Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities, Shouvik Chakraborty, Kalyani Mali

Автор: Shouvik Chakraborty, Kalyani Mali
Название: Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities
ISBN: 1799827364 ISBN-13(EAN): 9781799827368
Издательство: Mare Nostrum (Eurospan)
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Цена: 174630.00 T
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Описание: Computer vision and object recognition are two technological methods that are frequently used in various professional disciplines. In order to maintain high levels of quality and accuracy of services in these sectors, continuous enhancements and improvements are needed. The implementation of artificial intelligence and machine learning has assisted in the development of digital imaging, yet proper research on the applications of these advancing technologies is lacking.

Applications of Advanced Machine Intelligence in Computer Vision and Object Recognition: Emerging Research and Opportunities explores the theoretical and practical aspects of modern advancements in digital image analysis and object detection as well as its applications within healthcare, security, and engineering fields. Featuring coverage on a broad range of topics such as disease detection, adaptive learning, and automated image segmentation, this book is ideally designed for engineers, physicians, researchers, academicians, practitioners, scientists, industry professionals, scholars, and students seeking research on the current developments in object recognition using artificial intelligence.

Автор: Padmavathi Ganapathi, D. Shanmugapriya
Название: Handbook of Research on Machine and Deep Learning Applications for Cyber Security
ISBN: 1522596119 ISBN-13(EAN): 9781522596110
Издательство: Mare Nostrum (Eurospan)
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Цена: 264270.00 T
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Описание: As the advancement of technology continues, cyber security continues to play a significant role in today's world. With society becoming more dependent on the internet, new opportunities for virtual attacks can lead to the exposure of critical information. Machine and deep learning techniques to prevent this exposure of information are being applied to address mounting concerns in computer security.

The Handbook of Research on Machine and Deep Learning Applications for Cyber Security is a pivotal reference source that provides vital research on the application of machine learning techniques for network security research. While highlighting topics such as web security, malware detection, and secure information sharing, this publication explores recent research findings in the area of electronic security as well as challenges and countermeasures in cyber security research. It is ideally designed for software engineers, IT specialists, cybersecurity analysts, industrial experts, academicians, researchers, and post-graduate students.

Автор: Sathiyamoorthi Velayutham
Название: Handbook of Research on Applications and Implementations of Machine Learning Techniques
ISBN: 1522599029 ISBN-13(EAN): 9781522599029
Издательство: Mare Nostrum (Eurospan)
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Цена: 264270.00 T
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Описание: Artificial intelligence is at the forefront of research and implementation in many industries including healthcare and agriculture. Whether it's detecting disease or generating algorithms, deep learning techniques are advancing exponentially. Researchers and professionals need a platform in which they can keep up with machine learning trends and their developments in the real world.

The Handbook of Research on Applications and Implementations of Machine Learning Techniques provides innovative insights into the multi-disciplinary applications of machine learning algorithms for data analytics. The content within this publication examines disease identification, neural networks, and language support. It is designed for IT professionals, developers, data analysts, technology specialists, R&D professionals, industrialists, practitioners, researchers, academicians, and students seeking research on deep learning procedures and their enactments in the fields of medicine, engineering, and computer science.

Автор: Salman Khan, Hossein Rahmani, Syed Afaq Ali Shah, Mohammed Bennamoun
Название: A Guide to Convolutional Neural Networks for Computer Vision
ISBN: 1681732785 ISBN-13(EAN): 9781681732787
Издательство: Mare Nostrum (Eurospan)
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Цена: 102570.00 T
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Описание: Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision.This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation.This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.

Автор: Pablo Duboue
Название: The Art of Feature Engineering: Essentials for Machine Learning
ISBN: 1108709389 ISBN-13(EAN): 9781108709385
Издательство: Cambridge Academ
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Цена: 46470.00 T
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Описание: This is a guide for data scientists who want to use feature engineering to improve the performance of their machine learning solutions. The book provides a unified view of the field, beginning with basic concepts and techniques, followed by a cross-domain approach to advanced topics, like texts and images, with hands-on case studies.

Автор: Bradley Efron and Trevor Hastie
Название: Computer Age Statistical Inference
ISBN: 1107149894 ISBN-13(EAN): 9781107149892
Издательство: Cambridge Academ
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Цена: 60190.00 T
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Описание: The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. 'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

Автор: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy
Название: Machine Learning Applications: Emerging Trends
ISBN: 3110608537 ISBN-13(EAN): 9783110608533
Издательство: Walter de Gruyter
Цена: 123910.00 T
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Описание:

The publication is attempted to address emerging trends in machine learning applications. Recent trends in information identification have identified huge scope in applying machine learning techniques for gaining meaningful insights. Random growth of unstructured data poses new research challenges to handle this huge source of information. Efficient designing of machine learning techniques is the need of the hour. Recent literature in machine learning has emphasized on single technique of information identification. Huge scope exists in developing hybrid machine learning models with reduced computational complexity for enhanced accuracy of information identification. This book will focus on techniques to reduce feature dimension for designing light weight techniques for real time identification and decision fusion. Key Findings of the book will be the use of machine learning in daily lives and the applications of it to improve livelihood. However, it will not be able to cover the entire domain in machine learning in its limited scope. This book is going to benefit the research scholars, entrepreneurs and interdisciplinary approaches to find new ways of applications in machine learning and thus will have novel research contributions. The lightweight techniques can be well used in real time which will add value to practice.

