Machine Learning Algorithms For Signal And Image Processing
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Machine Learning Algorithms For Signal And Image Processing
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Author : Deepika Ghai
language : en
Publisher: John Wiley & Sons
Release Date : 2022-11-18
Machine Learning Algorithms For Signal And Image Processing written by Deepika Ghai and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-18 with Technology & Engineering categories.
Machine Learning Algorithms for Signal and Image Processing 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.
Deep Learning For Multimedia Processing Applications
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Author : Uzair Aslam Bhatti
language : en
Publisher: CRC Press
Release Date : 2024-02-21
Deep Learning For Multimedia Processing Applications written by Uzair Aslam Bhatti and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-21 with Computers categories.
Deep Learning for Multimedia Processing Applications is a comprehensive guide that explores the revolutionary impact of deep learning techniques in the field of multimedia processing. Written for a wide range of readers, from students to professionals, this book offers a concise and accessible overview of the application of deep learning in various multimedia domains, including image processing, video analysis, audio recognition, and natural language processing. Divided into two volumes, Volume Two delves into advanced topics such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs), explaining their unique capabilities in multimedia tasks. Readers will discover how deep learning techniques enable accurate and efficient image recognition, object detection, semantic segmentation, and image synthesis. The book also covers video analysis techniques, including action recognition, video captioning, and video generation, highlighting the role of deep learning in extracting meaningful information from videos. Furthermore, the book explores audio processing tasks such as speech recognition, music classification, and sound event detection using deep learning models. It demonstrates how deep learning algorithms can effectively process audio data, opening up new possibilities in multimedia applications. Lastly, the book explores the integration of deep learning with natural language processing techniques, enabling systems to understand, generate, and interpret textual information in multimedia contexts. Throughout the book, practical examples, code snippets, and real-world case studies are provided to help readers gain hands-on experience in implementing deep learning solutions for multimedia processing. Deep Learning for Multimedia Processing Applications is an essential resource for anyone interested in harnessing the power of deep learning to unlock the vast potential of multimedia data.
Machine Learning Methods For Signal Image And Speech Processing
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Author : M.A. Jabbar
language : en
Publisher: CRC Press
Release Date : 2022-09-01
Machine Learning Methods For Signal Image And Speech Processing written by M.A. Jabbar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-09-01 with Computers categories.
The signal processing (SP) landscape has been enriched by recent advances in artificial intelligence (AI) and machine learning (ML), yielding new tools for signal estimation, classification, prediction, and manipulation. Layered signal representations, nonlinear function approximation and nonlinear signal prediction are now feasible at very large scale in both dimensionality and data size. These are leading to significant performance gains in a variety of long-standing problem domains like speech and Image analysis. As well as providing the ability to construct new classes of nonlinear functions (e.g., fusion, nonlinear filtering). This book will help academics, researchers, developers, graduate and undergraduate students to comprehend complex SP data across a wide range of topical application areas such as social multimedia data collected from social media networks, medical imaging data, data from Covid tests etc. This book focuses on AI utilization in the speech, image, communications and yirtual reality domains.
Hyperspectral Image Analysis
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Author : Saurabh Prasad
language : en
Publisher: Springer Nature
Release Date : 2020-04-27
Hyperspectral Image Analysis written by Saurabh Prasad and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-27 with Computers categories.
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.
Machine Learning In Signal Processing
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Author : Sudeep Tanwar
language : en
Publisher: CRC Press
Release Date : 2021-12-10
Machine Learning In Signal Processing written by Sudeep Tanwar and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-10 with Technology & Engineering categories.
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.
Biomedical Signal And Image Processing With Artificial Intelligence
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Author : Chirag Paunwala
language : en
Publisher: Springer Nature
Release Date : 2023-01-09
Biomedical Signal And Image Processing With Artificial Intelligence written by Chirag Paunwala and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-09 with Technology & Engineering categories.
