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Machine Learning Concepts Techniques and Applications

Machine Learning: Concepts Techniques and Applications starts at basic conceptual level of explaining machine learning and goes on to explain the basis of machine learning algorithms. The mathematical foundations required are outlined along with their associations to machine learning. The book then goes on to describe important machine learning algorithms along with appropriate use cases. This approach enables the readers to explore the applicability of each algorithm by understanding the differences between them. A comprehensive account of various aspects of ethical machine learning has been discussed. An outline of deep learning models is also included. The use cases self-assessments exercises activities numerical problems and projects associated with each chapter aims to concretize the understanding. Features Concepts of Machine learning from basics to algorithms to implementation Comparison of Different Machine Learning Algorithms – When to use them & Why – for Application developers and Researchers Machine Learning from an Application Perspective – General & Machine learning for Healthcare Education Business Engineering Applications Ethics of machine learning including Bias Fairness Trust Responsibility Basics of Deep learning important deep learning models and applications Plenty of objective questions Use Cases Activity and Project based Learning Exercises The book aims to make the thinking of applications and problems in terms of machine learning possible for graduate students researchers and professionals so that they can formulate the problems prepare data decide features select appropriate machine learning algorithms and do appropriate performance evaluation. | Machine Learning Concepts Techniques and Applications

GBP 140.00

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Applied Machine Learning for Smart Data Analysis

The book focuses on how machine learning and the Internet of Things (IoT) has empowered the advancement of information driven arrangements including key concepts and advancements. Ontologies that are used in heterogeneous IoT environments have been discussed including interpretation context awareness analyzing various data sources machine learning algorithms and intelligent services and applications. Further it includes unsupervised and semi-supervised machine learning techniques with study of semantic analysis and thorough analysis of reviews. Divided into sections such as machine learning security IoT and data mining the concepts are explained with practical implementation including results. Key Features Follows an algorithmic approach for data analysis in machine learning Introduces machine learning methods in applications Address the emerging issues in computing such as deep learning machine learning Internet of Things and data analytics Focuses on machine learning techniques namely unsupervised and semi-supervised for unseen and seen data sets Case studies are covered relating to human health transportation and Internet applications | Applied Machine Learning for Smart Data Analysis

GBP 125.00

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Machine Learning Theory and Practice

Machine Learning: Theory and Practice provides an introduction to the most popular methods in machine learning. The book covers regression including regularization tree-based methods including Random Forests and Boosted Trees Artificial Neural Networks including Convolutional Neural Networks (CNNs) reinforcement learning and unsupervised learning focused on clustering. Topics are introduced in a conceptual manner along with necessary mathematical details. The explanations are lucid illustrated with figures and examples. For each machine learning method discussed the book presents appropriate libraries in the R programming language along with programming examples. Features: Provides an easy-to-read presentation of commonly used machine learning algorithms in a manner suitable for advanced undergraduate or beginning graduate students and mathematically and/or programming-oriented individuals who want to learn machine learning on their own. Covers mathematical details of the machine learning algorithms discussed to ensure firm understanding enabling further exploration Presents worked out suitable programming examples thus ensuring conceptual theoretical and practical understanding of the machine learning methods. This book is aimed primarily at introducing essential topics in Machine Learning to advanced undergraduates and beginning graduate students. The number of topics has been kept deliberately small so that it can all be covered in a semester or a quarter. The topics are covered in depth within limits of what can be taught in a short period of time. Thus the book can provide foundations that will empower a student to read advanced books and research papers. | Machine Learning Theory and Practice

GBP 110.00

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Machine Learning and Deep Learning Techniques for Medical Image Recognition

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas including chest X-ray breast computer-aided detection lung and chest microscopy and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering artificial intelligence and machine learning and biomedical imaging.

