Download or read book Cloud Computing written by Mohammad R. Khosravi and published by Springer Nature. This book was released on 2022-03-22 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 11th International Conference on Cloud Computing, CloudComp 2021, held in December 2021. Due to COVID-19 pandemic the conference was held virtually. The 17 full papers were carefully reviewed and selected from 40 submissions and detail cloud computing technologies for efficient and intelligent computing in secure and smart environments with distributed devices. The theme of CloudComp 2021 was “Cloud Computing for Secure and Smart Applications”. The book is organized in three general areas of data analytics for cloud systems with distributed applications, cloud architecture and challenges in real-world use, and security in cloud/edge platforms.

Download or read book Applied Mathematics Modeling and Computer Simulation written by C.-H. Chen and published by IOS Press. This book was released on 2022-02-25 with total page 1154 pages. Available in PDF, EPUB and Kindle. Book excerpt: The pervasiveness of computers in every field of science, industry and everyday life has meant that applied mathematics, particularly in relation to modeling and simulation, has become ever more important in recent years. This book presents the proceedings of the 2021 International Conference on Applied Mathematics, Modeling and Computer Simulation (AMMCS 2021), hosted in Wuhan, China, and held as a virtual event from 13 to 14 November 2021. The aim of the conference is to foster the knowledge and understanding of recent advances across the broad fields of applied mathematics, modeling and computer simulation, and it provides an annual platform for scholars and researchers to communicate important recent developments in their areas of specialization to colleagues and other scientists in related disciplines. This year more than 150 participants were able to exchange knowledge and discuss recent developments via the conference. The book contains 115 peer-reviewed papers, selected from more than 250 submissions and ranging from the theoretical and conceptual to the strongly pragmatic and all addressing industrial best practice. Topics covered include mathematical modeling and applications, engineering applications and scientific computations, and the simulation of intelligent systems. Providing an overview of recent development and with a mix of practical experiences and enlightening ideas, the book will be of interest to researchers and practitioners everywhere.

Download or read book Cyberspace Data and Intelligence and Cyber Living Syndrome and Health written by Huansheng Ning and published by Springer Nature. This book was released on 2020-12-01 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the proceedings of the Forth International Conference on Cyberspace Data and Intelligence, Cyber DI 2020, and the International Conference on Cyber-Living, Cyber-Syndrome, and Cyber-Health, CyberLife 2020, held under the umbrella of the 2020 Cyberspace Congress, held in Beijing, China, in December 2020.* The 13 full papers presented were carefully reviewed and selected from 36 submissions. The papers are grouped in the following topics: machine learning and ubiquitous and intelligent computing. * The conference was held virtually due to the COVID-19 pandemic.

Download or read book Proceedings of the International Conference on Cognitive and Intelligent Computing written by Amit Kumar and published by Springer Nature. This book was released on 2022-10-31 with total page 899 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents original, peer-reviewed select articles from the International Conference on Cognitive & Intelligent Computing (ICCIC – 2021), held on December 11–12, 2021, at Hyderabad, India. The proceedings has cutting edge Research outcome related to Machine learning in control applications, Soft computing, Pattern Recognition, Decision Support Systems, Text analytics and NLP, Statistical Learning, Neural Network Learning, Learning Through Fuzzy Logic, Learning Through Evolution (Evolutionary Algorithms), Reinforcement Learning, Multi-Strategy Learning, Cooperative Learning, Planning And Learning, Multi-Agent Learning, Online And Incremental Learning, Scalability Of Learning Algorithms, Inductive Learning, Inductive Logic Programming, Bayesian Networks, Support Vector Machines, Case-Based Reasoning, Multi-Agent Systems, Human–Computer Interaction, Data Mining and Knowledge Discovery, Knowledge Management and Networks, Data Intensive Computing Architecture, Medicine, Health, Bioinformatics, and Systems Biology, Industrial and Engineering Applications, Security Applications, Smart Cities, Game Playing and Problem Solving, Intelligent Virtual Environments, Economics, Business, And Forecasting Applications. Articles in the book are carefully selected on the basis of their application orientation. The content is expected to be especially useful for Professionals, Researchers, Research students working in the area of cognitive and intelligent computing.

