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Submit a ProposalProbabilistic Machine Learning in Sustainable Artificial Intelligence
 | A Practical Approach Edited by Richa Singh, Sayani Ghosal and Durgesh Srivastava Copyright: 2027 | Expected Pub Date:11/30/2026 ISBN: 9781394346400 | Hardcover | 467 pages Price: $245 USD  |
One Line Description
Discover how probabilistic machine learning bridges the gap between cutting-edge AI and environmental stewardship, equipping you with the essential tools to build responsible,
uncertainty-aware systems that drive sustainable development and a more resilient future.
Audience
Audience
Academics, researchers, and industry professionals who are looking to solve problems based on probabilistic machine learning in sustainable AI.
Audience
Academics, researchers, and industry professionals who are looking to solve problems based on probabilistic machine learning in sustainable AI.
Description
In an era defined by technological breakthroughs and environmental imperatives, the interplay between artificial intelligence and sustainability has become more critical than ever. As we strive to design intelligent systems that are not only innovative but also ethical and resource-efficient, probabilistic machine learning emerges as a transformative tool, offering robust approaches to address complex real-world challenges. This book serves as a pivotal resource at the intersection of machine learning and sustainable development. It delves into the application of probabilistic modeling techniques within the context of sustainability, addressing pressing environmental and societal challenges. By harnessing advanced machine learning algorithms, researchers and practitioners can develop AI systems that not only provide accurate predictions but also account for uncertainty inherent in complex real-world data. By elucidating the principles, methodologies, and applications of probabilistic machine learning in the context of sustainability, it empowers researchers, practitioners, policymakers, and industry professionals to develop intelligent and responsible AI systems that contribute to the advancement of sustainable development goals and foster a more equitable and resilient future.
Readers will find the volume:
• Provides methods for utilizing probabilistic machine learning to optimize resource allocation and utilization in sustainable systems;
• Tailors machine learning algorithms and models specifically to address challenges in sustainable development and environmental conservation;
• Explores decision support systems that leverage probabilistic machine learning to assist stakeholders in making informed decisions about sustainability initiatives;
• Recognizes the interdisciplinary nature of sustainability and adapts probabilistic machine learning techniques to diverse domains such as energy, water management, agriculture, biodiversity conservation, and urban planning;
• Emphasizes the use of probabilistic models that can capture uncertainty inherent in real-world data.
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In an era defined by technological breakthroughs and environmental imperatives, the interplay between artificial intelligence and sustainability has become more critical than ever. As we strive to design intelligent systems that are not only innovative but also ethical and resource-efficient, probabilistic machine learning emerges as a transformative tool, offering robust approaches to address complex real-world challenges. This book serves as a pivotal resource at the intersection of machine learning and sustainable development. It delves into the application of probabilistic modeling techniques within the context of sustainability, addressing pressing environmental and societal challenges. By harnessing advanced machine learning algorithms, researchers and practitioners can develop AI systems that not only provide accurate predictions but also account for uncertainty inherent in complex real-world data. By elucidating the principles, methodologies, and applications of probabilistic machine learning in the context of sustainability, it empowers researchers, practitioners, policymakers, and industry professionals to develop intelligent and responsible AI systems that contribute to the advancement of sustainable development goals and foster a more equitable and resilient future.
Readers will find the volume:
• Provides methods for utilizing probabilistic machine learning to optimize resource allocation and utilization in sustainable systems;
• Tailors machine learning algorithms and models specifically to address challenges in sustainable development and environmental conservation;
• Explores decision support systems that leverage probabilistic machine learning to assist stakeholders in making informed decisions about sustainability initiatives;
• Recognizes the interdisciplinary nature of sustainability and adapts probabilistic machine learning techniques to diverse domains such as energy, water management, agriculture, biodiversity conservation, and urban planning;
• Emphasizes the use of probabilistic models that can capture uncertainty inherent in real-world data.
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Author / Editor Details
Richa Singh, PhD is an Assistant Professor in the Computer Science and Engineering Department at the Krishna Institute of Engineering and Technology Group University, Ghaziabad, India She has published multiple research papers in national and international journals and conferences, and multiple book chapters. She specializes in AI, machine learning, and data science.
Richa Singh, PhD is an Assistant Professor in the Computer Science and Engineering Department at the Krishna Institute of Engineering and Technology Group University, Ghaziabad, India She has published multiple research papers in national and international journals and conferences, and multiple book chapters. She specializes in AI, machine learning, and data science.
Sayani Ghosal, PhD is an Assistant Professor in the Department Computer Science and Engineering, AI and Machine Learning at Krishna Institute of Engineering and Technology Group University, Ghaziabad India. She has published several papers in highly reputed international journals and conferences. Her research areas include natural language processing, fuzzy logic, soft computing, sentiment analysis, and machine learning.
Durgesh Srivastava, PhD is an Associate Professor at the Chitkara University Institute of Engineering and Technology at Chitkara University, Rajpura, Punjab, India. He has published more than 30 papers in reputed national and international journals, conferences, and seminars. His research interests include machine learning, soft computing, pattern recognition, and software engineering.
