-
Browse Book Series
- Adhesion and Adhesives: Fundamental and Applied Aspects
- Advances in Additive Manufacturing Technologies
- Advances in Antenna, Microwave and Communication Engineering
- Advances in Biofeedstocks and Biofuels
- Advances in Cyber Security
- Advances in Data Engineering and Machine Learning
- Advances in Disease Diagnosis, Drug Delivery and Treatment
- Advances in E-Mobility
- Advances in Hydrogen Production and Storage
- Advances in Intelligent and Scientific Computing (AISC)
- Advances in Learning Analytics for Intelligent Cloud-IoT Systems
- Advances in Membrane Processes
- Advances in Nanotechnology and Applications
- Advances in Natural Gas Engineering
- Advances in Pipes and Pipelines
- Advances in Production Engineering
- Advances in Solar Cell Materials and Storage
- Advances in Surface Engineering and Materials Processing
- Applied Functional Materials
- Artificial Intelligence and Soft Computing for Industrial Transformation
- Astrobiology Perspectives on Life in the Universe
- Bioprocessing in Food Science
- Computational Intelligence and Biomedical Engineering
- Computational Intelligence in Industrial Mathematics
- Concise Introductions to AI and Data Science
- Cyber Systems and Quantum Engineering
- Decentralized Systems and Next-Generation Internet
- Diatoms: Biology and Applications
- Digital Convergence in Engineering Systems
- Emerging Trends in Medicinal and Pharmaceutical Chemistry
- Engineering Systems Design for Sustainable Development
- Fintech in a Sustainable Digital Society
- Human Centric Sustainability and Next Generation Technology
- Industry 5.0 Transformation Applications
- Infectious and Rare Diseases
- Innovations in Materials and Manufacturing
- Leading-Edge Breakthroughs in Artificial Intelligence
- Lifeline Molecules: Exploring Antioxidants and Their Impact
- Machine Learning in Biomedical Science and Healthcare Informatics
- Marine Nanoscience and Nanotechnology
- Materials Degradation and Failure
- Mathematics and Computer Science
- Next-Generation Computing and Communication Engineering
- Performability Engineering Series
- Robotics and Automation for Modern Applications
- Rotating Machinery Fundamentals and Advances
- Semiconductor Devices and Their Applications
- Separation Science
- Studies in Computational Intelligence and Smart Technologies
- Sustainable Biopolymer Technologies and Applications
- Sustainable Computing and Optimization
- Thermoplastic Bionanocomposites Series
- Transformative Innovations in Materials Science
Browse Subject Areas
For Authors
Submit a ProposalArtificial Intelligence in Instrumentation, Control and Automation
 | Edited by B. Jaganatha Pandian, N. Amutha Prabha, Abhishek Gudipalli, and V. Indragandhi Copyright: 2026 | Expected Pub Date: 2026 ISBN: 9781394336135 | Hardcover | 648 pages Price: $225 USD  |
One Line Description
Revolutionize your industrial practice with this essential book, which provides a comprehensive overview of how artificial intelligence can be integrated into instrumentation and control systems to achieve unprecedented precision, efficiency, and autonomous optimization.
Audience
Academics, students, researchers, engineers, and industry professionals working in computer science, automation, and industrial automation.
Academics, students, researchers, engineers, and industry professionals working in computer science, automation, and industrial automation.
Description
Artificial Intelligence is increasingly becoming a game-changer in the fields of instrumentation, control, and automation, offering unprecedented capabilities to optimize processes, enhance efficiency, and drive innovation. AI algorithms and techniques could be integrated into traditional control systems to create smart and adaptive solutions capable of learning from data and making autonomous decisions for dynamic real-world environments. From predictive maintenance in manufacturing plants to navigation of autonomous vehicles in complex roadways, AI empowers instrumentation and control systems to operate with heightened precision and agility, revolutionizing industries across the board. This book will present the power of AI to optimize processes, enhance efficiency, and achieve unparalleled precision in instrumentation, control, and automation applications. It provides a comprehensive overview of these emerging technologies and their role in optimizing the instrumentation, control, and automation, covering topics such as neural networks and deep learning in control systems, data-driven modeling and prediction in automation, and optimization and decision-making in instrumentation using AI.
Back to Top
Artificial Intelligence is increasingly becoming a game-changer in the fields of instrumentation, control, and automation, offering unprecedented capabilities to optimize processes, enhance efficiency, and drive innovation. AI algorithms and techniques could be integrated into traditional control systems to create smart and adaptive solutions capable of learning from data and making autonomous decisions for dynamic real-world environments. From predictive maintenance in manufacturing plants to navigation of autonomous vehicles in complex roadways, AI empowers instrumentation and control systems to operate with heightened precision and agility, revolutionizing industries across the board. This book will present the power of AI to optimize processes, enhance efficiency, and achieve unparalleled precision in instrumentation, control, and automation applications. It provides a comprehensive overview of these emerging technologies and their role in optimizing the instrumentation, control, and automation, covering topics such as neural networks and deep learning in control systems, data-driven modeling and prediction in automation, and optimization and decision-making in instrumentation using AI.
Back to Top
Author / Editor Details
B. Jaganatha Pandian, PhD is an Associate Professor and the Head of the Control and Automation Department in the School of Electrical Engineering at the Vellore Institute of Technology with more than 20 years of experience. He has published more than 20 journal papers and more than 15 conference papers. His research interests span machine learning, process control, and intelligent systems.
B. Jaganatha Pandian, PhD is an Associate Professor and the Head of the Control and Automation Department in the School of Electrical Engineering at the Vellore Institute of Technology with more than 20 years of experience. He has published more than 20 journal papers and more than 15 conference papers. His research interests span machine learning, process control, and intelligent systems.
N. Amutha Prabha, PhD is a Professor in the Department of Instrumentation in the School of Electrical Engineering at the Vellore Institute of Technology with more than 25 years of teaching experience. She has published more than 20 journal papers and more than 15 conference papers. Her research includes wireless LAN networks and discrete-time linear systems.
Abhishek Gudipalli, PhD is an Associate Professor in the Department of Instrumentation in the School of Electrical Engineering at the Vellore Institute of Technology with more than 15 years of teaching experience. He has published more than 40 journal papers and more than 15 conference papers. His research interests include image processing, machine learning, IoT, and electric vehicles.
V. Indragandhi, PhD is a Professor in the Department of Energy and Power Electronics in the School of Electrical Engineering at the Vellore Institute of Technology. She has authored 100 research articles in leading peer-reviewed international journals, one book, and three patents. Her research interests include power generation, inverters, and photovoltaics.
