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Driving Innovation by Dynamic Optimization

The Challenges of Reshaping Industry

Edited by Ranjan Kumar, Sachi Nandan Mohanty, Arindam Dey, T. Rajasanthosh Kumar
Copyright: 2025   |   Expected Pub Date:2025/10/30
ISBN: 9781394315697  |  Hardcover  |  
660 pages

One Line Description
Future-proof your technical expertise by mastering the multidisciplinary synergy of AI-driven optimization and intelligent algorithms with this essential resource to optimization for the modern world.

Audience
Researchers, academics, industry experts, scientists, and technologists looking for insights into advances across optimization theory, machine learning, deep learning, cybersecurity, healthcare analytics, energy systems, and intelligent decision-making.

Description
The rapid evolution of artificial intelligence, machine learning, and data-driven optimization has transformed the landscape of scientific research and industrial innovation. This book presents a diverse and multidisciplinary collection of contemporary studies that advance theoretical foundations, computational techniques, and application-driven solutions across multiple domains to reflect the growing importance of optimization, intelligent systems, and hybrid analytical frameworks in solving complex real-world problems. This essential guide focuses on next-generation technologies such as AI-driven cybersecurity, dynamic optimization in 6G networks, fuzzy logic-based energy management, automated neural machine translation, and GAN-driven aerodynamic shape design, illustrating the synergy between intelligent algorithms and modern engineering systems. Whether you are a researcher, practitioner, or simply an enthusiast, this book will serve as an enlightening resource, providing valuable insights into the ever-evolving realm of optimization for the modern world.
Readers will find the volume:
• Provides comprehensive coverage of the backgrounds, foundations, and theoretical ideas of optimization methods and their potential applications;
• Introduces applications of fuzzy sets for solving optimization problems in real-life scenarios;
• Explores the transformative potential of fuzzy sets for optimization across several domains like healthcare, finance, and education.

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Author / Editor Details
Arindam Dey, PhD is an Associate Professor in the School of Computer Science at the Vellore Institute of Technology, Tamil Nadu, India, with more than 14 years of teaching and research experience. He has published more than 50 research articles in national and international peer-reviewed journals. His research focuses on fuzzy optimization and genetic algorithms.

Sachi Nandan Mohanty, PhD is a Professor in the School of Computer Science and Engineering at the Vellore Institute of Technology, Tamil Nadu, India. He has authored and edited 42 books and published more than 120 articles in international journals of repute. His research interests include data mining, big data analysis, cognitive science, fuzzy decision-making, brain-computer interface, cognition, and computational intelligence.

Ranjan Kumar, PhD is an Assistant Professor at MLSM College, Darbhanga, Bihar, India, with more than ten years of experience in research, teaching, and industry. He is a reputed academician with significant contributions as a reviewer, associate editor, book editor, and editorial board member for internationally acclaimed research journals. His research in mathematics focuses on fuzzy optimization.

T. Rajasanthosh Kumar, PhD is an Associate Professor at Puducherry Technological University, Puducherry, India. He has published 35 articles in internationals journals and conferences and two textbooks and has 25 granted patents to his credit. His research focuses on mechanical design and manufacturing.