GBP 110.00

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Stochastic Optimization for Large-scale Machine Learning

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms i. e. optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

GBP 150.00

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Machine Learning for Healthcare Handling and Managing Data

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them. Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy suitability and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms architecture design and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model performance evaluation and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning and is especially applicable to the healthcare sector. The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning along with recent research developments in healthcare sectors. | Machine Learning for Healthcare Handling and Managing Data

GBP 115.00

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Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques

The development of intelligent transportation systems especially autonomous underwater vehicles has become significant in marine engineering with an aim to enhance energy efficiency management and communication systems. This book covers different aspects of optimization of autonomous underwater vehicles and their propulsion systems via machine learning techniques. It further analyses hydrodynamic characteristics including the study of experimental investigation combined with hydrodynamic characteristics backed by MATLAB® codes and simulation study results. Features: Covers utilization of machine learning techniques with a focus on marine science and ocean engineering. Details effect of the intelligent transportation system (ITS) into the sustainable environment and ecology system. Evaluates performance of particle swarm intelligence-based optimization techniques. Reviews propulsion performance of the remote-controlled vehicles based on machine learning techniques. Includes MATLAB® examples and simulation study results. This book is aimed at graduate students and researchers in marine engineering and technology computer science and control system engineering. | Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques

GBP 110.00

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Machine Learning for Decision Sciences with Case Studies in Python

This book provides a detailed description of machine learning algorithms in data analytics data science life cycle Python for machine learning linear regression logistic regression and so forth. It addresses the concepts of machine learning in a practical sense providing complete code and implementation for real-world examples in electrical oil and gas e-commerce and hi-tech industries. The focus is on Python programming for machine learning and patterns involved in decision science for handling data. Features: Explains the basic concepts of Python and its role in machine learning. Provides comprehensive coverage of feature engineering including real-time case studies. Perceives the structural patterns with reference to data science and statistics and analytics. Includes machine learning-based structured exercises. Appreciates different algorithmic concepts of machine learning including unsupervised supervised and reinforcement learning. This book is aimed at researchers professionals and graduate students in data science machine learning computer science and electrical and computer engineering. | Machine Learning for Decision Sciences with Case Studies in Python

GBP 160.00

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Statistical Machine Learning A Unified Framework

The recent rapid growth in the variety and complexity of new machine learning architectures requires the development of improved methods for designing analyzing evaluating and communicating machine learning technologies. Statistical Machine Learning: A Unified Framework provides students engineers and scientists with tools from mathematical statistics and nonlinear optimization theory to become experts in the field of machine learning. In particular the material in this text directly supports the mathematical analysis and design of old new and not-yet-invented nonlinear high-dimensional machine learning algorithms. Features: Unified empirical risk minimization framework supports rigorous mathematical analyses of widely used supervised unsupervised and reinforcement machine learning algorithms Matrix calculus methods for supporting machine learning analysis and design applications Explicit conditions for ensuring convergence of adaptive batch minibatch MCEM and MCMC learning algorithms that minimize both unimodal and multimodal objective functions Explicit conditions for characterizing asymptotic properties of M-estimators and model selection criteria such as AIC and BIC in the presence of possible model misspecification This advanced text is suitable for graduate students or highly motivated undergraduate students in statistics computer science electrical engineering and applied mathematics. The text is self-contained and only assumes knowledge of lower-division linear algebra and upper-division probability theory. Students professional engineers and multidisciplinary scientists possessing these minimal prerequisites will find this text challenging yet accessible. About the Author: Richard M. Golden (Ph. D. M. S. E. E. B. S. E. E. ) is Professor of Cognitive Science and Participating Faculty Member in Electrical Engineering at the University of Texas at Dallas. Dr. Golden has published articles and given talks at scientific conferences on a wide range of topics in the fields of both statistics and machine learning over the past three decades. His long-term research interests include identifying conditions for the convergence of deterministic and stochastic machine learning algorithms and investigating estimation and inference in the presence of possibly misspecified probability models. | Statistical Machine Learning A Unified Framework

GBP 99.99

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Green Machine Learning Protocols for Future Communication Networks

