Search

Browse Subject Areas

For Authors

Submit a Proposal

Optimized Computational Intelligence Driven Decision-Making

Theory, Application and Challenges
Edited by Hrudaya Kumar Tripathy, Sushruta Mishra, Minakhi Rout, S. Balamurugan and Samaresh Mishra
Series: Industry 5.0 Transformation Applications
Copyright: 2024   |   Status: Contracted
ISBN: 9781394242535  |  Hardcover  |  
356 pages

One Line Description
This book covers a wide range of advanced techniques and approaches for designing and implementing computationally intelligent methods in different application domains which is of great use to not only researchers but also academicians and industry experts.

Audience
The book will interest a range of engineers and researchers in information technology, computer science, and artificial intelligence working in the interdisciplinary field of computational intelligence.

Description
Optimized Computational Intelligence (OCI) is a new, cutting-edge, and multidisciplinary research area that tackles the fundamental problems shared by modern informatics, biologically-inspired computation, software engineering, AI, cybernetics, cognitive science, medical science, systems science, philosophy, linguistics, economics, management science, and life sciences. OCI aims to apply modern computationally intelligent methods to generate optimum outcomes in various application domains. This book presents the latest technologies-driven material to explore optimized various computational intelligence domains and includes real-life case studies highlighting different advanced technologies in computational intelligence; provides a unique compendium of current and emerging hybrid intelligence paradigms for advanced informatics; reflects the diversity, complexity, and depth and breadth of this critical bio-inspired domain; offers a guided tour of computational intelligence algorithms, architecture design, and applications of learning in dealing with cognitive informatics challenges; presents a variety of intelligent and optimized techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional data analytics research in intelligent decision-making system dynamics; and includes architectural models and applications-based augmented solutions for optimized computational intelligence.

Back to Top
Author / Editor Details
Hrudaya Kumar Tripathy, PhD, is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, He has more than 20 years of teaching experience and his research interests include neural networks, pattern recognition, software engineering, machine learning, and big data. He has published several books and research papers in various journals and conferences. Tripathy received the 2013 Young IT Professional Award from the Computer Society of India.

Sushruta Mishra, PhD, is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, Odisha, India. He obtained his doctorate in 2017 and his research interests include image processing, machine learning, the Internet of Things, and cognitive computing. He has published 130+ research articles in international journals and conferences.

Minakhi Rout, PhD, is an associate professor in the School of Computer Engineering, KIIT Deemed to be University, Odisha, India. She obtained her PhD in 2015 and her research interests focus on computational finance, data mining, and machine learning. Rout has published 50+ research papers in international journals and conferences.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.

Samaresh Mishra, PhD, is the director of student affairs at KIIT Deemed to be University. He obtained a PhD in computer science from Utkal University. His research areas focus on software testing, machine learning, and cloud computing. He has published 30+ academic papers.