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Table of Contents
Preface
Part 1: Transforming Data Science and Machine Learning through Dynamic Optimization
1. Customized K-Means Clustering‑Based Color Image Segmentation
Babitha Lokula, V. Uma Shankar, L. Sushanth Gagan and N. Sudarshan
1.1 Introduction
1.2 Literature Survey
1.3 Existing Method
1.4 Problem Defined
1.5 Proposed Methodology
1.6 Result
1.6.1 Comparison between K-Means and Normalized Cut Clustering Using Ground Truth Segmentation
1.6.2 Tabular Columns
1.7 Conclusion
References
2. Optimizing Financial Forecasts and Integrating Utility Mining with Machine and Deep Learning for Stock Market Price Prediction
Venkatram Vennam, Ch Ramesh Babu and Amjan Shaik
2.1 Introduction
2.2 Related Work
2.3 Enhancing Deep and Stochastic Learning for Stock Market Price Prediction
2.3.1 Deep Learning Model Structure
2.3.2 Stochastic Model Integration
2.3.3 Combining Deep Learning with Stochastic Models
2.3.4 Error Correction in Predictions
2.3.5 Training Loss Function
2.4 Performance Evaluation
2.5 Experimental Results
2.6 Conclusion
References
3. Enhancing OCR Adaptability in Multimodal Environments: Challenges, Opportunities, and Insights
Praveen Kumar Nelapati, Arindam Dey, Avi Das and Likitha Chowdary Botta
3.1 Introduction
3.2 Literature Review
3.2.1 Traditional OCR Algorithms
3.2.2 Machine Learning-Based Approaches
3.2.3 Deep Learning-Based OCR Models
3.2.4 Hybrid OCR Systems
3.3 Classification of OCR Technologies
3.3.1 Classification Based on Algorithms
3.3.2 Classification Based on Techniques
3.3.3 Classification Based on Applications
3.4 Application of OCR Technologies
3.4.1 Document Digitization and Archiving
3.4.2 Accessibility Enhancement
3.4.3 Automated Data Entry in Business Environments
3.4.4 Text Analytics and Multimodal Integration
3.4.5 Medical Records Processing
3.5 Comparison of OCR Technologies
3.5.1 Strengths of OCR Technologies
3.5.2 Weaknesses and Challenges
3.5.3 Considerations in Selecting OCR Solutions
3.6 Conclusion
References
4. Text Generation, Classification, and Optimization Using Tokenization in NLP
Kuldeep Vayadande, Yogesh Bodhe, Ajit Patil, Amolkumar N. Jadhav, Devdatta K. Mokashi, Swapnil C. Mane, Dipali Ashutosh Alone and Praveenkumar Arjun Patel
4.1 Introduction
4.2 Literature Survey
4.3 Methodology
4.4 Results
4.4.1 SA Model Output
4.4.2 Question Answering Model Output
4.5 Scope of Research
4.6 Conclusion
Bibliography
5. Optimizing Decision Trees: Exploring Pruning Techniques and the Impact of Ensemble Classifiers
Suraj B. Madagaonkar, Ramyashree, Ishaan Singh, Aakarshee Jain, Harshitha G. M., Girija Attigeri and Ramya D. Shetty
5.1 Introduction
5.1.1 Need for Pruning
5.1.2 Types of Pruning
5.1.2.1 Pre-Pruning and Post-Pruning
5.2 Need for Ensembling
5.2.1 Boosting Techniques
5.3 Experiment Design and Results
5.3.1 Pruning Versus Ensembling—A Perspective
5.3.2 Pruning Versus Ensembling—An Experiment
5.4 Results on the Effect of Pruning on Decision Trees
5.4.1 Results on the Effect of Ensembling on Decision Trees
5.4.2 Results on Ensembling Versus Pruning and Ensembling
5.5 Conclusion and Future Work
Bibliography
6. Optimized Neural Machine Translation: A Review of Automated French-Hindi and Hindi-French Translation Systems
Chandan Vishwas
6.1 Introduction
6.1.1 Evolution of Machine Translation
6.1.1.1 Different Types of Machine Translation
6.2 Hypothesis
6.3 Methodology
6.4 Literature Survey
6.5 The Test of Neural Machine Translation
6.5.1 Text from Economics Domain: Le Figaro
6.5.2 A Text from Hindi Newspaper: दैनिक भास्कर
6.5.3 Sample Literary Text from the Novel Aphrodite: Moeurs Antiques, Written by Pierre Louÿs
6.5.4 Sample Text from Science and Technology Domain
6.5.5 Sample Text: Idioms and Expressions
6.6 Discussion on the Above Machine Translations
