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Author: Qi Xuan Publisher: World Scientific ISBN: 9811266921 Category : Computers Languages : en Pages : 309
Book Description
This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks.The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.
Author: Qi Xuan Publisher: World Scientific ISBN: 9811266921 Category : Computers Languages : en Pages : 309
Book Description
This book proposes various deep learning models featuring how deep learning algorithms have been applied and used in real-life settings. The complexity of real-world scenarios and constraints imposed by the environment, together with budgetary and resource limitations, have posed great challenges to engineers and developers alike, to come up with solutions to meet these demands. This book presents case studies undertaken by its contributors to overcome these problems. These studies can be used as references for designers when applying deep learning in solving real-world problems in the areas of vision, signals, and networks.The contents of this book are divided into three parts. In the first part, AI vision applications in plant disease diagnostics, PM2.5 concentration estimation, surface defect detection, and ship plate identification, are featured. The second part introduces deep learning applications in signal processing; such as time series classification, broad-learning based signal modulation recognition, and graph neural network (GNN) based modulation recognition. Finally, the last section of the book reports on graph embedding applications and GNN in AI for networks; such as an end-to-end graph embedding method for dispute detection, an autonomous System-GNN architecture to infer the relationship between Apache software, a Ponzi scheme detection framework to identify and detect Ponzi schemes, and a GNN application to predict molecular biological activities.
Author: Krishna Kant Singh Publisher: CRC Press ISBN: 1000565238 Category : Science Languages : en Pages : 289
Book Description
Covers both the fundamentals and the latest concepts in deep learning Presents some of the diverse applications of deep learning in visual computing and signal processing Includes over 90 figures and tables to elucidate the text
Author: Salman Khan Publisher: Morgan & Claypool Publishers ISBN: 1681732823 Category : Computers Languages : en Pages : 303
Book Description
Computer vision has become increasingly important and effective in recent years due to its wide-ranging applications in areas as diverse as smart surveillance and monitoring, health and medicine, sports and recreation, robotics, drones, and self-driving cars. Visual recognition tasks, such as image classification, localization, and detection, are the core building blocks of many of these applications, and recent developments in Convolutional Neural Networks (CNNs) have led to outstanding performance in these state-of-the-art visual recognition tasks and systems. As a result, CNNs now form the crux of deep learning algorithms in computer vision. This self-contained guide will benefit those who seek to both understand the theory behind CNNs and to gain hands-on experience on the application of CNNs in computer vision. It provides a comprehensive introduction to CNNs starting with the essential concepts behind neural networks: training, regularization, and optimization of CNNs. The book also discusses a wide range of loss functions, network layers, and popular CNN architectures, reviews the different techniques for the evaluation of CNNs, and presents some popular CNN tools and libraries that are commonly used in computer vision. Further, this text describes and discusses case studies that are related to the application of CNN in computer vision, including image classification, object detection, semantic segmentation, scene understanding, and image generation. This book is ideal for undergraduate and graduate students, as no prior background knowledge in the field is required to follow the material, as well as new researchers, developers, engineers, and practitioners who are interested in gaining a quick understanding of CNN models.
Author: Sudeep Tanwar Publisher: CRC Press ISBN: 1000487814 Category : Technology & Engineering Languages : en Pages : 488
Book Description
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.
Author: Shruti Jain Publisher: Springer Nature ISBN: 9811527407 Category : Technology & Engineering Languages : en Pages : 293
Book Description
This book highlights recent advances and emerging technologies that utilize computational intelligence in signal processing, computing, imaging science, artificial intelligence, and their applications. It covers all branches of artificial intelligence and machine learning that are based on computation at some level, e.g. artificial neural networks, evolutionary algorithms, fuzzy systems, and automatic medical identification systems. Exploring recent trends in research and applications, the book offers a valuable resource for professors, researchers, and engineers alike.
Author: Suman Lata Tripathi Publisher: John Wiley & Sons ISBN: 1119861829 Category : Technology & Engineering Languages : en Pages : 516
Book Description
Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.
Author: Mahmoud Hassaballah Publisher: CRC Press ISBN: 1351003801 Category : Computers Languages : en Pages : 261
Book Description
Deep learning algorithms have brought a revolution to the computer vision community by introducing non-traditional and efficient solutions to several image-related problems that had long remained unsolved or partially addressed. This book presents a collection of eleven chapters where each individual chapter explains the deep learning principles of a specific topic, introduces reviews of up-to-date techniques, and presents research findings to the computer vision community. The book covers a broad scope of topics in deep learning concepts and applications such as accelerating the convolutional neural network inference on field-programmable gate arrays, fire detection in surveillance applications, face recognition, action and activity recognition, semantic segmentation for autonomous driving, aerial imagery registration, robot vision, tumor detection, and skin lesion segmentation as well as skin melanoma classification. The content of this book has been organized such that each chapter can be read independently from the others. The book is a valuable companion for researchers, for postgraduate and possibly senior undergraduate students who are taking an advanced course in related topics, and for those who are interested in deep learning with applications in computer vision, image processing, and pattern recognition.
