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Submit a ProposalIndustrial Internet of Things and Sensor Data Aggregation and Fusion
 | Edited by Kanak Kalita, S. Vishnu Kumar, and M. Niranjanamurthhy Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394275489 | Hardcover | 428 pages Price: $225 USD  |
One Line Description
Master the complexity of modern networks with this essential guide, which provides the state-of-the-art AI and machine learning techniques needed to execute seamless sensor data fusion and energy-efficient aggregation across Industrial IoT and smart city environments.
Audience
Academics, researchers, graduate students, and professionals working in the fields of IoT, Industrial IoT (IIoT), and sensor data aggregation and fusion.
Academics, researchers, graduate students, and professionals working in the fields of IoT, Industrial IoT (IIoT), and sensor data aggregation and fusion.
Description
The use of artificial intelligence and machine learning techniques for data aggregation and fusion is becoming increasingly important, as these technologies can help extract important features and knowledge from data. Sensor data aggregation and fusion are essential components of IoT and Industrial IoT systems, as they enable the combination of data from multiple sources to provide a more comprehensive view of the system being monitored. This book is a comprehensive guide to the state-of-the-art techniques and methods used for sensor data aggregation and fusion in IoT and Industrial IoT environments, covering the fundamental principles of data aggregation and fusion, as well as the latest advancements and applications in the field. The book takes a practical approach to the subject matter, providing a deeper understanding of the challenges and opportunities associated with sensor data aggregation and fusion in IoT and Industrial IoT environments. It covers topics such as machine learning-based data aggregation, intelligent multi-sensor fusion, data aggregation and fusion in smart cities, and energy-efficient data aggregation and fusion. Written by leading experts in the field, the book will provide a comprehensive overview of the latest advancements in sensor data aggregation and fusion in IoT and Industrial IoT environments.
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The use of artificial intelligence and machine learning techniques for data aggregation and fusion is becoming increasingly important, as these technologies can help extract important features and knowledge from data. Sensor data aggregation and fusion are essential components of IoT and Industrial IoT systems, as they enable the combination of data from multiple sources to provide a more comprehensive view of the system being monitored. This book is a comprehensive guide to the state-of-the-art techniques and methods used for sensor data aggregation and fusion in IoT and Industrial IoT environments, covering the fundamental principles of data aggregation and fusion, as well as the latest advancements and applications in the field. The book takes a practical approach to the subject matter, providing a deeper understanding of the challenges and opportunities associated with sensor data aggregation and fusion in IoT and Industrial IoT environments. It covers topics such as machine learning-based data aggregation, intelligent multi-sensor fusion, data aggregation and fusion in smart cities, and energy-efficient data aggregation and fusion. Written by leading experts in the field, the book will provide a comprehensive overview of the latest advancements in sensor data aggregation and fusion in IoT and Industrial IoT environments.
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Author / Editor Details
Kanak Kalita, PhD is an accomplished professor and researcher in the field of Computational Engineering with more than ten years of experience. He has published more than 190 articles and five edited book volumes. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, the finite element method, and composites.
Kanak Kalita, PhD is an accomplished professor and researcher in the field of Computational Engineering with more than ten years of experience. He has published more than 190 articles and five edited book volumes. His research interests include machine learning, fuzzy decision making, metamodeling, process optimization, the finite element method, and composites.
S. Vishnu Kumar, PhD is an Assistant Professor in the Department of Electronics and Communication Engineering at the Vel Tech Rangarajan Dr. Sagunthala Research and Development Institute of Science and Technology. He has proven his expertise through publication and industrial consultancy projects, including the publication of five scientific research articles, two book chapters, and six research papers presented at international conferences. His research areas include embedded machine learning, Internet of Things, networking, and embedded system design.
