Deep Learning Applications in Operations Research

Deep Learning Applications in Operations Research explores cutting-edge applications of deep learning and optimization techniques across various domains. By delving into the innovative approaches and emerging trends in advanced intelligent applications, the book examines innovation and leveraging emerging technologies to drive intelligent solutions across diverse domains. It covers such key areas as: A comparative study of deep learning algorithms and genetic algorithms as stochastic optimizers, analyzing their effectiveness in operations research applications. An updated approach to Critical Path Method (CPM) that combines traditional scheduling with modern computational methods for dynamic project environments. A bibliometric analysis of smart warehousing trends in logistics operations management using R, providing data-driven insights into industry developments. An examination of edge computing optimization for real-time decision-making in operations research, focusing on latency reduction and computational efficiency. Development of a hybrid intrusion detection system for IoT networks, combining machine learning with anomaly and signature-based detection approaches. Introduction of SAI-GAN, a novel approach for masked face reconstruction, paired with a DCNN-ELM classifier for enhanced biometric authentication. Analysis of deep learning-driven mHealth applications in India's healthcare system, demonstrating how predictive analytics and real-time monitoring can improve healthcare accessibility. Exploration of machine learning-driven ontology evolution in multi-tenant cloud architectures, advancing automated knowledge engineering through deep learning models. Providing a wide-ranging overview of the field, the book helps researchers to navigate the rapidly evolving landscape of advanced intelligent applications. It demonstrates the transformative impact of deep learning in operations research by offering practical insights while establishing a foundation for future innovations.

Table of Contents:
1. Clustering for a Greener E-Commerce: Data-Driven Strategies for Sustainable Warehousing and Fulfillment 2. Employee Attrition Prediction using Optimized Deep Auto-Encoder and 1D Convolution Neural Network 3. Sooner-C Lightweight Cryptographic Scheme for Data Distribution Privacy in Smart Farming 4. Revolutionizing Operations Research with Deep Learning Techniques 5. Experimental Comparison of Deep Learning Algorithms and Genetic Algorithm as Stochastic Optimizers in Operations Research 6. Organizational Practices of Dynamic Project Scheduling Using CPM 7. Smart Warehousing in Logistics Operation Management: A Bibliometric Analysis by Using R 8. Towards Optimizing Edge Computing for Enhancing Decision-Making in Operations Research 9. Towards an Efficient Hybrid Intrusion Detection System for RPL-Based IoT Networks: A Machine Learning Approach 10. SAI-GAN for Masked Face Reconstruction with a Novel DCNN-ELM Classifier 11. Assessing the Role of Deep Learning-Driven mHealth Apps in Indian Healthcare System 12. Ontology Evolution in Multi-Tenant Cloud Architectures: A Machine Learning-Driven Approach Toward Automated Knowledge Engineering

About the Author :
Dr. Sanjay Misra is a senior scientist at the Department of Applied Data Science, Institute for Energy Technology Halden, Norway. Dr. Amit Jain is president and dean of the Faculty of Management, Amity University Rajasthan, India. Dr. Manju Kaushik is a professor of software engineering at the Amity Institute of Information Technology, Amity University Rajasthan. Dr. Chitreshh Banerjee is an associate professor at the Amity Institute of Information Technology, Amity University Rajasthan. Dr. Rakhi Mutha is an associate professor at the Amity Institute of Information Technology, Amity University Rajasthan.