Order Analysis Deep Learning And Connections To Optimization
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Order Analysis Deep Learning And Connections To Optimization
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Author : Johannes Jahn
language : en
Publisher: Springer Nature
Release Date : 2024-10-22
Order Analysis Deep Learning And Connections To Optimization written by Johannes Jahn and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-22 with Business & Economics categories.
This book introduces readers to order analysis and various aspects of deep learning, and describes important connections to optimization, such as nonlinear optimization as well as vector and set optimization. Besides a review of the essentials, this book consists of two main parts. The first main part focuses on the introduction of order analysis as an application-driven theory, which allows to treat order structures with an analytical approach. Applications of order analysis to nonlinear optimization, as well as vector and set optimization with fixed and variable order structures, are discussed in detail. This means there are close ties to finance, operations research, and multicriteria decision making. Deep learning is the subject of the second main part of this book. In addition to the usual basics, the focus is on gradient methods, which are investigated in the context of complex models with a large number of parameters. And a new fast variant of a gradient method is presented in this part. Finally, the deep learning approach is extended to data sets given by set-valued data. Although this set-valued approach is more computationally intensive, it has the advantage of producing more robust predictions. This book is primarily intended for researchers in the fields of optimization, order theory, or artificial intelligence (AI), but it will also benefit graduate students with a general interest in these fields. The book assumes that readers have a basic understanding of functional analysis or at least basic analysis. By unifying and streamlining existing approaches, this work will also appeal to professionals seeking a comprehensive and straightforward perspective on AI or order theory approaches.
Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems
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Author : Essam Halim Houssein
language : en
Publisher: Springer Nature
Release Date : 2022-06-04
Integrating Meta Heuristics And Machine Learning For Real World Optimization Problems written by Essam Halim Houssein and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-04 with Technology & Engineering categories.
This book collects different methodologies that permit metaheuristics and machine learning to solve real-world problems. This book has exciting chapters that employ evolutionary and swarm optimization tools combined with machine learning techniques. The fields of applications are from distribution systems until medical diagnosis, and they are also included different surveys and literature reviews that will enrich the reader. Besides, cutting-edge methods such as neuroevolutionary and IoT implementations are presented in some chapters. In this sense, the book provides theory and practical content with novel machine learning and metaheuristic algorithms. The chapters were compiled using a scientific perspective. Accordingly, the book is primarily intended for undergraduate and postgraduate students of Science, Engineering, and Computational Mathematics and can be used in courses on Artificial Intelligence, Advanced Machine Learning, among others. Likewise, the material can be helpful for research from the evolutionary computation, artificial intelligence communities.
Proceeding Of The International Conference On Connected Objects And Artificial Intelligence Cocia2024
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Author : Youssef Mejdoub
language : en
Publisher: Springer Nature
Release Date : 2024-10-12
Proceeding Of The International Conference On Connected Objects And Artificial Intelligence Cocia2024 written by Youssef Mejdoub and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-12 with Technology & Engineering categories.
’This book presents recent advances on Connected Objects, Systems, Telecommunications, Artificial Intelligence, and Electronic Engineering. On the connected objects side, the proceedings covered advancements in areas like sensor miniaturization, and networking to enable ever-more ubiquitous and autonomous IoT deployments. The AI-focused contributions explored novel machine learning architectures and training techniques tailored for resource-constrained edge devices. Key breakthroughs included federated learning models. In the telecommunications realm, the proceedings examined the critical role of 5G, 6G, and satellite communications in providing the high-bandwidth, low-latency connectivity required to unlock the full potential of AI-powered connected systems. This book is a collection of high-quality research papers presented at the 2nd International Conference on Connected Objects and Artificial Intelligence (COCIA'2024), held at High School of Technology, Hassan II University, Casablanca, Morocco, during 08–10 May 2024. This book features cutting-edge research and insights at the intersection of the important technology domains, Connected Objects, Systems, Telecommunications, Artificial Intelligence, and Electronic Engineering. It is designed for researchers, academicians, professionals, and graduates seeking to deepen their understanding and expertise at the intersection of IoT, AI, Telecommunications, and Electronic Engineering. This book includes: In-depth exploration of the latest advancements in connected objects and systems to enable autonomous IoT deployments. Detailed examinations of cutting-edge AI techniques optimized for edge computing environments, including federated learning and IA model compression. Insights into the critical role of 5G, 6G, and satellite communications in providing the high-performance connectivity required to unlock the full potential of intelligent, AI-powered IoT applications. With contributions from leading experts across academia and industry, this book equips readers with the knowledge and tools to drive innovation at the forefront of the connected intelligence revolution. It is an essential resource for anyone seeking to advance the state of the art in this rapidly evolving field.
