Driving Innovation by Dynamic Optimization by Arindam Dey (.PDF)
File Size: 35.4 MB
Driving Innovation by Dynamic Optimization: The Challenges of Reshaping Industry by Arindam Dey, Sachi Nandan Mohanty, Ranjan Kumar, T. Rajasanthosh Kumar
Requirements: .PDF reader, 35.4 MB | True PDF
Overview: Future-proof your technical expertise by mastering the multidisciplinary synergy of AI-driven optimization and intelligent algorithms with this essential resource to optimization for the modern world. The rapid evolution of Artificial Intelligence, Machine Learning, and data-driven optimization has transformed the landscape of scientific research and industrial innovation. This book presents a diverse and multidisciplinary collection of contemporary studies that advance theoretical foundations, computational techniques, and application-driven solutions across multiple domains to reflect the growing importance of optimization, intelligent systems, and hybrid analytical frameworks in solving complex real-world problems. This essential guide focuses on next-generation technologies such as AI-driven cybersecurity, dynamic optimization in 6G networks, fuzzy logic-based energy management, automated neural machine translation, and GAN-driven aerodynamic shape design, illustrating the synergy between intelligent algorithms and modern engineering systems. Whether you are a researcher, practitioner, or simply an enthusiast, this book will serve as an enlightening resource, providing valuable insights into the ever-evolving realm of optimization for the modern world. The collection begins with fundamental approaches, such as linear optimization using the Simplex Method, Big-M modeling, and assignment optimization, all implemented in Python and R, providing a strong computational base for learners and practitioners. It then advances into cutting-edge Machine Learning applications, including Deep Learning for medical imaging, precision healthcare, and biomedical modeling using entropy optimization.
Genre: Non-Fiction > Tech & Devices
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