Back to Top

Table of Contents
Preface
1. Emergence of Advanced Computational Intelligence Coupled with Smart Environment
Risha Rani and Tirtha Deb
1.1 Introduction
1.2 Background Works
1.3 Integrated Smart Environment
1.4 Proposed Models for Smart Intelligent Environment
1.4.1 Smart Cities
1.4.1.1 Garbage Monitoring System
1.4.1.2 Accident Sensing System
1.4.2 Smart Healthcare
1.4.3 Smart Homes
1.4.3.1 Weather Monitoring IoT-Based System
1.4.3.2 Air Pollution Monitoring IoT-Based System
1.4.3.3 Noise Pollution Monitoring IoT-Based System
1.4.3.4 Forest Fire Detection IoT-Based System
1.5 IoT Architecture
1.5.1 Perception Layer
1.5.1.1 Privacy and Verification
1.5.1.2 Network Availability
1.5.1.3 Service Integrity
1.5.1.4 Jamming
1.5.1.5 Eavesdropping
1.5.1.6 Replay Attack
1.5.1.7 Man-in-the-Middle (MITM) Attack
1.5.1.8 Denial of Service (DoS)
1.5.1.9 Tag Cloning
1.5.1.10 Take Off i.e. Spoofing
1.5.1.11 Device Tampering
1.5.1.12 Outage of Nodes
1.5.1.13 Leakage of Information
1.5.2 Network Layer
1.5.2.1 Selective Forwarding
1.5.2.2 Sybil Attack
1.5.2.3 Sinkhole/Black Hole Attack
1.5.2.4 Wormhole
1.5.2.5 Attacks of Hello Flood
1.5.3 Support Layer
1.5.3.1 Data Tampering
1.5.3.2 Unauthorized Access
1.5.3.3 DoS Attack
1.5.4 Application Layer
1.5.4.1 Sniffer
1.5.4.2 Injection
1.5.4.3 Session Hijacking
1.5.4.4 Distributed Denial of Service (DDoS)
1.5.4.5 Social Engineering
1.6 Smart Environment and Advanced Computational Intelligence
1.7 Advanced Computational Intelligences: Possible Uses in Smart Environment
1.7.1 Smart Infrastructure and Green Economy
1.7.2 Resolving the Issue of Sustainability
1.8 Conclusion
References
2. Machine Learning-Enabled Integrated Information Platform for Educational Universities
Sai Smurti Sahu, Rishav Kumar, Soumya Sahoo, Balwant Kumar and Padmabati Mohanta
2.1 Introduction
2.2 Cloud-Based Web Application for University
2.2.1 Overview
2.2.2 Working Principles
2.2.3 Cloud Computing Techniques
2.2.4 Cloud Database
2.2.5 Advantages of Cloud-Based Database
2.2.6 How Cloud-Based Website is Better than Non-Cloud Websites
2.3 Integrated Information Platform of Indian Universities Using Machine Learning
2.3.1 Overview
2.3.2 Applications of Machine Learning in Integrated Information Platform
2.3.3 What are Uses of Machine Learning in This Platform
2.4 Applications Used to Designed This Web Platform
2.4.1 Front-End Development
2.4.2 Backend Development
2.5 Analysis Result
2.5.1 Home Page
2.5.2 Sign Up/Log In Page
2.5.3 Explore University
2.5.4 Recommended Comparison
2.5.5 Manually Comparison
Conclusion
References
3. False Data Injection Attack Detection Using Machine Learning in Industrial Internet of Things
Hafizunisa, Prerna Rai and Damini Sinha
3.1 Introduction
3.2 Literature Review
3.3 Technical Methodology
3.3.1 Autoencoders (AE) used for Identifying False Data
3.3.1.1 Encoder Network
3.3.1.2 Decoder Network
3.3.2 Denoising Autoencoder (DAE) used for Data Recovery
3.4 Proposed Model for Detecting False Data and its Correction
3.4.1 Attack Detection Algorithm
3.4.2 Denoising Autoencoders-Based Data Cleaning
3.4.3 Algorithm for False Data Detection and Correction
3.5 Complexity Analysis of Proposed Model
3.6 Advantages of the Model
3.7 Future Scope and Limitations of the Proposed Model
3.8 Conclusion
References
4. Fake News Detection: Restricting Spreading of Misinformation Using Machine Learning
Shubham Choudhary and Pratyush Mishra
4.1 Introduction
4.2 Scope of False News Detection
4.3 Main Highlights of the Analysis
4.3.1 Approach
4.3.2 Naive Bayes
4.3.3 Support Vector Machine (SVM)
4.4 A Novel Model for False News Detection
4.4.1 Aggregator
4.4.2 News Authentication
4.4.3 News Suggestion/Recommendation System
4.5 Literature Review
4.6 Results and Analysis
4.7 Conclusion
References