M. Niranjanamurthy, PhD is an Assistant Professor in the Department of Artificial Intelligence and Machine Learning at the Bhusanayana Mukundadas Sreenivasaiah Institute of Technology and Management. He has published 25 books and 95 articles in various national and international conferences and journals and filed 30 patents, six of which were granted. His areas of interest are data science, machine learning, e-commerce, and m-commerce.
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Table of Contents
Preface
Part I: Foundations and Frameworks in IoT and Data Aggregation
1. Enhancing Privacy and Efficiency in IoT‑Enabled Business Information Analytics and Blockchain‑Based Contingency
P. Kumaresan and R. Ramprashath
1.1 Introduction
1.2 Literature Survey
1.3 Proposed System
1.4 Results
1.5 Discussion of Results
1.6 Conclusion
1.7 Limitation
References
2. Supporting Authorized Duplicate Check in Hybrid Cloud Architecture Using IoT
Balaji B. and Ram Prashath R.
2.1 Introduction
2.2 Literature Review
2.3 Research Methodology
2.4 Implementation
2.4.1 System Analysis
2.4.1.1 The Duplicate-Check Token’s Unforgery
2.5 Evaluation
2.5.1 File Size
2.5.2 Deduplication Ratio
2.6 Discussion
2.6.1 Adaptable Attribute-Based Encryption
2.7 Results
2.8 Future Discussion
2.9 Conclusion
References
3. Design and Analysis of Multiband Microstrip Patch Antenna for IoT Applications
R. Ramasamy, Arunachala Perumal C., Bharathi C. Ramachandra, Srikusan A. and Vijayan S.
3.1 Introduction
3.2 Related Works
3.3 Design Calculation
3.4 Patch Antenna Design
3.4.1 Block Diagram
3.4.2 Antenna Design
3.4.3 Ground Plane
3.4.4 Patch
3.4.5 Multiband MPA
3.5 Results and Discussion
3.5.1 Gain
3.5.2 Radiation Pattern
3.6 Conclusion
References
4. Simulation and Implementation of Advanced Adder and Hybrid Multiplier for FIR Filter
V. Magesh, T. Solai Mithelesh, S. Ponmaniselvan and P. Praveen Kumar
4.1 Introduction
4.2 Literature Survey
4.3 Proposed System
4.4 Proposed Multiplier
4.5 Results and Discussion
4.6 Performance Analysis
4.7 Conclusion
References
5. Comparison of the Design and Implementation of Smartphone Charging Controllers Using Arduino Mega and Raspberry Pi
Yuvaraj D. and N. P. G. Bhavani
Introduction
Materials and Methods
Statistical Analysis
Results
Discussion
Conclusion
References
6. Enhancing the Accuracy in Designing an Image with Style Transfer Learning Method Using Visual Geometry Group (VGG16) over InceptionV3
Boobathy A. and Rashmita Khilar
Introduction
Materials and Methods
VGG16
InceptionV3
Statistical Analysis
Results
Discussion
Conclusion
References
Part II: IoT in Smart Cities and Infrastructure
7. Efficient Traffic Detection and Localization in 5G Networks Using IoT‑Enhanced Dynamic Ad-Hoc Clusters
Nirmalkumar K. and Ramprashath R.
7.1 Introduction
7.2 Proposed System
7.3 Related Work
7.4 Routing Optimization
7.5 Clustering for Network Planning
7.6 Results and Discussion
7.7 Conclusion
References
8. RFID-Based Smart Parking Management System Using IoT
Anil Kumar C. S., Shailendra Kumar Mishra, Ali Baig Mohammad and Vishnu Kumar. S.
