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Submit a ProposalEnvironmental Monitoring Using Artificial Intelligence
 | Edited by A. Suresh, T. Devi, N. Deepa, and Ali Kashif Bashir Copyright: 2025 | Expected Pub Date:2025// ISBN: 9781394270361 | Hardcover | 417 pages Price: $225 USD  |
One Line Description
Environmental Monitoring Using Artificial Intelligence is a vital resource for anyone looking to leverage cutting-edge technologies in artificial intelligence and sensor systems to effectively address environmental challenges, offering innovative solutions and insights essential for creating a sustainable future.
Audience
Environmental engineers, data and environmental scientists, AI specialists, ecologists, agricultural engineers, agronomists, urban planners, policymakers, researchers, and students interested in renewable energy and smart cities
Environmental engineers, data and environmental scientists, AI specialists, ecologists, agricultural engineers, agronomists, urban planners, policymakers, researchers, and students interested in renewable energy and smart cities
Description
Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability.
From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you’re exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.
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Environmental Monitoring Using Artificial Intelligence provides a comprehensive exploration of the cutting-edge technologies transforming environmental monitoring. This book bridges the gap between artificial intelligence (AI), natural language processing (NLP), and sensor-based systems, highlighting their potential to revolutionize the way we address pressing environmental challenges. Each chapter presents innovative case studies, real-world applications, and the latest research on how these technologies are being utilized to monitor and manage ecosystems, water resources, air quality, and urban sustainability.
From advanced sensor networks to machine learning models, this book covers a broad spectrum of topics, including smart water solutions, biodiversity conservation, waste management, and agricultural sustainability. It offers an interdisciplinary approach, making it an essential resource for environmental engineers, data scientists, researchers, and policymakers. Whether you’re exploring smart city innovations, renewable energy monitoring, or AI-driven solutions for environmental protection, Environmental Monitoring Using Artificial Intelligence equips readers with the knowledge and tools to leverage technology for a sustainable future.
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Author / Editor Details
Annamalai Suresh, PhD is an associate professor in the Department of Networking and Communications in the School of Computing, at the SRM Institute of Science and Technology, Tamil Nadu, India with over decades of experience. He has been granted nine patents, published over 150 papers in technical journals and over 100 papers at international scientific conferences, as well as several books. Additionally, he has served as an editor and reviewer for various journals and a chair of several conferences and workshops.
Annamalai Suresh, PhD is an associate professor in the Department of Networking and Communications in the School of Computing, at the SRM Institute of Science and Technology, Tamil Nadu, India with over decades of experience. He has been granted nine patents, published over 150 papers in technical journals and over 100 papers at international scientific conferences, as well as several books. Additionally, he has served as an editor and reviewer for various journals and a chair of several conferences and workshops.
T. Devi, PhD is a professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has published her research work in reputed international journals and presented papers in several national and international and conferences and has published patents in the computer science and engineering field. Her areas of specialization include cryptography, network security, cloud computing, artificial intelligence, machine learning, and blockchain.
N. Deepa, PhD is an associate professor in the Department of Computer Science and Engineering at the Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India. She has presented papers in several national and international onferences, as well as published her research work in reputed international journals and conferences and patents in the computer science and engineering field. Her areas of specialization include deep learning, machine learning, mobile computing, cryptography, network security, and blockchain.
Ali Kashif Bashir, PhD is a professor at the School of Computing and Mathematics, Manchester Metropolitan University, United Kingdom and an adjunct professor at the School of Electrical Engineering and Computer Science, National University of Science and Technology, Islamabad. Additionally, he has authored over 90 peer-reviewed articles and advised several startups in the field of STEM-based education, robotics, and smart homes. He is a senior member of the Institute of Electrical and Electronics Engineers and a Distinguished Speaker for the Association for Computing Machinery and serves as an editor and reviewer for a number of journals.