Synergizing Data Envelopment Analysis And Machine Learning For Performance Optimization In Healthcare
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Author : Ajibesin, Adeyemi Abel
language : en
Publisher: IGI Global
Release Date : 2025-05-02
Synergizing Data Envelopment Analysis And Machine Learning For Performance Optimization In Healthcare written by Ajibesin, Adeyemi Abel and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-02 with Medical categories.
Healthcare systems face the challenge of delivering high-quality care while efficiently managing costs and resources. Traditional methods of performance evaluation often fall short when addressing the complex and diverse nature of healthcare operations. Data envelopment analysis (DEA) has been used to measure the efficiency of healthcare providers, but its linear, deterministic nature limits its adaptability to dynamic environments. In contrast, machine learning (ML) can handle complex, non-linear relationships and high-dimensional data, offering deeper insights and predictive capabilities. The synergy between DEA and ML presents an opportunity to overcome these limitations and drive more effective performance optimization. It leads to efficiency assessments through predictive analytics and improved resource allocation with data-driven insights and optimizing clinical pathways and decision support systems for better patient outcomes. Synergizing Data Envelopment Analysis and Machine Learning for Performance Optimization in Healthcare explores the integration of DEA and ML to enhance performance optimization in healthcare, improving efficiency, care quality, and resource management. It examines theoretical foundations, methodological innovations, and practical applications, providing a comprehensive resource with a key focus on development of algorithms to address challenges in healthcare optimization. Covering topics such as healthcare equipment manufacturing, human augmentation, and robotic surgery, this book is an excellent resource for hospital administrators, clinical managers, clinical decision-makers, policymakers, public health officials, professionals, researchers, scholars, academics, and more.
Ai Based Advanced Optimization Techniques For Edge Computing
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Author : Mohit Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2025-03-27
Ai Based Advanced Optimization Techniques For Edge Computing written by Mohit Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-03-27 with Computers categories.
The book offers cutting-edge insights into AI-driven optimization algorithms and their crucial role in enhancing real-time applications within fog and Edge IoT networks and addresses current challenges and future opportunities in this rapidly evolving field. This book focuses on artificial intelligence-induced adaptive optimization algorithms in fog and Edge IoT networks. Artificial intelligence, fog, and edge computing, together with IoT, are the next generation of paradigms offering services to people to improve existing services for real-time applications. Over the past few years, there has been rigorous growth in AI-based optimization algorithms and Edge and IoT paradigms. However, despite several applications and advancements, there are still some limitations and challenges to address including security, adaptive, complex, and heterogeneous IoT networks, protocols, intelligent offloading decisions, latency, energy consumption, service allocation, and network lifetime. This volume aims to encourage industry professionals to initiate a set of architectural strategies to solve open research computation challenges. The authors achieve this by defining and exploring emerging trends in advanced optimization algorithms, AI techniques, and fog and Edge technologies for IoT applications. Solutions are also proposed to reduce the latency of real-time applications and improve other quality of service parameters using adaptive optimization algorithms in fog and Edge paradigms. The book provides information on the full potential of IoT-based intelligent computing paradigms for the development of suitable conceptual and technological solutions using adaptive optimization techniques when faced with challenges. Additionally, it presents in-depth discussions in emerging interdisciplinary themes and applications reflecting the advancements in optimization algorithms and their usage in computing paradigms. Audience Researchers, industrial engineers, and graduate/post-graduate students in software engineering, computer science, electronic and electrical engineering, data analysts, and security professionals working in the fields of intelligent computing paradigms and similar areas.