8.1 Introduction
8.2 Related Works
8.3 Methodology
8.4 Algorithm
8.5 Results and Discussion
8.6 Conclusions
References
9. IoT-Based Animal Watch Safety Using Green Technology with Deep Learning Approach
Vineet Saxena, Prashant Dhage, Ghouse Basha M. A., Trupti Patil, Mritunjay Rai and Vishal Sharma
9.1 Introduction
9.1.1 Man–Wildlife Conflict and the Role of Image Processing
9.1.2 YOLO Model for Detection of Animals and a Safety System Using Machine Learning
9.1.3 CNN and Visual Geometry Group Networks in Animal Detection for Training
9.1.4 Scaling the Object Detection System Made Using CNN to Real-World Scenario
9.1.5 Improvement in Object Detection for Animals Over the Past Few Years
9.1.6 Characteristics of Object Detection Using YOLO Algorithm in Animal Security System
9.1.7 Animal Security and Advanced System for a Security System
9.2 Literature Review
9.3 Problem Statement
9.4 Proposed Work
9.5 Results and Discussion
9.6 Conclusion
9.7 Future Scope
References
10. Detection of Barcode for Automatic Fastai and EDA Considering Big Data for Green Cities
Santosh S. Chowhan, Vivek Veeraiah, Sukhvinder Singh Dari, Sovers Singh Bisht, Rohit Anand, Ritu Shree and M. Niranjanamurthy
10.1 Introduction
10.1.1 Introduction to Barcode Detection and Its Significance in Surveillance Video Analysis
10.1.2 Overview of Deep Learning Methods and Their Application in Computer Vision Tasks
10.1.3 Theoretical Background of CNNs and Role in Barcode Detection
10.1.4 Transfer Learning and Its Relevance in Training Deep Learning Models for Barcode Recognition
10.1.5 The ResNet34 Architecture and Its Suitability for the Barcode Identification Task
10.1.6 Utilization of the Fastai Library for Efficient Handling of the Barcode Dataset
10.1.7 Data Processing and Model Building
10.1.8 Determining the Learning Rate and Training the Model
10.2 Literature Review
10.3 Problem Statement
10.4 Proposed Work
10.5 Results and Discussion
10.6 Conclusion
10.7 Future Scope
References
11. Number Plate Detection to Automatic Ticket Repeat Offenders in Traffic Violation Using Green Technology
Anishkumar Dhablia, Bharti Sharma, Alka Singh, Jayaprakash B., Rajendra P. Pandey and Adapa Gopi
11.1 Introduction
11.1.1 Introduction to Automated Ticketing Systems for Traffic Violations
11.1.2 Overview of the Proposed Method for Number Plate Detection and Extraction
11.1.3 Explanation of Computer Vision Techniques Used in the Suggested Approach
11.1.4 The Two-Step Procedure for Extracting License Plate Numbers
11.1.5 Deep Learning Models and Their Role in Identifying and Segmenting License Plates
11.1.6 Machine Learning Approaches and Feature Extraction Techniques for Character Recognition
11.1.7 Integration of the Suggested Method into Existing Traffic Monitoring Infrastructure
11.2 Literature Review
11.2.1 Problem Statement
11.2.2 Proposed Work
11.3 Results and Discussion
11.4 Conclusion
11.5 Future Scope
References
12. IoT-Based Object Detection in Green Cities by Making Use of Data Center Based on YOLOv3 Model
Jayant S. Rohankar, Shaziya Islam, Priyanka Chandani, Aditee Godbole, Neeraj Kumari and Dharmesh Dhabliya
12.1 Introduction
12.1.1 Importance of Monitoring and Surveillance in Data Centers
12.1.2 Challenges Specific to Data Center Monitoring in Content of Machine Learning
12.1.3 Collaboration with Human Operators in Object Detection
12.1.4 Scalability and Adaptability of Such Systems
12.1.5 Data Management and Privacy in Data Centers
12.1.6 Performance Evaluation and Optimization in Data Centers through Security Measures
12.2 Literature Review
12.2.1 Problem Statement
12.2.2 Proposed Work
12.3 Results and Discussion
12.4 Conclusion
12.5 Future Scope
References
13. Facemask Detection for Passengers’ Safety Using Green Technology by Fine Tuning on Object Detection Model in IoT
Mohd. Asif Iqbal, Panduranga Rao M. V., Anisha Soni, Priyank Singhal, Ankur Gupta and Sharayu Ikhar
13.1 Introduction
13.1.1 Fine-Tuning Object Detection Models for Mask Detection in Public Transportation
13.1.2 Enhancing Passenger Health and Well-Being through Mask Detection in Public Transportation
13.1.3 Gender Analysis and Mask Detection for Safety Implications in Public Transportation
13.1.4 Significance of Face Masks in Disease Transmission Mitigation and Mask Detection in Public Transportation
13.1.5 Implementing Mask Detection Systems as Crucial Safety Measures in Public Transportation Environments
13.2 Literature Review
13.3 Problem Statement
13.4 Proposed Work
13.5 Results and Discussion
13.6 Conclusion
13.7 Future Scope
References
Part III: Industrial Applications and Predictive Maintenance
14. Enhancing Error Prediction in Machineries through CNN and Random Forest Models Using IoT with Sensor Data Fusion
Thishan S. and Senthil Kumar K.