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Table of Contents
Preface
1. Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
Leena Sri R., Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Karthikeyan Thangavel
1.1 Introduction
1.2 Literature Verticals
1.3 Key Methodologies in Literature Review
1.4 Most Common Methods in Environmental Monitoring
1.5 AI Architectures for Environmental Monitoring
1.6 Applications of AI in Environmental Monitoring
1.7 Challenges and Limitations of Using AI in Environment Modeling
1.8 Future Directions
1.9 Conclusion
Acknowledgements
References
2. Fundamentals of AI and NLP in Environmental Analysis
Sreedevi Chikkudu and Suresh Annamalai
2.1 Introduction
2.2 AI and NLP Techniques
2.2.1 Artificial Neural Network (ANN)
2.2.2 Support Vector Machine (SVM)
2.2.3 Linear Regression (LR)
2.2.4 Random Forests (RF) and Decision Trees (DT)
2.2.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
2.2.6 Batch-Normalization (BN)
2.2.7 Convolutional Neural Networks (CNNs)
2.2.8 Deep Neural Networks (DNNs)
2.2.9 Genetic Algorithm (GA)
2.3 AI Models and NLP System with Data Science Cycle
2.3.1 Supervised Learning
2.3.2 Semi-Unsupervised Learning/Unsupervised Learning
2.3.3 Reinforcement Learning
2.4 Environmental Analysis Using AIoT and NLP
Bibliography
3. Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements
D. Roja Ramani, B. Ben Sujitha and Shrikant Tangade
3.1 Introduction
3.2 Essential Elements and Factors for Environmental Monitoring with IoT
3.2.1 Sensor Devices
3.2.2 Data Acquisition
3.2.3 Data Mastery and Storage Brilliance
3.2.4 Data Analysis
3.2.5 Visualizing Insights
3.2.6 Alert Mechanism
3.2.7 Synergizing Connectivity
3.2.8 Energy Efficiency
3.2.9 Securing Environmental Data
3.2.10 Adhering to Regulatory Standards
3.3 Diverse Avenues and Methodologies in IoT Environmental Applications
3.3.1 Agricultural Surveillance
3.3.2 Air Quality Surveillance
3.3.3 Aquatic Quality Surveillance
3.3.4 Urban Intelligence
3.3.5 Preserving Wildlife
3.3.6 Sustainable Industrial Practices
3.4 Conclusion
References
4. Remote Monitoring Advancements: A New Approach to Biodiversity Conservation
D. Roja Ramani, K. Kalaiarasan and Shrikant Tangade
4.1 Introduction
4.2 Indicators of Primary Biodiversity
4.3 Exploring Biodiversity Conservation Strategies
4.4 AI Enhancing Animal Observation Images
4.5 AI and ML for Preserving Flora
4.6 Deep Learning Tracks Terrestrial Mammals via Satellites
4.7 Conclusion
References
5. Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank
I. Baskar, A. Haamidh, S. Suriya and K. Parameswari
5.1 Introduction
5.2 Software Used
5.2.1 GPS Essentials Application
5.2.2 Google Earth Pro
5.2.3 Drone Deploy
5.2.4 QGIS
5.2.5 Pix 4D Mapper
5.3 Methodology
5.3.1 Drone Preprocessing
5.3.1.1 Collection of GPS Coordinates
5.3.1.2 Generation of .kmlfile
5.3.1.3 Generation of the Ground Map
5.3.1.4 Creating Flight Plan
5.3.1.5 Collection of Data
5.3.2 Drone Data Preprocessing
5.3.2.1 Reviewing the Data