Modeling Analysis And Optimization For Space Ground Integrated Networks
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Author : Min Sheng
language : en
Publisher: Springer Nature
Release Date : 2024-12-17
Modeling Analysis And Optimization For Space Ground Integrated Networks written by Min Sheng and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-17 with Computers categories.
This book delves into the burgeoning field of space-ground integrated networks (SGINs), focusing on the evolution, architecture and application scenarios of large satellite constellations. As global communication demands surge, the next-generation SGINs are transitioning towards large-scale, hyperscale, and hierarchical constellations to address diverse user needs, such as ultra-long-haul, high-speed and instantaneous communications. Through an in-depth exploration of network working principles, traffic and link modeling, and performance analysis and optimization methods, this book offers substantial guidance to readers. It aims to uncover pathways for enhancing network service capability and to establish a solid theoretical foundation for the future development of mega-satellite constellations within the SGIN framework. Furthermore, this book provides a comprehensive analysis of SGINs, examining its network architecture, developmental trajectory and application scenarios. It delves into the core challenges faced by SGINs and proposes response strategies. This book introduces various methods for service capability modeling and quantitative performance analysis across different traffic types, emphasizing the intricate relationships between network performance, traffic patterns, nodes, links and network characteristics. Through an exhaustive study of large-scale satellite network operations, this book illuminates avenues for improving network service capacity, thereby paving the way for theoretical guidance and technical support for future ultra-large-scale SGINs. By offering systematic analyses and insights, this book aims to equip readers with a profound understanding and practical guidance for SGIN network planning and optimization. It also highlights future trends and potential application scenarios in the evolving landscape of network technology. This book targets advanced level students, professors, researchers and scientists working in the fields of telecommunications, satellite communications and network engineering, who wish to gain an in-depth understanding of SGIN principles, performance analysis and optimization techniques. Practitioners working in aerospace or telecommunications policy making to gain insights into future trends and challenges in satellite network design and optimization will want to purchase this book as well.
Optimization In Machine Learning And Applications
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Author : Anand J. Kulkarni
language : en
Publisher: Springer
Release Date : 2020-12-10
Optimization In Machine Learning And Applications written by Anand J. Kulkarni and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-10 with Technology & Engineering categories.
This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.
Deep Learning For Toxicity And Disease Prediction
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Author : Ping Gong
language : en
Publisher: Frontiers Media SA
Release Date : 2020-04-01
Deep Learning For Toxicity And Disease Prediction written by Ping Gong and has been published by Frontiers Media SA this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-01 with categories.
Math Optimization For Artificial Intelligence
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Author : Umesh Kumar Lilhore
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2025-04-21
Math Optimization For Artificial Intelligence written by Umesh Kumar Lilhore and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-21 with Mathematics categories.
The book presents powerful optimization approaches for integrating AI into daily life. This book explores how heuristic and metaheuristic methodologies have revolutionized the fields of robotics and machine learning. The book covers the wide range of tools and methods that have emerged as part of the AI revolution, from state-of-the-art decision-making algorithms for robots to data-driven machine learning models. Each chapter offers a meticulous examination of the theoretical foundations and practical applications of mathematical optimization, helping readers understand how these methods are transforming the field of technology. This book is an invaluable resource for researchers, practitioners, and students. It makes AI optimization accessible and comprehensible, equipping the next generation of innovators with the knowledge and skills to further advance robotics and machine learning. While artificial intelligence constantly evolves, this book sheds light on the path ahead.
Mathematical Analysis Of Machine Learning Algorithms
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Author : Tong Zhang
language : en
Publisher: Cambridge University Press
Release Date : 2023-08-10
Mathematical Analysis Of Machine Learning Algorithms written by Tong Zhang and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-10 with Computers categories.
Introduction to the mathematical foundation for understanding and analyzing machine learning algorithms for AI students and researchers.