5. Adaptability, Flexibility, and Accessibility Through Telemedicine
Dipti Verma, Somyajyoti Talukdar and Kumari Alankrita Sharma
5.1 Introduction
5.2 Related Works
5.3 Proposed Model for Remote Health Monitoring System
5.3.1 Microcontroller and Sensor
5.4 Benefits of the Proposed Model
5.5 Constraints of the Proposed Model
5.6 Conclusion
5.7 Future Works
References
6. Crop Prediction by Implementing Machine Learning in an IoT-Based System
Vivian Rawade and Shubham Sahoo
6.1 Introduction
6.2 Literature Review
6.3 Proposed Model for Crop Prediction
6.4 Results and Analysis
6.5 Challenges Faced
6.6 Advantages of the Proposed Model
6.7 Disadvantages of the Proposed Model
6.8 Conclusion
References
7. Relevance of Smart Management of Road Traffic System Using Advanced Intelligence
Koustab Chowdhury and Rishabh Kapoor
7.1 Introduction
7.2 Related Works
7.2.1 Traffic Lighting System
7.2.2 Smart Parking System
7.2.3 Vehicle Theft Detection System
7.3 Proposed Model of Traffic Management System
7.3.1 Traffic Lighting System
7.3.2 Smart Parking System
7.3.3 Vehicle Theft Detection System
7.4 Role of AI in Traffic Management
7.5 Conclusion and Future Works
References
8. Visualization of Textual Corpora Using Social Network Analysis
Indu Rodda and Durga Bhavani S.
8.1 Introduction
8.1.1 Importance of Character Networks
8.1.2 Visualization of Dynamic Networks
8.1.3 Contributions
8.2 Related Literature
8.2.1 Visualization of Social Networks
8.2.2 Community Discovery (CD)
8.2.3 Community Discovery in Dynamic Networks
8.3 Proposed Method
8.3.1 Basic Idea of Algorithm
8.3.2 Life Cycle of Dynamic Communities
8.3.3 Notation
8.3.4 Algorithm
8.4 Implementation and Results
8.4.1 Pre-Processing the Data
8.4.2 Generating Graph
8.4.3 Community Detection
8.4.4 Score-Similarity Measure
8.4.5 Visualization of Network
8.4.6 Visualization of Snapshots
8.4.7 Analysis of Results
8.5 Conclusion and Future Work
References
9. Autonomous Intelligent Vehicles: Impact, Current Market, Future Trends, Challenges, and Limitations
Kamalanathan Shanmugam, Muhammad Ehsan Rana and Felix Ting Yu Hong
9.1 Introduction
9.2 The Global Impact of the AV Industry
9.3 Role of Machine Learning in Autonomous Vehicles
9.4 Significance of the AV Industry in Various Sectors
9.4.1 Traffic Management
9.4.2 Roads and Urban Infrastructure
9.4.3 Logistics
9.4.4 Healthcare
9.4.5 Job Market
9.4.6 Environment and Society
9.5 Current Market and Future Trends in AV Industry
9.5.1 Tesla and Waymo: Two Key Players in the Autonomous Vehicle Industry
9.5.2 AI Datasets and ML-Based Development
9.5.3 Use of Sensors and Other Hardware
9.6 Challenges and Limitations
9.6.1 Data Privacy
9.6.2 Cybersecurity
9.6.3 Policies and Regulations
9.6.4 Ethical Issues
9.6.5 Other Common Challenges
9.7 Conclusion
References
10. Role of Smart and Predictive Healthcare in Modern Society
Muhammad Ehsan Rana and Manoj Jayabalan
10.1 Introduction
10.2 Healthcare System
10.3 Role of Predictive Analytics in Healthcare
10.3.1 Disease Prevention
10.3.2 Early Detection
10.3.3 Diagnosis
10.3.4 Treatment Planning
10.3.5 Resource Optimization
10.4 Application of IoT in Healthcare
10.4.1 Home Healthcare
10.4.2 m-Health
10.4.3 Electronic Health Record (EHR)
10.5 IoT Based Healthcare Management Framework
10.5.1 Data Collection Layer
10.5.2 Connectivity Layer
10.5.3 Cloud Layer
10.5.4 Application Layer
10.5.5 Consumer Layer
10.6 Future Recommendations for Research
10.7 Conclusion
References
11. An Analytical Study on Depression Detection Using Machine Learning
Angelia Melani Adrian and Junaidy Budi Sanger
11.1 Introduction
11.2 Literature Survey
11.3 Proposed System
11.4 Challenges of Machine Learning in Depression Detection
11.5 Conclusion and Future Work
References
12. Revolutionizing Healthcare: Empowering Faster Treatment with IoT-Powered Smart Healthcare