5.3.2.2 Uploading the Data
5.3.2.3 Addition of Ground Control Points (GCPs)
5.3.2.4 Aero Triangulation
5.3.2.5 Reconstruction Settings
5.3.2.6 Production
5.3.3 Drone Data: Post-Processing-Outputs
5.3.3.1 Orthomosaic Map
5.3.3.2 Digital Surface Model (DSM)
5.3.3.3 3D Model
5.3.3.4 Contour Map
5.3.4 Hydrological Analysis
5.3.4.1 Fill and Flow Direction
5.3.4.2 Streamline and Stream Order
5.3.4.3 Water Outlet and Water Catchment Areas
5.3.4.4 Fill Volume Estimation
5.3.4.5 Terrain Elevation Range
5.3.4.6 Stream Order Overlayed on Orthomosaic
5.3.4.7 Expected Earthwork Estimation
5.3.4.8 Outlet Trace
5.4 Conclusion and Recommendation
Acknowledgement
References
6. Sustainable Waste Management as a Key Feature for Smart City: A Case Study of Vadodara, Gujarat, India
Sahil Menghani, Hardik Giri Gosai, Parashuram Kallem, Payal Desai and Uma Hapani
6.1 Introduction
6.1.1 Essential Features of Indian Smart Cities
6.1.2 Waste Management in Smart Cities
6.1.3 Indian Status of Solid Waste Management
6.2 Material and Methodology
6.2.1 Study Area
6.2.2 Design of Questionnaire for Survey, and Data Analysis
6.2.3 Comparison Study of Waste Management Scenario in Vadodara
6.3 Result and Discussion
6.3.1 Socio-Demographic Profile of Respondents
6.3.2 Awareness and Willingness of Respondents Towards Waste Management
6.3.3 Comparative Assessment of Generalised Waste Collection with KSA Waste Collection
6.4 Limitation of Study
6.5 Conclusion and Future Prospects
References
7. Sensor Technologies for Environmental Data Collection
Adimulam Raghuvira Pratap and Suresh Annamalai
7.1 Introduction
7.2 Sensor Technologies
7.3 Background of Sensing
7.4 Types of Sensors
7.5 Applications of Sensors
7.6 Challenges of Sensors
7.7 Environmental Sensors
7.7.1 Standards and Regulations
7.7.2 Drivers of Environmental Sensing
7.7.3 Network and Communications Technologies in Conjunction with Environmental Sensing
7.7.4 Environmental Sensors
7.7.4.1 Metal Trace Detectors
7.7.4.2 Radioisotope Sensors
7.7.4.3 Cadmium Zinc Telluride (CZT) Detectors
7.7.4.4 Minimal-Power Pin Diode Beta Spectrometer
7.7.4.5 Thermoluminescent Dosimeter (TLD)
7.7.4.6 Identification Gamma Detector for Isotopes
7.7.4.7 Neutrino Projector for Detection of Nuclear Material
7.7.4.8 Non-Sandia Radiation Detectors
7.7.4.9 Sensors for Organic Pollutants
7.7.4.10 MicroChemLab (Gas Phase)
7.7.4.11 Biological Sensors
7.7.4.12 iDEP (Insulator-Based Dielectrophoresis)
7.8 Summary and Recommendations
Bibliography
8. Significance and Advancement of Sensor Technologies for Environmental Analysis
S. Thanga Revathi, Mary Subaja Christo, A. Sathya and Suresh Annamalai
8.1 Introduction
8.2 Sensing and Sensor Fundamentals
8.2.1 Sensing Modalities
8.2.2 Sensor Types
8.2.3 Sensor Characteristics
8.3 Key Sensor Technology Components
8.4 Regulations and Standards - Sensor Technologies
8.5 Conclusion
Bibliography
9. Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Ganesha Ram G., Ashween Raj V. S. and Godwin Wilfred Raj A.
