Advanced Information Networking and Applications: Proceedings of the 34th International Conference on Advanced Information Networking and Applications, Barolli Leonard, Amato Flora, Moscato Francesco
Автор: Saifullah Khalid Название: Applications of Artificial Intelligence in Electrical Engineering ISBN: 1799827186 ISBN-13(EAN): 9781799827184 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 42451.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Artificial intelligence is increasingly finding its way into industrial and manufacturing contexts. The prevalence of AI in industry from stock market trading to manufacturing makes it easy to forget how complex artificial intelligence has become. Engineering provides various current and prospective applications of these new and complex artificial intelligence technologies.
Applications of Artificial Intelligence in Electrical Engineering is a critical research book that examines the advancing developments in artificial intelligence with a focus on theory and research and their implications. Highlighting a wide range of topics such as evolutionary computing, image processing, and swarm intelligence, this book is essential for engineers, manufacturers, technology developers, IT specialists, managers, academicians, researchers, computer scientists, and students.
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.
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.
Adaptive Multiple-view Label Propagation for Semi-Supervised Classication.- Container Damage Identication based on Fmask-RCNN.- Multiagent Reinforcement Learning for Combinatorial Optimization.- Extended Kalman Filter-Based Adaptively Sliding Mode Control with Dead-Zone Compensator for An Anchor-Hole Driller.- Exploring Multi-scale Deep Encoder-decoder and PatchGAN for Perceptual Ultrasound Image Super-resolution.- Reliable neighbors-based Collaborative Filtering for Recommendation Systems.- Downhole condition identication for geological drilling processes based on qualitative trend analysis and expert rules.- Adaptive Neural Network Control for Double-Pendulum Tower Crane Systems.- Mengh Generalized Locally-linear embedding: A Neural Network Implementation.- Semi-supervised Feature Selection Using Sparse Laplacian Support Vector Machine.- Tailored Pruning via Rollback Learning.- Coordinative Hyper-Heuristic Resource Scheduling in Mobile Cellular Networks.- Latent Sparse Discriminative Learning for Face Image Set Classication.- Sparse multi-task least-squares support vector machine.- Discriminative Subspace Learning for Cross-view Classication with Simultaneous Local and Global Alignment.- A Recognition Method of Hand Gesture based on Dual-SDAE.- Image Generation from Layout via Pair-wise RaGAN.- Learning Unsupervised Video Summarization with Semantic-consistent Network.- Sustainable Competitiveness Evaluation for Container Liners Using a Novel Hybrid Method with Intuitionistic Fuzzy Linguistic Variables.- 2-Dimensional Interval Neutrosophic Linguistic Numbers and Their Utilization in Group Decision Making.- Cross-Modal N-Pair Network for Generalized Zero-Shot Learning.- A Bi-Directional Relation Aware Network for Link Prediction in Knowledge Graph.- Deep K-Means: A Simple and Eective Method for Data Clustering.- Brain Storm Optimization Algorithms: A Brief Review.- A Binary Superior Tracking Articial Bee Colony for Feature Selection.- A Hybrid Neural Network RBERT-C based on Pre-trained RoBERTa and CNN for User Intent Classication.- Image Registration Algorithm Based on Manifold Regularization with Thin-plate Spline Model.- Optimal Control of Nonlinear Time-delay Systems with Input Constraints Using Reinforcement Learning.- Disaggregated Power System Signal Recognition using Capsule Network.- Design of echo state network with coordinate descent method and l1 regularization.- An advanced actor-critic algorithm for training video game AI.- Neural Network-Based Adaptive Control for EMS Type Maglev Vehicle Systems with Time-Varying Mass.- A Novel Collision-Avoidance TDMA MAC Protocol for Fog-assisted VCNs.- Design optimization of plate-n heat exchanger using sine cosine algorithm.- Mr-ResNeXt: A Multi-resolution Network Architecture for Detection of Obstructive Sleep Apnea.- Scalable Multi-Agent Reinforcement Learning Architecture for Semi-MDP Real-Time Strategy Games.- Stacked Deep Learning Structure with Bidirectional Long-Short Term Memory for Stock Market Prediction.- Multi-period Distributed Delay-sensitive Tasks Ooading in a Two-layer Vehicular Fog Computing Architecture.- Template-Enhanced Aspect Term Extraction with Bi-contextual Convolutional Neural Networks.- Privacy Sensitive Large-Margin Model for Face De-identication.- Balancing of Bike-sharing networks via constrained model predictive control.- RLV Reentry Trajectory Optimization Design Based on Improved Differential Evolution Algorithm.- A Tree-structure Convolutional Neural Network for Temporal Features Exaction on Sensor-based Multi-resident Activity Recognition.