14.1 Introduction
14.2 Literature Survey
14.3 Proposed System
14.4 Training and Classification
14.5 Implementation
14.6 Commonly Monitored Machines
14.7 Model Architecture
14.8 Results
14.9 Conclusion
Bibliography
15. Real-Time Flight Delay Prediction with Live Data from IoT and Airline Operations Optimization Using the KNN Algorithm
B. Praveen Kumar and R. Ramprashath
15.1 Introduction
15.2 Literature Review
15.3 Existing System
15.4 Proposed System
15.5 Dataset Description
15.6 Results and Discussion
15.7 Conclusion
15.8 Future Scope
References
16. Flight Delay Analysis Using XGBoost on Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion
Gayathri S., Venkata Veerendra Naveen Guthurthi, Varri Venkata Jyothi, Abirami R. and Priyadharshini S.
16.1 Introduction
16.1.1 Records Specification
16.1.2 Data Cleansing and Data Preparation
16.1.3 Predictive Model
16.2 Background Study
16.3 System Design
16.4 Methodology
16.5 Results and Discussion
16.6 Conclusion
Bibliography
17. An Analysis of the Traffic Loads in the Servers Using Thermal Images Utilizing Empirical Wavelet Transform with Dyadic Wavelet Transform
Madhan Kumar Reddy and A. Selva Kumar
Introduction
Materials and Methods
Empirical Wavelet Transform
Dyadic Wavelet Transform
Results
Discussion
Conclusion
References
18. Comparison of Discrete Wavelet Transforms and Stationary Wavelets for the Accurate Diagnosis of Server Issues Using Thermal Images
Madhan Kumar Reddy and A. Selva Kumar
Introduction
Materials and Methods
Statistical Analysis
Results
Discussion
Conclusion
References
Part IV: Health, Safety, and Security Applications
19. Diagnosis of Diseases Using Machine Learning
Sujita Godishala, Vakalapudi Sumavi, M. Saravanan and P.S. Maya Gopal
19.1 Introduction
19.2 Literature Survey
19.3 Existing System
19.4 Disadvantages of Existing System
19.5 Proposed System
19.6 Advantages of Proposed System
19.7 Results
19.8 Conclusion
References
20. Client Attrition Prediction in Multiple Sectors with Customized Machine Learning Models Using IoT
R. Balaji and R. Ramprashath
20.1 Introduction
20.2 Literature Survey
20.3 Proposed System
20.4 Methodology
20.5 Discussion
20.6 Conclusion
20.7 Limitations
References
21. Blood Transfusion System Using Data Mining Techniques and Grey Relational Analysis (GRA) Using Decision Tree Compared with Naive Bayes
K. Manimaran and T. Poovizhi
Introduction
Materials and Methods
Decision Tree
Statistical Analysis
Results
Discussion
Conclusion
References
22. IoT-Integrated Detection and Classification of Deepfake Images and Videos Using Custom Deep Learning Models
K. Praveen Kumar and R. Ramprashath
22.1 Introduction
22.2 Literature Survey
22.3 Proposed System
22.4 Results
22.5 Discussion of Results
22.6 Conclusion
References
23. Reducing the False Rejection Using Novel Iris Recognition by Comparing with Elastic Bunch Graph Matching for Smartphones
Kamalesh S. and V. Nagaraju
Introduction
Materials and Methods
VGG16 Architecture
Elastic Bunch Graph Matching
Statistical Analysis
Results
Discussion
Conclusion
References
24. Intelligent IoT-Enabled Privacy-Preserving Course Recommendation System: Leveraging NLP Chatbot and Federated Learning with Federated Linear Regression
Dhivyaprabha G. and Ram Prashath R.