Back to Top
Table of Contents
Preface
1. One-Shot Learning for Inertial Measurement Unit-Based Phone Gesture Recognition
Subramaniyaswamy Vairavasundaram, Indragandhi V., Arnav Jain, Pavan Dheeraj, Pragun Gurkhi Chetan and Srinath Chitrala
1.1 Introduction
1.2 Related Works
1.3 Siamese Architecture
1.3.1 Design
1.3.2 Architecture Modules
1.3.2.1 Input
1.3.2.2 Dataset
1.3.2.3 Embedder Architecture
1.3.3 Embedder Algorithm
1.4 Experimental Results
1.4.1 Similarity Metrics
1.4.2 Hyperparameter Tuning
1.4.3 K-Fold Cross-Validation Results
1.5 Model Conversion and Deployment
1.6 Conclusion and Future Work
References
2. Optimizing Hydrogen Consumption from Fuel Cell in DC Microgrid Energy Management Using Efficient State Machine-Fractional Order PID Control
Shashi Bhushan Mohanty, Satyajit Mohanty, Mrutunjaya Panda, Sandeep S. D.
and Soumya Ranjan Mahapatro 2.1 Introduction
2.1.1 Research Gap
2.1.2 Contributions
2.2 Proposed DC Microgrid Structure Modeling
2.3 Energy Management Strategies
2.4 Result Analysis
2.4.1 Hydrogen Utilization and Comprehensive Efficiency
2.5 Conclusion
References
3. Forecasting of Electromagnetic Relay Epoch Using Artificial Intelligence
T. Maris Murugan, E. Sathish, C. Jayabharathi and A. Malligarjun
3.1 Introduction
3.2 Methods and Materials
3.3 Results and Discussion
3.4 Conclusion and Future Scope
Acknowledgement
References
4. Cancer Prediction Using Machine Learning
Anuprabha S.S., Abinaya P., Pooja Nandhini N., Satyanarayan G.D., Revathi S. and N.S. Raghavee
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.3.1 Data Cleaning
4.3.2 Machine Learning
4.4 Result Analysis
4.5 Conclusion
References
5. Parkinson’s Disease Prediction Using Convolutional Neural Network (CNN)
Sheeba Rachel S., J. Godwin Ponsam, Bharath S. and Saiganesh V.
5.1 Introduction
5.2 Literature Review
5.3 Proposed System
5.3.1 Stage 1 – Mild Symptoms
5.3.2 Stage 2 – Moderate Symptoms
5.3.3 Stage 3 – Mid Stage
5.3.4 Stage 4 – Severe Symptoms
5.3.5 Stage 5 – Advanced Stage
5.4 Vocal Cord Problem in Parkinson’s Disease
5.4.1 Voicebox (Larynx)
5.4.2 Throat
5.4.3 Respiratory Muscles
5.4.4 Roof of Mouth, Tongue and Lips
5.4.5 Face Muscle Movements
5.5 Modular Design
5.5.1 Data Preprocessing Module
5.5.1.1 Handling Null Values
5.5.1.2 Splitting the Dataset
5.5.2 CNN Implementation
5.5.3 Prediction
5.5.4 Comparison Table for Draw, Wave, and Spirals
5.6 Results and Outcome
5.7 Conclusion
References
6. Crop Yield Prediction Using Machine Learning and Deep Learning Techniques
Angulakshmi Maruthamuthu, Balasingam B. and Khushi K. S.
6.1 Introduction
6.2 Related Works
6.3 Proposed Work
6.3.1 Data Collection
6.3.2 Pre-Processing
6.3.3 Feature Selection
6.3.4 Splitting Data for Training and Testing
6.3.5 Random Forest
6.3.6 Linear Regression
6.3.7 Multilayer Perceptron
6.3.8 Artificial Neural Network
6.4 Experimental Analysis
6.4.1 Performance Metrics
6.5 Conclusions
References
7. A Deep Learning Approach for Signature Recognition
Jasmine Pemeena Priyadarsini M., Viswa Brahmana Nanda Kishore, Dhavileswarapu Meher Anand, Murra Anil Kumar Reddy, Tamizharasi R.,
Ganesan Subramanian, Ernest Bravin Clinton S. and G.K. Rajini
7.1 Introduction
7.1.1 Related Work
7.2 Proposed Methodology
7.2.1 Methodology Overview
7.2.2 Data Collection and Preprocessing
7.2.3 Data Augmentation
7.2.4 Data Splitting
7.2.5 Feature Extraction
7.2.6 Model Training and Testing
7.2.7 CNN Algorithm
7.2.8 Capsule Network
7.2.9 LSTM Algorithm
7.2.10 Radial Basis Function
7.2.11 Siamese Network
7.2.12 Recognition Model
7.3 Results and Discussion
7.3.1 Accuracy Test
7.3.2 Comparison Graph
7.3.3 Outputs
7.4 Results
7.5 Conclusion
References
Abbreviations
8. Modeling Efficient Deep Learning Network on Imbalanced Data for Pest Prediction
D. Lakshmi Sreenivasa Reddy, S. Siva Priyanka, C. Venkata Narasimhulu and Dhana Lakshmi N.
8.1 Introduction
8.2 Literature Review
8.2.1 EfficientNet Architecture for Pest Prediction
8.2.2 Compound Scaling Method
8.2.3 Baseline Architecture
8.2.4 Model Training
8.2.5 Application to Pest Prediction
8.3 Methodology
8.4 Results and Discussion
8.5 Conclusion and Future Work
References
9. PPG as a Biometric: A Study on the Effectiveness of Statistical Input-Based ML Algorithms in Disadvantageous Scenarios
Sathvik Rajampalli, Kaviya Dharshini A. S., Nithillen Jayaseelan and Jeeva J. B.
9.1 Introduction 123
9.2 Overview of PPG Biometrics
9.3 Methodology
9.3.1 Key Biomarkers
9.3.2 Feature Extraction
9.3.3 ML Algorithm Selection
9.3.4 Implementation of ML Models
9.4 Results
9.4.1 Random Forest
9.4.2 Gradient Boosting Machine (GBM)
9.4.3 K-Nearest Neighbor
9.5 Conclusion
References
10. Classification of Physical Fitness Using Neural Networks
Shovan Sahoo, Piyush Kumar Sinha, T. Shankar, Ravi S. and Marimuthu R.
10.1 Introduction
10.1.1 Problem Statement
10.2 Methodology
10.3 Results and Inference
10.3.1 Statistical Chi-Square Test
10.3.2 Correlation Matrix and Eigenvectors
10.4 Conclusion
10.5 Future Scope
Data Availability
Acknowledgement
References
11. Performance Evaluation of Convolution Neural Network Architectures for Deepfake Detection
Anitha Julian, Hariharan E., Ramyadevi R. and Lingasri P.
11.1 Introduction
11.2 Related Works
11.3 Existing System
11.4 Proposed Methodology
11.4.1 Deepfake
11.5 Implementation and Results
11.5.1 Collection of Data
11.5.2 Classification Setup
11.5.3 Image Classification Results
11.5.4 Image Aggregation
11.5.5 Aggregation on Intra-Frames
11.5.6 Intuition behind the Network
11.6 Conclusion
References
12. Structural Defect Detection in Walls Using Convolutional Neural Networks
Anitha Julian, Thanga Deepika R., Naveenaa V. R. and Pradeepasri S.