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Table of Contents
Preface
Part I: Foundations of Sustainable AI and Probabilistic Machine Learning
1. Introduction to Sustainable AI and Stochastic AI
Shelly Gupta and Kumud Alok
1.1 Introduction
1.2 Probabilistic Machine Learning: What and Why
1.3 Probabilistic Machine Learning Methods
1.4 Sustainable Stochastic AI: A New Frontier
1.5 Discussion and Conclusion
References
2. Navigating Uncertainty: Probabilistic ML across Diverse
Domains
Sanket Ramakant Lodha and Durgesh Srivastava
2.1 Introduction
2.2 Foundations of Probabilistic Machine Learning
2.2.1 Principles of Probability in Machine Learning
2.2.2 Important Concepts: Bayesian Inference and Quantifying Uncertainty
2.2.2.1 Bayesian Inference Process
2.2.2.2 Quantifying Uncertainty
2.2.3 Probabilistic Models
2.2.3.1 Gaussian Processes (GPs)
2.2.3.2 Hidden Markov Models (HMMs)
2.2.3.3 Bayesian Neural Networks (BNNs)
2.2.3.4 Other Notable Models
2.2.4 Advantages Over Deterministic Approaches
2.3 Methodologies in Probabilistic Machine Learning
2.3.1 Bayesian Networks and Applications
2.3.1.1 Components of Bayesian Networks
2.3.1.2 Advantages
2.3.1.3 Applications
2.3.2 Variational Inference and Sampling Techniques
2.3.2.1 Steps in Variational Inference
2.3.2.2 Markov Chain Monte Carlo (MCMC)
2.3.3 Probabilistic Programming Frameworks
2.3.3.1 Popular Frameworks
2.3.3.2 Benefits of Probabilistic Programming
2.3.4 Monte Carlo Methods: Importance and Applications
2.3.4.1 Importance
2.3.4.2 Applications
2.3.4.3 Challenges and Mitigations
2.4 Applications across Diverse Domains
2.4.1 Medicine: Personalized Diagnosis and Healthcare
2.4.1.1 Diagnosis of Disease
2.4.1.2 Personalized Medicine
2.4.1.3 Predictive Analytics in Epidemics
2.4.2 Finance: Risk Modeling and Fraud Detection
2.4.2.1 Risk Assessment and Portfolio Optimization
2.4.2.2 Fraud Detection
2.4.2.3 Algorithmic Trading
2.4.3 Autonomous Systems: Perception and Control
2.4.3.1 Perception
2.4.3.2 Decision-Making and Planning
2.4.3.3 Control Systems
2.4.4 Climate Modeling: Forecasting and Policy Formulation
2.4.4.1 Weather Forecasting
2.4.4.2 Climate Change Projections
2.4.4.3 Disaster Preparedness
2.5 Cross-Disciplinary Advances in Probabilistic Machine Learning
2.5.1 Bioinformatics and Genomics
2.5.1.1 Gene Expression Analysis
2.5.1.2 Protein Structure Prediction
2.5.1.3 Evolutionary Analysis
2.5.2 Social Sciences and Behavioral Analysis
2.5.2.1 Opinion Dynamics and Sentiment Analysis
2.5.2.2 Network Analysis
2.5.2.3 Behavioral Prediction
2.5.3 Natural Language Processing (NLP) and Linguistics
2.5.3.1 Language Modeling
2.5.3.2 Speech Recognition
2.5.3.3 Sentiment and Context Understanding
2.5.4 Quantum Computing and Probabilistic ML
2.5.4.1 Quantum Probabilistic Models
2.5.4.2 Quantum Sampling
2.5.4.3 Hybrid Quantum-Classical Models
2.5.5 Art and Creativity
2.5.5.1 Generative Models for Art
2.5.5.2 Music Composition
2.5.5.3 Storytelling and Content Generation
2.6 Challenges and Future Directions in Probabilistic Machine
Learning
2.6.1 Challenges in Probabilistic ML
2.6.1.1 Scalability and Computational Complexity
2.6.1.2 Interpretability vs. Complexity
2.6.1.3 Data Quality and Availability
2.6.1.4 Lack of Standardization
2.6.1.5 Integration with Emerging Technologies
2.6.2 Future Directions in Probabilistic ML
2.6.2.1 Improving Scalable Inference Methods
2.6.2.2 Hybrid Models
2.6.2.3 Improved Interpretability
2.6.2.4 Noisy and Biased Data Robustness
2.6.2.5 Integration with Edge and Quantum Computing
2.6.2.6 Automated Probabilistic ML
2.6.2.7 Ethical and Fair Probabilistic Modeling
2.6.3 Long-Term Vision
2.7 Conclusion
2.7.1 Key Takeaways
2.7.2 Broader Implications
2.7.3 Vision for the Future
References
3. Iris Image Recognition through Goggles Using CNN and Mathematical Models in Probabilistic ML
Ridhima Chopra, Subhadeep Mal and Rajini G.K.
3.1 Introduction
3.2 Related Work
3.3 Methodology
3.4 Pre-Processing
3.5 Iris Localization
3.5.1 Mathematical Model Approach
3.5.2 CNN Approach
3.6 Iris Segmentation
3.7 Iris Normalization
3.8 Feature Extraction
3.8.1 Discrete Wavelet Transform + Principal Component Analysis
3.8.2 Local Binary Pattern
3.8.3 Gabor Filter
3.9 Feature Matching
3.10 Tackling Specular Reflections
3.11 Experimental Results
3.12 Future Scope
3.13 Conclusion
References
Part II: Applications of Probabilistic ML in Key Industries
4. Energy Consumption Optimization Using Deep Learning in Probabilistic ML
Monika Sharma, Dimple Tiwari and Sayani Ghosal
4.1 Introduction
4.2 Motivation
4.3 Reasons for Energy Consumption
4.4 Current Challenges for Consumption of Energy
4.5 Applications of Energy Consumption
4.6 Related Work
4.7 Primary Principle of Deep Learning
4.8 Overview of Deep Learning
4.9 Types of Deep Learning Models Used in Energy Consumption
4.10 Key Applications of Deep Learning in Energy Consumption
4.11 Data Sources for Deep Learning Models
4.12 Data Pre-Processing and Feature Engineering
4.13 Training Deep Learning Models
4.14 Techniques and Algorithms for Online Energy Consumption
Models
4.15 Deep Learning Model Design and Implementation
4.16 Energy Consumption Training Model
4.17 Future Directions and Enhancements
4.18 Conclusion
References
5. Influence of Deep Learning Models on Energy Efficiency
