Machine Learning For Audio Image And Video Analysis

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Machine Learning For Audio Image And Video Analysis

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Author : Francesco Camastra
language : en
Publisher: Springer
Release Date : 2015-07-21

Machine Learning For Audio Image And Video Analysis written by Francesco Camastra and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-07-21 with Computers categories.

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine Learning includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part Applications shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

Strengthening Deep Neural Networks

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Author : Katy Warr
language : en
Publisher: O'Reilly Media
Release Date : 2019-07-03

Strengthening Deep Neural Networks written by Katy Warr and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-03 with Computers categories.

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re a data scientist developing DNN algorithms, a security architect interested in how to make AI systems more resilient to attack, or someone fascinated by the differences between artificial and biological perception, this book is for you. Delve into DNNs and discover how they could be tricked by adversarial input Investigate methods used to generate adversarial input capable of fooling DNNs Explore real-world scenarios and model the adversarial threat Evaluate neural network robustness; learn methods to increase resilience of AI systems to adversarial data Examine some ways in which AI might become better at mimicking human perception in years to come

Machine Learning For Spatial Environmental Data

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Author : Mikhail Kanevski
language : en
Publisher: CRC Press
Release Date : 2009-06-09

Machine Learning For Spatial Environmental Data written by Mikhail Kanevski and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-06-09 with Computers categories.

This book discusses machine learning algorithms, such as artificial neural networks of different architectures, statistical learning theory, and Support Vector Machines used for the classification and mapping of spatially distributed data. It presents basic geostatistical algorithms as well. The authors describe new trends in machine lea

Machine Learning Paradigms

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Author : Aristomenis S. Lampropoulos
language : en
Publisher: Springer
Release Date : 2015-06-13

Machine Learning Paradigms written by Aristomenis S. Lampropoulos and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-06-13 with Technology & Engineering categories.

This timely book presents Applications in Recommender Systems which are making recommendations using machine learning algorithms trained via examples of content the user likes or dislikes. Recommender systems built on the assumption of availability of both positive and negative examples do not perform well when negative examples are rare. It is exactly this problem that the authors address in the monograph at hand. Specifically, the books approach is based on one-class classification methodologies that have been appearing in recent machine learning research. The blending of recommender systems and one-class classification provides a new very fertile field for research, innovation and development with potential applications in “big data” as well as “sparse data” problems. The book will be useful to researchers, practitioners and graduate students dealing with problems of extensive and complex data. It is intended for both the expert/researcher in the fields of Pattern Recognition, Machine Learning and Recommender Systems, as well as for the general reader in the fields of Applied and Computer Science who wishes to learn more about the emerging discipline of Recommender Systems and their applications. Finally, the book provides an extended list of bibliographic references which covers the relevant literature completely.

Deep Learning For Multimedia Forensics

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Author : Irene Amerini
language : en
Publisher:
Release Date : 2021-08-31

Deep Learning For Multimedia Forensics written by Irene Amerini and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-31 with Computers categories.

In this survey, the latest trends and deep-learning-based techniques for multimedia forensics are introduced, in both architectural and data-processing. The publication is intended for researchers, students and professionals active in the fields of Deep Learning and Multimedia Forensics.

Fundamentals And Methods Of Machine And Deep Learning

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Author : Pradeep Singh
language : en
Publisher: John Wiley & Sons
Release Date : 2022-03-02

Fundamentals And Methods Of Machine And Deep Learning written by Pradeep Singh 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 2022-03-02 with Computers categories.

FUNDAMENTALS AND METHODS OF MACHINE AND DEEP LEARNING The book provides a practical approach by explaining the concepts of machine learning and deep learning algorithms, evaluation of methodology advances, and algorithm demonstrations with applications. Over the past two decades, the field of machine learning and its subfield deep learning have played a main role in software applications development. Also, in recent research studies, they are regarded as one of the disruptive technologies that will transform our future life, business, and the global economy. The recent explosion of digital data in a wide variety of domains, including science, engineering, Internet of Things, biomedical, healthcare, and many business sectors, has declared the era of big data, which cannot be analysed by classical statistics but by the more modern, robust machine learning and deep learning techniques. Since machine learning learns from data rather than by programming hard-coded decision rules, an attempt is being made to use machine learning to make computers that are able to solve problems like human experts in the field. The goal of this book is to present a??practical approach by explaining the concepts of machine learning and deep learning algorithms with applications. Supervised machine learning algorithms, ensemble machine learning algorithms, feature selection, deep learning techniques, and their applications are discussed. Also included in the eighteen chapters is unique information which provides a clear understanding of concepts by using algorithms and case studies illustrated with applications of machine learning and deep learning in different domains, including disease prediction, software defect prediction, online television analysis, medical image processing, etc. Each of the chapters briefly described below provides both a chosen approach and its implementation. Audience Researchers and engineers in artificial intelligence, computer scientists as well as software developers.

