1st Edition

Deep Learning Applications in Operations Research

289 Pages 80 B/W Illustrations

by Routledge

289 Pages 80 B/W Illustrations

by Routledge

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Description

Deep Learning Applications in Operations Research explores cutting-edge applications of deep learning and optimization techniques across various domains. By exploring innovative approaches and emerging trends in advanced intelligent applications, the book examines how innovation and emerging technologies can be leveraged to drive intelligent solutions across diverse domains. It covers such key... Read more

Deep Learning Applications in Operations Research explores cutting-edge applications of deep learning and optimization techniques across various domains. By exploring innovative approaches and emerging trends in advanced intelligent applications, the book examines how innovation and emerging technologies can be leveraged to drive intelligent solutions across diverse domains. It covers such key areas as follows:

A comparative study of deep learning algorithms and genetic algorithms as stochastic optimizers, analyzing their effectiveness in operations research applications
An updated approach to the 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 navigate the rapidly evolving landscape of advanced intelligent applications. It demonstrates the transformative impact of deep learning on operations research by offering practical insights and establishing a foundation for future innovations.

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1. Clustering for a Greener E-Commerce: Data-Driven Strategies for Sustainable Warehousing and Fulfillment
Yash Chawla, Ewa Prałat, and Adam Wasilewski

2. Employee Attrition Prediction Using Optimized Deep Auto-Encoder and 1D Convolution Neural Network
Karabi Konar, Saptarshi Das, Samiran Das, and Sanjay Misra

3. Sooner-C Lightweight Cryptographic Scheme for Data Distribution Privacy in Smart Farming
A. A. Alfa, J. K. Alhassan, O. M. Olaniyi, M. Morufu, and Sanjay Misra

4. Revolutionizing Operations Research with Deep Learning Techniques
Aarti Chugh, Akshat Agrawal, and Arpita Soni

5. Experimental Comparison of Deep Learning Algorithms and Genetic Algorithm as Stochastic Optimizers in Operations Research
Emmanuel Okewu and Sanjay Misra

6. Organizational Practices of Dynamic Project Scheduling using CPM
Chamundeswari Arumugam, Senthil Velan Suganantham, and Srinivasan Vaidyanathan

7. Smart Warehousing in Logistics Operation Management: A Bibliometric Analysis by Using R
Shruti Traymbak

8. Towards Optimizing Edge Computing for Enhancing Decision-Making in Operations Research
Bilkisu L. Muhammad-Bello, Sanjay Misra, and Muhammad Bello Kusharki

9. Towards an Efficient Hybrid Intrusion Detection System for RPL-Based IoT Networks: A Machine Learning Approach
Abubakar Wakili, Sara Bakkali, and Ahmed El Hilali Alaou

10. SAI-GAN for Masked Face Reconstruction with a Novel DCNN-ELM Classifier
Chandni Agarwal and Anurag Mishra

11. Assessing the Role of Deep Learning-Driven mHealth Apps in Indian Healthcare System
Pallavi Mishra and Tanushri Mukherjee

12. Ontology Evolution in Multi-Tenant Cloud Architectures: A Machine Learning-Driven Approach Toward Automated Knowledge Engineering
Dimpy Jindal, Manju Kaushik, and Barkha Bahl

Editor(s)

Biography

Sanjay Misra is a senior scientist at the Department of Digital Sovereignty, Institute for Energy Technology, Halden, Norway.

Amit Jain is president, vice chancellor, and director of Amity University Rajasthan, India.

Manju Kaushik is a professor of software engineering at the Amity Institute of Information Technology and director of E-Cell and Center of IoT, Amity University Rajasthan, India.

Chitresh Banerjee is an associate professor at the Amity Institute of Information Technology, Amity University Rajasthan, India.

Rakhi Mutha is an associate professor at the Amity Institute of Information Technology, Amity University, Rajasthan, India.

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Deep Learning Applications in Operations Research

Description

Table of Contents

Editor(s)

Biography

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