in Sustainable AI
Shagun Gupta, Aakash and Shalini
5.1 Introduction
5.1.1 Deep Learning
5.1.2 Energy Efficiency’s Significance in the Context of Sustainability
5.1.3 What Will Be Elucidated in This Chapter
5.2 Related Works
5.3 Deep Learning Approaches for Energy Optimization
5.3.1 CNN (Convolutional Neural Networks)
5.3.2 RNN (Recurrent Neural Networks)
5.3.3 LSTM (Long Short-Term Memory)
5.3.4 GRU (Gated Recurrent Units)
5.3.5 Autoencoders (AE)
5.3.6 Performance Metrics in DL
5.3.7 Analysis of Deep Learning Approaches for Energy Efficiency in Sustainable AI
5.4 Deep Learning’s Role in Achieving Energy Efficiency
5.4.1 Predictive Modeling for Energy Consumption
5.4.2 Intelligent Building Control Systems
5.4.3 Forecasting and Integrating Renewable Energy
5.4.4 Deep Learning: A Catalyst for SDG
5.4.5 Deep Learning-Based Energy Management Systems for Smart Buildings
5.5 Challenges in Implementing Deep Learning for Sustainable AI
5.6 Conclusion and Future Work
Abbreviations
References
6. Reliable Solutions Using Probabilistic ML and Sustainable AI in Healthcare Applications
Snigdha Bhardwaj, Kandasamy Nagarajan and Tulika Bhardwaj
6.1 Introduction
6.1.1 History of AI in Healthcare
6.1.2 Scope
6.2 Landscape Assessment of AI and Big Data Analytics Unfolding in Health Care
6.3 Interplay of AI-ML for Aligning Biomedical Science and Precision Medicine
6.3.1 Importance and Benefits
6.4 Significant Applications of AI-ML in Healthcare
6.4.1 Clinical Medicines
6.4.2 Medical Diagnosis
6.4.3 Drug Discovery
6.4.4 Patient’s Experience
6.4.5 Healthcare Data Management
6.4.6 Robotic Surgery
6.5 Challenges of Machine Learning in Clinical Applications
6.6 Regulatory Challenges in Governing Artificial Intelligence
in Healthcare Sector
6.7 Future Scope
6.8 Conclusion
References
7. Exploring Probabilistic Machine Learning in Healthcare
with Heart Disease Prediction
Noor Mohammad, Sayani Ghosal and Korhan Cengiz
7.1 Introduction
7.2 Probabilistic Approach in Machine Learning
7.3 Background
7.4 Implementation: Logistic Regression
7.5 Conclusion
References
8. Recent Advances and Smart Farming in Digital Agriculture
Using Probabilistic ML and Sustainable AI
Veena Parihar, Bhawna, Richa Singh and Jabar H. Yousif
8.1 Introduction
8.2 Latest Advancements in Digital Agriculture
8.3 Market of AI in Agriculture
8.4 Related Work in the Field of Digital Agriculture
8.5 Probabilistic ML
8.5.1 Types of Probabilistic ML Models
8.5.2 Implementation of Probabilistic ML Model for Yield Prediction
8.6 Current Challenges and Future Directions
8.7 Conclusion
References
9. IoT-Driven Resource Allocation in Smart Cities Using Probabilistic Models
Vedprakash Maralapalle, Jayatheja Muktinutalapati, Abdullah Ansari and A. Chithambar Ganesh
9.1 Introduction to Smart Cities and the Role of IoT
9.1.1 IoT Applications in Smart Cities
9.2 Challenges in Resource Allocation for Smart Cities
9.2.1 Handling Big Data from IoT Devices
9.2.2 Challenges in Handling Big Data from IoT Devices
9.2.3 Leveraging Big Data for Resource Allocation
9.2.4 Challenges to Real-Time Responsiveness
9.2.5 Balancing Competing Objectives
9.2.6 Competing Objectives in Smart Cities
9.2.7 Role of IoT and Probabilistic Models
9.3 Probabilistic Data Structures for Smart City Data Management
9.4 Bloom Filters
9.4.1 Background
9.4.2 Bloom Filters Offer Several Advantages for Resource
Allocation and Data Management in Smart Cities
9.4.3 Challenges of Bloom Filters
9.4.4 Sketches
9.4.5 Applications in Smart Cities
9.4.6 Advantages of Using Sketches
9.4.7 Applications in Smart City Resource Allocation
9.5 Machine Learning Algorithms for Resource Allocation
9.5.1 Various Domains within Smart Cities to Enhance Resource Allocation
9.5.2 The Potential of Machine Learning in Resource Allocation is Significant, But Several Challenges Must Be Addressed
9.5.3 Reinforcement Learning
9.5.4 Multi-Agent Systems
9.5.5 Applications of MAS in Smart Cities
9.5.6 Advantages of MAS for Resource Allocation
9.5.7 Optimization Techniques
9.5.8 Various Optimization Algorithms
9.6 Proposed Framework for Resource Allocation in Smart Cities
9.6.1 Integration of PDS and Machine Learning
9.6.2 Synergy between PDS and ML
9.6.3 Dynamic Resource Allocation Strategies
9.6.4 Role of IoT and Probabilistic Models
9.6.5 Case Studies and Simulations
9.6.6 Simulations
9.7 Practical Considerations and Barriers to Adoption
9.7.1 Scalability and Reliability
9.7.2 Importance of Reliability
9.7.3 Challenges to Scalability and Reliability
9.8 Conclusion and Future Directions
9.8.1 Future Directions
References
10. Resource Allocation in Smart Cities Using IoT and Probabilistic Models
Disha Sharma, Sandeep Singh Kang and Vidhushi Gupta
10.1 Introduction
10.2 Challenges in Resource Allocation
10.3 IoT Architecture in Smart Cities
10.3.1 Traffic Sensors
10.3.2 Environmental Sensors
10.3.3 Smart Meters
10.3.4 Actuators
10.3.5 Smart Traffic Lights
10.3.6 Smart Streetlights
10.3.7 Water Valves
10.3.7.1 Communication Networks
10.3.7.2 Wireless Networks
10.3.7.3 Cellular (4G/5G)
10.3.7.4 LoRa (Long Range) and NB-IoT (Narrowband IoT)
10.3.7.5 Bluetooth and Zigbee
10.3.7.6 Ethernet
10.3.7.7 Protocols
10.3.7.8 MQTT (Message Queuing Telemetry Transport)
10.3.7.9 CoAP (Constrained Application Protocol)
10.3.7.10 IoT Edge Devices and Gateways