6.7 Challenges in French-Hindi NMT
6.7.1 Data Scarcity
6.7.2 Linguistic Differences
6.7.3 Morphological Complexity
6.8 Approaches to Overcome Challenges
6.8.1 Learning
6.8.2 Multilingual NMT
6.8.3 Synthetic Data Generation
6.9 Future Directions
6.9.1 Improving Data Resources
6.9.2 Enhanced Contextual Understanding
6.9.3 Interactive and Adaptive Systems
6.10 Conclusion
Bibliography
7. Optimizing Assignment Solutions: Integrating R and Python for Enhanced Workforce Efficiency
Bijin Sanny P.R., Ankit Dubey, Miriyala Navya Pratyusha, Arindam Dey and Ranjan Kumar
7.1 Introduction
7.1.1 Assignment Problem Mathematical Formulation
7.2 Review of Previous Work
7.2.1 Flowchart
7.2.2 Main Objective
7.3 Application (Illustrate Example)
7.3.1 Algorithm in R
7.3.2 Algorithm in Python
7.4 Conclusion
References
8. Practical Optimization: Big M with Python and R
Bijin Sanny P.R., Ankit Dubey, Miriyala Navya Pratyusha, Arindam Dey and Ranjan Kumar
8.1 Introduction
8.1.1 Nature of the Big M Problem
8.2 Computational Code of the Big M Method in Different Environments
8.2.1 Code in R
8.2.2 Code in Python
8.2.3 Main Objective
8.3 Numerical Example
8.3.1 Example: Solve the LP Problem Using the Penalty Method
8.3.2 Computational Code Example in R Environment
8.3.3 Computational Code Example in Python Environment
8.4 Conclusion
References
9. Leveraging R and Python for Advanced Graphical Optimization Solutions
Bijin Sanny P. R., Ankit Dubey, Miriyala Navya Pratyusha, Arindam Dey and Ranjan Kumar
9.1 Introduction
9.1.1 Mathematical Formulation of the Graphical Problem
9.2 Review of Previous Work
9.2.1 Flowchart (Graphical Method)
9.2.2 Main Objective
9.3 Application (Illustrate Example)
9.3.1 Algorithm in R
9.3.2 Algorithm in Python
9.4 Conclusion
References
10. Linear Optimization: Simplex Method in Python and R
Bijin Sanny P. R., Ankit Dubey, Miriyala Navya Pratyusha, Arindam Dey and Ranjan Kumar
10.1 Introduction
10.1.1 Nature of Simplex Problems
10.1.2 Review of Previous Work
10.2 Code in R and Python and Flowchart for SM
10.2.1 Flowchart
10.2.2 Main Objective
10.3 Numerical Example
10.3.1 Example
10.3.2 Computational Code Example in R Environment
10.3.3 Computational Code Example in Python Environment
10.4 Conclusion
References
Part 2: Revolutionizing Healthcare with AI-Driven Optimization
11. Efficient Transfer Learning Methods for MIA: An Optimization Perspective
Ch. V. Bhargavi and K. Subhadra
11.1 Introduction
11.2 Research Problem and Objectives
11.3 Background
11.4 Transfer Learning Model
11.4.1 Transfer Learning Methods in MIA
11.5 Pretrained DL Architectures
11.5.1 VGG (Visual Geometry Group)
11.5.2 ResNet (Residual Network)
11.5.3 Inception (GoogLeNet)
11.5.4 DenseNet (Densely Connected Convolutional Networks)
11.5.5 MobileNet
11.5.6 EfficientNet
11.5.7 Xception (Extreme Inception)
11.6 Comparative Analysis
11.6.1 Data Selection
11.6.2 Performance Metrics
11.6.3 Pretrained Architectures
11.7 Future Research Directions
11.8 Conclusions
References
12. Optimized Deep Learning Approach for Alzheimer’s Prediction Using CNN on MRI Images
G. Deepika, M. Vazralu, Patil Meenakshi and D. Mounika
12.1 Introduction
12.1.1 Alzheimer’s Disease
12.1.2 Methodology
12.2 Literature Survey
12.3 System Analysis
12.3.1 Existing System
12.3.2 Proposed System
12.4 System Architecture
12.4.1 Brain MRI Input Image
12.5 Algorithm
12.5.1 Convolutional Neural Network Algorithm
12.6 The Experimental Procedure
12.7 Results
12.8 Conclusion
12.9 Future Work
Bibliography
13. Precision Care: Exposing Machine Learning-Based Optimization Methods in Epileptic Seizure Diseases
Sunkara Mounika and Reeja S. R.
13.1 Introduction
13.2 Primer on EEG: Basics and Beyond
13.2.1 Modernizing Seizure Prediction: Advancing EEG Methodologies
