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Author: Wenwu Zhu
Publisher: Springer Nature
ISBN: 3030881326
Category : Computers
Languages : en
Pages : 240
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Book Description
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Author: Wenwu Zhu
Publisher: Springer Nature
ISBN: 3030881326
Category : Computers
Languages : en
Pages : 240
Get Book Here
Book Description
This book disseminates and promotes the recent research progress and frontier development on AutoML and meta-learning as well as their applications on computer vision, natural language processing, multimedia and data mining related fields. These are exciting and fast-growing research directions in the general field of machine learning. The authors advocate novel, high-quality research findings, and innovative solutions to the challenging problems in AutoML and meta-learning. This topic is at the core of the scope of artificial intelligence, and is attractive to audience from both academia and industry. This book is highly accessible to the whole machine learning community, including: researchers, students and practitioners who are interested in AutoML, meta-learning, and their applications in multimedia, computer vision, natural language processing and data mining related tasks. The book is self-contained and designed for introductory and intermediate audiences. No special prerequisite knowledge is required to read this book.
Author: Koushlendra Kumar Singh
Publisher: Springer Nature
ISBN: 9819743591
Category :
Languages : en
Pages : 875
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Book Description
Author: Suman Kumar Swarnkar, Sapna Singh Kshatri, Virendra Kumar Swarnkar, Tien Anh Tran
Publisher: Bentham Science Publishers
ISBN: 9815196456
Category : Computers
Languages : en
Pages : 134
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Book Description
This book explains different applications of supervised and unsupervised data engineering for working with multimedia objects. Throughout this book, the contributors highlight the use of Artificial Intelligence-based soft computing and machine techniques in the field of medical diagnosis, biometrics, networking, automation in vehicle manufacturing, data science and automation in electronics industries. The book presents seven chapters which present use-cases for AI engineering that can be applied in many fields. The book concludes with a final chapter that summarizes emerging AI trends in intelligent and interactive multimedia systems. Key features: - A concise yet diverse range of AI applications for multimedia data engineering - Covers both supervised and unsupervised machine learning techniques - Summarizes emerging AI trends in data engineering - Simple structured chapters for quick reference and easy understanding - References for advanced readers This book is a primary reference for data science and engineering students, researchers and academicians who need a quick and practical understanding of AI supplications in multimedia analysis for undertaking or designing courses. It also serves as a secondary reference for IT and AI engineers and enthusiasts who want to grasp advanced applications of the basic machine learning techniques in everyday applications
Author: Sharma, Ashok
Publisher: IGI Global
ISBN: 1668476614
Category : Computers
Languages : en
Pages : 271
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Book Description
Meta-learning, or learning to learn, has been gaining popularity in recent years to adapt to new tasks systematically and efficiently in machine learning. In the book, Meta-Learning Frameworks for Imaging Applications, experts from the fields of machine learning and imaging come together to explore the current state of meta-learning and its application to medical imaging and health informatics. The book presents an overview of the meta-learning framework, including common versions such as model-agnostic learning, memory augmentation, prototype networks, and learning to optimize. It also discusses how meta-learning can be applied to address fundamental limitations of deep neural networks, such as high data demand, computationally expensive training, and limited ability for task transfer. One critical topic in imaging is image segmentation, and the book explores how a meta-learning-based framework can help identify the best image segmentation algorithm, which would be particularly beneficial in the healthcare domain. This book is relevant to healthcare institutes, e-commerce companies, and educational institutions, as well as professionals and practitioners in the intelligent system, computational data science, network applications, and biomedical applications fields. It is also useful for domain developers and project managers from diagnostic and pharmacy companies involved in the development of medical expert systems. Additionally, graduate and master students in intelligent systems, big data management, computational intelligent approaches, computer vision, and biomedical science can use this book for their final projects and specific courses.