10.3.7.11 Smart Traffic Management
10.3.7.12 Cloud Platforms
10.3.7.13 Big Data Analytics
10.3.7.14 Real-Time Processing of Data
10.3.7.15 Predictive Analytics is Used for Strategic Planning
10.3.7.16 AI and Machine Learning
10.3.7.17 APIs and Integration Platforms
10.3.8 Data Collection and Processing in IoT Systems
10.4 Probabilistic Model for Resource Allocation
10.4.1 Bayesian Networks
10.4.2 Markov Chains
10.4.3 Stochastic Processes
10.4.4 Application of Probabilistic Model in IoT
10.5 Resource Allocation Technologies
10.6 Integration of IoT and Probabilistic Models
10.6.1 Data Collection Layer
10.6.2 Data Processing and Storage Layer
10.6.3 Probabilistic Model Layer
10.6.4 Decision-Making and Actuation Layer
10.6.5 Algorithm Design
10.6.6 Scalability and Real-Time Processing
10.7 Challenges Faced in Resource Allocation in Smart Cities
Using IoT and Probabilistic Models
10.7.1 Scalability
10.7.2 Latency in the Network
10.7.3 Data Integration
10.7.4 Data Volume
10.7.5 Data Integrity and Accuracy
10.7.6 Data Processing
10.7.7 Power management for IoT devices
10.7.8 Energy Balance
10.7.9 Cybersecurity Risks
10.7.10 Data Privacy Issues
10.7.11 DDoS attacks
10.7.12 Model Accuracy
10.7.13 Equity and Bias
10.7.14 Overfitting Historical Data
10.7.15 Future Directions in “Resource Allocation for Smart
Cities Using IoT and Probabilistic Models”
10.7.16 Advanced IoT Technologies for Resource Allocation
10.8 Future Scope
10.9 Conclusion
References
11. Probabilistic Machine Learning and Large Language Models to Address Social Issues and Their Solutions
Shweta Thakur
11.1 Introduction
11.1.1 Application Areas
11.2 Fundamentals of Probabilistic Machine Learning
11.2.1 Basics Concepts and Techniques in Probabilistic Machine Learning
11.2.2 Types of Probabilistic Models
11.2.3 Advantages of Using Probabilistic Approaches in Social Issue Analysis
11.3 Large Language Models: An Overview
11.3.1 Evolution and Development of LLMs
11.3.2 Key Large Language Model (LLM) Architectures
11.3.3 Capabilities and Limitations of LLMs in Social Contexts
11.4 Related Work
11.5 Methodology
11.6 Challenges and Ethical Considerations
11.7 Future Directions
11.8 Conclusion
Bibliography
Part III: Advanced Innovations and Ethical Considerations
12. Enhancement of Multimodal Dialog System Via Knowledge Graph with OpenAI GPT-4 Model in Sustainable AI
Richa Singh, Gaurav Srivastav and Korhan Cengiz
12.1 Introduction
12.1.1 Vector Database
12.1.2 Knowledge Graph
12.2 Literature Survey
12.2.1 Forecasting Multimodal Links
12.2.2 Extraction of Multimodal Relations
12.2.3 Named Entity Recognition Using Multimodal Approach
12.2.4 Multimodal Pre-Trained Representation
12.2.5 Multimodal Knowledge Graphs
12.3 Problem Statement
12.4 Proposed Approach
12.4.1 Ontology of Knowledge Graphs
12.4.2 Text2Graph
12.4.3 Picture2Graph
12.4.4 Code2Graph
12.4.5 Aligning Knowledge Graphs
12.5 Result and Discussion
12.6 Conclusion and Future Work
References
13. Impact of Probabilistic Graphical Model Approaches for Environmental Analysis
Vishal Jain and Archan Mitra
13.1 Introduction
13.1.1 Background of the Study
13.1.2 Problem Statement
13.1.3 Research Objective
13.1.4 Research Questions
13.2 Literature Review
13.2.1 Literature Survey
13.2.1.1 Overview of Probabilistic Graphical Models (PGMs)
13.2.1.2 Previous Studies Using PGMs in Various Fields, with a Focus on Environmental Applications
13.2.1.3 Existing Methods in Environmental Analysis
13.2.1.4 Studies Demonstrating the Limitations of Non-Probabilistic Approaches
13.2.1.5 Applications of PGMs in Environmental Science
13.2.1.6 Gaps in Current Research
13.3 Methodology
13.4 Discussion and Findings
13.4.1 Discussion of Results
13.4.1.1 Accuracy of PGMs in Predicting Environmental Phenomena
13.4.1.2 Applications of PGMs in Simulating Environmental Data
13.4.1.3 Handling Uncertainties in Environmental Datasets
13.4.2 Key Findings
13.4.3 Limitations of the Study
13.4.4 Future Scope
13.5 Conclusion
References
14. Optimizing Fault Prediction in AI Software Systems for Enhanced Economic Sustainability
J. Ramkumar and D. R. Medhunhashini
14.1 Introduction to Fault Prediction in AI Software Systems
14.1.1 Overview of Fault Prediction
14.1.2 Significance in AI Software Systems
14.1.3 Historical Context and Evolution
14.2 Economic Sustainability in AI Development
14.2.1 Defining Economic Sustainability
14.2.2 Role of AI in Economic Efficiency
14.2.3 Impact of Fault Prediction on Cost Savings
14.2.4 Energy Efficiency in Fault Prediction Systems
14.2.5 Future Directions and Trends
14.2.6 Emerging Technologies in Fault Prediction
14.2.7 Ethical and Regulatory Aspects of Fault Prediction
14.2.8 AI Fault Prediction in Smart Cities and IoT
14.3 Techniques for Fault Prediction
14.3.1 Statistical Methods
14.3.2 Machine Learning Approaches
14.3.3 Hybrid Techniques
14.4 Advanced Machine Learning Algorithms for Fault Prediction
14.4.1 Ensemble Methods for Enhanced Accuracy
14.4.2 Neural Networks and Deep Learning for Complex Patterns
14.4.3 Reinforcement Learning for Adaptive Fault Prediction
14.5 Real-Time Monitoring and Predictive Maintenance
14.5.1 Importance of Real-Time Monitoring
14.5.2 Tools for Real-Time Fault Detection
14.5.3 Strategies for Predictive Maintenance