Описание: This book gathers selected papers presented at the conference "Advances in 3D Image and Graphics Representation, Analysis, Computing and Information Technology," one of the first initiatives devoted to the problems of 3D imaging in all contemporary scientific and application areas.
Описание: This proceedings book presents the latest research findings, and theoretical and practical perspectives on innovative methods and development techniques related to the emerging areas of Web computing, intelligent systems and Internet computing.
Автор: Leonard Barolli; Makoto Takizawa; Fatos Xhafa; Tom Название: Advanced Information Networking and Applications ISBN: 3030150313 ISBN-13(EAN): 9783030150310 Издательство: Springer Рейтинг: Цена: 27950.00 р. Наличие на складе: Поставка под заказ.
Описание: The aim of the book is to provide latest research findings, innovative research results, methods and development techniques from both theoretical and practical perspectives related to the emerging areas of information networking and applications. Networks of today are going through a rapid evolution and there are many emerging areas of information networking and their applications. Heterogeneous networking supported by recent technological advances in low power wireless communications along with silicon integration of various functionalities such as sensing, communications, intelligence and actuations are emerging as a critically important disruptive computer class based on a new platform, networking structure and interface that enable novel, low cost and high volume applications. Several of such applications have been difficult to realize because of many interconnections problems. To fulfill their large range of applications different kinds of networks need to collaborate and wired and next generation wireless systems should be integrated in order to develop high performance computing solutions to problems arising from the complexities of these networks. This book covers the theory, design and applications of computer networks, distributed computing and information systems.
Описание: This book constitutes the refereed proceedings of the 34th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2020, held in Regensburg, Germany, in June 2020.*The 14 full papers and 8 short papers presented were carefully reviewed and selected from 39 submissions.
Описание: The papers are grouped in topical sections on world wide web, recommendation, query processing and algorithm, natural language processing, machine learning, graph query, edge computing and data mining, data privacy and security, and blockchain.
Автор: Siddhartha Bhattacharyya, Vaclav Snasel, Aboul Ella Hassanien, Satadal Saha, B. K. Tripathy Название: Deep Learning: Research and Applications ISBN: 3110670798 ISBN-13(EAN): 9783110670790 Издательство: Walter de Gruyter Цена: 20446.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book will focus on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it would provide an insight of deep neural networks in action with illustrative coding examples. Moreover, the book will also provide video demonstrations on each chapter. Deep learning is a new area of machine learning research, which has been introduced with the objective of moving ML closer to one of its original goals, i.e. artificial intelligence. Deep learning was developed as an ML approach to deal with complex input-output mappings. While traditional methods successfully solve problems where final value is a simple function of input data, deep learning techniques are able to capture composite relations between non immediately related fields, for example between air pressure recordings and english words, millions of pixels and textual description, brand-related news and future stock prices and almost all real world problems. Deep learning is a class of nature inspired machine learning algorithms that uses a cascade of multiple layers of nonlinear processing units for feature extraction and transformation. Each successive layer uses the output from the previous layer as input. The learning may be supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners. These algorithms learn multiple levels of representations that correspond to different levels of abstraction by resorting to some form of gradient descent for training via backpropagation. Layers that have been used in deep learning include hidden layers of an artificial neural network and sets of propositional formulas. They may also include latent variables organized layer-wise in deep generative models such as the nodes in deep belief networks and deep boltzmann machines. Deep learning is part of state-of-the-art systems in various disciplines, particularly computer vision, automatic speech recognition (ASR) and human action recognition. The unique features of this book include: • tutorials on deep learning framework with focus on tensor flow, keras etc. • video demonstration of each chapter for enabling the readers to have a good understanding of the chapter contents. • a score of worked out examples on real life applications. • illustrative diagrams • coding examples
Автор: Rik Das, Siddhartha Bhattacharyya, Sudarshan Nandy Название: Machine Learning Applications: Emerging Trends ISBN: 3110608537 ISBN-13(EAN): 9783110608533 Издательство: Walter de Gruyter Цена: 18586.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
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.
Описание: This book constitutes the refereed proceedings of the 32nd International Conference on Advanced Information Systems Engineering, CAiSE 2020, held in Grenoble, France, in June 2020.*The 33 full papers presented in this volume were carefully reviewed and selected from 185 submissions.
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