Download or read book Advances in Artificial Intelligence and Machine Learning in Big Data Processing written by R. Geetha and published by Springer Nature. This book was released on with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Towards sustainable urban development Use of geographic big data for spatial planning written by Ying Jing and published by Frontiers Media SA. This book was released on 2023-06-01 with total page 374 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Extended Selected Papers of the 14th International Conference on Information Intelligence Systems and Applications written by Nikolaos Bourbakis and published by Springer Nature. This book was released on with total page 439 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Proceedings of the 5th International Conference on Big Data and Internet of Things written by Mohamed Lazaar and published by Springer Nature. This book was released on 2022-07-03 with total page 600 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a collection of papers in the research area of big data, cloud computing, cybersecurity, machine learning, deep learning, e-learning, Internet of Things, reinforcement learning, information system, social media and natural language processing. This book includes papers presented at the 5th International Conference on Big Data Cloud and Internet of Things, BDIoT 2021 during March 17–18, 2021, at ENSIAS, Mohammed V University in Rabat, Morocco.

Download or read book Recurrent Neural Networks for Short Term Load Forecasting written by Filippo Maria Bianchi and published by Springer. This book was released on 2017-11-09 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: The key component in forecasting demand and consumption of resources in a supply network is an accurate prediction of real-valued time series. Indeed, both service interruptions and resource waste can be reduced with the implementation of an effective forecasting system. Significant research has thus been devoted to the design and development of methodologies for short term load forecasting over the past decades. A class of mathematical models, called Recurrent Neural Networks, are nowadays gaining renewed interest among researchers and they are replacing many practical implementations of the forecasting systems, previously based on static methods. Despite the undeniable expressive power of these architectures, their recurrent nature complicates their understanding and poses challenges in the training procedures. Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet. This work performs a comparative study on the problem of Short-Term Load Forecast, by using different classes of state-of-the-art Recurrent Neural Networks. The authors test the reviewed models first on controlled synthetic tasks and then on different real datasets, covering important practical cases of study. The text also provides a general overview of the most important architectures and defines guidelines for configuring the recurrent networks to predict real-valued time series.

Download or read book Introduction to Deep Learning written by Sandro Skansi and published by Springer. This book was released on 2018-02-04 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook presents a concise, accessible and engaging first introduction to deep learning, offering a wide range of connectionist models which represent the current state-of-the-art. The text explores the most popular algorithms and architectures in a simple and intuitive style, explaining the mathematical derivations in a step-by-step manner. The content coverage includes convolutional networks, LSTMs, Word2vec, RBMs, DBNs, neural Turing machines, memory networks and autoencoders. Numerous examples in working Python code are provided throughout the book, and the code is also supplied separately at an accompanying website. Topics and features: introduces the fundamentals of machine learning, and the mathematical and computational prerequisites for deep learning; discusses feed-forward neural networks, and explores the modifications to these which can be applied to any neural network; examines convolutional neural networks, and the recurrent connections to a feed-forward neural network; describes the notion of distributed representations, the concept of the autoencoder, and the ideas behind language processing with deep learning; presents a brief history of artificial intelligence and neural networks, and reviews interesting open research problems in deep learning and connectionism. This clearly written and lively primer on deep learning is essential reading for graduate and advanced undergraduate students of computer science, cognitive science and mathematics, as well as fields such as linguistics, logic, philosophy, and psychology.

Download or read book Advanced Intelligent Technologies and Sustainable Society written by Kazumi Nakamatsu and published by Springer Nature. This book was released on with total page 632 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. This book provides a synthesis and overview of graph representation learning. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs—a nascent but quickly growing subset of graph representation learning.

