Deep Learning Applications in Operations Research - Chaudhary, Aryan; Basu Mallik, Biswadip; Mukherjee, Gunjan;(ed.) - Prospero Internet Bookshop
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Deep Learning Applications in Operations Research

 
Edition number: 1
Publisher: Auerbach Publications
Date of Publication:
 
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Short description:

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.

Long description:

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.

Table of Contents:

1. Predicting Crop Yield using Quantum Neural Networks 2. A Comprehensive Survey on Risk Factor Monitoring Using Deep Learning Methods on Electrocardiogram Data 3. Data-centric in AI Perspective Challenges in Deep Learning 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 Some Indian Hospital?s ICU Patients 8. Darknet in Multiple Gun Detection for Suspicious Activity Detection and Crime Prediction 9. Image Edge Detection Using Fireflies Fine-Tuned Deep Convolution Networks 10. Application of Machine Learning, Deep Learning and Econometric Models in Stock Price Movement of Rain Industry: An In-Depth Analysis 11. Performance Analysis of U-Net and Fully Convolutional Regression Network on Jetson Nano for the Real-Time Inventory Analysis 12. Clinical Decision Support System for Prevention of Puberty Disorders and Infertility Problems 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 Detection Using Gaussian Mixture Model: A Machine Learning Approach Using Image Classification Technique 16. Multi-Objective Optimization of Economic Development and   Environmental Issues on Yangtze River Basin, China 17. Qualitative Study on E-Commerce and Brick-and-Mortar: 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