14.6 Evaluating the Economic Impact of Fault Prediction
14.6.1 Metrics for Economic Evaluation
14.6.2 Case Studies on Cost Reduction
14.7 Conclusion
References
15. Security Measures for Fraud Detection Using Sustainable AI in the Finance Sector
Sneha Srijaya and Aarti Goel
15.1 Introduction
15.1.1 Impact on Businesses, Consumers, and the Global Economy
15.1.2 The Growing Threat of Fraud in the Digital Age
15.1.3 Introduction to AI in Fraud Detection
15.2 Technological Advancements in AI for Fraud Detection
15.2.1 Machine Learning
15.2.2 Big Data
15.2.3 Natural Learning Processing (NLP)
15.2.4 Behavioral Analytics
15.2.5 Blockchain
15.3 Security Measures in AI-Driven Fraud Detection
15.3.1 Data Encryption and Tokenization
15.3.2 Multi-Factor Authentication (MFA) and Biometrics
15.3.3 Real-Time Transaction Monitoring
15.3.4 Explainability and Transparency in AI Models
15.4 Sustainability in AI-Driven Fraud Detection
15.4.1 What is Sustainable AI?
15.4.2 Energy Efficiency in AI Models
15.4.3 Reducing Carbon Footprints of AI Technologies
15.4.4 Impact Assessment of AI on Environmental Sustainability
15.5 Case Studies: AI in Fraud Detection in Finance
15.5.1 Case Study I: AI in Credit Card Fraud Detection
15.5.2 Case Study II: Global Investment Bank Reduces Payment Fraud
15.6 Challenges and Future Directions
15.6.1 Challenges in AI-Driven Fraud Detection
15.6.2 Future Trends in AI and Fraud Detection
15.7 Conclusion
Bibliography
Index
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Preface
Part I: Foundations of Sustainable AI and Probabilistic Machine Learning
1. Introduction to Sustainable AI and Stochastic AI
Shelly Gupta and Kumud Alok
1.1 Introduction
1.2 Probabilistic Machine Learning: What and Why
1.3 Probabilistic Machine Learning Methods
1.4 Sustainable Stochastic AI: A New Frontier
1.5 Discussion and Conclusion
References
2. Navigating Uncertainty: Probabilistic ML across Diverse
Domains
Sanket Ramakant Lodha and Durgesh Srivastava
2.1 Introduction
2.2 Foundations of Probabilistic Machine Learning
2.2.1 Principles of Probability in Machine Learning
2.2.2 Important Concepts: Bayesian Inference and Quantifying Uncertainty
2.2.2.1 Bayesian Inference Process
2.2.2.2 Quantifying Uncertainty
2.2.3 Probabilistic Models
2.2.3.1 Gaussian Processes (GPs)
2.2.3.2 Hidden Markov Models (HMMs)
2.2.3.3 Bayesian Neural Networks (BNNs)
2.2.3.4 Other Notable Models
2.2.4 Advantages Over Deterministic Approaches
2.3 Methodologies in Probabilistic Machine Learning
2.3.1 Bayesian Networks and Applications
2.3.1.1 Components of Bayesian Networks
2.3.1.2 Advantages
2.3.1.3 Applications
2.3.2 Variational Inference and Sampling Techniques
2.3.2.1 Steps in Variational Inference
2.3.2.2 Markov Chain Monte Carlo (MCMC)
2.3.3 Probabilistic Programming Frameworks
2.3.3.1 Popular Frameworks
2.3.3.2 Benefits of Probabilistic Programming
2.3.4 Monte Carlo Methods: Importance and Applications
2.3.4.1 Importance
2.3.4.2 Applications
2.3.4.3 Challenges and Mitigations
2.4 Applications across Diverse Domains
2.4.1 Medicine: Personalized Diagnosis and Healthcare
2.4.1.1 Diagnosis of Disease
2.4.1.2 Personalized Medicine
2.4.1.3 Predictive Analytics in Epidemics
2.4.2 Finance: Risk Modeling and Fraud Detection
2.4.2.1 Risk Assessment and Portfolio Optimization
2.4.2.2 Fraud Detection
2.4.2.3 Algorithmic Trading
2.4.3 Autonomous Systems: Perception and Control
2.4.3.1 Perception
2.4.3.2 Decision-Making and Planning
2.4.3.3 Control Systems
2.4.4 Climate Modeling: Forecasting and Policy Formulation
2.4.4.1 Weather Forecasting
2.4.4.2 Climate Change Projections
2.4.4.3 Disaster Preparedness
2.5 Cross-Disciplinary Advances in Probabilistic Machine Learning
2.5.1 Bioinformatics and Genomics
2.5.1.1 Gene Expression Analysis
2.5.1.2 Protein Structure Prediction
2.5.1.3 Evolutionary Analysis
2.5.2 Social Sciences and Behavioral Analysis
2.5.2.1 Opinion Dynamics and Sentiment Analysis
2.5.2.2 Network Analysis
2.5.2.3 Behavioral Prediction
2.5.3 Natural Language Processing (NLP) and Linguistics
2.5.3.1 Language Modeling
2.5.3.2 Speech Recognition
2.5.3.3 Sentiment and Context Understanding
2.5.4 Quantum Computing and Probabilistic ML
2.5.4.1 Quantum Probabilistic Models
2.5.4.2 Quantum Sampling
2.5.4.3 Hybrid Quantum-Classical Models
2.5.5 Art and Creativity
2.5.5.1 Generative Models for Art
2.5.5.2 Music Composition
2.5.5.3 Storytelling and Content Generation
2.6 Challenges and Future Directions in Probabilistic Machine
Learning
2.6.1 Challenges in Probabilistic ML
2.6.1.1 Scalability and Computational Complexity
2.6.1.2 Interpretability vs. Complexity
2.6.1.3 Data Quality and Availability
2.6.1.4 Lack of Standardization
2.6.1.5 Integration with Emerging Technologies
2.6.2 Future Directions in Probabilistic ML
2.6.2.1 Improving Scalable Inference Methods
2.6.2.2 Hybrid Models
2.6.2.3 Improved Interpretability
2.6.2.4 Noisy and Biased Data Robustness
2.6.2.5 Integration with Edge and Quantum Computing
2.6.2.6 Automated Probabilistic ML
2.6.2.7 Ethical and Fair Probabilistic Modeling
2.6.3 Long-Term Vision
2.7 Conclusion
2.7.1 Key Takeaways
2.7.2 Broader Implications
2.7.3 Vision for the Future
References
3. Iris Image Recognition through Goggles Using CNN and Mathematical Models in Probabilistic ML
Ridhima Chopra, Subhadeep Mal and Rajini G.K.