12.1 Introduction
12.2 Related Literature
12.3 Proposed Methodology
12.3.1 Dataset Collection
12.3.2 Convolution
12.3.3 Pooling
12.3.4 Image Recognition and Categorizing
12.4 Results and Discussion
12.5 Conclusion
References
13. Non-Invasive Temperature Monitoring for Testicular Health
P. Sinthia, Madesh M. and Suriyaprakash K.
13.1 Introduction
13.2 Related Works
13.3 Causes
13.3.1 Industrial Chemicals
13.3.2 Heavy Metal Exposure
13.3.3 Radiation or X-Rays
13.3.4 Overheating the Testicles
13.3.5 Sitting for Long Periods
13.4 Diagnosis
13.4.1 Medical History and Lifestyle Assessment
13.4.2 Physical Examination
13.4.3 Laboratory Tests
13.4.4 Imaging Studies
13.4.5 Specialized Tests
13.4.6 Evaluation of Sexual Function
13.5 Methodology
13.5.1 Circuit Design and Simulation
13.5.2 Schematic and Workflow
13.5.3 Testing and Evaluation
13.6 Results and Discussion
13.7 Conclusion
Limitations
Bibliography
14. Driver Sleep Detection and Emergency Word Spotting Using Similarity Map and Bi-LSTM
Arun S. L., Tarun Raj R. and Vijayapriya R.
14.1 Introduction
14.2 Methodology
14.2.1 Driver Monitoring
14.2.1.1 Facial Detection
14.2.1.2 Eyelid Detection
14.2.2 Emergency Word Prediction
14.2.2.1 Visual Feature Extractor
14.2.2.2 Lip Point Sequence
14.3 Experiments
14.3.1 Dataset
14.3.2 Training
14.4 Process Flow
14.5 Results
14.5.1 Prediction of Drowsiness
14.5.2 Emergency Word Detection
14.6 Conclusion
References
15. A System Theoretic Stochastic Adaptive Model of Neuron with Probability of Firing
Chittotosh Ganguly
15.1 Introduction
15.2 A Stochastic Adaptive Neuron Model
15.3 Probability of Spike Generation
15.4 Results and Related Discussion
15.5 Conclusion
References
16. An Overview of Capacitor-Diode Voltage Multiplier-Based High Voltage Pulse Generators
Shanmuga Sundari A. and Vijayakumar D.
16.1 Introduction
16.2 Pulse Generation and Its Diverse Waveforms
16.3 Remedies for Contradictory CDVM Topologies in Pulse Power Applications
16.4 Distinct CDVM Topologies
16.5 Conclusion
Bibliography
17. Optimal Tuning of PI Parameters for Speed Control of BLDC Motor
P.V.S.K. Kousik, Namra Nadem and Bagyaveereswaran V.
17.1 Introduction
17.2 Research Methodology
17.3 Overview of BLDC Motor Control and the Role of PI Controller
17.3.1 BLDC Motor Control
17.3.2 Role of PI Controller in BLDC Motor
17.3.3 Challenges in PI Controller Tuning
17.4 Problem Formulation
17.5 Evaluation of Tuning Methods
17.5.1 Introduction
17.5.1.1 Particle Swarm Optimization
17.5.1.2 Auto-Tuning
17.5.2 Implementation in MATLAB Simulink
17.5.2.1 Particle Swarm Optimization (PSO)
17.5.2.2 Auto-Tuning
17.5.3 Results and Analysis
17.5.3.1 Particle Swarm Optimization
17.5.3.2 Auto-Tuning
17.6 Comparative Analysis
17.7 Conclusion
References
18. Tracking Control of a Robotic Arm Using Model-Based Neural Control Approach
K. Jaswanth, B. Jaganatha Pandian and Nohaidda Sariff
18.1 Introduction
18.2 System Description – Robot Arm
18.3 Model Reference Controller (MRC)
18.4 Neural Network Predictive Controller (NNPC)
18.5 Results and Discussion
18.5.1 MRC Control Realization
18.5.2 NNPC Realization
18.6 Conclusion
References
19. Design of Static Output Feedback Controller for Positive Systems: An LMI Approach
Jitendra Kumar Goyal, Adithya Suresh, Adhiraj Kaushik, Mathew Santosh, Amutha Prabha N., Ankit Sachan, Sandeep Kumar Soni and Chockalingam Aravind
19.1 Introduction
19.2 Problem Statement
19.3 Preliminaries
19.4 Main Results
19.5 Numerical Examples
19.6 Conclusion
Acknowledgement
References
20. Model Free Robust Controller Design for Half Quad Rotor Aero System: An Experimental Study
Jitendra Kumar Goyal, Jonnada Harshavardhan, Yashwant Ramesh Dhote, Partha P. Katkar, Amutha Prabha N., Ankit Sachan, Sandeep Kumar Soni and Swee King Phang
20.1 Introduction
20.2 Objective
20.3 System Modeling
20.3.1 Transfer Function Model
20.3.2 State-Space Model
20.4 Intelligent P Control
20.5 Hardware Implementation
20.5.1 Simulink Model
20.5.2 Results
20.6 Conclusion
20.6.1 Future Scope
References
21. The Design and Control of Grid-Connected PWM Rectifiers Using a Soft Switching Control Strategy
S. Suba, M. Malukannan, R. Uthirasamy, M. Dinesh, P. C. Sivakumar and S. Dinesh
21.1 Introduction
21.2 Theoretical Background
21.3 Methodology
21.3.1 Types of Active PFCs
21.3.2 PWM Rectifier with Buck-Boost Topology
21.4 Motor Modeling
21.5 Elimination of Harmonics
21.6 Results and Discussion
21.7 Conclusion
References
22. Software Defect Prediction by Exploiting Semantic and Syntax Information
M. Senthil, Arunkumar C. and Sabarish B.A.
22.1 Introduction
22.2 Literature Survey
22.3 Dataset Description
22.4 Problem Statement
22.5 Proposed Methodology
22.6 Results and Discussion
22.7 Conclusion and Future Scope
References
23. Evolutionary Interfaces: Unleashing Creativity in UI Design with Generative AI
M. Ravi Teja, Sabarish B.A. and Arunkumar C.
23.1 Introduction
23.2 Literature Survey
23.3 Dataset Description
23.4 Problem Statement
23.5 Proposed Methodology
23.6 Results and Conclusion
23.7 Future Scope
References
24. Hybrid PV and Supercapacitor-Powered UAVs for Optimized SWIPT in IoT Networks Using DDPG and Game Theory Model
P. Keerthana and A. Vijayalakshmi
24.1 Introduction
24.2 Proposed System
24.3 System Model and Problem Formulation
24.4 Problem Formulation
24.5 UAV for Optimized SWIPT Networks
24.6 Algorithm Design
24.7 DDPG Approach
24.8 Game Theory
24.9 Simulation Results
24.10 Conclusion
References
25. Design of a Smart Restaurant Ordering System Utilizing Human Activity Recognition and Machine Learning in a Digital Infrastructure
Gundumalle Ashlin Joel, Navamani T.M., Archita Sharma, Vansh Harkut, Mansi Saxena and Arnav Goenka
25.1 Introduction
25.2 Literature Review
25.3 Algorithm Development
25.4 Results and Analysis
25.5 Conclusion and Future Work
References
26. IoT-Based Smart Electricity Energy Meter
Hariny A., R. Resmi and Ashwini K.
