Misra S. Deep Learning Applications in Operations Research 2026

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Misra S. Deep Learning Applications in Operations Research 2026

Textbook in PDF format 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 In ‘Employee Attrition Prediction Using Optimized Deep Auto-encoder and 1D Convolution Neural Network’, readers will discover how deep learning models can transform human resource management. This chapter demonstrates the application of auto-encoders and convolutional neural networks to predict employee attrition, helping organizations retain talent and improve workforce planning. As data privacy becomes increasingly paramount, Sooner-C: Lightweight Cryptographic Scheme for Data Distribution Privacy in Smart Farming introduces a novel cryptographic scheme designed to safeguard data in smart farming systems. This chapter bridges deep learning with cryptographic solutions, ensuring secure data exchange while maintaining efficiency in agricultural operations. Revolutionizing Operations Research with Deep Learning Techniques offers a comprehensive overview of the transformative impact of deep learning on the field of operations research. This chapter examines cutting-edge applications, methodologies, and future directions, setting the stage for further innovations in the discipline. An Experimental Comparison of Deep Learning Algorithms and Genetic Algorithm as Stochastic Optimizers in Operations Research presents a comparative analysis of optimization techniques. By evaluating the performance of deep learning algorithms alongside genetic algorithms, this chapter sheds light on their respective strengths and suitability for various operational challenges... 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