4 Command and Control
4.4.1.5 Plan Management
4.4.1.6 Security Privacy
4.4.1.7 Remote Maintenance
4.4.1.8 Online Upgrade
4.4.1.9 Command Management
4.4.1.10 Statistical Decision
4.4.2 Experimental Setup
4.5 Conclusion and Future Scope
References
5. Smart Agriculture Applications Using Cloud and IoT
Keshav Kaushik
5.1 Role of IoT and Cloud in Smart Agriculture
5.2 Applications of IoT and Cloud in Smart Agriculture
5.3 Security Challenges in Smart Agriculture
5.4 Open Research Challenges for IoT and Cloud in Smart Agriculture
5.5 Conclusion
References
6. Applications of Federated Learning in Computing Technologies
Sambit Kumar Mishra, Kotipalli Sindhu, Mogaparthi Surya Teja, Vutukuri Akhil, Ravella Hari Krishna, Pakalapati Praveen and Tapas Kumar Mishra
6.1 Introduction
6.1.1 Federated Learning in Cloud Computing
6.1.1.1 Cloud-Mobile Edge Computing
6.1.1.2 Cloud Edge Computing
6.1.2 Federated Learning in Edge Computing
6.1.2.1 Vehicular Edge Computing
6.1.2.2 Intelligent Recommendation
6.1.3 Federated Learning in IoT (Internet of Things)
6.1.3.1 Federated Learning for Wireless Edge Intelligence
6.1.3.2 Federated Learning for Privacy Protected Information
6.1.4 Federated Learning in Medical Computing Field
6.1.4.1 Federated Learning in Medical Healthcare
6.1.4.2 Data Privacy in Healthcare
6.1.5 Federated Learning in Blockchain
6.1.5.1 Blockchain-Based Federated Learning Against End-Point Adversarial Data
6.2 Advantages of Federated Learning
6.3 Conclusion
References
7. Analyzing the Application of Edge Computing in Smart Healthcare
Parul Verma and Umesh Kumar
7.1 Internet of Things (IoT)
7.1.1 IoT Communication Models
7.1.2 IoT Architecture
7.1.3 Protocols for IoT
7.1.3.1 Physical/Data Link Layer Protocols
7.1.3.2 Network Layer Protocols
7.1.3.3 Transport Layer Protocols
7.1.3.4 Application Layer Protocols
7.1.4 IoT Applications
7.1.5 IoT Challenges
7.2 Edge Computing
7.2.1 Cloud vs. Fog vs. Edge
7.2.2 Existing Edge Computing Reference Architecture
7.2.2.1 FAR-EDGE Reference Architecture
7.2.2.2 Intel-SAP Joint Reference Architecture (RA)
7.2.3 Integrated Architecture for IoT and Edge
7.2.4 Benefits of Edge Computing Based IoT Architecture
7.3 Edge Computing and Real Time Analytics in Healthcare
7.4 Edge Computing Use Cases in Healthcare
7.5 Future of Healthcare and Edge Computing
7.6 Conclusion
References
8. Fog-IoT Assistance-Based Smart Agriculture Application
Pawan Whig, Arun Velu and Rahul Reddy Nadikattu
8.1 Introduction
8.1.1 Difference Between Fog and Edge Computing
8.1.1.1 Bandwidth
8.1.1.2 Confidence
8.1.1.3 Agility
8.1.2 Relation of Fog with IoT
8.1.3 Fog Computing in Agriculture
8.1.4 Fog Computing in Smart Cities
8.1.5 Fog Computing in Education
8.1.6 Case Study
Conclusion and Future Scope
References
9. Internet of Things in the Global Impacts of COVID-19: A Systematic Study
Shalini Sharma Goel, Anubhav Goel, Mohit Kumar and Sachin Sharma
9.1 Introduction
9.2 COVID-19 – Misconceptions
9.3 Global Impacts of COVID-19 and Significant Contributions of IoT in Respective Domains to Counter the Pandemic
9.3.1 Impact on Healthcare and Major Contributions of IoT
9.3.2 Social Impacts of COVID-19 and Role of IoT
9.3.3 Financial and Economic Impact and How IoT Can Help to Shape Businesses
9.3.4 Impact on Education and Part Played by IoT
9.3.5 Impact on Climate and Environment and Indoor Air Quality Monitoring Using IoT
9.3.6 Impact on Travel and Tourism and Aviation Industry and How IoT is Shaping its Future
9.4 Conclusions
References
10. An Efficient Solar Energy Management Using IoT-Enabled Arduino-Based MPPT Techniques
Rita Banik and Ankur Biswas
List of Symbols
10.1 Introduction
10.2 Impact of Irradiance on PV Efficiency
10.2.1 PV Reliability and Irradiance Optimization
10.2.1.1 PV System Level Reliability
10.2.1.2 PV Output with Varying Irradiance
10.2.1.3 PV Output with Varying Tilt
10.3 Design and Implementation
10.3.1 The DC to DC Buck Converter
10.3.2 The Arduino Microcontroller
10.3.3 Dynamic Response
10.4 Result and Discussions
10.5 Conclusions
References
11. Axiomatic Analysis of Pre-Processing Methodologies Using Machine Learning in Text Mining: A Social Media Perspective in Internet of Things