26.1 Introduction
26.2 Methodology
26.2.1 Methods of AC and DC Energy Measurement
26.2.1.1 AC Voltage and Current Sensing
26.2.1.2 DC Voltage and Current Sensing
26.2.2 System Layout
26.2.3 Working System
26.3 Result and Discussion
26.4 Conclusion
Acknowledgments
References
27. IoT-Enabled Framework for OpenViBE‑Based Online BCI with an OSC-Driven Somatosensory Stimulator and Unity Animation Control
Vadivelan Ramu and Kishor Lakshminarayanan
27.1 Introduction
27.2 Architecture of the Proposed IoT-Based Framework of Online BCI for Imagery Paradigms
27.3 Basic Set of Scenarios for OpenViBE for Online BCI
27.4 Structure of OSC Messages
27.4.1 Addressing Scheme of OSC
27.5 Representative Result
27.6 Discussion
27.7 Conclusion
References
28. Design of Low-Cost Durable System Using IoT Technology for Signal Violated Vehicle Detection
Sujatha Canavoy Narahari, Abhishek Gudipalli, K. Saiteja, K. Hemchandra, K. Sudhamsh and Wei Jen Chew
28.1 Introduction
28.2 Respective Background Research Work
28.2.1 Smart Prepaid Traffic Fines System Using RFID, IoT and Mobile App
28.2.2 The Prototype of Traffic Violation Detection System Based on Internet of Things
28.3 Proposed Method
28.4 Working Model
28.5 Outcomes
28.5.1 Data Card
28.5.2 Processing Unit
28.5.3 Prototype Model
28.6 Conclusion
References
29. IoT in an E-Bike with Theft Detection and Accident Alert
Rajesh Kannan Megalingam, Puppala Gautham Prasad, Sreehari Sahadevan, Bhupathiraju Mohan Varma and Kummara Anand Dileep
29.1 Introduction
29.2 Problem Statement
29.3 Related Works
29.4 Methodology
29.4.1 System Architecture
29.4.2 App Methodology
29.4.3 Dashboard Methodology
29.4.4 Theft Detection Methodology
29.4.5 Accident Detection Methodology
29.5 Experiment and Results
29.5.1 Dashboard
29.5.2 Mobile App
29.5.3 Theft Detection
29.5.4 Accident Detection
29.6 Conclusion
Acknowledgment
References
30. IoT-Based Soil Fertilizer Dispensing System for Smart Agriculture
Karthikeyan A., Manmeet Singha, Abhiansh Wadegaonkara and Rajalakshmi S.
30.1 Introduction
30.2 Related Study
30.3 Methodology
30.3.1 Control Unit
30.3.2 Soil Moisture Sensor
30.3.3 MQ-7 Gas Sensor
30.3.4 DHT-11 Sensor
30.4 Results and Discussions
30.5 Conclusion and Future Scope
References
31. Deep Reinforcement Learning for Autonomous Vehicle and Surface Vessel Navigation Using Unreal Engine
Mukund Pareek, Muthunagai S. U., Ramya G. and Nivitha K.
31.1 Introduction
31.2 Related Work
31.2.1 Deep Reinforcement Learning for Autonomous Navigation
31.2.2 Multi-Agent Reinforcement Learning (MARL)
31.2.3 Maritime Navigation and USV Swarms
31.2.4 Simulation Platforms for Autonomous Systems
31.3 Proposed Methodology
31.3.1 Simulation Environment
31.3.2 Application of Multi-Agent Reinforcement Learning (MARL)
31.3.3 Strategies for Evading Intrusions
31.3.4 Path Planning for USV Swarms
31.4 Performance Evaluation
31.4.1 Experiment Setup
31.4.2 Vehicle Navigation
31.4.3 USV Swarm Path Planning
31.4.4 Intrusion Evasion Strategies
31.5 Conclusion
References
32. Design of a Smart Bin System for Efficient Waste Management
Yi Wen Tan, Wai Leong Pang, Hui Hwang Goh, Kah Yoong Chan, Gwo Chin Chung and N. Amutha Prabha
32.1 Introduction
32.2 Materials and Methods
32.2.1 The Smart Bin’s Functions
32.2.2 Smart Bin’s Circuit Design
32.3 Results and Discussion
32.4 Conclusion
Acknowledgement
References
33. Enhancing Energy Efficiency in Cooling Systems: Benchmarking Machine Learning Algorithms for Cooler Energy Consumption Prediction
Mayeesha Bashar, Chockalingam Aravind Vaithilingam, Manee Sangaran Diagarajan, Jitendra Kumar Goyal and Nagentrau Muniandy
33.1 Introduction
33.2 Literature Review
33.3 Methodology
33.3.1 Objective-Oriented Framework for Research Evaluation
33.3.2 Datasets
33.3.2.1 Test Dataset 1
33.3.2.2 Test Dataset 2
33.3.2.3 Main Dataset
33.3.3 Dataset Preprocessing
33.3.4 Machine Learning Algorithms
33.3.5 Training Testing and Evaluation Setup
33.3.5.1 Training and Testing
33.3.5.2 Fixed ML Algorithm Parameters
33.3.5.3 Evaluation Metrics
33.4 Results and Discussion
33.4.1 Results and Discussion for Test Dataset
33.4.2 Results and Discussion for Main Dataset
33.4.3 Implications for Chiller Energy Consumption Prediction
33.4.4 Improvement Percentage Analysis
33.5 Conclusion
Acknowledgement
References
34. Enhanced Fault Detection for Solar Panels with YOLOv7 on RGB Images Using Augmentation Strategies and an Early Stopper
Weng Ti Wong, Swee King Phang, Nohaidda Sariff, Husna Sarirah Husin and B. Jaganatha Pandian
34.1 Introduction
34.1.1 Faults in Solar Panels
34.1.2 Fault Detection Using Infrared Thermography
34.1.3 Applications of Deep Learning Algorithms
34.1.4 Research Statement
34.2 Research Methodology
34.2.1 Evaluation of the Trained Result
34.2.2 Data Augmentation
34.2.3 Early Stopper
34.3 Results and Discussion
34.3.1 Data Augmentation
34.3.2 Early Stopper
34.4 Conclusions and Future Works
References
35. Automated Plaque Identification in Arterial Walls Using MATLAB
Nazarkar Pravalika, Jabeena A., Vetriveeran Rajamani, Jasmin Pemeena Priyadarisini M. and Nazarkar Archana
35.1 Introduction
35.2 Methodology
35.3 Results
35.4 Conclusion
35.5 Future Scope
References
36. Hybrid PSO-SA Algorithm for Efficient 3D Path Planning of UAVs
Shankar Thangavelu, Lavanya Nagarajan, Akshay Narendran, Samarth Begari A. and Marimuthu R.
