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Author: George A. Tsihrintzis
Publisher: Springer Nature
ISBN: 3030767949
Category : Technology & Engineering
Languages : en
Pages : 237
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Book Description
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Author: George A. Tsihrintzis
Publisher: Springer Nature
ISBN: 3030767949
Category : Technology & Engineering
Languages : en
Pages : 237
Get Book
Book Description
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, “Society 5.0”, the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Author: Maria Virvou
Publisher: Springer
ISBN: 3030137430
Category : Technology & Engineering
Languages : en
Pages : 223
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Book Description
This book presents recent machine learning paradigms and advances in learning analytics, an emerging research discipline concerned with the collection, advanced processing, and extraction of useful information from both educators’ and learners’ data with the goal of improving education and learning systems. In this context, internationally respected researchers present various aspects of learning analytics and selected application areas, including: • Using learning analytics to measure student engagement, to quantify the learning experience and to facilitate self-regulation; • Using learning analytics to predict student performance; • Using learning analytics to create learning materials and educational courses; and • Using learning analytics as a tool to support learners and educators in synchronous and asynchronous eLearning. The book offers a valuable asset for professors, researchers, scientists, engineers and students of all disciplines. Extensive bibliographies at the end of each chapter guide readers to probe further into their application areas of interest.
Author: George A. Tsihrintzis
Publisher:
ISBN: 9783030767952
Category :
Languages : en
Pages : 0
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Book Description
As the 4th Industrial Revolution is restructuring human societal organization into, so-called, "Society 5.0", the field of Machine Learning (and its sub-field of Deep Learning) and related technologies is growing continuously and rapidly, developing in both itself and towards applications in many other disciplines. Researchers worldwide aim at incorporating cognitive abilities into machines, such as learning and problem solving. When machines and software systems have been enhanced with Machine Learning/Deep Learning components, they become better and more efficient at performing specific tasks. Consequently, Machine Learning/Deep Learning stands out as a research discipline due to its worldwide pace of growth in both theoretical advances and areas of application, while achieving very high rates of success and promising major impact in science, technology and society. The book at hand aims at exposing its readers to some of the most significant Advances in Machine Learning/Deep Learning-based Technologies. The book consists of an editorial note and an additional ten (10) chapters, all invited from authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into five parts, namely (i) Machine Learning/Deep Learning in Socializing and Entertainment, (ii) Machine Learning/Deep Learning in Education, (iii) Machine Learning/Deep Learning in Security, (iv) Machine Learning/Deep Learning in Time Series Forecasting, and (v) Machine Learning in Video Coding and Information Extraction. This research book is directed towards professors, researchers, scientists, engineers and students in Machine Learning/Deep Learning-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent Machine Learning/Deep Learning-based technologies. An extensive list of bibliographic references at the end of each chapter guides the readers to probe further into the application areas of interest to them.
Author: George A. Tsihrintzis
Publisher: Springer Nature
ISBN: 3030497240
Category : Computers
Languages : en
Pages : 429
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Book Description
At the dawn of the 4th Industrial Revolution, the field of Deep Learning (a sub-field of Artificial Intelligence and Machine Learning) is growing continuously and rapidly, developing both theoretically and towards applications in increasingly many and diverse other disciplines. The book at hand aims at exposing its reader to some of the most significant recent advances in deep learning-based technological applications and consists of an editorial note and an additional fifteen (15) chapters. All chapters in the book were invited from authors who work in the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into six parts, namely (1) Deep Learning in Sensing, (2) Deep Learning in Social Media and IOT, (3) Deep Learning in the Medical Field, (4) Deep Learning in Systems Control, (5) Deep Learning in Feature Vector Processing, and (6) Evaluation of Algorithm Performance. This research book is directed towards professors, researchers, scientists, engineers and students in computer science-related disciplines. It is also directed towards readers who come from other disciplines and are interested in becoming versed in some of the most recent deep learning-based technological applications. An extensive list of bibliographic references at the end of each chapter guides the readers to probe deeper into their application areas of interest.
Author: M. Arif Wani
Publisher: Springer
ISBN: 9811367949
Category : Technology & Engineering
Languages : en
Pages : 149
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Book Description
This book introduces readers to both basic and advanced concepts in deep network models. It covers state-of-the-art deep architectures that many researchers are currently using to overcome the limitations of the traditional artificial neural networks. Various deep architecture models and their components are discussed in detail, and subsequently illustrated by algorithms and selected applications. In addition, the book explains in detail the transfer learning approach for faster training of deep models; the approach is also demonstrated on large volumes of fingerprint and face image datasets. In closing, it discusses the unique set of problems and challenges associated with these models.
Author: China Info & Comm Tech Grp Corp
Publisher: Springer Nature
ISBN: 9811545847
Category : Computers
Languages : en
Pages : 68
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Book Description
This book is a part of the Blue Book series “Research on the Development of Electronic Information Engineering Technology in China,” which explores the cutting edge of deep learning studies. A subfield of machine learning, deep learning differs from conventional machine learning methods in its ability to learn multiple levels of representation and abstraction by using several layers of nonlinear modules for feature extraction and transformation. The extensive use and huge success of deep learning in speech, CV, and NLP have led to significant advances toward the full materialization of AI. Focusing on the development of deep learning technologies, this book also discusses global trends, the status of deep learning development in China and the future of deep learning.
