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Broad Learning Through Fusions by Jiawei Zhang,Philip S. Yu Pdf
This book offers a clear and comprehensive introduction to broad learning, one of the novel learning problems studied in data mining and machine learning. Broad learning aims at fusing multiple large-scale information sources of diverse varieties together, and carrying out synergistic data mining tasks across these fused sources in one unified analytic. This book takes online social networks as an application example to introduce the latest alignment and knowledge discovery algorithms. Besides the overview of broad learning, machine learning and social network basics, specific topics covered in this book include network alignment, link prediction, community detection, information diffusion, viral marketing, and network embedding.
Integrating Artificial Intelligence and Machine Learning with Blockchain Security by D. Jeya Mala,R. Ganesan Pdf
Due to its transparency and dependability in secure online transactions, blockchain technology has grown in prominence in recent years. Several industries, including those of finance, healthcare, energy and utilities, manufacturing, retail marketing, entertainment and media, supply chains, e-commerce, and e-business, among others, use blockchain technology. In order to enable intelligent decision-making to prevent security assaults, particularly in permission-less blockchain platforms, artificial intelligence (AI) techniques and machine learning (ML) algorithms are used. By exploring the numerous use cases and security methods used in each of them, this book offers insight on the application of AI and ML in blockchain security principles. The book argues that it is crucial to include artificial intelligence and machine learning techniques in blockchain technology in order to increase security.
Machine Learning for Transportation Research and Applications by Yinhai Wang,Zhiyong Cui,Ruimin Ke Pdf
Transportation is a combination of systems that presents a variety of challenges often too intricate to be addressed by conventional parametric methods. Increasing data availability and recent advancements in machine learning provide new methods to tackle challenging transportation problems. This textbookis designed for college or graduate-level students in transportation or closely related fields to study and understand fundamentals in machine learning. Readers will learn how to develop and apply various types of machine learning models to transportation-related problems. Example applications include traffic sensing, data-quality control, traffic prediction, transportation asset management, traffic-system control and operations, and traffic-safety analysis. Introduces fundamental machine learning theories and methodologies Presents state-of-the-art machine learning methodologies and their incorporation into transportationdomain knowledge Includes case studies or examples in each chapter that illustrate the application of methodologies andtechniques for solving transportation problems Provides practice questions following each chapter to enhance understanding and learning Includes class projects to practice coding and the use of the methods
Cognitive Systems and Information Processing by Fuchun Sun,Qinghu Meng,Zhumu Fu,Bin Fang Pdf
The two-volume set CCIS 1918 and 1919 constitutes the refereed post-conference proceedings of the 8th International Conference on Cognitive Systems and Information Processing, ICCSIP 2023, held in Luoyang, China, during August 10–12, 2023. The 52 full papers presented in these proceedings were carefully reviewed and selected from 136 submissions. The papers are organized in the following topical sections: Volume I : Award; Algorithm & Control; and Application. Volume II: Robotics & Bioinformatics; and Vision.
Data Fusion Techniques and Applications for Smart Healthcare by Amit Kumar Singh,Stefano Berretti Pdf
Medical data exists in several formats, from structured data and medical reports to 1D signals, 2D images, 3D volumes, or even higher dimensional data such as temporal 3D sequences. Healthcare experts can make auscultation reports in text format; electrocardiograms can be printed in time series format, x-rays saved as images; volume can be provided through angiography; temporal information by echocardiograms, and 4D information extracted through flow MRI. Another typical source of variability is the existence of data from different time points, such as pre and post treatment, for instance. These large and highly diverse amounts of information need to be organized and mined in an appropriate way so that meaningful information can be extracted. New multimodal data fusion techniques are able to combine salient information into one single source to ensure better diagnostic accuracy and assessment. Data Fusion Techniques and Applications for Smart Healthcare covers cutting-edge research from both academia and industry with a particular emphasis on recent advances in algorithms and applications that involve combining multiple sources of medical information. This book can be used as a reference for practicing engineers, scientists, and researchers. It will also be useful for graduate students and practitioners from government and industry as well as healthcare technology professionals working on state-of-the-art information fusion solutions for healthcare applications. Presents broad coverage of applied case studies using data fusion techniques to mine, organize, and interpret medical data Investigates how data fusion techniques offer a new solution for dealing with massive amounts of medical data coming from diverse sources and multiple formats Focuses on identifying challenges, solutions, and new directions that will be useful for graduate students, researchers, and practitioners from government, academia, industry, and healthcare
Deep Learning by Ian Goodfellow,Yoshua Bengio,Aaron Courville Pdf
An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
Social Computing with Artificial Intelligence by Xun Liang Pdf
This book provides a comprehensive introduction to the application of artificial intelligence in social computing, from fundamental data processing to advanced social network computing. To broaden readers’ understanding of the topics addressed, it includes extensive data and a large number of charts and references, covering theories, techniques and applications. It particularly focuses on data collection, data mining, artificial intelligence algorithms in social computing, and several key applications of social computing application, and also discusses network propagation mechanisms and dynamic analysis, which provide useful insights into how information is disseminated in online social networks. This book is intended for readers with a basic knowledge of advanced mathematics and computer science.
Emotion Recognition and Understanding for Emotional Human-Robot Interaction Systems by Luefeng Chen,Min Wu,Witold Pedrycz,Kaoru Hirota Pdf
This book focuses on the key technologies and scientific problems involved in emotional robot systems, such as multimodal emotion recognition (i.e., facial expression/speech/gesture and their multimodal emotion recognition) and emotion intention understanding, and presents the design and application examples of emotional HRI systems. Aiming at the development needs of emotional robots and emotional human–robot interaction (HRI) systems, this book introduces basic concepts, system architecture, and system functions of affective computing and emotional robot systems. With the professionalism of this book, it serves as a useful reference for engineers in affective computing, and graduate students interested in emotion recognition and intention understanding. This book offers the latest approaches to this active research area. It provides readers with the state-of-the-art methods of multimodal emotion recognition, intention understanding, and application examples of emotional HRI systems.