Prerna Kumari, Rupali Agarwal and Shruti Kumari
12.1 Introduction
12.1.1 Main Contribution of the Paper
12.1.1.1 Using IoT to Track Abnormalities
12.1.1.2 Emergency Alerts for Patients
12.1.1.3 Ambulance Notification
12.1.1.4 Patient Medical History and Family Contacts
12.1.1.5 Early Access to Treatment
12.2 Scope/Motivation
12.3 Literature Survey
12.4 Smart Technology
12.4.1 IoT-Enabled Healthcare
12.4.2 Importance of Smart Healthcare System
12.5 Methods and Materials
12.5.1 Smart Sensors Deployed in Model
12.5.1.1 Heart Rate Sensor
12.5.1.2 Blood Pressure Sensor
12.5.1.3 Body Temperature Sensor
12.5.1.4 Accelerometer
12.5.1.5 Gyrosco
12.5.1.6 Magnetometer
12.5.1.7 Barometric Pressure Sensor
12.5.1.8 Oximetry Sensor
12.5.1.9 Bioimpedance Sensor
12.5.2 Working of Model
12.6 Result
12.7 Conclusion
References
13. Machine Learning Algorithms for Initial Diagnosis of Parkinson’s Disease
Udayan Das, Manish Jena and Manish Roy
13.1 Overview of Parkinson’s Disease
13.2 Scope
13.3 Related Works
13.4 Comparative Analysis of Parkinson’s Disease
13.5 Pros and Cons Using ML Algorithms
13.6 Conclusion and Future Works
13.7 Bibliography
References
14. Towards a Sustainable Future: Harnessing the Power of Computational Intelligence to Track Climate Change
Satyam Sinha, Shreyash Kumar Agnihotri and Oshmita Sarkar
14.1 Introduction
14.2 Artificial Intelligence and Climate Change Adaptation
14.3 Related Works
14.4 Comparative Analysis of Technological Frameworks to Handle Climate Crisis
14.4.1 Artificial Intelligence for Drought Assessment and Forecasting
14.4.2 Carbon Dioxide Capture by Help of Synthetic Intelligence
14.4.3 Discussion of the Case Studies
14.5 Future Scope of Climatic Crisis Handling with AI
14.6 Conclusion
References
15. Impact of Computational Intelligence and Modeling in Tackling Weather Fluctuation
Rohan Karn, Aniket Rouniyar, Ranjit Kumar Das and Amit Gupta
15.1 Introduction
15.2 Objective
15.3 Causes of Climate Crisis
15.3.1 Greenhouse Gases
15.3.2 Fossil Fuels
15.3.3 Deforestation
15.3.4 Agriculture and Livestock
15.3.5 Industrial Processes
15.3.6 Transportation
15.4 Significance of AI and Modeling on Climate Crisis
15.4.1 Design and Optimization of Renewable Energy Systems
15.4.2 Develop AI-Driven Solutions to Reduce Deforestation
15.4.2.1 Detection and Monitoring of Deforestation
15.4.3 Forest Fire Prediction and Prevention
15.4.3.1 Forest Restoration
15.4.4 Create AI-Driven Solutions to Improve Agricultural Practices to Reduce
Carbon Emissions
15.4.5 Precision Farming
15.4.6 Sustainable Livestock Production
15.4.7 Carbon Sequestration
15.4.8 Energy Efficiency
15.4.9 Analyze and Predict the Effects of Climate Change
15.4.10 Studying Ecosystems
15.4.11 Predicting Human Health Impacts
15.4.12 Predicting Economic Impacts
15.5 Plastic Waste Detection Model
15.5.1 Convolutional Neural Networks (CNNs)
15.5.1.1 Image Classification
15.5.1.2 Object Detection
15.5.1.3 Segmentation
15.5.1.4 Time-Series Analysis
15.5.1.5 Accuracy
15.5.1.6 Efficiency
15.5.1.7 Flexibility
15.5.2 Deep Belief Networks (DBNs)
15.5.2.1 Feature Extraction
15.5.2.2 Object Detection
15.5.2.3 Transfer Learning
15.5.3 Advantages of Using DBNs for Plastic Waste Detection
15.5.3.1 Robustness
15.5.3.2 Scalability
15.5.3.3 Flexibility
15.6 Forest Fire Prediction Models Using AI
15.6.1 How ML Models Can Help to Prevent Forest Fire
15.6.1.1 Early Warning Systems
15.6.1.2 Predictive Modeling
15.6.1.3 Image Analysis
15.6.1.4 Real-Time Monitoring
15.6.1.5 Improved Firefighting Techniques
15.6.1.6 Early Detection
15.7 Results
15.7.1 Plastic Waste Detection
15.7.2 Forest Fire Prediction Model
15.7.2.1 Data Preprocessing
15.7.2.2 Training and Validation
15.8 Conclusion
References
Index

Back to Top



Description
Author/Editor Details
Table of Contents
Bookmark this page