36.1 Introduction
36.1.1 Motivation
36.2 Background
36.3 Proposed Fitness Function
36.4 Methodology
36.4.1 PSO Algorithm
36.4.2 SA Algorithm
36.4.3 GWO Algorithm
36.4.3.1 Encircling Equations
36.4.3.2 Hunting Equations
36.4.4 PSO-SA Algorithm
36.5 Simulation Results and Analysis
36.5.1 Experimental Setup
36.6 Conclusions
36.7 Limitations and Constraints
References
37. Voice-Activated Alert System for the Hearing-Impaired
Dinesh N., Harshavardhan J. and M. Manimozhi
37.1 Introduction
37.2 Objective
37.3 Implementation
37.4 Proposed System Operational Flowchart
37.5 Hardware Description
37.5.1 NodeMCU ESP8366
37.5.2 Micro Vibration Motor
37.5.3 Buzzer
37.5.4 OLED Screen
37.6 Software Description
37.6.1 Arduino Integrated Development Environment (IDE)
37.6.2 MIT App Inventor
37.6.3 Arduino Libraries
37.6.3.1 NTPClient
37.6.3.2 WiFiUdp
37.6.3.3 Adafruit_SSD1306
37.6.3.4 Adafruit_GFX
37.7 Hardware and Software Interfacing
37.8 Conclusion
37.9 Future Scope
References
38. Energy Efficient Cluster Using Floyd Warshall Shortest Path with Artificial Bee Colony Optimization for Data Transmission in MANET
S. Usha Devi and K. Preetha
38.1 Introduction
38.2 Related Works
38.3 Proposed FWSP-ABC Optimization-Based Data Collection in MANET
38.3.1 The Proposed FWSP-ABC Optimization Technique
38.4 Simulation Results and Parameter Values
38.4.1 Packet Delivery Ratio
38.4.2 End to End Delay Time
38.4.3 Remaining Energy
38.4.4 Link Losses Occurrences
38.4.5 Throughput
38.5 Conclusion
Bibliography
39. ADAS for Improving Road Safety: YOLOv10-Based Detection of Cows, Potholes, and Traffic Signboards
Jayakrishnan P, Sunsitha Varshini Pugalaendhi and Sanaputur Sai Charan
39.1 Introduction
39.2 Related Works
39.3 Methodology
39.3.1 Data Collection
39.3.2 Data Annotation
39.3.3 Data Preprocessing
39.3.4 Data Augmentation
39.3.5 Model Selection
39.3.6 Training Procedure
39.3.7 Evaluation Metrics
39.3.8 Inference
39.4 Results
39.4.1 Class-Wise Metrics
39.4.2 Speed Metrics
39.4.3 Visual Outputs
39.4.4 Custom Inference
39.5 Conclusion
39.6 Future Work
References
40. Design, Development, Implementation and Optimization of a Low-Cost CNC Pen Plotter Using GRBL
Jibesh Mahanta and Sankardoss Varadhan
40.1 Introduction
40.2 System Overview
40.2.1 Hardware Components
40.2.1.1 Arduino Uno with GRBL Firmware
40.2.1.2 28BYJ-48 Stepper Motors and ULN2003 Driver Boards
40.2.1.3 Servo Motor for Z-Axis Control
40.2.2 Mechanical Design
40.2.2.1 Telescopic Drawer Hinges
40.2.2.2 Threads Instead of Belts
40.3 Design Considerations
40.3.1 Motor Selection
40.3.2 Servo Motor for Z-Axis Movement
40.4 Interface and Software Design
40.4.1 Project Software
40.4.2 Inkscape
40.4.3 Universal G-Code Sender
40.4.4 Arduino Integrated Development Environment (IDE)
40.5 Experimental Results
40.5.1 Movement Accuracy
40.6 Conclusion
Bibliography
About the Editors
Index
Back to Top
Preface
1. One-Shot Learning for Inertial Measurement Unit-Based Phone Gesture Recognition
Subramaniyaswamy Vairavasundaram, Indragandhi V., Arnav Jain, Pavan Dheeraj, Pragun Gurkhi Chetan and Srinath Chitrala
1.1 Introduction
1.2 Related Works
1.3 Siamese Architecture
1.3.1 Design
1.3.2 Architecture Modules
1.3.2.1 Input
1.3.2.2 Dataset
1.3.2.3 Embedder Architecture
1.3.3 Embedder Algorithm
1.4 Experimental Results
1.4.1 Similarity Metrics
1.4.2 Hyperparameter Tuning
1.4.3 K-Fold Cross-Validation Results
1.5 Model Conversion and Deployment
1.6 Conclusion and Future Work
References
2. Optimizing Hydrogen Consumption from Fuel Cell in DC Microgrid Energy Management Using Efficient State Machine-Fractional Order PID Control
Shashi Bhushan Mohanty, Satyajit Mohanty, Mrutunjaya Panda, Sandeep S. D.
and Soumya Ranjan Mahapatro 2.1 Introduction
2.1.1 Research Gap
2.1.2 Contributions
2.2 Proposed DC Microgrid Structure Modeling
2.3 Energy Management Strategies
2.4 Result Analysis
2.4.1 Hydrogen Utilization and Comprehensive Efficiency
2.5 Conclusion
References
3. Forecasting of Electromagnetic Relay Epoch Using Artificial Intelligence
T. Maris Murugan, E. Sathish, C. Jayabharathi and A. Malligarjun
3.1 Introduction
3.2 Methods and Materials
3.3 Results and Discussion
3.4 Conclusion and Future Scope
Acknowledgement
References
4. Cancer Prediction Using Machine Learning
Anuprabha S.S., Abinaya P., Pooja Nandhini N., Satyanarayan G.D., Revathi S. and N.S. Raghavee
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.3.1 Data Cleaning
4.3.2 Machine Learning
4.4 Result Analysis
4.5 Conclusion
References
5. Parkinson’s Disease Prediction Using Convolutional Neural Network (CNN)
Sheeba Rachel S., J. Godwin Ponsam, Bharath S. and Saiganesh V.
5.1 Introduction
5.2 Literature Review
5.3 Proposed System
5.3.1 Stage 1 – Mild Symptoms
5.3.2 Stage 2 – Moderate Symptoms
5.3.3 Stage 3 – Mid Stage
5.3.4 Stage 4 – Severe Symptoms
5.3.5 Stage 5 – Advanced Stage
5.4 Vocal Cord Problem in Parkinson’s Disease
5.4.1 Voicebox (Larynx)
5.4.2 Throat
5.4.3 Respiratory Muscles
5.4.4 Roof of Mouth, Tongue and Lips
5.4.5 Face Muscle Movements
5.5 Modular Design
5.5.1 Data Preprocessing Module
5.5.1.1 Handling Null Values
5.5.1.2 Splitting the Dataset
5.5.2 CNN Implementation
5.5.3 Prediction
5.5.4 Comparison Table for Draw, Wave, and Spirals
5.6 Results and Outcome
5.7 Conclusion
References
6. Crop Yield Prediction Using Machine Learning and Deep Learning Techniques
Angulakshmi Maruthamuthu, Balasingam B. and Khushi K. S.