This book focuses on advanced techniques used for feature extraction, analysis, recognition, and classification in the area of biomedical signal and image processing. Contributions cover all aspects of artificial intelligence, machine learning, and deep learning in the field of biomedical signal and image processing using novel and unexplored techniques and methodologies. The book covers recent developments in both medical images and signals analyzed by artificial intelligence techniques. The authors also cover topics related to development based artificial intelligence, which includes machine learning, neural networks, and deep learning. This book will provide a platform for researchers who are working in the area of artificial intelligence for biomedical applications. Provides insights into medical signal and image analysis using artificial intelligence; Includes novel and recent trends of decision support system for medical research; Outlines employment of evolutionary algorithms for biomedical data, big data analysis for medical databases, and reliability, opportunities, and challenges in clinical data.
Neural Networks Machine Learning And Image Processing
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Author : Manoj Sahni
language : en
Publisher: CRC Press
Release Date : 2022-12-15
Neural Networks Machine Learning And Image Processing written by Manoj Sahni and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-15 with Computers categories.
SECTION I Mathematical Modeling and Neural Network’ Mathematical Essence Chapter 1 Mathematical Modeling on Thermoregulation in Sarcopenia 1.1. Introduction 1.2. Discretization 1.3. Modeling and Simulation of Basal Metabolic Rate and Skin Layers Thickness 1.4. Mathematical Model and Boundary Conditions 1.5. Solution of the Model 1.6. Numerical Results and discussion 1.7. Conclusion References Chapter 2 Multi-objective University Course Scheduling for Uncertainly Generated Courses 2.1 Introduction 2.2 Literature review 2.3 Formulation of problem 2.4 Methodology 2.5 Numerical Example 2.6 Result and Discussion 2.7 Conclusion References Chapter 3 MChCNN : A Deep Learning Approach to Detect Text based Hate Speech 3.1. Introduction Background and Driving Forces 3.2. Related Work 3.3. Experiment and Results 3.4. Conclusion References Chapter 4 PSO Based PFC Cuk Converter fed BLDC Motor Drive for Automotive Applications 4.1. Introduction 4.2. Operation of Cuk converter fed BLDC motor drive system 4.3. Controller Operation 4.4. Result and Discussion 4.5. Conclusion References Chapter 5 Optimize Feature Selection for Condition based monitoring of Cylindrical bearing using Wavelet transform and ANN 5.1. Introduction 5.2. Methodology 5.3. Data Preparation 5.4. Result and Discussion 5.5. Conclusion References Chapter 6 SafeShop - An integrated system for safe pickup of items during COVID-19 6.1. Introduction 6.2. Literature Survey 6.3. Methodology 6.4. Result and Discussion 6.5. Conclusion References Chapter 7 Solution of First Order Fuzzy Differential Equation using Numerical Method 7.1. Introduction 7.2. Preliminaries 7.3. Methodology 7.4. Illustration 7.5. Conclusion References SECTION II Simulations in Machine Learning and Image Processing Chapter 8 Multi-layer Encryption Algorithm for Data Integrity in Cloud Computing 8.1. Introduction 8.2. Related works 8.3. Algorithm description 8.4. Simulation and performance analysis 8.5. Conclusion and Future Work References Chapter 9 Anomaly detection using class of supervised and unsupervised learning algorithms 9. 1. Introduction 9.2. Adaptive threshold and regression techniques for anomaly detection 9.3. Unsupervised Learning techniques for anomaly detection 9.4. Description of the dataset 9.5 Results and Discussions 9.6. Conclusion References Chapter 10 Improving Support Vector Machine accuracy with Shogun’s multiple kernel learning 10. 1. Introduction 10. 2. Support Vector Machine Statistics 10.3. Experiment and Result 10.4 Conclusion References Chapter 11 An Introduction to Parallelisable String-Based SP-Languages 11.1. Introduction 11.2. Parallelisable string-based SP-languages 11.3. Parallel Regular Expression 11.4. Equivalence of Parallel Regular Expression and Branching Automaton 11.5. Parallelisable String-Based SP-Grammar 11.6. Parallelisable String-Based SP-Parallel Grammar 11.7. Conclusion 11.8. Applications 11.9. Future Scope References Chapter 12 Detection of Disease using Machine Learning 12.1. Introduction 12.2. Techniques Applied 12.3. GENERAL ARCHITECTURE OF AI/ML 12.4. EXPERIMENTAL OUTCOMES 12.5. Conclusion References Chapter 13 Driver Drowsiness Detection Using Eye Tracing System 13.1. Introduction 13.2. Literature Review 13.3. Research Method 13.4. Observations and Results 13.5. Conclusion References Chapter 14 An Efficient Image Encryption Scheme Combining Rubik Cube Principle with Masking 14.1 Introduction 14.2 Preliminary Section 14.3 Proposed Work 14. 4 Experimental Setup and Simulation Analysis 14.5 Conclusion References