9.1 Introduction
9.2 Related Works
9.3 Proposed Work
9.3.1 Conceptual Basis for Pesticidal Detection
9.3.2 Image Preprocessing
9.3.3 Local Binary Patterns
9.3.4 Feature Extraction
9.3.5 Support Vector Machine
9.3.6 Cross-Validation Techniques
9.3.7 Evaluation Metrics and Parameters
9.4 Implementation and Results
9.4.1 Dataset and Textural Insights from LBP
9.4.2 Performance Metrics
9.4.3 Performance Analysis
9.5 Conclusion
References
10. Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis
D. Roja Ramani, Naveen Chandra Gowda, S. Sreejith and Shrikant Tangade
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Results and Discussion
10.5 Conclusion
10.6 Future Directions
References
11. A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
Jenefa J., Divya Vetriveeran, Rakoth Kandan Sambandam, Vinodha D., S. Thaiyalnayaki and P. Karthikeyan
11.1 Introduction
11.2 Related Works
11.3 AlexNet
11.4 ResNet
11.5 Proposed Work
11.6 Conclusion
Acknowledgments
References
12. Comparison of Borewell Rescue L-Type Different Arm with Different Materials
K.P. Sridhar, Arun M., C. Prajitha, S. Deepa, Abubeker K.M. and Rajalakshmi Selvaraj
12.1 Introduction
12.2 Related Works
12.3 Proposed Method
12.4 Cylinder
12.4.1 Aluminium
12.4.2 Plastic
12.4.3 Stainless Steel
12.4.4 Steel
12.5 Ellipse
12.5.1 Aluminium
12.5.2 Plastic
12.5.3 Stainless Steel
12.5.4 Steel
12.6 I-Beam
12.6.1 Aluminium
12.6.2 Plastic
12.6.3 Stainless Steel
12.6.4 Steel
12.7 L-Angle
12.7.1 Aluminium
12.7.2 Plastic
12.7.3 Stainless Steel
12.7.4 Steel
12.8 Mathematical Analysis
12.8.1 Performance Ratio
12.8.2 Accuracy Ratio
12.8.3 Efficiency Ratio
12.8.4 Deformation Ratio
12.8.5 Stress Comparison Ratio
12.8.6 Summary of the Proposed Method
12.9 Results and Discussion
12.9.1 Performance Comparison
12.9.2 Accuracy Comparison
12.9.3 Efficiency of Our Proposed Method
12.9.4 Deformation Comparison
12.9.5 Stress Analysis
12.10 Conclusion
References
13. Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment
D. Roja Ramani, N. Deepa, Naveen Chandra Gowda and Naandhini Sidnal
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.4 Results and Discussion
13.5 Conclusion
References
14. Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis
Naveen Chandra Gowda, Veena H. N., Aghila Rajagopal and Shrikant Tangade
14.1 Introduction
14.2 Review of Relevant Literature
14.3 Methodological Framework
14.3.1 Drone and IoT Configuration
14.3.2 Data Gathering
14.3.3 Study Site
14.3.4 Data Examination
14.3.4.1 Parameter Details and Drone Specifications
14.3.4.2 Drone Specifications
14.3.4.3 Data Collection Approach
14.3.4.4 Data Collection Intervals
14.3.4.5 Altitude Range
14.3.4.6 Sensor Array
14.3.4.7 The Sensor Types for the Measured Parameters
14.3.4.8 Noise Mitigation
14.3.4.9 Geospatial Mapping
14.3.4.10 Mapping Techniques
14.3.4.11 Input for Predictive Framework
14.3.4.12 Focus of Study
14.4 Outcomes and Discourse
14.5 Conclusion
References
15. Voltage Veggies: A Shocking Revolution in Agriculture
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Rageshwaran H.R., Praveen Kumar D., Praveen Kumar S. and Mugunthan Kennedy K.