24.1 Introduction
24.1.1 Motivation
24.1.2 Research Gap
24.1.3 Significance
24.1.4 Objectives
24.2 Related Works
24.3 Proposed System
24.4 Results and Discussion
24.5 Conclusion
24.6 Future Enhancements
References
25. Harnessing YOLO-Powered Drones for Cloud-Based Weed Density Mapping Focusing Agri 4.0
S. Vishnu Kumar, G. Aloy Anuja Mary, B. Sathyasri, Murali Kalipindi and Chivon Choeung
25.1 Integration of Internet of Things and Machine Learning in Agriculture
25.2 Significance of Automated Weed Removal in Agriculture
25.3 Challenges in Developing Weed Density MAP
25.4 Development of YOLO and Cloud-Based Weed Density Mapping System
25.4.1 Drone—Data Collection and Transmission
25.4.1.1 Hardware Connection
25.4.1.2 YOLO-Based Weed Detection Model
25.4.1.3 Publish Data to Google Cloud Using MQTT
25.4.1.4 Python Code
25.4.2 Visualizing Weed Density as a Heat Map at Cloud
25.5 Conclusion
References
About the Editors
Index
Back to Top
Preface
Part I: Foundations and Frameworks in IoT and Data Aggregation
1. Enhancing Privacy and Efficiency in IoT‑Enabled Business Information Analytics and Blockchain‑Based Contingency
P. Kumaresan and R. Ramprashath
1.1 Introduction
1.2 Literature Survey
1.3 Proposed System
1.4 Results
1.5 Discussion of Results
1.6 Conclusion
1.7 Limitation
References
2. Supporting Authorized Duplicate Check in Hybrid Cloud Architecture Using IoT
Balaji B. and Ram Prashath R.