3.1 Introduction
3.2 Related Work
3.3 Methodology
3.4 Pre-Processing
3.5 Iris Localization
3.5.1 Mathematical Model Approach
3.5.2 CNN Approach
3.6 Iris Segmentation
3.7 Iris Normalization
3.8 Feature Extraction
3.8.1 Discrete Wavelet Transform + Principal Component Analysis
3.8.2 Local Binary Pattern
3.8.3 Gabor Filter
3.9 Feature Matching
3.10 Tackling Specular Reflections
3.11 Experimental Results
3.12 Future Scope
3.13 Conclusion
References
Part II: Applications of Probabilistic ML in Key Industries
4. Energy Consumption Optimization Using Deep Learning in Probabilistic ML
Monika Sharma, Dimple Tiwari and Sayani Ghosal
4.1 Introduction
4.2 Motivation
4.3 Reasons for Energy Consumption
4.4 Current Challenges for Consumption of Energy
4.5 Applications of Energy Consumption
4.6 Related Work
4.7 Primary Principle of Deep Learning
4.8 Overview of Deep Learning
4.9 Types of Deep Learning Models Used in Energy Consumption
4.10 Key Applications of Deep Learning in Energy Consumption
4.11 Data Sources for Deep Learning Models
4.12 Data Pre-Processing and Feature Engineering
4.13 Training Deep Learning Models
4.14 Techniques and Algorithms for Online Energy Consumption
Models
4.15 Deep Learning Model Design and Implementation
4.16 Energy Consumption Training Model
4.17 Future Directions and Enhancements
4.18 Conclusion
References
5. Influence of Deep Learning Models on Energy Efficiency
in Sustainable AI
Shagun Gupta, Aakash and Shalini
5.1 Introduction
5.1.1 Deep Learning
5.1.2 Energy Efficiency’s Significance in the Context of Sustainability
5.1.3 What Will Be Elucidated in This Chapter
5.2 Related Works
5.3 Deep Learning Approaches for Energy Optimization
5.3.1 CNN (Convolutional Neural Networks)
5.3.2 RNN (Recurrent Neural Networks)
5.3.3 LSTM (Long Short-Term Memory)
5.3.4 GRU (Gated Recurrent Units)
5.3.5 Autoencoders (AE)
5.3.6 Performance Metrics in DL
5.3.7 Analysis of Deep Learning Approaches for Energy Efficiency in Sustainable AI
5.4 Deep Learning’s Role in Achieving Energy Efficiency
5.4.1 Predictive Modeling for Energy Consumption
5.4.2 Intelligent Building Control Systems
5.4.3 Forecasting and Integrating Renewable Energy
5.4.4 Deep Learning: A Catalyst for SDG
5.4.5 Deep Learning-Based Energy Management Systems for Smart Buildings
5.5 Challenges in Implementing Deep Learning for Sustainable AI
5.6 Conclusion and Future Work
Abbreviations
References
6. Reliable Solutions Using Probabilistic ML and Sustainable AI in Healthcare Applications
Snigdha Bhardwaj, Kandasamy Nagarajan and Tulika Bhardwaj
6.1 Introduction
6.1.1 History of AI in Healthcare
6.1.2 Scope
6.2 Landscape Assessment of AI and Big Data Analytics Unfolding in Health Care
6.3 Interplay of AI-ML for Aligning Biomedical Science and Precision Medicine
6.3.1 Importance and Benefits
6.4 Significant Applications of AI-ML in Healthcare
6.4.1 Clinical Medicines
6.4.2 Medical Diagnosis
6.4.3 Drug Discovery
6.4.4 Patient’s Experience
6.4.5 Healthcare Data Management
6.4.6 Robotic Surgery
6.5 Challenges of Machine Learning in Clinical Applications
6.6 Regulatory Challenges in Governing Artificial Intelligence
in Healthcare Sector
6.7 Future Scope
6.8 Conclusion
References
7. Exploring Probabilistic Machine Learning in Healthcare
with Heart Disease Prediction
Noor Mohammad, Sayani Ghosal and Korhan Cengiz
7.1 Introduction
7.2 Probabilistic Approach in Machine Learning
7.3 Background
7.4 Implementation: Logistic Regression
7.5 Conclusion
References
8. Recent Advances and Smart Farming in Digital Agriculture
Using Probabilistic ML and Sustainable AI
Veena Parihar, Bhawna, Richa Singh and Jabar H. Yousif
8.1 Introduction
8.2 Latest Advancements in Digital Agriculture
8.3 Market of AI in Agriculture
8.4 Related Work in the Field of Digital Agriculture
8.5 Probabilistic ML
8.5.1 Types of Probabilistic ML Models
8.5.2 Implementation of Probabilistic ML Model for Yield Prediction
8.6 Current Challenges and Future Directions
8.7 Conclusion
References
9. IoT-Driven Resource Allocation in Smart Cities Using Probabilistic Models
Vedprakash Maralapalle, Jayatheja Muktinutalapati, Abdullah Ansari and A. Chithambar Ganesh
9.1 Introduction to Smart Cities and the Role of IoT
9.1.1 IoT Applications in Smart Cities
9.2 Challenges in Resource Allocation for Smart Cities
9.2.1 Handling Big Data from IoT Devices
9.2.2 Challenges in Handling Big Data from IoT Devices
9.2.3 Leveraging Big Data for Resource Allocation
9.2.4 Challenges to Real-Time Responsiveness