After her mother dies in 1975, ten-year-old Lemonade must live with her grandfather in a small town famous for Bigfoot sitings and soon becomes friends with Tobin, a quirky Bigfoot investigator.
e-Learning, e-Education, and Online Training by Weina Fu,Guanglu Sun Pdf
The two-volume set, LNICST 453 and 454 constitutes the proceedings of the 8th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2022, held in Harbin, China, in July 2022. The 111 papers presented in this volume were carefully reviewed and selected from 226 submissions. This conference has brought researchers, developers and practitioners around the world who are leveraging and developing e-educational technologies as well as related learning, training, and practice methods. The theme of eLEOT 2022 was “New Trend of Information Technology and Artificial Intelligence in Education”. They were organized in topical sections as follows: IT promoted Teaching Platforms and Systems; AI based Educational Modes and Methods; Automatic Educational Resource Processing; Educational Information Evaluation.
Application of Intelligent Systems in Multi-modal Information Analytics by Vijayan Sugumaran,A. G. Sreedevi,Zheng Xu Pdf
This book provides comprehensive coverage of the latest advances and trends in information technology, science and engineering. Specifically, it addresses a number of broad themes, including multi-modal informatics, data mining, agent-based and multi-agent systems for health and education informatics, which inspire the development of intelligent information technologies. The book covers a wide range of topics such as AI applications and innovations in health and education informatics; data and knowledge management; multi-modal application management; and web/social media mining for multi-modal informatics. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and a useful reference guide for newcomers to the field. This book is a compilation of the papers presented in the 4th International Conference on Multi-modal Information Analytics, held online, on April 23, 2022.
Computational Data and Social Networks by Andrea Tagarelli,Hanghang Tong Pdf
This book constitutes the refereed proceedings of the 8th International Conference on Computational Data and Social Networks, CSoNet 2019, held in Ho Chi Minh City, Vietnam, in November 2019. The 22 full and 8 short papers presented in this book were carefully reviewed and selected from 120 submissions. The papers appear under the following topical headings: Combinatorial Optimization and Learning; Influence Modeling, Propagation, and Maximization; NLP and Affective Computing; Computational Methods for Social Good; and User Profiling and Behavior Modeling.
Advances in Intelligent Information Hiding and Multimedia Signal Processing by Shu-Chuan Chu,Shi-Huang Chen,Zhenyu Meng,Keun Ho Ryu,George A. Tsihrintzis Pdf
The book presents selected papers from the 17th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, in conjunction with the 14th International Conference on Frontiers of Information Technology, Applications and Tools, held on October 29 – 31, 2021, in Kaohsiung, Taiwan. It is divided into two volumes and discusses latest research outcomes in the field of information technology (IT) including but not limited to information hiding, multimedia signal processing, big data, data mining, bioinformatics, database, industrial and Internet of things, and their applications.
Author : CJ Meadows Publisher : Walter de Gruyter GmbH & Co KG Page : 498 pages File Size : 48,5 Mb Release : 2020-01-20 Category : Business & Economics ISBN : 9781547401505
Just as nuclear fusion produces massive energy from combining two nuclei, a fusion in business, technology, and the arts can release massive value—creating whole new companies, industries, and human capabilities. Examples of the fusion technique for high-value, radical innovation are presented in this unique collection of stories about innovating across industries, fields, organizational silos, nations, social class, and more. This book is the result of a global research study of 30 world-class innovators who have collectively created billions of dollars’ worth of business value, as well as new advances in the arts and sciences that bring joy to the world and can save millions of lives. Insights from the journeys of the innovators provided in this book will help leaders, organizations, and individuals succeed in their innovative endeavors. In addition, each chapter provides a link to a short video that provides further insights, mostly from the innovators themselves. Innovation through Fusion is essential reading for individual innovators who would like to create the future; teams and organizations that need to craft radical or high-value innovations (especially across industries or organizational silos); and leaders concerned about declining returns on innovation efforts and uncertain about organizational survival in a disruptive world. The author provides a new model of lateral innovation—useful both as an innovation process and as a framework to assess your lateral innovation capabilities. The book is replete with value-creation examples of lives saved, billions of dollars of savings/growth, and new products, services, and companies, as well as stories of leading lateral innovators—who they are and how they succeeded. For the author’s talk on Fusion at EmTech Asia/MIT Technology Review, featured in Asian Scientist magazine, click here: https://www.asianscientist.com/2019/04/features/ipi-singapore-emtech-asia-cj-meadows-innovation/
Deep Learning for Robot Perception and Cognition by Alexandros Iosifidis,Anastasios Tefas Pdf
Deep Learning for Robot Perception and Cognition introduces a broad range of topics and methods in deep learning for robot perception and cognition together with end-to-end methodologies. The book provides the conceptual and mathematical background needed for approaching a large number of robot perception and cognition tasks from an end-to-end learning point-of-view. The book is suitable for students, university and industry researchers and practitioners in Robotic Vision, Intelligent Control, Mechatronics, Deep Learning, Robotic Perception and Cognition tasks. Presents deep learning principles and methodologies Explains the principles of applying end-to-end learning in robotics applications Presents how to design and train deep learning models Shows how to apply deep learning in robot vision tasks such as object recognition, image classification, video analysis, and more Uses robotic simulation environments for training deep learning models Applies deep learning methods for different tasks ranging from planning and navigation to biosignal analysis