Machine learning has shown tremendous benefits in solving complex network problems and providing situation and parameter prediction. However heavy resources are required to process and analyze the data which can be done either offline or using edge computing but also requires heavy transmission resources to provide a timely response. The need here is to provide lightweight machine learning protocols that can process and analyze the data at run time and provide a timely and efficient response. These algorithms have grown in terms of computation and memory requirements due to the availability of large data sets. These models/algorithms also require high levels of resources such as computing memory communication and storage. The focus so far was on producing highly accurate models for these communication networks without considering the energy consumption of these machine learning algorithms. For future scalable and sustainable network applications efforts are required toward designing new machine learning protocols and modifying the existing ones which consume less energy i. e. green machine learning protocols. In other words novel and lightweight green machine learning algorithms/protocols are required to reduce energy consumption which can also reduce the carbon footprint. To realize the green machine learning protocols this book presents different aspects of green machine learning for future communication networks. This book highlights mainly the green machine learning protocols for cellular communication federated learning-based models and protocols for Beyond Fifth Generation networks approaches for cloud-based communications and Internet-of-Things. This book also highlights the design considerations and challenges for green machine learning protocols for different future applications. | Green Machine Learning Protocols for Future Communication Networks

GBP 110.00

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Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas such as game development decision making and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning which is followed by Gaussian process introduction classification regression covariance and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates researchers and professionals in the field of data science and machine learning.

GBP 105.00

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VLSI and Hardware Implementations using Modern Machine Learning Methods

Machine learning is a potential solution to resolve bottleneck issues in VLSI via optimizing tasks in the design process. This book aims to provide the latest machine-learning–based methods algorithms architectures and frameworks designed for VLSI design. The focus is on digital analog and mixed-signal design techniques device modeling physical design hardware implementation testability reconfigurable design synthesis and verification and related areas. Chapters include case studies as well as novel research ideas in the given field. Overall the book provides practical implementations of VLSI design IC design and hardware realization using machine learning techniques. Features: Provides the details of state-of-the-art machine learning methods used in VLSI design Discusses hardware implementation and device modeling pertaining to machine learning algorithms Explores machine learning for various VLSI architectures and reconfigurable computing Illustrates the latest techniques for device size and feature optimization Highlights the latest case studies and reviews of the methods used for hardware implementation This book is aimed at researchers professionals and graduate students in VLSI machine learning electrical and electronic engineering computer engineering and hardware systems. | VLSI and Hardware Implementations using Modern Machine Learning Methods

GBP 115.00

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Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

The book focuses on the recent developments in the areas of error reduction resource optimization and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4. 0 for better quality products and efficient manufacturing methods. Focusses on machine learning applications in Industry 4. 0 ecosystem such as resource optimization data analysis and predictions. Highlights the importance of the explainable machine learning model in the manufacturing processes. Presents the integration of machine learning and big data analytics from an industry 4. 0 perspective. Discusses advanced computational techniques for sustainable manufacturing. Examines environmental impacts of operations and supply chain from an industry 4. 0 perspective. This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection cyber-attack prediction and inventory management. It further discusses resource optimization using machine learning in industry 4. 0 and explainable machine learning models for industry 4. 0. It will serve as an ideal reference text for senior undergraduate graduate students and academic researchers in the fields including mechanical engineering manufacturing engineering production engineering aerospace engineering and computer engineering. | Machine Learning for Sustainable Manufacturing in Industry 4. 0 Concept Concerns and Applications

GBP 110.00

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Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Artificial Intelligence (AI) when incorporated with machine learning and deep learning algorithms has a wide variety of applications today. This book focuses on the implementation of various elementary and advanced approaches in AI that can be used in various domains to solve real-time decision-making problems. The book focuses on concepts and techniques used to run tasks in an automated manner. It discusses computational intelligence in the detection and diagnosis of clinical and biomedical images covers the automation of a system through machine learning and deep learning approaches presents data analytics and mining for decision-support applications and includes case-based reasoning natural language processing computer vision and AI approaches in real-time applications. Academic scientists researchers and students in the various domains of computer science engineering electronics and communication engineering and information technology as well as industrial engineers biomedical engineers and management will find this book useful. By the end of this book you will understand the fundamentals of AI. Various case studies will develop your adaptive thinking to solve real-time AI problems. Features Includes AI-based decision-making approaches Discusses computational intelligence in the detection and diagnosis of clinical and biomedical images Covers automation of systems through machine learning and deep learning approaches and its implications to the real world Presents data analytics and mining for decision-support applications Offers case-based reasoning | Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