15.1 Introduction
15.1.1 Plant vs. Animal Nervous System
15.1.2 Unraveling Plant Action Potentials
15.1.3 Plant Impulses: Decoding Nature’s Signals
15.1.4 Related Works
15.2 Proposed Methodology
15.2.1 Role in Plant Physiology
15.2.2 Recording and Understanding
15.2.3 Signal Analysis Techniques
15.2.3.1 Advanced Techniques in Signal Analysis
15.2.3.2 Experimental Validation and Application
15.2.4 AI Model Development
15.2.4.1 AI Model Architecture: Decoding the Framework
15.2.4.2 Training Process: Nurturing Intelligence through Data
15.2.4.3 Advancements in Magnetic Field Application: A Symbiotic Interaction
15.2.4.4 Fostering Intelligent Plant-Environment Interaction
15.2.4.5 Adaptive Response Modeling
15.2.5 Transferring Signals to Defective Plants through Magnetic Impulses
15.2.5.1 Magnetic Field Generation Methods
15.2.5.2 Experimental Insights and Observations
15.3 Experimental Approach
15.3.1 Unraveling the Dynamics of Plant Impulse Manipulation
15.3.2 Challenges and Limitations in AI-Driven Plant Impulse Manipulation: A Critical Examination
15.3.2.1 Challenges Faced During the Research Process
15.3.3 Limitations of the Proposed Method and Areas for Improvement
15.4 Conclusion and Future Research Directions
15.4.1 Future Research Directions
15.5 Conclusion
References
16. Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design
N.L. Venkataraman, S. Sumithra, S. Suresh Kumarm, K. Kokulavani and Gunasekaran Thangevel
16.1 Introduction
16.2 Related Works
16.3 Design of Routerless NoC
16.4 Emperor Penguin Optimized (EPO) Loop Selection
16.4.1 To Generate the 4 × 4 Grid
16.4.2 Determine the Delay, Power Loss and Data Traffic
16.4.3 Difference between the Best Loops
16.4.4 To Find the Best Loop
16.5 Result and Discussion
16.6 Conclusion
References
17. Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants
K.P. Sridhar, C. Prajitha, S. Deepa, Rinesh S., Arun M. and Srinath Doss
17.1 Introduction
17.2 Related Study
17.3 Case Study on Flyover Construction and the Air Quality Measurement
17.3.1 Case Study on Gounder Mills
17.4 Conclusion
References
About the Editors
Index
Back to Top
Preface
1. Transformative Trends in AI for Environmental Monitoring: Challenges, Applications
Leena Sri R., Divya Vetriveeran, Rakoth Kandan Sambandam, Jenefa J. and Karthikeyan Thangavel
1.1 Introduction
1.2 Literature Verticals
1.3 Key Methodologies in Literature Review
1.4 Most Common Methods in Environmental Monitoring
1.5 AI Architectures for Environmental Monitoring
1.6 Applications of AI in Environmental Monitoring
1.7 Challenges and Limitations of Using AI in Environment Modeling
1.8 Future Directions
1.9 Conclusion
Acknowledgements
References
2. Fundamentals of AI and NLP in Environmental Analysis
Sreedevi Chikkudu and Suresh Annamalai
2.1 Introduction
2.2 AI and NLP Techniques
2.2.1 Artificial Neural Network (ANN)
2.2.2 Support Vector Machine (SVM)
2.2.3 Linear Regression (LR)
2.2.4 Random Forests (RF) and Decision Trees (DT)
2.2.5 Adaptive Neuro-Fuzzy Inference System (ANFIS)
2.2.6 Batch-Normalization (BN)
2.2.7 Convolutional Neural Networks (CNNs)
2.2.8 Deep Neural Networks (DNNs)
2.2.9 Genetic Algorithm (GA)
2.3 AI Models and NLP System with Data Science Cycle
2.3.1 Supervised Learning
2.3.2 Semi-Unsupervised Learning/Unsupervised Learning
2.3.3 Reinforcement Learning
2.4 Environmental Analysis Using AIoT and NLP
Bibliography
3. Smart Environmental Monitoring Systems: IoT and Sensor-Based Advancements
D. Roja Ramani, B. Ben Sujitha and Shrikant Tangade
3.1 Introduction
3.2 Essential Elements and Factors for Environmental Monitoring with IoT
3.2.1 Sensor Devices
3.2.2 Data Acquisition
3.2.3 Data Mastery and Storage Brilliance
3.2.4 Data Analysis
3.2.5 Visualizing Insights
3.2.6 Alert Mechanism
3.2.7 Synergizing Connectivity
3.2.8 Energy Efficiency
3.2.9 Securing Environmental Data
3.2.10 Adhering to Regulatory Standards
3.3 Diverse Avenues and Methodologies in IoT Environmental Applications
3.3.1 Agricultural Surveillance
3.3.2 Air Quality Surveillance
3.3.3 Aquatic Quality Surveillance
3.3.4 Urban Intelligence
3.3.5 Preserving Wildlife
3.3.6 Sustainable Industrial Practices