Author: Management Association, Information Resources
Publisher: IGI Global
ISBN: 1609608194
Category : Computers
Languages : en
Pages : 2174
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Book Description
"This reference offers a wide-ranging selection of key research in a complex field of study,discussing topics ranging from using machine learning to improve the effectiveness of agents and multi-agent systems to developing machine learning software for high frequency trading in financial markets"--Provided by publishe
Author: Keshav Kaushik
Publisher: CRC Press
ISBN: 1000520595
Category : Computers
Languages : en
Pages : 258
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Book Description
This book addresses the topics related to artificial intelligence, the Internet of Things, blockchain technology, and machine learning. It brings together researchers, developers, practitioners, and users interested in cybersecurity and forensics. The first objective is to learn and understand the need for and impact of advanced cybersecurity and forensics and its implementation with multiple smart computational technologies. This objective answers why and how cybersecurity and forensics have evolved as one of the most promising and widely-accepted technologies globally and has widely-accepted applications. The second objective is to learn how to use advanced cybersecurity and forensics practices to answer computational problems where confidentiality, integrity, and availability are essential aspects to handle and answer. This book is structured in such a way so that the field of study is relevant to each reader’s major or interests. It aims to help each reader see the relevance of cybersecurity and forensics to their career or interests. This book intends to encourage researchers to develop novel theories to enrich their scholarly knowledge to achieve sustainable development and foster sustainability. Readers will gain valuable knowledge and insights about smart computing technologies using this exciting book. This book: • Includes detailed applications of cybersecurity and forensics for real-life problems • Addresses the challenges and solutions related to implementing cybersecurity in multiple domains of smart computational technologies • Includes the latest trends and areas of research in cybersecurity and forensics • Offers both quantitative and qualitative assessments of the topics Includes case studies that will be helpful for the researchers Prof. Keshav Kaushik is Assistant Professor in the Department of Systemics, School of Computer Science at the University of Petroleum and Energy Studies, Dehradun, India. Dr. Shubham Tayal is Assistant Professor at SR University, Warangal, India. Dr. Akashdeep Bhardwaj is Professor (Cyber Security & Digital Forensics) at the University of Petroleum & Energy Studies (UPES), Dehradun, India. Dr. Manoj Kumar is Assistant Professor (SG) (SoCS) at the University of Petroleum and Energy Studies, Dehradun, India.
Author: Carina S. González-González
Publisher: Springer Nature
ISBN: 3031330234
Category : Computers
Languages : en
Pages : 553
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Book Description
This book constitutes the refereed conference proceedings of the 21st International Conference on Web-Based Learning, ICWL 2022 and 7th International Symposium on Emerging Technologies for Education, SETE 2022, held in Tenerife, Spain in November 21–23, 2022. The 45 full papers and 5 short papers included in this book were carefully reviewed and selected from 82 submissions. The topics proposed in the ICWL&SETE Call for Papers included several relevant issues, ranging from Semantic Web for E-Learning, through Learning Analytics, Computer-Supported Collaborative Learning, Assessment, Pedagogical Issues, E-learning Platforms, and Tools, to Mobile Learning. In addition to regular papers, ICWL&SETE 2022 also featured a set of special workshops and tracks: The 5th International Workshop on Educational Technology for Language Learning (ETLL 2022), The 6th International Symposium on User Modeling and Language Learning (UMLL 2022), Digitalization in Language and Cross-Cultural Education, First Workshop on Hardware and software systems as enablers for lifelong learning (HASSELL).
Author: Vineeth Balasubramanian
Publisher: Newnes
ISBN: 0124017150
Category : Computers
Languages : en
Pages : 323
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Book Description
The conformal predictions framework is a recent development in machine learning that can associate a reliable measure of confidence with a prediction in any real-world pattern recognition application, including risk-sensitive applications such as medical diagnosis, face recognition, and financial risk prediction. Conformal Predictions for Reliable Machine Learning: Theory, Adaptations and Applications captures the basic theory of the framework, demonstrates how to apply it to real-world problems, and presents several adaptations, including active learning, change detection, and anomaly detection. As practitioners and researchers around the world apply and adapt the framework, this edited volume brings together these bodies of work, providing a springboard for further research as well as a handbook for application in real-world problems. - Understand the theoretical foundations of this important framework that can provide a reliable measure of confidence with predictions in machine learning - Be able to apply this framework to real-world problems in different machine learning settings, including classification, regression, and clustering - Learn effective ways of adapting the framework to newer problem settings, such as active learning, model selection, or change detection
Author: Thomas, J. Joshua
Publisher: IGI Global
ISBN: 1799811948
Category : Computers
Languages : en
Pages : 355
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Book Description
Many approaches have sprouted from artificial intelligence (AI) and produced major breakthroughs in the computer science and engineering industries. Deep learning is a method that is transforming the world of data and analytics. Optimization of this new approach is still unclear, however, and there’s a need for research on the various applications and techniques of deep learning in the field of computing. Deep Learning Techniques and Optimization Strategies in Big Data Analytics is a collection of innovative research on the methods and applications of deep learning strategies in the fields of computer science and information systems. While highlighting topics including data integration, computational modeling, and scheduling systems, this book is ideally designed for engineers, IT specialists, data analysts, data scientists, engineers, researchers, academicians, and students seeking current research on deep learning methods and its application in the digital industry.
Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Category : Computers
Languages : en
Pages : 415
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Book Description
Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.