13.3 Artificial Intelligence in Epilepsy Care
13.4 Task-Driven ML Optimizations
13.4.1 Flower Pollination Algorithm
13.4.2 Moth-Flame Optimization
13.4.3 Bat Algorithm
13.4.4 Firefly Algorithm
13.4.5 Cuckoo Search
13.5 Conclusion
References
14. Optimized Deep Learning Models to Identify Skin Malignancy through Skin Lesion Images
Shaik Reshma and Reeja S.R.
14.1 Introduction
14.1.1 Biology of Skin
14.1.2 Skin Diseases
14.1.3 Skin Cancer Forms and Incidence
14.1.4 Global Skin Cancer Statistics
14.1.5 Diagnosing Methodology
14.1.5.1 Medical Examinations
14.1.5.2 Screening Methods
14.2 Optimization
14.2.1 Metaheuristic Optimizer
14.2.2 Teaching-Learning-Based Optimizer
14.2.3 Grey Wolf Optimizer
14.2.4 Dragonfly Algorithm
14.2.5 Wildebeest Herd Optimizer
14.3 Methodology
14.3.1 Preprocessing
14.3.2 Deep Learning Models
14.3.2.1 ResNet50
14.3.2.2 GoogLeNet
14.3.3 Optimization of DL Models
14.4 Experimental Results
14.4.1 Database
14.4.2 Performance Metrics
14.4.3 Model Evolution
14.5 Conclusion
References
15. Hybrid Optimization Techniques for Ayurvedic Medicine Recommendation: Bridging Tradition and Technology
Kuldeep Vayadande, Yogesh Bodhe, Ajit Patil, Amolkumar N. Jadhav, Devdatta K. Mokashi, Swapnil C. Mane, Radhika Mahesh Mane and Renuka Vinayak Jadhav
15.1 Introduction
15.2 Literature Survey
15.3 Methodology
15.4 Proposed System
15.5 Results and Discussion
15.6 Scope of Research
15.7 Future Scope
15.8 Conclusion
Bibliography
16. Entropy Optimization in Casson Tri‑Mixed Nanofluid Flow on a Curved Sheet Utilizing the Cattaneo-Christov Model: Biomedical Applications
K. Sakkaravarthi, P. Bala Anki Reddy, N.N.V. Sakuntala and Vangapelli Nagaraju
16.1 Introduction
16.1.1 Mathematical Formulation
16.1.2 Examination of Entropy
16.1.3 Examination of Entropy
16.1.4 Examination of Bejan
16.2 Methodology of Numerical Procedure
16.3 Results and Discussion
16.4 Profile of Velocity
16.5 Profile of Temperature
16.6 Entropy Generation
16.7 Result Analysis of Physical Quantities
16.8 Conclusion
Nomenclature
References
Part 3: Engineering the Future: Optimization in Technology and Smart Systems
17. Optimized Performance of Grid-Integrated Hybrid Energy Systems Using Fuzzy Logic-Based MPPT
K. Vijaya Bhaskar Reddy, A. Murali, Srinivasa Rao Balasani, P. Venkata Kishore, Golla Naresh Kumar and D. Sri Varasidhi Vinay
17.1 Introduction
17.2 Existing System
17.3 Proposed System Architecture
17.4 Power Generation Using WECS
17.4.1 Wind Turbine Demonstrating
17.4.2 Solar Pv Module
17.4.3 Fuel Cell
17.4.4 Buck-Boost Converter
17.4.5 DC-AC Converter (Inverter)
17.5 Proposed MPPT Technique
17.6 Simulation and Results
17.6.1 Wind Energy Conversion Scheme
17.6.2 PV Scheme
17.6.3 Fuel Cell Scheme
17.6.4 Hybrid Scheme
17.7 Conclusion
Bibliography
18. Dynamic Optimization of 6G Networks with AI-Driven SDN Approaches
Rohit Kumar Das and Monali Bordoloi
18.1 Introduction
18.2 SDN in 6G Networks
18.2.1 Evolution of Software-Defined Networking
18.2.2 Importance of SDN in Network Management
18.3 Challenges in 6G Network Management
18.4 AI Integration in SDN for 6G Networks
18.4.1 Role of AI in Network Optimization
18.4.2 Optimization of Autonomous Decision-Making
18.4.3 6G Opportunities in Transforming Applications and Services
18.5 Literature Review
18.6 Conclusion
References
19. Efficient EV Journeys: Balancing Route Optimization and Power Management
Simran Sahoo, Meenakshi Kandpal, Shivani Agarwal, Jyotirmayee Rautaray, Pranati Mishra and Manjit Patra
19.1 Introduction
19.1.1 Objectives
19.1.2 Contributions
19.1.3 Organization
19.2 Literature Review
19.3 Proposed Framework
19.4 Model Architecture Information
19.4.1 Dataset
19.4.1.1 Bhubaneswar Charging Stations
19.4.1.2 EV Arrival and Departure Times
19.4.1.3 EV Charging Station Finder
19.4.1.4 Dijkstra’s Algorithm
19.4.1.5 NSGA-III