Download or read book Guide to Convolutional Neural Networks written by Hamed Habibi Aghdam and published by Springer. This book was released on 2017-05-17 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This must-read text/reference introduces the fundamental concepts of convolutional neural networks (ConvNets), offering practical guidance on using libraries to implement ConvNets in applications of traffic sign detection and classification. The work presents techniques for optimizing the computational efficiency of ConvNets, as well as visualization techniques to better understand the underlying processes. The proposed models are also thoroughly evaluated from different perspectives, using exploratory and quantitative analysis. Topics and features: explains the fundamental concepts behind training linear classifiers and feature learning; discusses the wide range of loss functions for training binary and multi-class classifiers; illustrates how to derive ConvNets from fully connected neural networks, and reviews different techniques for evaluating neural networks; presents a practical library for implementing ConvNets, explaining how to use a Python interface for the library to create and assess neural networks; describes two real-world examples of the detection and classification of traffic signs using deep learning methods; examines a range of varied techniques for visualizing neural networks, using a Python interface; provides self-study exercises at the end of each chapter, in addition to a helpful glossary, with relevant Python scripts supplied at an associated website. This self-contained guide will benefit those who seek to both understand the theory behind deep learning, and to gain hands-on experience in implementing ConvNets in practice. As no prior background knowledge in the field is required to follow the material, the book is ideal for all students of computer vision and machine learning, and will also be of great interest to practitioners working on autonomous cars and advanced driver assistance systems.

Download or read book Proceedings of the 2nd International Conference on Internet of Things Communication and Intelligent Technology written by Jian Dong and published by Springer Nature. This book was released on with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Deep Learning for Robot Perception and Cognition written by Alexandros Iosifidis and published by Academic Press. This book was released on 2022-02-04 with total page 638 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Download or read book Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence written by Anitha S. Pillai and published by Academic Press. This book was released on 2022-02-23 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Augmenting Neurological Disorder Prediction and Rehabilitation Using Artificial Intelligence focuses on how the neurosciences can benefit from advances in AI, especially in areas such as medical image analysis for the improved diagnosis of Alzheimer's disease, early detection of acute neurologic events, prediction of stroke, medical image segmentation for quantitative evaluation of neuroanatomy and vasculature, diagnosis of Alzheimer's Disease, autism spectrum disorder, and other key neurological disorders. Chapters also focus on how AI can help in predicting stroke recovery, and the use of Machine Learning and AI in personalizing stroke rehabilitation therapy. Other sections delve into Epilepsy and the use of Machine Learning techniques to detect epileptogenic lesions on MRIs and how to understand neural networks. - Provides readers with an understanding on the key applications of artificial intelligence and machine learning in the diagnosis and treatment of the most important neurological disorders - Integrates recent advancements of artificial intelligence and machine learning to the evaluation of large amounts of clinical data for the early detection of disorders such as Alzheimer's Disease, autism spectrum disorder, Multiple Sclerosis, headache disorder, Epilepsy, and stroke - Provides readers with illustrative examples of how artificial intelligence can be applied to outcome prediction, neurorehabilitation and clinical exams, including a wide range of case studies in predicting and classifying neurological disorders

Download or read book Machine Learning and Knowledge Discovery in Databases Research Track written by Nuria Oliver and published by Springer Nature. This book was released on 2021-09-09 with total page 838 pages. Available in PDF, EPUB and Kindle. Book excerpt: The multi-volume set LNAI 12975 until 12979 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2021, which was held during September 13-17, 2021. The conference was originally planned to take place in Bilbao, Spain, but changed to an online event due to the COVID-19 pandemic. The 210 full papers presented in these proceedings were carefully reviewed and selected from a total of 869 submissions. The volumes are organized in topical sections as follows: Research Track: Part I: Online learning; reinforcement learning; time series, streams, and sequence models; transfer and multi-task learning; semi-supervised and few-shot learning; learning algorithms and applications. Part II: Generative models; algorithms and learning theory; graphs and networks; interpretation, explainability, transparency, safety. Part III: Generative models; search and optimization; supervised learning; text mining and natural language processing; image processing, computer vision and visual analytics. Applied Data Science Track: Part IV: Anomaly detection and malware; spatio-temporal data; e-commerce and finance; healthcare and medical applications (including Covid); mobility and transportation. Part V: Automating machine learning, optimization, and feature engineering; machine learning based simulations and knowledge discovery; recommender systems and behavior modeling; natural language processing; remote sensing, image and video processing; social media.