9.2.5 Balancing Competing Objectives
9.2.6 Competing Objectives in Smart Cities
9.2.7 Role of IoT and Probabilistic Models
9.3 Probabilistic Data Structures for Smart City Data Management
9.4 Bloom Filters
9.4.1 Background
9.4.2 Bloom Filters Offer Several Advantages for Resource
Allocation and Data Management in Smart Cities
9.4.3 Challenges of Bloom Filters
9.4.4 Sketches
9.4.5 Applications in Smart Cities
9.4.6 Advantages of Using Sketches
9.4.7 Applications in Smart City Resource Allocation
9.5 Machine Learning Algorithms for Resource Allocation
9.5.1 Various Domains within Smart Cities to Enhance Resource Allocation
9.5.2 The Potential of Machine Learning in Resource Allocation is Significant, But Several Challenges Must Be Addressed
9.5.3 Reinforcement Learning
9.5.4 Multi-Agent Systems
9.5.5 Applications of MAS in Smart Cities
9.5.6 Advantages of MAS for Resource Allocation
9.5.7 Optimization Techniques
9.5.8 Various Optimization Algorithms
9.6 Proposed Framework for Resource Allocation in Smart Cities
9.6.1 Integration of PDS and Machine Learning
9.6.2 Synergy between PDS and ML
9.6.3 Dynamic Resource Allocation Strategies
9.6.4 Role of IoT and Probabilistic Models
9.6.5 Case Studies and Simulations
9.6.6 Simulations
9.7 Practical Considerations and Barriers to Adoption
9.7.1 Scalability and Reliability
9.7.2 Importance of Reliability
9.7.3 Challenges to Scalability and Reliability
9.8 Conclusion and Future Directions
9.8.1 Future Directions
References
10. Resource Allocation in Smart Cities Using IoT and Probabilistic Models
Disha Sharma, Sandeep Singh Kang and Vidhushi Gupta
10.1 Introduction
10.2 Challenges in Resource Allocation
10.3 IoT Architecture in Smart Cities
10.3.1 Traffic Sensors
10.3.2 Environmental Sensors
10.3.3 Smart Meters
10.3.4 Actuators
10.3.5 Smart Traffic Lights
10.3.6 Smart Streetlights
10.3.7 Water Valves
10.3.7.1 Communication Networks
10.3.7.2 Wireless Networks
10.3.7.3 Cellular (4G/5G)
10.3.7.4 LoRa (Long Range) and NB-IoT (Narrowband IoT)
10.3.7.5 Bluetooth and Zigbee
10.3.7.6 Ethernet
10.3.7.7 Protocols
10.3.7.8 MQTT (Message Queuing Telemetry Transport)
10.3.7.9 CoAP (Constrained Application Protocol)
10.3.7.10 IoT Edge Devices and Gateways
10.3.7.11 Smart Traffic Management
10.3.7.12 Cloud Platforms
10.3.7.13 Big Data Analytics
10.3.7.14 Real-Time Processing of Data
10.3.7.15 Predictive Analytics is Used for Strategic Planning
10.3.7.16 AI and Machine Learning
10.3.7.17 APIs and Integration Platforms
10.3.8 Data Collection and Processing in IoT Systems
10.4 Probabilistic Model for Resource Allocation
10.4.1 Bayesian Networks
10.4.2 Markov Chains
10.4.3 Stochastic Processes
10.4.4 Application of Probabilistic Model in IoT
10.5 Resource Allocation Technologies
10.6 Integration of IoT and Probabilistic Models
10.6.1 Data Collection Layer
10.6.2 Data Processing and Storage Layer
10.6.3 Probabilistic Model Layer
10.6.4 Decision-Making and Actuation Layer
10.6.5 Algorithm Design
10.6.6 Scalability and Real-Time Processing
10.7 Challenges Faced in Resource Allocation in Smart Cities
Using IoT and Probabilistic Models
10.7.1 Scalability
10.7.2 Latency in the Network
10.7.3 Data Integration
10.7.4 Data Volume
10.7.5 Data Integrity and Accuracy
10.7.6 Data Processing
10.7.7 Power management for IoT devices
10.7.8 Energy Balance
10.7.9 Cybersecurity Risks
10.7.10 Data Privacy Issues
10.7.11 DDoS attacks
10.7.12 Model Accuracy
10.7.13 Equity and Bias
10.7.14 Overfitting Historical Data
10.7.15 Future Directions in “Resource Allocation for Smart
Cities Using IoT and Probabilistic Models”
10.7.16 Advanced IoT Technologies for Resource Allocation
10.8 Future Scope
10.9 Conclusion
References
11. Probabilistic Machine Learning and Large Language Models to Address Social Issues and Their Solutions
Shweta Thakur
11.1 Introduction
11.1.1 Application Areas
11.2 Fundamentals of Probabilistic Machine Learning
11.2.1 Basics Concepts and Techniques in Probabilistic Machine Learning
11.2.2 Types of Probabilistic Models
11.2.3 Advantages of Using Probabilistic Approaches in Social Issue Analysis
11.3 Large Language Models: An Overview
11.3.1 Evolution and Development of LLMs
11.3.2 Key Large Language Model (LLM) Architectures
11.3.3 Capabilities and Limitations of LLMs in Social Contexts
11.4 Related Work
11.5 Methodology
11.6 Challenges and Ethical Considerations
11.7 Future Directions
11.8 Conclusion
Bibliography
Part III: Advanced Innovations and Ethical Considerations