3.4 Conclusion
References
4. Remote Monitoring Advancements: A New Approach to Biodiversity Conservation
D. Roja Ramani, K. Kalaiarasan and Shrikant Tangade
4.1 Introduction
4.2 Indicators of Primary Biodiversity
4.3 Exploring Biodiversity Conservation Strategies
4.4 AI Enhancing Animal Observation Images
4.5 AI and ML for Preserving Flora
4.6 Deep Learning Tracks Terrestrial Mammals via Satellites
4.7 Conclusion
References
5. Smart Water Solutions: A Case Study on Drone-Led Hydrological Investigation of Water Diversion from Lakshmiyapuram Catchment to Sivakasi Periyakulam Tank
I. Baskar, A. Haamidh, S. Suriya and K. Parameswari
5.1 Introduction
5.2 Software Used
5.2.1 GPS Essentials Application
5.2.2 Google Earth Pro
5.2.3 Drone Deploy
5.2.4 QGIS
5.2.5 Pix 4D Mapper
5.3 Methodology
5.3.1 Drone Preprocessing
5.3.1.1 Collection of GPS Coordinates
5.3.1.2 Generation of .kmlfile
5.3.1.3 Generation of the Ground Map
5.3.1.4 Creating Flight Plan
5.3.1.5 Collection of Data
5.3.2 Drone Data Preprocessing
5.3.2.1 Reviewing the Data
5.3.2.2 Uploading the Data
5.3.2.3 Addition of Ground Control Points (GCPs)
5.3.2.4 Aero Triangulation
5.3.2.5 Reconstruction Settings
5.3.2.6 Production
5.3.3 Drone Data: Post-Processing-Outputs
5.3.3.1 Orthomosaic Map
5.3.3.2 Digital Surface Model (DSM)
5.3.3.3 3D Model
5.3.3.4 Contour Map
5.3.4 Hydrological Analysis
5.3.4.1 Fill and Flow Direction
5.3.4.2 Streamline and Stream Order
5.3.4.3 Water Outlet and Water Catchment Areas
5.3.4.4 Fill Volume Estimation
5.3.4.5 Terrain Elevation Range
5.3.4.6 Stream Order Overlayed on Orthomosaic
5.3.4.7 Expected Earthwork Estimation
5.3.4.8 Outlet Trace
5.4 Conclusion and Recommendation
Acknowledgement
References
6. Sustainable Waste Management as a Key Feature for Smart City: A Case Study of Vadodara, Gujarat, India
Sahil Menghani, Hardik Giri Gosai, Parashuram Kallem, Payal Desai and Uma Hapani
6.1 Introduction
6.1.1 Essential Features of Indian Smart Cities
6.1.2 Waste Management in Smart Cities
6.1.3 Indian Status of Solid Waste Management
6.2 Material and Methodology
6.2.1 Study Area
6.2.2 Design of Questionnaire for Survey, and Data Analysis
6.2.3 Comparison Study of Waste Management Scenario in Vadodara
6.3 Result and Discussion
6.3.1 Socio-Demographic Profile of Respondents
6.3.2 Awareness and Willingness of Respondents Towards Waste Management
6.3.3 Comparative Assessment of Generalised Waste Collection with KSA Waste Collection
6.4 Limitation of Study
6.5 Conclusion and Future Prospects
References
7. Sensor Technologies for Environmental Data Collection
Adimulam Raghuvira Pratap and Suresh Annamalai
7.1 Introduction
7.2 Sensor Technologies
7.3 Background of Sensing
7.4 Types of Sensors
7.5 Applications of Sensors
7.6 Challenges of Sensors
7.7 Environmental Sensors
7.7.1 Standards and Regulations
7.7.2 Drivers of Environmental Sensing
7.7.3 Network and Communications Technologies in Conjunction with Environmental Sensing
7.7.4 Environmental Sensors
7.7.4.1 Metal Trace Detectors
7.7.4.2 Radioisotope Sensors
7.7.4.3 Cadmium Zinc Telluride (CZT) Detectors
7.7.4.4 Minimal-Power Pin Diode Beta Spectrometer
7.7.4.5 Thermoluminescent Dosimeter (TLD)
7.7.4.6 Identification Gamma Detector for Isotopes
7.7.4.7 Neutrino Projector for Detection of Nuclear Material
7.7.4.8 Non-Sandia Radiation Detectors
7.7.4.9 Sensors for Organic Pollutants
7.7.4.10 MicroChemLab (Gas Phase)
7.7.4.11 Biological Sensors
7.7.4.12 iDEP (Insulator-Based Dielectrophoresis)
7.8 Summary and Recommendations
Bibliography
8. Significance and Advancement of Sensor Technologies for Environmental Analysis
S. Thanga Revathi, Mary Subaja Christo, A. Sathya and Suresh Annamalai
8.1 Introduction
8.2 Sensing and Sensor Fundamentals
8.2.1 Sensing Modalities
8.2.2 Sensor Types
8.2.3 Sensor Characteristics
8.3 Key Sensor Technology Components
8.4 Regulations and Standards - Sensor Technologies
8.5 Conclusion
Bibliography
9. Texture-Based Classification of Organic and Pesticidal Spinach Using Machine Learning
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Ganesha Ram G., Ashween Raj V. S. and Godwin Wilfred Raj A.