6.1 Introduction
6.2 Related Works
6.3 Proposed Work
6.3.1 Data Collection
6.3.2 Pre-Processing
6.3.3 Feature Selection
6.3.4 Splitting Data for Training and Testing
6.3.5 Random Forest
6.3.6 Linear Regression
6.3.7 Multilayer Perceptron
6.3.8 Artificial Neural Network
6.4 Experimental Analysis
6.4.1 Performance Metrics
6.5 Conclusions
References
7. A Deep Learning Approach for Signature Recognition
Jasmine Pemeena Priyadarsini M., Viswa Brahmana Nanda Kishore, Dhavileswarapu Meher Anand, Murra Anil Kumar Reddy, Tamizharasi R.,
Ganesan Subramanian, Ernest Bravin Clinton S. and G.K. Rajini
7.1 Introduction
7.1.1 Related Work
7.2 Proposed Methodology
7.2.1 Methodology Overview
7.2.2 Data Collection and Preprocessing
7.2.3 Data Augmentation
7.2.4 Data Splitting
7.2.5 Feature Extraction
7.2.6 Model Training and Testing
7.2.7 CNN Algorithm
7.2.8 Capsule Network
7.2.9 LSTM Algorithm
7.2.10 Radial Basis Function
7.2.11 Siamese Network
7.2.12 Recognition Model
7.3 Results and Discussion
7.3.1 Accuracy Test
7.3.2 Comparison Graph
7.3.3 Outputs
7.4 Results
7.5 Conclusion
References
Abbreviations
8. Modeling Efficient Deep Learning Network on Imbalanced Data for Pest Prediction
D. Lakshmi Sreenivasa Reddy, S. Siva Priyanka, C. Venkata Narasimhulu and Dhana Lakshmi N.
8.1 Introduction
8.2 Literature Review
8.2.1 EfficientNet Architecture for Pest Prediction
8.2.2 Compound Scaling Method
8.2.3 Baseline Architecture
8.2.4 Model Training
8.2.5 Application to Pest Prediction
8.3 Methodology
8.4 Results and Discussion
8.5 Conclusion and Future Work
References
9. PPG as a Biometric: A Study on the Effectiveness of Statistical Input-Based ML Algorithms in Disadvantageous Scenarios
Sathvik Rajampalli, Kaviya Dharshini A. S., Nithillen Jayaseelan and Jeeva J. B.
9.1 Introduction 123
9.2 Overview of PPG Biometrics
9.3 Methodology
9.3.1 Key Biomarkers
9.3.2 Feature Extraction
9.3.3 ML Algorithm Selection
9.3.4 Implementation of ML Models
9.4 Results
9.4.1 Random Forest
9.4.2 Gradient Boosting Machine (GBM)
9.4.3 K-Nearest Neighbor
9.5 Conclusion
References
10. Classification of Physical Fitness Using Neural Networks
Shovan Sahoo, Piyush Kumar Sinha, T. Shankar, Ravi S. and Marimuthu R.
10.1 Introduction
10.1.1 Problem Statement
10.2 Methodology
10.3 Results and Inference
10.3.1 Statistical Chi-Square Test
10.3.2 Correlation Matrix and Eigenvectors
10.4 Conclusion
10.5 Future Scope
Data Availability
Acknowledgement
References
11. Performance Evaluation of Convolution Neural Network Architectures for Deepfake Detection
Anitha Julian, Hariharan E., Ramyadevi R. and Lingasri P.
11.1 Introduction
11.2 Related Works
11.3 Existing System
11.4 Proposed Methodology
11.4.1 Deepfake
11.5 Implementation and Results
11.5.1 Collection of Data
11.5.2 Classification Setup
11.5.3 Image Classification Results
11.5.4 Image Aggregation
11.5.5 Aggregation on Intra-Frames
11.5.6 Intuition behind the Network
11.6 Conclusion
References
12. Structural Defect Detection in Walls Using Convolutional Neural Networks
Anitha Julian, Thanga Deepika R., Naveenaa V. R. and Pradeepasri S.
12.1 Introduction
12.2 Related Literature
12.3 Proposed Methodology
12.3.1 Dataset Collection
12.3.2 Convolution
12.3.3 Pooling
12.3.4 Image Recognition and Categorizing
12.4 Results and Discussion
12.5 Conclusion
References
13. Non-Invasive Temperature Monitoring for Testicular Health
P. Sinthia, Madesh M. and Suriyaprakash K.
13.1 Introduction
13.2 Related Works
13.3 Causes
13.3.1 Industrial Chemicals
13.3.2 Heavy Metal Exposure
13.3.3 Radiation or X-Rays
13.3.4 Overheating the Testicles
13.3.5 Sitting for Long Periods
13.4 Diagnosis
13.4.1 Medical History and Lifestyle Assessment
13.4.2 Physical Examination
13.4.3 Laboratory Tests
13.4.4 Imaging Studies
13.4.5 Specialized Tests
13.4.6 Evaluation of Sexual Function
13.5 Methodology
13.5.1 Circuit Design and Simulation
13.5.2 Schematic and Workflow
13.5.3 Testing and Evaluation
13.6 Results and Discussion
13.7 Conclusion
Limitations
Bibliography
14. Driver Sleep Detection and Emergency Word Spotting Using Similarity Map and Bi-LSTM
Arun S. L., Tarun Raj R. and Vijayapriya R.
14.1 Introduction
14.2 Methodology
14.2.1 Driver Monitoring
14.2.1.1 Facial Detection
14.2.1.2 Eyelid Detection
14.2.2 Emergency Word Prediction
14.2.2.1 Visual Feature Extractor
14.2.2.2 Lip Point Sequence
14.3 Experiments
14.3.1 Dataset
14.3.2 Training
14.4 Process Flow
14.5 Results
14.5.1 Prediction of Drowsiness
14.5.2 Emergency Word Detection
14.6 Conclusion
References
15. A System Theoretic Stochastic Adaptive Model of Neuron with Probability of Firing
Chittotosh Ganguly
15.1 Introduction
15.2 A Stochastic Adaptive Neuron Model
15.3 Probability of Spike Generation
15.4 Results and Related Discussion
15.5 Conclusion
References
16. An Overview of Capacitor-Diode Voltage Multiplier-Based High Voltage Pulse Generators
Shanmuga Sundari A. and Vijayakumar D.
16.1 Introduction
16.2 Pulse Generation and Its Diverse Waveforms
16.3 Remedies for Contradictory CDVM Topologies in Pulse Power Applications
16.4 Distinct CDVM Topologies
16.5 Conclusion
Bibliography
17. Optimal Tuning of PI Parameters for Speed Control of BLDC Motor
P.V.S.K. Kousik, Namra Nadem and Bagyaveereswaran V.