Author: Luca Oneto
Publisher: Springer
ISBN: 3030168417
Category : Computers
Languages : en
Pages : 392
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Book Description
This book presents the original articles that have been accepted in the 2019 INNS Big Data and Deep Learning (INNS BDDL) international conference, a major event for researchers in the field of artificial neural networks, big data and related topics, organized by the International Neural Network Society and hosted by the University of Genoa. In 2019 INNS BDDL has been held in Sestri Levante (Italy) from April 16 to April 18. More than 80 researchers from 20 countries participated in the INNS BDDL in April 2019. In addition to regular sessions, INNS BDDL welcomed around 40 oral communications, 6 tutorials have been presented together with 4 invited plenary speakers. This book covers a broad range of topics in big data and deep learning, from theoretical aspects to state-of-the-art applications. This book is directed to both Ph.D. students and Researchers in the field in order to provide a general picture of the state-of-the-art on the topics addressed by the conference.
Author: Bharatendra Rai
Publisher: Packt Publishing Ltd
ISBN: 1789534984
Category : Computers
Languages : en
Pages : 339
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Book Description
Discover best practices for choosing, building, training, and improving deep learning models using Keras-R, and TensorFlow-R libraries Key FeaturesImplement deep learning algorithms to build AI models with the help of tips and tricksUnderstand how deep learning models operate using expert techniquesApply reinforcement learning, computer vision, GANs, and NLP using a range of datasetsBook Description Deep learning is a branch of machine learning based on a set of algorithms that attempt to model high-level abstractions in data. Advanced Deep Learning with R will help you understand popular deep learning architectures and their variants in R, along with providing real-life examples for them. This deep learning book starts by covering the essential deep learning techniques and concepts for prediction and classification. You will learn about neural networks, deep learning architectures, and the fundamentals for implementing deep learning with R. The book will also take you through using important deep learning libraries such as Keras-R and TensorFlow-R to implement deep learning algorithms within applications. You will get up to speed with artificial neural networks, recurrent neural networks, convolutional neural networks, long short-term memory networks, and more using advanced examples. Later, you'll discover how to apply generative adversarial networks (GANs) to generate new images; autoencoder neural networks for image dimension reduction, image de-noising and image correction and transfer learning to prepare, define, train, and model a deep neural network. By the end of this book, you will be ready to implement your knowledge and newly acquired skills for applying deep learning algorithms in R through real-world examples. What you will learnLearn how to create binary and multi-class deep neural network modelsImplement GANs for generating new imagesCreate autoencoder neural networks for image dimension reduction, image de-noising and image correctionImplement deep neural networks for performing efficient text classificationLearn to define a recurrent convolutional network model for classification in KerasExplore best practices and tips for performance optimization of various deep learning modelsWho this book is for This book is for data scientists, machine learning practitioners, deep learning researchers and AI enthusiasts who want to develop their skills and knowledge to implement deep learning techniques and algorithms using the power of R. A solid understanding of machine learning and working knowledge of the R programming language are required.
Author: George A. Tsihrintzis
Publisher: Springer Nature
ISBN: 3030871320
Category : Technology & Engineering
Languages : en
Pages : 317
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Book Description
This book aims at exposing its readers to some of the most significant advances in assistive technologies. Assistive technologies develop devices to assist vulnerable people, including elderly people, people with cognitive disabilities such as memory problems or dementia, and people with functional disabilities such as those needing support in toileting, moving around, eating, bathing, dressing, grooming, and taking personal care. The ever-increasing life expectancy and world population, along with the emergence of new diseases and the regrettable continuation of armed conflicts, press for development and availability of assistive technologies that offer people in need the opportunity to live secure and controlled lives with more active participation in society and at a reduced assistive cost. This task can be accomplished only with more advanced devices than traditional-powered wheelchairs or eating/drinking devices, including prosthetics, exoskeletons, visual and hearing aids, cognitive aids, and devices enhanced with artificial intelligence. The book consists of an editorial note and an additional twelve (12) chapters, all of which have been written by authors who work on the corresponding chapter theme and are recognized for their significant research contributions. In more detail, the chapters in the book are organized into four parts, namely (i) Advances in Assistive Technologies in Healthcare, (ii) Advances in Assistive Technologies in Medical Diagnosis, (iii) Advances in Assistive Technologies in Mobility and Navigation, and (iv) Advances in Privacy and Explainability in Assistive Technologies. This research book is directed towards professors, researchers, scientists, engineers, and students in assistive technologies. It is also directed towards readers who come from other disciplines and is interested in becoming versed in some of the most recent assistive technologies. An extensive list of bibliographic references at the end of each chapter guides its readers to probe further into the application areas of interest to them.
Author: M. Arif Wani
Publisher: Springer
ISBN: 9789811567582
Category : Technology & Engineering
Languages : en
Pages : 300
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Book Description
This book presents selected papers from the 18th IEEE International Conference on Machine Learning and Applications (IEEE ICMLA 2019). It focuses on deep learning networks and their application in domains such as healthcare, security and threat detection, fault diagnosis and accident analysis, and robotic control in industrial environments, and highlights novel ways of using deep neural networks to solve real-world problems. Also offering insights into deep learning architectures and algorithms, it is an essential reference guide for academic researchers, professionals, software engineers in industry, and innovative product developers.