2.1 Introduction
2.2 Literature Review
2.3 Research Methodology
2.4 Implementation
2.4.1 System Analysis
2.4.1.1 The Duplicate-Check Token’s Unforgery
2.5 Evaluation
2.5.1 File Size
2.5.2 Deduplication Ratio
2.6 Discussion
2.6.1 Adaptable Attribute-Based Encryption
2.7 Results
2.8 Future Discussion
2.9 Conclusion
References
3. Design and Analysis of Multiband Microstrip Patch Antenna for IoT Applications
R. Ramasamy, Arunachala Perumal C., Bharathi C. Ramachandra, Srikusan A. and Vijayan S.
3.1 Introduction
3.2 Related Works
3.3 Design Calculation
3.4 Patch Antenna Design
3.4.1 Block Diagram
3.4.2 Antenna Design
3.4.3 Ground Plane
3.4.4 Patch
3.4.5 Multiband MPA
3.5 Results and Discussion
3.5.1 Gain
3.5.2 Radiation Pattern
3.6 Conclusion
References
4. Simulation and Implementation of Advanced Adder and Hybrid Multiplier for FIR Filter
V. Magesh, T. Solai Mithelesh, S. Ponmaniselvan and P. Praveen Kumar
4.1 Introduction
4.2 Literature Survey
4.3 Proposed System
4.4 Proposed Multiplier
4.5 Results and Discussion
4.6 Performance Analysis
4.7 Conclusion
References
5. Comparison of the Design and Implementation of Smartphone Charging Controllers Using Arduino Mega and Raspberry Pi
Yuvaraj D. and N. P. G. Bhavani
Introduction
Materials and Methods
Statistical Analysis
Results
Discussion
Conclusion
References
6. Enhancing the Accuracy in Designing an Image with Style Transfer Learning Method Using Visual Geometry Group (VGG16) over InceptionV3
Boobathy A. and Rashmita Khilar
Introduction
Materials and Methods
VGG16
InceptionV3
Statistical Analysis
Results
Discussion
Conclusion
References
Part II: IoT in Smart Cities and Infrastructure
7. Efficient Traffic Detection and Localization in 5G Networks Using IoT‑Enhanced Dynamic Ad-Hoc Clusters
Nirmalkumar K. and Ramprashath R.
7.1 Introduction
7.2 Proposed System
7.3 Related Work
7.4 Routing Optimization
7.5 Clustering for Network Planning
7.6 Results and Discussion
7.7 Conclusion
References
8. RFID-Based Smart Parking Management System Using IoT
Anil Kumar C. S., Shailendra Kumar Mishra, Ali Baig Mohammad and Vishnu Kumar. S.
8.1 Introduction
8.2 Related Works
8.3 Methodology
8.4 Algorithm
8.5 Results and Discussion
8.6 Conclusions
References
9. IoT-Based Animal Watch Safety Using Green Technology with Deep Learning Approach
Vineet Saxena, Prashant Dhage, Ghouse Basha M. A., Trupti Patil, Mritunjay Rai and Vishal Sharma
9.1 Introduction
9.1.1 Man–Wildlife Conflict and the Role of Image Processing
9.1.2 YOLO Model for Detection of Animals and a Safety System Using Machine Learning
9.1.3 CNN and Visual Geometry Group Networks in Animal Detection for Training
9.1.4 Scaling the Object Detection System Made Using CNN to Real-World Scenario
9.1.5 Improvement in Object Detection for Animals Over the Past Few Years
9.1.6 Characteristics of Object Detection Using YOLO Algorithm in Animal Security System
9.1.7 Animal Security and Advanced System for a Security System
9.2 Literature Review
9.3 Problem Statement
9.4 Proposed Work
9.5 Results and Discussion
9.6 Conclusion
9.7 Future Scope
References
10. Detection of Barcode for Automatic Fastai and EDA Considering Big Data for Green Cities
Santosh S. Chowhan, Vivek Veeraiah, Sukhvinder Singh Dari, Sovers Singh Bisht, Rohit Anand, Ritu Shree and M. Niranjanamurthy
10.1 Introduction
10.1.1 Introduction to Barcode Detection and Its Significance in Surveillance Video Analysis
10.1.2 Overview of Deep Learning Methods and Their Application in Computer Vision Tasks
10.1.3 Theoretical Background of CNNs and Role in Barcode Detection
10.1.4 Transfer Learning and Its Relevance in Training Deep Learning Models for Barcode Recognition
10.1.5 The ResNet34 Architecture and Its Suitability for the Barcode Identification Task
10.1.6 Utilization of the Fastai Library for Efficient Handling of the Barcode Dataset