Автор: Huang Thomas S., Wang Zhaowen, Yang Jianchao
Название: Sparse Coding and Its Applications in Computer Vision
ISBN: 9814725048 ISBN-13(EAN): 9789814725040
Издательство: World Scientific Publishing
Цена: 89760.00 T
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Описание: This book provides a broader introduction to the theories and applications of sparse coding techniques in computer vision research.

Автор: Jennifer Boger, Victoria Young, Jesse Hoey, Tizneem Jiancaro, Alex Mihailidis
Название: Zero-Effort Technologies: Considerations, Challenges, and Use in Health, Wellness, and Rehabilitation, Second Edition
ISBN: 1681732866 ISBN-13(EAN): 9781681732862
Издательство: Mare Nostrum (Eurospan)
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Цена: 82230.00 T
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Описание: This book introduces zero-effort technologies (ZETs), an emerging class of technologies that require little or no effort from the people who use them. ZETs use advanced computing techniques, such as computer vision, sensor fusion, decision-making and planning, machine learning, and the Internet of Things to autonomously perform the collection, analysis, and application of data about the user and/or his/her context. This book begins with an overview of ZETs, then presents concepts related to their development, including pervasive intelligent technologies and environments, design principles, and considerations regarding use. The book discusses select examples of the latest in ZET development before concluding with thoughts regarding future directions of the field.

Название: Handbook of research on emerging trends and applications of machine learning
ISBN: 1522596437 ISBN-13(EAN): 9781522596431
Издательство: Mare Nostrum (Eurospan)
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Цена: 335410.00 T
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Описание: Provides a high-level understanding of various machine learning algorithms along with modern tools and techniques using Artificial Intelligence. In addition, the book explores the critical role that machine learning plays in a variety of professional fields including healthcare, business, and computer science.

Автор: Sugiyama Shigeki
Название: Human Behavior and Another Kind in Consciousness: Emerging Research and Opportunities
ISBN: 1522582177 ISBN-13(EAN): 9781522582175
Издательство: Mare Nostrum (Eurospan)
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Цена: 132130.00 T
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Описание: New technological communication methods have created new kinds of interactions among us that have allowed people across the globe to become closer, but they have also created more complex global dynamics. These dynamics have expanded the workings of human behavior, making human-seeming artificial intelligence a more difficult goal to achieve. Human Behavior and Another Kind in Consciousness: Emerging Research and Opportunities is a crucial reference book that examines human consciousness and how it can translate into artificial intelligence. Covering important topics such as cloud computing, human behavior, and intelligent systems, this book is ideal for engineers, researchers, academicians, and students in the fields of computer science, artificial intelligence, operations research, and intelligent systems.

Автор: Walter J. Scheirer
Название: Extreme Value Theory-Based Methods for Visual Recognition
ISBN: 1627057005 ISBN-13(EAN): 9781627057004
Издательство: Turpin
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Цена: 68930.00 T
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Описание: A common feature of many approaches to modeling sensory statistics is an emphasis on capturing the ""average."" From early representations in the brain, to highly abstracted class categories in machine learning for classification tasks, central-tendency models based on the Gaussian distribution are a seemingly natural and obvious choice for modeling sensory data. However, insights from neuroscience, psychology, and computer vision suggest an alternate strategy: preferentially focusing representational resources on the extremes of the distribution of sensory inputs. The notion of treating extrema near a decision boundary as features is not necessarily new, but a comprehensive statistical theory of recognition based on extrema is only now just emerging in the computer vision literature. This book begins by introducing the statistical Extreme Value Theory (EVT) for visual recognition. In contrast to central-tendency modeling, it is hypothesized that distributions near decision boundaries form a more powerful model for recognition tasks by focusing coding resources on data that are arguably the most diagnostic features. EVT has several important properties: strong statistical grounding, better modeling accuracy near decision boundaries than Gaussian modeling, the ability to model asymmetric decision boundaries, and accurate prediction of the probability of an event beyond our experience. The second part of the book uses the theory to describe a new class of machine learning algorithms for decision making that are a measurable advance beyond the state-of-the-art. This includes methods for post-recognition score analysis, information fusion, multi-attribute spaces, and calibration of supervised machine learning algorithms.