Driving Innovation by Dynamic Optimization : The Challenges of Reshaping Industry.

Format:

Book

Author/Creator:

Dey, Arindam.

Language:

English

Subjects (All):

Mathematical optimization--Technological innovations.

Mathematical optimization.

Physical Description:

1 online resource (662 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2026.

Summary:

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.

Contents:

Cover

Series Page

Title Page

Copyright Page

Contents

Preface

Part 1: Transforming Data Science and Machine Learning through Dynamic Optimization

Chapter 1 Customized K-Means Clustering-Based Color Image Segmentation

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

Chapter 2 Optimizing Financial Forecasts and Integrating Utility Mining with Machine and Deep Learning for Stock Market Price Prediction

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

Chapter 3 Enhancing OCR Adaptability in Multimodal Environments: Challenges, Opportunities, and Insights

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

Chapter 4 Text Generation, Classification, and Optimization Using Tokenization in NLP

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

Chapter 5 Optimizing Decision Trees: Exploring Pruning Techniques and the Impact of Ensemble Classifiers

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

Chapter 6 Optimized Neural Machine Translation: A Review of Automated French-Hindi and Hindi-French Translation Systems

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:

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

Chapter 7 Optimizing Assignment Solutions: Integrating R and Python for Enhanced Workforce Efficiency

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

Chapter 8 Practical Optimization: Big M with Python and R

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

Chapter 9 Leveraging R and Python for Advanced Graphical Optimization Solutions

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

Chapter 10 Linear Optimization: Simplex Method in Python and R

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

Part 2: Revolutionizing Healthcare with AI-Driven Optimization

Chapter 11 Efficient Transfer Learning Methods for MIA: An Optimization Perspective

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

Chapter 12 Optimized Deep Learning Approach for Alzheimer's Prediction Using CNN on MRI Images

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

Chapter 13 Precision Care: Exposing Machine Learning-Based Optimization Methods in Epileptic Seizure Diseases

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

Chapter 14 Optimized Deep Learning Models to Identify Skin Malignancy through Skin Lesion Images

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

14.3.2.1 ResNet50

14.3.2.2 GoogLeNet

14.3.3 Optimization of DL Models

14.3.1 Preprocessing

14.3.2 Deep Learning Models

14.4 Experimental Results

14.4.1 Database

14.4.2 Performance Metrics

14.4.3 Model Evolution

14.5 Conclusion

Chapter 15 Hybrid Optimization Techniques for Ayurvedic Medicine Recommendation: Bridging Tradition and Technology

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

Chapter 16 Entropy Optimization in Casson Tri-Mixed Nanofluid Flow on a Curved Sheet Utilizing the Cattaneo-Christov Model: Biomedical Applications

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

Part 3: Engineering the Future: Optimization in Technology and Smart Systems.

Chapter 17 Optimized Performance of Grid-Integrated Hybrid Energy Systems Using Fuzzy Logic-Based MPPT.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-31572-4

1-394-31571-6

OCLC:

1587898132