The model-based approach for carrying out classification and identification of tasks has led to the pervading progress of machine learning paradigm in diversified fields of technology. Deep Learning Applications in Operations Research presents the varied applications of this model-based approach. Apart from the classification process, the machine learning (ML) model has become effective enough to predict future trends of any sort of phenomena. Such fields as object classification, speech recognition, and face detection have sought extensive application of artificial intelligence (AI) and machine learning as well. The application of AI and ML has also the domains of agriculture, health sectors, and insurance.
Operations research is the branch of mathematics for performing so many operational tasks in other allied domains, and the book explains how the implementation of automated strategies in optimization and parameter selection can be carried out by AI and ML. Operations research has many beneficial aspects for decision making. Discussing how the proper decision depends on a number of factors, the book examines how AI and ML can be used to model equations and define constraints to solve more easily problems and discover proper and valid solutions. It also looks at how automation plays a significant role in minimizing human labor and thereby minimizes overall time and cost. Case studies look at how to streamline operations and unearth data to make better business decisions. The concepts presented in this book can bring about and guide unique research directions to the future application of AI enabled technologies.
The book delves into how to apply deep learning to areas of operations research. The book focuses on decision modeling and model optimization and features case studies.
1. Predicting Crop Yield Using Quantum Neural Networks,
2. A
Comprehensive Survey on Risk Factor Monitoring Using Deep Learning Methods on
Electrocardiogram Data,
3. AI-Powered Data-Centric Approaches: Enhancing
Information Quality for Deep Learning Algorithms,
4. Multi-Attribute Decision
Modeling,
5. Unmasking Transformations: CNNs for Detecting Land Cover Changes
in Satellite Imagery,
6. Leafine: An AI Tool to Recognize and Perceive Leaf
Illness with Manure Suggestions,
7. An Expansive Performance Analysis and
Comparison between Different Supervised and Unsupervised ML Algorithms for
Categorization of ICU Patients at an Indian Hospital,
8. Darknet for Gun and
Suspicious Activity Detection and Crime Prediction,
9. Image Edge Detection
Using Fireflies to Fine-Tuned Deep Convolution Networks,
10. Application of
Machine Learning, Deep Learning, and Econometric Models in Stock Price
Movement of Rain Industries: An In-Depth Analysis,
11. Performance Analysis
of U-Net and Fully Convolutional Regression Network on Jetson Nano for
Real-Time Inventory Analysis,
12. Clinical Decision Support System for
Prevention of Puberty Disorders and Fertility Issues due to Noyyal River
Pollution using Ensemble Learning Techniques,
13. Obesity Prediction Using
Machine Learning,
14. Intuitionistic Fuzzy Dombi-Archimedean Weighted
Aggregation Operators and Their Applications in Sustainable Material
Selection,
15. Identification of Rice Leaf Disease Using Gaussian Mixture
Model: A Machine Learning Approach Using Image Classification Techniques,
16.
Multi-Objective Optimization of Economic Development and Environmental Issues
in the Yangtze River Basin, China,
17. Qualitative Study on E-Commerce and
Brick-and-Mortar Stores: A Machine Learning Approach,
18. Design of Novel
Energy Management System in Solar PV Powered EV Charging Station Using
Artificial Gorilla Troops Optimization,
19. School Students Cataract
Prediction Using Machine Learning,
20. Minimization of the Threat of Diabetic
Kidney Disease through the Lens of Machine Learning,
21. A Novel Segmentation
and Feature Extraction-Based Plant Disease Diagnosis Method Based on Stacked
Ensemble Learning
Biswadip Basu Mallik is a Senior Assistant Professor of Mathematics in the Department of Basic Sciences & Humanities at Institute of Engineering & Management, Kolkata, India.
Gunjan Mukherjee is an Assistant professor in the Department of Computational Science, Brainware University, Barasat, India.
Rahul Kar holds a master's degree in mathematics from Burdwan University and is currently working as a SACT-II Mathematics faculty of Kalyani Mahavidyalaya, Kalyani, Nadia, West Bengal.
Aryan Chaudhary is the Research Head and Lead Member of the research project launched by Nijji Healthcare Pvt Ltd.
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