Advanced Machine Intelligence And Signal Processing
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Author : Deepak Gupta
language : en
Publisher: Springer Nature
Release Date : 2022-06-25
Advanced Machine Intelligence And Signal Processing written by Deepak Gupta and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-06-25 with Technology & Engineering categories.
This book covers the latest advancements in the areas of machine learning, computer vision, pattern recognition, computational learning theory, big data analytics, network intelligence, signal processing, and their applications in real world. The topics covered in machine learning involve feature extraction, variants of support vector machine (SVM), extreme learning machine (ELM), artificial neural network (ANN), and other areas in machine learning. The mathematical analysis of computer vision and pattern recognition involves the use of geometric techniques, scene understanding and modeling from video, 3D object recognition, localization and tracking, medical image analysis, and so on. Computational learning theory involves different kinds of learning like incremental, online, reinforcement, manifold, multitask, semi-supervised, etc. Further, it covers the real-time challenges involved while processing big data analytics and stream processing with the integration of smart data computing services and interconnectivity. Additionally, it covers the recent developments to network intelligence for analyzing the network information and thereby adapting the algorithms dynamically to improve the efficiency. In the last, it includes the progress in signal processing to process the normal and abnormal categories of real-world signals, for instance signals generated from IoT devices, smart systems, speech, videos, etc., and involves biomedical signal processing: electrocardiogram (ECG), electroencephalogram (EEG), magnetoencephalography (MEG), and electromyogram (EMG).
Adaptive Blind Signal And Image Processing
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Author : Andrzej Cichocki
language : en
Publisher: John Wiley & Sons
Release Date : 2002-06-14
Adaptive Blind Signal And Image Processing written by Andrzej Cichocki and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2002-06-14 with Science categories.
Im Mittelpunkt dieses modernen und spezialisierten Bandes stehen adaptive Strukturen und unüberwachte Lernalgorithmen, besonders im Hinblick auf effektive Computersimulationsprogramme. Anschauliche Illustrationen und viele Beispiele sowie eine interaktive CD-ROM ergänzen den Text.
Learning Approaches In Signal Processing
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Author : Francis Ring
language : en
Publisher: CRC Press
Release Date : 2018-12-07
Learning Approaches In Signal Processing written by Francis Ring and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-07 with Computers categories.
Coupled with machine learning, the use of signal processing techniques for big data analysis, Internet of things, smart cities, security, and bio-informatics applications has witnessed explosive growth. This has been made possible via fast algorithms on data, speech, image, and video processing with advanced GPU technology. This book presents an up-to-date tutorial and overview on learning technologies such as random forests, sparsity, and low-rank matrix estimation and cutting-edge visual/signal processing techniques, including face recognition, Kalman filtering, and multirate DSP. It discusses the applications that make use of deep learning, convolutional neural networks, random forests, etc. The applications include super-resolution imaging, fringe projection profilometry, human activities detection/capture, gesture recognition, spoken language processing, cooperative networks, bioinformatics, DNA, and healthcare.