19.4.1.6 NSGA-II
19.4.1.7 Key Features of NSGA-II
19.5 Result and Discussion
19.6 Comparison and Analysis
19.7 Conclusion
Acknowledgments
References
20. Optimization Techniques in AI-Driven Cybersecurity: Enhancing IoT and Social Media Security Frameworks
Kuldeep Vayadande, Yogesh Bodhe, Ajit Patil, Amolkumar N. Jadhav, Devdatta K. Mokashi, Swapnil C. Mane, Vijay Chougule and Rajkumar Kundlik Chougale
20.1 Introduction
20.2 Literature Survey
20.3 Results
20.4 Discussions
20.5 Research Gap Identified
20.6 Conclusion
References
21. Efficient Book Genre Classification Using NLP and Optimization
Kuldeep Vayadande, Yogesh Bodhe, Ajit Patil, Amolkumar N. Jadhav, Devdatta K. Mokashi, Swapnil C. Mane, Praveenkumar Arjun Patel and Rajkumar Kundlik Chougale
21.1 Introduction
21.2 Literature Surey
21.3 Methodology
21.4 Proposed System
21.5 Table of Analysis
21.6 Results
21.7 Scope of Research
21.8 Future Scope
21.9 Conclusion
References
22. A Data-Driven and Optimized Approach to Umpiring: Integrating AI and Machine Learning for Better Decision-Making
Aditya Guntupalli, Aravind Kumar Muddana, Karthika Thota, Kowshik Eswara Chaitanya Venigalla, Phani Kumar Turlapati, Siddhartha Paturi, Sravya Kusam
and Mukkoti Maruthi Venkata Chalapathi
22.1 Introduction
22.2 Literature Review
22.2.1 Automated Decision-Making in Cricket
22.2.2 RNNs in Sports Analytics
22.2.3 Challenges in Cricket Ball Movement Detection
22.2.4 Contextual Information Integration
22.2.5 Real-Time Processing Challenges
22.3 Methodology
22.3.1 Data Acquisition and Preprocessing
22.3.2 Feature Extraction
22.3.3 Model Architecture
22.3.4 Classification
22.3.5 Model Training and Evaluation
22.4 Main Body
22.4.1 Optimization Strategies for Algorithm
22.4.2 Proposed Approach with RNNs and Attention
22.5 Limitations and Advantages
22.5.1 Limitations
22.5.2 Advantages
22.6 Conclusion
References
23. Automated Aerodynamic Shape Optimization through GAN-Driven 3D Model Generation
Matta Sai Kiran Goud, Anila Macharla, G. Kiran Kumar, Karthik Alluri and S. China Ramu
23.1 Introduction
23.2 Related Work
23.2.1 Shape Generation
23.2.2 Shape Optimization
23.3 State-of-the Art Models
23.3.1 3D Model Generation Using Differentiable Rendering
23.3.2 Aerodynamic Shape Optimization Based on Discrete Adjoint and RBF
23.3.3 Automated Aerodynamic Vehicle Shape Optimization Using Neural Networks
and Evolutionary Optimization
23.3.4 Automation of Vehicle Aerodynamic Shape Exploration and Optimization Using Integrated Mesh Morphing and CFD
23.3.5 Machine Learning in Aerodynamic Shape Optimization
23.3.6 Multi-Fidelity Deep Neural Network Surrogate Model for Aerodynamic Shape Optimization
23.4 Results and Discussions
23.5 Conclusion
Bibliography
Part 4: Sustainable Growth: Dynamic Optimization in Agriculture, Environment, and Industry
24. Study of Optimization Techniques in Agriculture
Preethi Nanjundan, Indu P.V. and Lijo Thomas
24.1 Introduction
24.1.1 Optimization Techniques
24.1.2 Linear Programming and Nonlinear Programming
24.1.3 Genetic Algorithms and Machine Learning
24.2 Applications
24.2.1 Crop Planning and Irrigation Management
24.2.2 Pest Control
24.3 Case Studies
24.3.1 Crop Rotation
24.3.2 Precision Agriculture
24.4 Challenges and Future Directions
24.4.1 Data Quality
24.4.2 Computational Complexity
24.4.3 IoT Integration
24.4.4 AI Advancements
24.5 Conclusion
References
25. An Optimization-Based Prediction Model for Agricultural Soil Health Using Stacking Ensemble Approach
Amjan Shaik, Vidya Rajasekaran, N. Arul and P. Deepan
25.1 Introduction
25.2 Literature Survey
25.3 Proposed Methodology
25.4 Stacking Ensemble Model
25.5 Optimization Approach
25.6 Performance Evaluation
25.7 Results and Discussion
25.8 Conclusion
References

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