9.1 Introduction
9.2 Related Works
9.3 Proposed Work
9.3.1 Conceptual Basis for Pesticidal Detection
9.3.2 Image Preprocessing
9.3.3 Local Binary Patterns
9.3.4 Feature Extraction
9.3.5 Support Vector Machine
9.3.6 Cross-Validation Techniques
9.3.7 Evaluation Metrics and Parameters
9.4 Implementation and Results
9.4.1 Dataset and Textural Insights from LBP
9.4.2 Performance Metrics
9.4.3 Performance Analysis
9.5 Conclusion
References
10. Deep Bidirectional LSTM for Emotion Detection through Mobile Sensor Analysis
D. Roja Ramani, Naveen Chandra Gowda, S. Sreejith and Shrikant Tangade
10.1 Introduction
10.2 Literature Survey
10.3 Methodology
10.4 Results and Discussion
10.5 Conclusion
10.6 Future Directions
References
11. A Comparative Analysis of AlexNet and ResNet for Pneumonia Detection
Jenefa J., Divya Vetriveeran, Rakoth Kandan Sambandam, Vinodha D., S. Thaiyalnayaki and P. Karthikeyan
11.1 Introduction
11.2 Related Works
11.3 AlexNet
11.4 ResNet
11.5 Proposed Work
11.6 Conclusion
Acknowledgments
References
12. Comparison of Borewell Rescue L-Type Different Arm with Different Materials
K.P. Sridhar, Arun M., C. Prajitha, S. Deepa, Abubeker K.M. and Rajalakshmi Selvaraj
12.1 Introduction
12.2 Related Works
12.3 Proposed Method
12.4 Cylinder
12.4.1 Aluminium
12.4.2 Plastic
12.4.3 Stainless Steel
12.4.4 Steel
12.5 Ellipse
12.5.1 Aluminium
12.5.2 Plastic
12.5.3 Stainless Steel
12.5.4 Steel
12.6 I-Beam
12.6.1 Aluminium
12.6.2 Plastic
12.6.3 Stainless Steel
12.6.4 Steel
12.7 L-Angle
12.7.1 Aluminium
12.7.2 Plastic
12.7.3 Stainless Steel
12.7.4 Steel
12.8 Mathematical Analysis
12.8.1 Performance Ratio
12.8.2 Accuracy Ratio
12.8.3 Efficiency Ratio
12.8.4 Deformation Ratio
12.8.5 Stress Comparison Ratio
12.8.6 Summary of the Proposed Method
12.9 Results and Discussion
12.9.1 Performance Comparison
12.9.2 Accuracy Comparison
12.9.3 Efficiency of Our Proposed Method
12.9.4 Deformation Comparison
12.9.5 Stress Analysis
12.10 Conclusion
References
13. Optimizing Almond and Walnut Farming: A U-Net-Powered Deep Learning Approach for Energy Efficiency Prediction and Damage Assessment
D. Roja Ramani, N. Deepa, Naveen Chandra Gowda and Naandhini Sidnal
13.1 Introduction
13.2 Literature Survey
13.3 Methodology
13.4 Results and Discussion
13.5 Conclusion
References
14. Enhancing Sustainable Management of Waste Dump Sites with Smart Drones and Geospatial Tech: Air Quality Monitoring and Analysis
Naveen Chandra Gowda, Veena H. N., Aghila Rajagopal and Shrikant Tangade
14.1 Introduction
14.2 Review of Relevant Literature
14.3 Methodological Framework
14.3.1 Drone and IoT Configuration
14.3.2 Data Gathering
14.3.3 Study Site
14.3.4 Data Examination
14.3.4.1 Parameter Details and Drone Specifications
14.3.4.2 Drone Specifications