Tajinder Singh, Madhu Kumari, Daya Sagar Gupta and Nikolai Siniak
11.1 Introduction
11.2 Text Pre-Processing – Role and Characteristics
11.3 Modern Pre-Processing Methodologies and Their Scope
11.4 Text Stream and Role of Clustering in Social Text Stream
11.5 Social Text Stream Event Analysis
11.6 Embedding
11.6.1 Type of Embeddings
11.7 Description of Twitter Text Stream
11.8 Experiment and Result
11.9 Applications of Machine Learning in IoT (Internet of Things)
11.10 Conclusion
References
12. APP-Based Agriculture Information System for Rural Farmers in India
Ashwini Kumar, Dilip Kumar Choubey, Manish Kumar and Santosh Kumar
12.1 Introduction
12.2 Motivation
12.3 Related Work
12.4 Proposed Methodology and Experimental Results Discussion
12.4.1 Mobile Cloud Computing
12.4.2 XML Parsing and Computation Offloading
12.4.3 Energy Analysis for Computation Offloading
12.4.4 Virtual Database
12.4.5 App Engine
12.4.6 User Interface
12.4.7 Securing Data
12.5 Conclusion and Future Work
References
13. SSAMH – A Systematic Survey on AI-Enabled Cyber Physical Systems in Healthcare
Kamalpreet Kaur, Renu Dhir and Mariya Ouaissa
13.1 Introduction
13.2 The Architecture of Medical Cyber-Physical Systems
13.3 Artificial Intelligence-Driven Medical Devices
13.3.1 Monitoring Devices
13.3.2 Delivery Devices
13.3.3 Network Medical Device Systems
13.3.4 IT-Based Medical Device Systems
13.3.5 Wireless Sensor Network-Based Medical Driven Systems
13.4 Certification and Regulation Issues
13.5 Big Data Platform for Medical Cyber-Physical Systems
13.6 The Emergence of New Trends in Medical Cyber-Physical Systems
13.7 Eminence Attributes and Challenges
13.8 High-Confidence Expansion of a Medical Cyber-Physical Expansion
13.9 Role of the Software Platform in the Interoperability of Medical Devices
13.10 Clinical Acceptable Decision Support Systems
13.11 Prevalent Attacks in the Medical Cyber-Physical Systems
13.12 A Suggested Framework for Medical Cyber-Physical System
13.13 Conclusion
References
14. ANN-Aware Methanol Detection Approach with CuO-Doped SnO2 in Gas Sensor
Jitendra K. Srivastava, Deepak Kumar Verma, Bholey Nath Prasad and Chayan Kumar Mishra
14.1 Introduction
14.1.1 Basic ANN Model
14.1.2 ANN Data Pre- and Post-Processing
14.1.2.1 Activation Function
14.2 Network Architectures
14.2.1 Feed Forward ANNs
14.2.2 Recurrent ANNs Topologies
14.2.3 Learning Processes
14.2.3.1 Supervised Learning
14.2.3.2 Unsupervised Learning
14.2.4 ANN Methodology
14.2.5 1%CuO–Doped SnO2 Sensor for Methanol
14.2.6 Experimental Result
References
15. Detecting Heart Arrhythmias Using Deep Learning Algorithms
Dilip Kumar Choubey, Chandan Kumar Jha, Niraj Kumar, Neha Kumari and Vaibhav Soni
15.1 Introduction
15.1.1 Deep Learning
15.2 Motivation
15.3 Literature Review
15.4 Proposed Approach
15.4.1 Dataset Descriptions
15.4.2 Algorithms Description
15.4.2.1 Dense Neural Network
15.4.2.2 Convolutional Neural Network
15.4.2.3 Long Short-Term Memory
15.5 Experimental Results of Proposed Approach
15.6 Conclusion and Future Scope
References
16. Artificial Intelligence Approach for Signature Detection
Amar Shukla, Rajeev Tiwari, Saurav Raghuvanshi, Shivam Sharma and Shridhar Avinash
16.1 Introduction
16.2 Literature Review
16.3 Problem Definition
16.4 Methodology
16.4.1 Data Flow Process
16.4.2 Algorithm
16.5 Result Analysis
16.6 Conclusion
References
17. Comparison of Various Classification Models Using Machine Learning to Predict Mobile Phones Price Range
Chinu Singla and Chirag Jindal
17.1 Introduction
17.2 Materials and Methods
17.2.1 Dataset
17.2.2 Decision Tree
17.2.2.1 Basic Algorithm
17.2.3 Gaussian Naive Bayes (GNB)
17.2.3.1 Basic Algorithm
17.2.4 Support Vector Machine
17.2.4.1 Basic Algorithm
17.2.5 Logistic Regression (LR)
17.2.5.1 Basic Algorithm
17.2.6 K-Nearest Neighbor
17.2.6.1 Basic Algorithm
17.2.7 Evaluation Metrics
17.3 Application of the Model
17.3.1 Decision Tree (DT)
17.3.2 Gaussian Naive Bayes
17.3.3 Support Vector Machine
17.3.4 Logistic Regression
17.3.5 K Nearest Neighbor
17.4 Results and Comparison
17.5 Conclusion and Future Scope
References
Index

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