Author: M.G. Sumithra Publisher: John Wiley & Sons ISBN: 1119857201 Category : Computers Languages : en Pages : 325
Book Description
BRAIN-COMPUTER INTERFACE It covers all the research prospects and recent advancements in the brain-computer interface using deep learning. The brain-computer interface (BCI) is an emerging technology that is developing to be more functional in practice. The aim is to establish, through experiences with electronic devices, a communication channel bridging the human neural networks within the brain to the external world. For example, creating communication or control applications for locked-in patients who have no control over their bodies will be one such use. Recently, from communication to marketing, recovery, care, mental state monitoring, and entertainment, the possible application areas have been expanding. Machine learning algorithms have advanced BCI technology in the last few decades, and in the sense of classification accuracy, performance standards have been greatly improved. For BCI to be effective in the real world, however, some problems remain to be solved. Research focusing on deep learning is anticipated to bring solutions in this regard. Deep learning has been applied in various fields such as computer vision and natural language processing, along with BCI growth, outperforming conventional approaches to machine learning. As a result, a significant number of researchers have shown interest in deep learning in engineering, technology, and other industries; convolutional neural network (CNN), recurrent neural network (RNN), and generative adversarial network (GAN). Audience Researchers and industrialists working in brain-computer interface, deep learning, machine learning, medical image processing, data scientists and analysts, machine learning engineers, electrical engineering, and information technologists.
Author: Saurabh Prasad Publisher: Springer Nature ISBN: 3030386171 Category : Computers Languages : en Pages : 464
Book Description
This book reviews the state of the art in algorithmic approaches addressing the practical challenges that arise with hyperspectral image analysis tasks, with a focus on emerging trends in machine learning and image processing/understanding. It presents advances in deep learning, multiple instance learning, sparse representation based learning, low-dimensional manifold models, anomalous change detection, target recognition, sensor fusion and super-resolution for robust multispectral and hyperspectral image understanding. It presents research from leading international experts who have made foundational contributions in these areas. The book covers a diverse array of applications of multispectral/hyperspectral imagery in the context of these algorithms, including remote sensing, face recognition and biomedicine. This book would be particularly beneficial to graduate students and researchers who are taking advanced courses in (or are working in) the areas of image analysis, machine learning and remote sensing with multi-channel optical imagery. Researchers and professionals in academia and industry working in areas such as electrical engineering, civil and environmental engineering, geosciences and biomedical image processing, who work with multi-channel optical data will find this book useful.
Author: Avik Santra Publisher: Artech House ISBN: 1630817473 Category : Technology & Engineering Languages : en Pages : 358
Book Description
This exciting new resource covers various emerging applications of short range radars, including people counting and tracking, gesture sensing, human activity recognition, air-drawing, material classification, object classification, vital sensing by extracting features such as range-Doppler Images (RDI), range-cross range images, Doppler Spectrogram or directly feeding raw ADC data to the classifiers. The book also presents how deep learning architectures are replacing conventional radar signal processing pipelines enabling new applications and results. It describes how deep convolutional neural networks (DCNN), long-short term memory (LSTM), feedforward networks, regularization, optimization algorithms, connectionist This exciting new resource presents emerging applications of artificial intelligence and deep learning in short-range radar. The book covers applications ranging from industrial, consumer space to emerging automotive applications. The book presents several human-machine interface (HMI) applications, such as gesture recognition and sensing, human activity classification, air-writing, material classification, vital sensing, people sensing, people counting, people localization and in-cabin automotive occupancy and smart trunk opening. The underpinnings of deep learning are explored, outlining the history of neural networks and the optimization algorithms to train them. Modern deep convolutional neural network (DCNN), popular DCNN architectures for computer vision and their features are also introduced. The book presents other deep learning architectures, such as long-short term memory (LSTM), auto-encoders, variational auto-encoders (VAE), and generative adversarial networks (GAN). The application of human activity recognition as well as the application of air-writing using a network of short-range radars are outlined. This book demonstrates and highlights how deep learning is enabling several advanced industrial, consumer and in-cabin applications of short-range radars, which weren't otherwise possible. It illustrates various advanced applications, their respective challenges, and how they are been addressed using different deep learning architectures and algorithms.
Author: Qi Xuan
Author: Qi Xuan
Author: Krishna Kant Singh
Author: Salman Khan
Author: Sudeep Tanwar
Author: Shruti Jain
Author: Suman Lata Tripathi
Author: Mahmoud Hassaballah
Author: M.G. Sumithra
Author: Saurabh Prasad
Author: Avik Santra