14.3.4.3 Data Collection Approach
14.3.4.4 Data Collection Intervals
14.3.4.5 Altitude Range
14.3.4.6 Sensor Array
14.3.4.7 The Sensor Types for the Measured Parameters
14.3.4.8 Noise Mitigation
14.3.4.9 Geospatial Mapping
14.3.4.10 Mapping Techniques
14.3.4.11 Input for Predictive Framework
14.3.4.12 Focus of Study
14.4 Outcomes and Discourse
14.5 Conclusion
References
15. Voltage Veggies: A Shocking Revolution in Agriculture
P. Prittopaul, M. Usha, Mervin Retnadhas Mary, Rageshwaran H.R., Praveen Kumar D., Praveen Kumar S. and Mugunthan Kennedy K.
15.1 Introduction
15.1.1 Plant vs. Animal Nervous System
15.1.2 Unraveling Plant Action Potentials
15.1.3 Plant Impulses: Decoding Nature’s Signals
15.1.4 Related Works
15.2 Proposed Methodology
15.2.1 Role in Plant Physiology
15.2.2 Recording and Understanding
15.2.3 Signal Analysis Techniques
15.2.3.1 Advanced Techniques in Signal Analysis
15.2.3.2 Experimental Validation and Application
15.2.4 AI Model Development
15.2.4.1 AI Model Architecture: Decoding the Framework
15.2.4.2 Training Process: Nurturing Intelligence through Data
15.2.4.3 Advancements in Magnetic Field Application: A Symbiotic Interaction
15.2.4.4 Fostering Intelligent Plant-Environment Interaction
15.2.4.5 Adaptive Response Modeling
15.2.5 Transferring Signals to Defective Plants through Magnetic Impulses
15.2.5.1 Magnetic Field Generation Methods
15.2.5.2 Experimental Insights and Observations
15.3 Experimental Approach
15.3.1 Unraveling the Dynamics of Plant Impulse Manipulation
15.3.2 Challenges and Limitations in AI-Driven Plant Impulse Manipulation: A Critical Examination
15.3.2.1 Challenges Faced During the Research Process
15.3.3 Limitations of the Proposed Method and Areas for Improvement
15.4 Conclusion and Future Research Directions
15.4.1 Future Research Directions
15.5 Conclusion
References
16. Emperor Penguin Optimized Loop Selection Process for Routerless NoC Design
N.L. Venkataraman, S. Sumithra, S. Suresh Kumarm, K. Kokulavani and Gunasekaran Thangevel
16.1 Introduction
16.2 Related Works
16.3 Design of Routerless NoC
16.4 Emperor Penguin Optimized (EPO) Loop Selection
16.4.1 To Generate the 4 × 4 Grid
16.4.2 Determine the Delay, Power Loss and Data Traffic
16.4.3 Difference between the Best Loops
16.4.4 To Find the Best Loop
16.5 Result and Discussion
16.6 Conclusion
References
17. Case Study on Flyover Construction and the Air Quality Measurement by the Emission Level of Pollutants
K.P. Sridhar, C. Prajitha, S. Deepa, Rinesh S., Arun M. and Srinath Doss
17.1 Introduction
17.2 Related Study
17.3 Case Study on Flyover Construction and the Air Quality Measurement
17.3.1 Case Study on Gounder Mills
17.4 Conclusion
References
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