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:
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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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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Hardcover. Condition: new. Hardcover. 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 applicationsAn updated approach to the critical path method (CPM) that combines traditional scheduling with modern computational methods for dynamic project environmentsA bibliometric analysis of smart warehousing trends in logistics operations management using R, providing data- driven insights into industry developmentsAn examination of edge computing optimization for real-time decision-making in operations research, focusing on latency reduction and computational efficiencyDevelopment of a hybrid intrusion detection system for IoT networks, combining machine learning with anomaly and signature-based detection approachesIntroduction of SAI-GAN, a novel approach for masked face reconstruction, paired with a DCNN-ELM classifier for enhanced biometric authenticationAnalysis of deep learning-driven mHealth applications in Indias healthcare system, demonstrating how predictive analytics and real-time monitoring can improve healthcare accessibilityExploration of machine learning-driven ontology evolution in multi-tenant cloud architectures, advancing automated knowledge engineering through deep learning modelsProviding 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. Emphasizing practical implementation, this book features real-world use cases, industry applications, and success stories. It examines how intelligent applications are transforming industries such as healthcare, finance, manufacturing, and transportation. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781032709185
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Hardcover. Condition: new. Hardcover. 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 applicationsAn updated approach to the critical path method (CPM) that combines traditional scheduling with modern computational methods for dynamic project environmentsA bibliometric analysis of smart warehousing trends in logistics operations management using R, providing data- driven insights into industry developmentsAn examination of edge computing optimization for real-time decision-making in operations research, focusing on latency reduction and computational efficiencyDevelopment of a hybrid intrusion detection system for IoT networks, combining machine learning with anomaly and signature-based detection approachesIntroduction of SAI-GAN, a novel approach for masked face reconstruction, paired with a DCNN-ELM classifier for enhanced biometric authenticationAnalysis of deep learning-driven mHealth applications in Indias healthcare system, demonstrating how predictive analytics and real-time monitoring can improve healthcare accessibilityExploration of machine learning-driven ontology evolution in multi-tenant cloud architectures, advancing automated knowledge engineering through deep learning modelsProviding 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. Emphasizing practical implementation, this book features real-world use cases, industry applications, and success stories. It examines how intelligent applications are transforming industries such as healthcare, finance, manufacturing, and transportation. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781032709185
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