17.1 Introduction
17.2 Research Methodology
17.3 Overview of BLDC Motor Control and the Role of PI Controller
17.3.1 BLDC Motor Control
17.3.2 Role of PI Controller in BLDC Motor
17.3.3 Challenges in PI Controller Tuning
17.4 Problem Formulation
17.5 Evaluation of Tuning Methods
17.5.1 Introduction
17.5.1.1 Particle Swarm Optimization
17.5.1.2 Auto-Tuning
17.5.2 Implementation in MATLAB Simulink
17.5.2.1 Particle Swarm Optimization (PSO)
17.5.2.2 Auto-Tuning
17.5.3 Results and Analysis
17.5.3.1 Particle Swarm Optimization
17.5.3.2 Auto-Tuning
17.6 Comparative Analysis
17.7 Conclusion
References
18. Tracking Control of a Robotic Arm Using Model-Based Neural Control Approach
K. Jaswanth, B. Jaganatha Pandian and Nohaidda Sariff
18.1 Introduction
18.2 System Description – Robot Arm
18.3 Model Reference Controller (MRC)
18.4 Neural Network Predictive Controller (NNPC)
18.5 Results and Discussion
18.5.1 MRC Control Realization
18.5.2 NNPC Realization
18.6 Conclusion
References
19. Design of Static Output Feedback Controller for Positive Systems: An LMI Approach
Jitendra Kumar Goyal, Adithya Suresh, Adhiraj Kaushik, Mathew Santosh, Amutha Prabha N., Ankit Sachan, Sandeep Kumar Soni and Chockalingam Aravind
19.1 Introduction
19.2 Problem Statement
19.3 Preliminaries
19.4 Main Results
19.5 Numerical Examples
19.6 Conclusion
Acknowledgement
References
20. Model Free Robust Controller Design for Half Quad Rotor Aero System: An Experimental Study
Jitendra Kumar Goyal, Jonnada Harshavardhan, Yashwant Ramesh Dhote, Partha P. Katkar, Amutha Prabha N., Ankit Sachan, Sandeep Kumar Soni and Swee King Phang
20.1 Introduction
20.2 Objective
20.3 System Modeling
20.3.1 Transfer Function Model
20.3.2 State-Space Model
20.4 Intelligent P Control
20.5 Hardware Implementation
20.5.1 Simulink Model
20.5.2 Results
20.6 Conclusion
20.6.1 Future Scope
References
21. The Design and Control of Grid-Connected PWM Rectifiers Using a Soft Switching Control Strategy
S. Suba, M. Malukannan, R. Uthirasamy, M. Dinesh, P. C. Sivakumar and S. Dinesh
21.1 Introduction
21.2 Theoretical Background
21.3 Methodology
21.3.1 Types of Active PFCs
21.3.2 PWM Rectifier with Buck-Boost Topology
21.4 Motor Modeling
21.5 Elimination of Harmonics
21.6 Results and Discussion
21.7 Conclusion
References
22. Software Defect Prediction by Exploiting Semantic and Syntax Information
M. Senthil, Arunkumar C. and Sabarish B.A.
22.1 Introduction
22.2 Literature Survey
22.3 Dataset Description
22.4 Problem Statement
22.5 Proposed Methodology
22.6 Results and Discussion
22.7 Conclusion and Future Scope
References
23. Evolutionary Interfaces: Unleashing Creativity in UI Design with Generative AI
M. Ravi Teja, Sabarish B.A. and Arunkumar C.
23.1 Introduction
23.2 Literature Survey
23.3 Dataset Description
23.4 Problem Statement
23.5 Proposed Methodology
23.6 Results and Conclusion
23.7 Future Scope
References
24. Hybrid PV and Supercapacitor-Powered UAVs for Optimized SWIPT in IoT Networks Using DDPG and Game Theory Model
P. Keerthana and A. Vijayalakshmi
24.1 Introduction
24.2 Proposed System
24.3 System Model and Problem Formulation
24.4 Problem Formulation
24.5 UAV for Optimized SWIPT Networks
24.6 Algorithm Design
24.7 DDPG Approach
24.8 Game Theory
24.9 Simulation Results
24.10 Conclusion
References
25. Design of a Smart Restaurant Ordering System Utilizing Human Activity Recognition and Machine Learning in a Digital Infrastructure
Gundumalle Ashlin Joel, Navamani T.M., Archita Sharma, Vansh Harkut, Mansi Saxena and Arnav Goenka
25.1 Introduction
25.2 Literature Review
25.3 Algorithm Development
25.4 Results and Analysis
25.5 Conclusion and Future Work
References
26. IoT-Based Smart Electricity Energy Meter
Hariny A., R. Resmi and Ashwini K.
26.1 Introduction
26.2 Methodology
26.2.1 Methods of AC and DC Energy Measurement
26.2.1.1 AC Voltage and Current Sensing
26.2.1.2 DC Voltage and Current Sensing
26.2.2 System Layout
26.2.3 Working System
26.3 Result and Discussion
26.4 Conclusion
Acknowledgments
References
27. IoT-Enabled Framework for OpenViBE‑Based Online BCI with an OSC-Driven Somatosensory Stimulator and Unity Animation Control
Vadivelan Ramu and Kishor Lakshminarayanan
27.1 Introduction
27.2 Architecture of the Proposed IoT-Based Framework of Online BCI for Imagery Paradigms
27.3 Basic Set of Scenarios for OpenViBE for Online BCI
27.4 Structure of OSC Messages
27.4.1 Addressing Scheme of OSC
27.5 Representative Result
27.6 Discussion
27.7 Conclusion
References
28. Design of Low-Cost Durable System Using IoT Technology for Signal Violated Vehicle Detection
Sujatha Canavoy Narahari, Abhishek Gudipalli, K. Saiteja, K. Hemchandra, K. Sudhamsh and Wei Jen Chew
28.1 Introduction
28.2 Respective Background Research Work
28.2.1 Smart Prepaid Traffic Fines System Using RFID, IoT and Mobile App
28.2.2 The Prototype of Traffic Violation Detection System Based on Internet of Things
28.3 Proposed Method
28.4 Working Model
28.5 Outcomes
28.5.1 Data Card
28.5.2 Processing Unit
28.5.3 Prototype Model
28.6 Conclusion
References
29. IoT in an E-Bike with Theft Detection and Accident Alert
Rajesh Kannan Megalingam, Puppala Gautham Prasad, Sreehari Sahadevan, Bhupathiraju Mohan Varma and Kummara Anand Dileep
29.1 Introduction
29.2 Problem Statement
29.3 Related Works
29.4 Methodology
29.4.1 System Architecture
29.4.2 App Methodology
29.4.3 Dashboard Methodology
29.4.4 Theft Detection Methodology
29.4.5 Accident Detection Methodology
29.5 Experiment and Results
29.5.1 Dashboard
29.5.2 Mobile App
29.5.3 Theft Detection
29.5.4 Accident Detection
29.6 Conclusion
Acknowledgment
References
30. IoT-Based Soil Fertilizer Dispensing System for Smart Agriculture
Karthikeyan A., Manmeet Singha, Abhiansh Wadegaonkara and Rajalakshmi S.
30.1 Introduction
30.2 Related Study
30.3 Methodology
30.3.1 Control Unit
30.3.2 Soil Moisture Sensor
30.3.3 MQ-7 Gas Sensor
30.3.4 DHT-11 Sensor
30.4 Results and Discussions
30.5 Conclusion and Future Scope
References
31. Deep Reinforcement Learning for Autonomous Vehicle and Surface Vessel Navigation Using Unreal Engine
Mukund Pareek, Muthunagai S. U., Ramya G. and Nivitha K.