Multimedia Services In Intelligent Environments

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Author : George A Tsihrintzis
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-09-08

Multimedia Services In Intelligent Environments written by George A Tsihrintzis and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-09-08 with Technology & Engineering categories.

KES International (KES) is a worldwide organisation that provides a professional community and association for researchers, originally in the discipline of Knowledge Based and Intelligent Engineering Systems, but now extending into other related areas. Through this, KES provides its members with opportunities for publication and beneficial interaction. The focus of KES is research and technology transfer in the area of Intelligent S- tems, i.e. computer-based software systems that operate in a manner analogous to the human brain, in order to perform advanced tasks. Recently KES has started to extend its area of interest to encompass the contribution that intelligent systems can make to sustainability and renewable energy, and also the knowledge transfer, innovation and enterprise agenda. Involving several thousand researchers, managers and engineers drawn from u- versities and companies world-wide, KES is in an excellent position to facilitate - ternational research co-operation and generate synergy in the area of artificial intel- gence applied to real-world ‘Smart’ systems and the underlying related theory. The KES annual conference covers a broad spectrum of intelligent systems topics and attracts several hundred delegates from a range of countries round the world. KES also organises symposia on specific technical topics, for example, Agent and Multi Agent Systems, Intelligent Decision Technologies, Intelligent Interactive M- timedia Systems and Services, Sustainability in Energy and Buildings and Innovations through Knowledge Transfer. KES is responsible for two peer-reviewed journals, the International Journal of Knowledge based and Intelligent Engineering Systems, and Intelligent Decision Technologies: an International Journal.

Machine Learning In Signal Processing

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Author : Sudeep Tanwar
language : en
Publisher: CRC Press
Release Date : 2021-12-10

Machine Learning In Signal Processing written by Sudeep Tanwar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Technology & Engineering categories.

Machine Learning in Signal Processing: Applications, Challenges, and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML, as the driving force of the wave of artificial intelligence (AI), provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML, and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students, post-graduate students, research scholars, faculties, and academicians of computer science and engineering, computer science and applications, and electronics and telecommunication engineering.

Agents And Artificial Intelligence

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Author : Joaquim Filipe
language : en
Publisher: Springer
Release Date : 2014-10-30

Agents And Artificial Intelligence written by Joaquim Filipe and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-30 with Computers categories.

This book constitutes the thoroughly refereed post-conference proceedings of the 5th International Conference on Agents and Artificial Intelligence, ICAART 2013, held in Barcelona, Spain, in February 2013. The 20 revised full papers presented together with one invited paper were carefully reviewed and selected from 269 submissions. The papers are organized in two topical sections on artificial intelligence and on agents.

Machine Learning Applications In Electromagnetics And Antenna Array Processing

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Author : Manel Martínez-Ramón
language : en
Publisher: Artech House
Release Date : 2021-04-30

Machine Learning Applications In Electromagnetics And Antenna Array Processing written by Manel Martínez-Ramón and has been published by Artech House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-30 with Technology & Engineering categories.

This practical resource provides an overview of machine learning (ML) approaches as applied to electromagnetics and antenna array processing. Detailed coverage of the main trends in ML, including uniform and random array processing (beamforming and detection of angle of arrival), antenna optimization, wave propagation, remote sensing, radar, and other aspects of electromagnetic design are explored. An introduction to machine learning principles and the most common machine learning architectures and algorithms used today in electromagnetics and other applications is presented, including basic neural networks, gaussian processes, support vector machines, kernel methods, deep learning, convolutional neural networks, and generative adversarial networks. Applications in electromagnetics and antenna array processing that are solved using machine learning are discussed, including antennas, remote sensing, and target classification.