10.1.7 Data Processing and Model Building
10.1.8 Determining the Learning Rate and Training the Model
10.2 Literature Review
10.3 Problem Statement
10.4 Proposed Work
10.5 Results and Discussion
10.6 Conclusion
10.7 Future Scope
References
11. Number Plate Detection to Automatic Ticket Repeat Offenders in Traffic Violation Using Green Technology
Anishkumar Dhablia, Bharti Sharma, Alka Singh, Jayaprakash B., Rajendra P. Pandey and Adapa Gopi
11.1 Introduction
11.1.1 Introduction to Automated Ticketing Systems for Traffic Violations
11.1.2 Overview of the Proposed Method for Number Plate Detection and Extraction
11.1.3 Explanation of Computer Vision Techniques Used in the Suggested Approach
11.1.4 The Two-Step Procedure for Extracting License Plate Numbers
11.1.5 Deep Learning Models and Their Role in Identifying and Segmenting License Plates
11.1.6 Machine Learning Approaches and Feature Extraction Techniques for Character Recognition
11.1.7 Integration of the Suggested Method into Existing Traffic Monitoring Infrastructure
11.2 Literature Review
11.2.1 Problem Statement
11.2.2 Proposed Work
11.3 Results and Discussion
11.4 Conclusion
11.5 Future Scope
References
12. IoT-Based Object Detection in Green Cities by Making Use of Data Center Based on YOLOv3 Model
Jayant S. Rohankar, Shaziya Islam, Priyanka Chandani, Aditee Godbole, Neeraj Kumari and Dharmesh Dhabliya
12.1 Introduction
12.1.1 Importance of Monitoring and Surveillance in Data Centers
12.1.2 Challenges Specific to Data Center Monitoring in Content of Machine Learning
12.1.3 Collaboration with Human Operators in Object Detection
12.1.4 Scalability and Adaptability of Such Systems
12.1.5 Data Management and Privacy in Data Centers
12.1.6 Performance Evaluation and Optimization in Data Centers through Security Measures
12.2 Literature Review
12.2.1 Problem Statement
12.2.2 Proposed Work
12.3 Results and Discussion
12.4 Conclusion
12.5 Future Scope
References
13. Facemask Detection for Passengers’ Safety Using Green Technology by Fine Tuning on Object Detection Model in IoT
Mohd. Asif Iqbal, Panduranga Rao M. V., Anisha Soni, Priyank Singhal, Ankur Gupta and Sharayu Ikhar
13.1 Introduction
13.1.1 Fine-Tuning Object Detection Models for Mask Detection in Public Transportation
13.1.2 Enhancing Passenger Health and Well-Being through Mask Detection in Public Transportation
13.1.3 Gender Analysis and Mask Detection for Safety Implications in Public Transportation
13.1.4 Significance of Face Masks in Disease Transmission Mitigation and Mask Detection in Public Transportation
13.1.5 Implementing Mask Detection Systems as Crucial Safety Measures in Public Transportation Environments
13.2 Literature Review
13.3 Problem Statement
13.4 Proposed Work
13.5 Results and Discussion
13.6 Conclusion
13.7 Future Scope
References
Part III: Industrial Applications and Predictive Maintenance
14. Enhancing Error Prediction in Machineries through CNN and Random Forest Models Using IoT with Sensor Data Fusion
Thishan S. and Senthil Kumar K.
14.1 Introduction
14.2 Literature Survey
14.3 Proposed System
14.4 Training and Classification
14.5 Implementation
14.6 Commonly Monitored Machines
14.7 Model Architecture
14.8 Results
14.9 Conclusion
Bibliography
15. Real-Time Flight Delay Prediction with Live Data from IoT and Airline Operations Optimization Using the KNN Algorithm
B. Praveen Kumar and R. Ramprashath
15.1 Introduction
15.2 Literature Review
15.3 Existing System
15.4 Proposed System
15.5 Dataset Description
15.6 Results and Discussion
15.7 Conclusion
15.8 Future Scope
References
16. Flight Delay Analysis Using XGBoost on Industrial Internet of Things and Advanced Techniques for Sensor Data Aggregation and Fusion
Gayathri S., Venkata Veerendra Naveen Guthurthi, Varri Venkata Jyothi, Abirami R. and Priyadharshini S.