12. Enhancement of Multimodal Dialog System Via Knowledge Graph with OpenAI GPT-4 Model in Sustainable AI
Richa Singh, Gaurav Srivastav and Korhan Cengiz
12.1 Introduction
12.1.1 Vector Database
12.1.2 Knowledge Graph
12.2 Literature Survey
12.2.1 Forecasting Multimodal Links
12.2.2 Extraction of Multimodal Relations
12.2.3 Named Entity Recognition Using Multimodal Approach
12.2.4 Multimodal Pre-Trained Representation
12.2.5 Multimodal Knowledge Graphs
12.3 Problem Statement
12.4 Proposed Approach
12.4.1 Ontology of Knowledge Graphs
12.4.2 Text2Graph
12.4.3 Picture2Graph
12.4.4 Code2Graph
12.4.5 Aligning Knowledge Graphs
12.5 Result and Discussion
12.6 Conclusion and Future Work
References
13. Impact of Probabilistic Graphical Model Approaches for Environmental Analysis
Vishal Jain and Archan Mitra
13.1 Introduction
13.1.1 Background of the Study
13.1.2 Problem Statement
13.1.3 Research Objective
13.1.4 Research Questions
13.2 Literature Review
13.2.1 Literature Survey
13.2.1.1 Overview of Probabilistic Graphical Models (PGMs)
13.2.1.2 Previous Studies Using PGMs in Various Fields, with a Focus on Environmental Applications
13.2.1.3 Existing Methods in Environmental Analysis
13.2.1.4 Studies Demonstrating the Limitations of Non-Probabilistic Approaches
13.2.1.5 Applications of PGMs in Environmental Science
13.2.1.6 Gaps in Current Research
13.3 Methodology
13.4 Discussion and Findings
13.4.1 Discussion of Results
13.4.1.1 Accuracy of PGMs in Predicting Environmental Phenomena
13.4.1.2 Applications of PGMs in Simulating Environmental Data
13.4.1.3 Handling Uncertainties in Environmental Datasets
13.4.2 Key Findings
13.4.3 Limitations of the Study
13.4.4 Future Scope
13.5 Conclusion
References
14. Optimizing Fault Prediction in AI Software Systems for Enhanced Economic Sustainability
J. Ramkumar and D. R. Medhunhashini
14.1 Introduction to Fault Prediction in AI Software Systems
14.1.1 Overview of Fault Prediction
14.1.2 Significance in AI Software Systems
14.1.3 Historical Context and Evolution
14.2 Economic Sustainability in AI Development
14.2.1 Defining Economic Sustainability
14.2.2 Role of AI in Economic Efficiency
14.2.3 Impact of Fault Prediction on Cost Savings
14.2.4 Energy Efficiency in Fault Prediction Systems
14.2.5 Future Directions and Trends
14.2.6 Emerging Technologies in Fault Prediction
14.2.7 Ethical and Regulatory Aspects of Fault Prediction
14.2.8 AI Fault Prediction in Smart Cities and IoT
14.3 Techniques for Fault Prediction
14.3.1 Statistical Methods
14.3.2 Machine Learning Approaches
14.3.3 Hybrid Techniques
14.4 Advanced Machine Learning Algorithms for Fault Prediction
14.4.1 Ensemble Methods for Enhanced Accuracy
14.4.2 Neural Networks and Deep Learning for Complex Patterns
14.4.3 Reinforcement Learning for Adaptive Fault Prediction
14.5 Real-Time Monitoring and Predictive Maintenance
14.5.1 Importance of Real-Time Monitoring
14.5.2 Tools for Real-Time Fault Detection
14.5.3 Strategies for Predictive Maintenance
14.6 Evaluating the Economic Impact of Fault Prediction
14.6.1 Metrics for Economic Evaluation
14.6.2 Case Studies on Cost Reduction
14.7 Conclusion
References
15. Security Measures for Fraud Detection Using Sustainable AI in the Finance Sector
Sneha Srijaya and Aarti Goel
15.1 Introduction
15.1.1 Impact on Businesses, Consumers, and the Global Economy
15.1.2 The Growing Threat of Fraud in the Digital Age
15.1.3 Introduction to AI in Fraud Detection
15.2 Technological Advancements in AI for Fraud Detection
15.2.1 Machine Learning
15.2.2 Big Data
15.2.3 Natural Learning Processing (NLP)
15.2.4 Behavioral Analytics
15.2.5 Blockchain
15.3 Security Measures in AI-Driven Fraud Detection
15.3.1 Data Encryption and Tokenization
15.3.2 Multi-Factor Authentication (MFA) and Biometrics
15.3.3 Real-Time Transaction Monitoring
15.3.4 Explainability and Transparency in AI Models
15.4 Sustainability in AI-Driven Fraud Detection
15.4.1 What is Sustainable AI?
15.4.2 Energy Efficiency in AI Models
15.4.3 Reducing Carbon Footprints of AI Technologies
15.4.4 Impact Assessment of AI on Environmental Sustainability
15.5 Case Studies: AI in Fraud Detection in Finance
15.5.1 Case Study I: AI in Credit Card Fraud Detection
15.5.2 Case Study II: Global Investment Bank Reduces Payment Fraud
15.6 Challenges and Future Directions
15.6.1 Challenges in AI-Driven Fraud Detection
15.6.2 Future Trends in AI and Fraud Detection
15.7 Conclusion
Bibliography
Index
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