31.1 Introduction
31.2 Related Work
31.2.1 Deep Reinforcement Learning for Autonomous Navigation
31.2.2 Multi-Agent Reinforcement Learning (MARL)
31.2.3 Maritime Navigation and USV Swarms
31.2.4 Simulation Platforms for Autonomous Systems
31.3 Proposed Methodology
31.3.1 Simulation Environment
31.3.2 Application of Multi-Agent Reinforcement Learning (MARL)
31.3.3 Strategies for Evading Intrusions
31.3.4 Path Planning for USV Swarms
31.4 Performance Evaluation
31.4.1 Experiment Setup
31.4.2 Vehicle Navigation
31.4.3 USV Swarm Path Planning
31.4.4 Intrusion Evasion Strategies
31.5 Conclusion
References
32. Design of a Smart Bin System for Efficient Waste Management
Yi Wen Tan, Wai Leong Pang, Hui Hwang Goh, Kah Yoong Chan, Gwo Chin Chung and N. Amutha Prabha
32.1 Introduction
32.2 Materials and Methods
32.2.1 The Smart Bin’s Functions
32.2.2 Smart Bin’s Circuit Design
32.3 Results and Discussion
32.4 Conclusion
Acknowledgement
References
33. Enhancing Energy Efficiency in Cooling Systems: Benchmarking Machine Learning Algorithms for Cooler Energy Consumption Prediction
Mayeesha Bashar, Chockalingam Aravind Vaithilingam, Manee Sangaran Diagarajan, Jitendra Kumar Goyal and Nagentrau Muniandy
33.1 Introduction
33.2 Literature Review
33.3 Methodology
33.3.1 Objective-Oriented Framework for Research Evaluation
33.3.2 Datasets
33.3.2.1 Test Dataset 1
33.3.2.2 Test Dataset 2
33.3.2.3 Main Dataset
33.3.3 Dataset Preprocessing
33.3.4 Machine Learning Algorithms
33.3.5 Training Testing and Evaluation Setup
33.3.5.1 Training and Testing
33.3.5.2 Fixed ML Algorithm Parameters
33.3.5.3 Evaluation Metrics
33.4 Results and Discussion
33.4.1 Results and Discussion for Test Dataset
33.4.2 Results and Discussion for Main Dataset
33.4.3 Implications for Chiller Energy Consumption Prediction
33.4.4 Improvement Percentage Analysis
33.5 Conclusion
Acknowledgement
References
34. Enhanced Fault Detection for Solar Panels with YOLOv7 on RGB Images Using Augmentation Strategies and an Early Stopper
Weng Ti Wong, Swee King Phang, Nohaidda Sariff, Husna Sarirah Husin and B. Jaganatha Pandian
34.1 Introduction
34.1.1 Faults in Solar Panels
34.1.2 Fault Detection Using Infrared Thermography
34.1.3 Applications of Deep Learning Algorithms
34.1.4 Research Statement
34.2 Research Methodology
34.2.1 Evaluation of the Trained Result
34.2.2 Data Augmentation
34.2.3 Early Stopper
34.3 Results and Discussion
34.3.1 Data Augmentation
34.3.2 Early Stopper
34.4 Conclusions and Future Works
References
35. Automated Plaque Identification in Arterial Walls Using MATLAB
Nazarkar Pravalika, Jabeena A., Vetriveeran Rajamani, Jasmin Pemeena Priyadarisini M. and Nazarkar Archana
35.1 Introduction
35.2 Methodology
35.3 Results
35.4 Conclusion
35.5 Future Scope
References
36. Hybrid PSO-SA Algorithm for Efficient 3D Path Planning of UAVs
Shankar Thangavelu, Lavanya Nagarajan, Akshay Narendran, Samarth Begari A. and Marimuthu R.
36.1 Introduction
36.1.1 Motivation
36.2 Background
36.3 Proposed Fitness Function
36.4 Methodology
36.4.1 PSO Algorithm
36.4.2 SA Algorithm
36.4.3 GWO Algorithm
36.4.3.1 Encircling Equations
36.4.3.2 Hunting Equations
36.4.4 PSO-SA Algorithm
36.5 Simulation Results and Analysis
36.5.1 Experimental Setup
36.6 Conclusions
36.7 Limitations and Constraints
References
37. Voice-Activated Alert System for the Hearing-Impaired
Dinesh N., Harshavardhan J. and M. Manimozhi
37.1 Introduction
37.2 Objective
37.3 Implementation
37.4 Proposed System Operational Flowchart
37.5 Hardware Description
37.5.1 NodeMCU ESP8366
37.5.2 Micro Vibration Motor
37.5.3 Buzzer
37.5.4 OLED Screen
37.6 Software Description
37.6.1 Arduino Integrated Development Environment (IDE)
37.6.2 MIT App Inventor
37.6.3 Arduino Libraries
37.6.3.1 NTPClient
37.6.3.2 WiFiUdp
37.6.3.3 Adafruit_SSD1306
37.6.3.4 Adafruit_GFX
37.7 Hardware and Software Interfacing
37.8 Conclusion
37.9 Future Scope
References
38. Energy Efficient Cluster Using Floyd Warshall Shortest Path with Artificial Bee Colony Optimization for Data Transmission in MANET
S. Usha Devi and K. Preetha
38.1 Introduction
38.2 Related Works
38.3 Proposed FWSP-ABC Optimization-Based Data Collection in MANET
38.3.1 The Proposed FWSP-ABC Optimization Technique
38.4 Simulation Results and Parameter Values
38.4.1 Packet Delivery Ratio
38.4.2 End to End Delay Time
38.4.3 Remaining Energy
38.4.4 Link Losses Occurrences
38.4.5 Throughput
38.5 Conclusion
Bibliography
39. ADAS for Improving Road Safety: YOLOv10-Based Detection of Cows, Potholes, and Traffic Signboards
Jayakrishnan P, Sunsitha Varshini Pugalaendhi and Sanaputur Sai Charan
39.1 Introduction
39.2 Related Works
39.3 Methodology
39.3.1 Data Collection
39.3.2 Data Annotation
39.3.3 Data Preprocessing
39.3.4 Data Augmentation
39.3.5 Model Selection
39.3.6 Training Procedure
39.3.7 Evaluation Metrics
39.3.8 Inference
39.4 Results
39.4.1 Class-Wise Metrics
39.4.2 Speed Metrics
39.4.3 Visual Outputs
39.4.4 Custom Inference
39.5 Conclusion
39.6 Future Work
References
40. Design, Development, Implementation and Optimization of a Low-Cost CNC Pen Plotter Using GRBL
Jibesh Mahanta and Sankardoss Varadhan
40.1 Introduction
40.2 System Overview
40.2.1 Hardware Components
40.2.1.1 Arduino Uno with GRBL Firmware
40.2.1.2 28BYJ-48 Stepper Motors and ULN2003 Driver Boards
40.2.1.3 Servo Motor for Z-Axis Control
40.2.2 Mechanical Design
40.2.2.1 Telescopic Drawer Hinges
40.2.2.2 Threads Instead of Belts
40.3 Design Considerations
40.3.1 Motor Selection
40.3.2 Servo Motor for Z-Axis Movement
40.4 Interface and Software Design
40.4.1 Project Software
40.4.2 Inkscape
40.4.3 Universal G-Code Sender
40.4.4 Arduino Integrated Development Environment (IDE)
40.5 Experimental Results
40.5.1 Movement Accuracy
40.6 Conclusion
Bibliography
About the Editors
Index
Back to Top