16.1 Introduction
16.1.1 Records Specification
16.1.2 Data Cleansing and Data Preparation
16.1.3 Predictive Model
16.2 Background Study
16.3 System Design
16.4 Methodology
16.5 Results and Discussion
16.6 Conclusion
Bibliography
17. An Analysis of the Traffic Loads in the Servers Using Thermal Images Utilizing Empirical Wavelet Transform with Dyadic Wavelet Transform
Madhan Kumar Reddy and A. Selva Kumar
Introduction
Materials and Methods
Empirical Wavelet Transform
Dyadic Wavelet Transform
Results
Discussion
Conclusion
References
18. Comparison of Discrete Wavelet Transforms and Stationary Wavelets for the Accurate Diagnosis of Server Issues Using Thermal Images
Madhan Kumar Reddy and A. Selva Kumar
Introduction
Materials and Methods
Statistical Analysis
Results
Discussion
Conclusion
References
Part IV: Health, Safety, and Security Applications
19. Diagnosis of Diseases Using Machine Learning
Sujita Godishala, Vakalapudi Sumavi, M. Saravanan and P.S. Maya Gopal
19.1 Introduction
19.2 Literature Survey
19.3 Existing System
19.4 Disadvantages of Existing System
19.5 Proposed System
19.6 Advantages of Proposed System
19.7 Results
19.8 Conclusion
References
20. Client Attrition Prediction in Multiple Sectors with Customized Machine Learning Models Using IoT
R. Balaji and R. Ramprashath
20.1 Introduction
20.2 Literature Survey
20.3 Proposed System
20.4 Methodology
20.5 Discussion
20.6 Conclusion
20.7 Limitations
References
21. Blood Transfusion System Using Data Mining Techniques and Grey Relational Analysis (GRA) Using Decision Tree Compared with Naive Bayes
K. Manimaran and T. Poovizhi
Introduction
Materials and Methods
Decision Tree
Statistical Analysis
Results
Discussion
Conclusion
References
22. IoT-Integrated Detection and Classification of Deepfake Images and Videos Using Custom Deep Learning Models
K. Praveen Kumar and R. Ramprashath
22.1 Introduction
22.2 Literature Survey
22.3 Proposed System
22.4 Results
22.5 Discussion of Results
22.6 Conclusion
References
23. Reducing the False Rejection Using Novel Iris Recognition by Comparing with Elastic Bunch Graph Matching for Smartphones
Kamalesh S. and V. Nagaraju
Introduction
Materials and Methods
VGG16 Architecture
Elastic Bunch Graph Matching
Statistical Analysis
Results
Discussion
Conclusion
References
24. Intelligent IoT-Enabled Privacy-Preserving Course Recommendation System: Leveraging NLP Chatbot and Federated Learning with Federated Linear Regression
Dhivyaprabha G. and Ram Prashath R.
24.1 Introduction
24.1.1 Motivation
24.1.2 Research Gap
24.1.3 Significance
24.1.4 Objectives
24.2 Related Works
24.3 Proposed System
24.4 Results and Discussion
24.5 Conclusion
24.6 Future Enhancements
References
25. Harnessing YOLO-Powered Drones for Cloud-Based Weed Density Mapping Focusing Agri 4.0
S. Vishnu Kumar, G. Aloy Anuja Mary, B. Sathyasri, Murali Kalipindi and Chivon Choeung
25.1 Integration of Internet of Things and Machine Learning in Agriculture
25.2 Significance of Automated Weed Removal in Agriculture
25.3 Challenges in Developing Weed Density MAP
25.4 Development of YOLO and Cloud-Based Weed Density Mapping System
25.4.1 Drone—Data Collection and Transmission
25.4.1.1 Hardware Connection
25.4.1.2 YOLO-Based Weed Detection Model
25.4.1.3 Publish Data to Google Cloud Using MQTT
25.4.1.4 Python Code
25.4.2 Visualizing Weed Density as a Heat Map at Cloud
25.5 Conclusion
References
About the Editors
Index
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