GBP 145.00

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Marketing Analytics A Machine Learning Approach

With businesses becoming ever more competitive marketing strategies need to be more precise and performance oriented. Companies are investing considerably in analytical infrastructure for marketing. This new volume Marketing Analytics: A Machine Learning Approach enlightens readers on the application of analytics in marketing and the process of analytics providing a foundation on the concepts and algorithms of machine learning and statistics. The book simplifies analytics for businesses and explains its uses in different aspects of marketing in a way that even marketers with no prior analytics experience will find it easy to follow giving them to tools to make better business decisions. This volume gives a comprehensive overview of marketing analytics incorporating machine learning methods of data analysis that automates analytical model building. The volume covers the important aspects of marketing analytics including segmentation and targeting analysis statistics for marketing marketing metrics consumer buying behavior neuromarketing techniques for consumer analytics new product development forecasting sales and price web and social media analytics and much more. This well-organized and straight-forward volume will be valuable for marketers managers decision makers and research scholars and faculty in business marketing and information technology and would also be suitable for classroom use. | Marketing Analytics A Machine Learning Approach

GBP 124.00

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Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Technology is moving at an exponential pace in this era of computational intelligence. Machine learning has emerged as one of the most promising tools used to challenge and think beyond current limitations. This handbook will provide readers with a leading edge to improving their products and processes through optimal and smarter machine learning techniques. This handbook focuses on new machine learning developments that can lead to newly developed applications. It uses a predictive and futuristic approach which makes machine learning a promising tool for processes and sustainable solutions. It also promotes newer algorithms that are more efficient and reliable for new dimensions in discovering other applications and then goes on to discuss the potential in making better use of machines in order to ensure optimal prediction execution and decision-making. Individuals looking for machine learning-based knowledge will find interest in this handbook. The readership ranges from undergraduate students of engineering and allied courses to researchers professionals and application designers. | Handbook of Machine Learning for Computational Optimization Applications and Case Studies

GBP 150.00

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Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

This book describes the use of machine learning techniques to build predictive models of uncertainty with application to hydrological models focusing mainly on the development and testing of two different models. The first focuses on parameter uncertainty analysis by emulating the results of Monte Carlo simulation of hydrological models using efficient machine learning techniques. The second method aims at modelling uncertainty by building an ensemble of specialized machine learning models on the basis of past hydrological model‘s performance. The book then demonstrates the capacity of machine learning techniques for building accurate and efficient predictive models of uncertainty. | Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

GBP 175.00

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Recent Trends in Computational Sciences Proceedings of the Fourth Annual International Conference on Data Science Machine Learning and Bloc

This book is a compilation of research papers and presentations from the Fourth Annual International Conference on Data Science Machine Learning and Blockchain Technology (AICDMB 2023 Mysuru India 16-17 March 2023). The book covers a wide range of topics including data mining natural language processing deep learning computer vision big data analytics cryptography smart contracts decentralized applications and blockchain-based solutions for various industries such as healthcare finance and supply chain management. The research papers presented in this book highlight the latest advancements and practical applications in data science machine learning and blockchain technology and provide insights into the future direction of these fields. The book serves as a valuable resource for researchers students and professionals in the areas of data science machine learning and blockchain technology. | Recent Trends in Computational Sciences Proceedings of the Fourth Annual International Conference on Data Science Machine Learning and Bloc

GBP 94.99

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Machine Learning for Criminology and Crime Research At the Crossroads

Machine Learning for Criminology and Crime Research: At the Crossroads reviews the roots of the intersection between machine learning artificial intelligence (AI) and research on crime; examines the current state of the art in this area of scholarly inquiry; and discusses future perspectives that may emerge from this relationship. As machine learning and AI approaches become increasingly pervasive it is critical for criminology and crime research to reflect on the ways in which these paradigms could reshape the study of crime. In response this book seeks to stimulate this discussion. The opening part is framed through a historical lens with the first chapter dedicated to the origins of the relationship between AI and research on crime refuting the novelty narrative that often surrounds this debate. The second presents a compact overview of the history of AI further providing a nontechnical primer on machine learning. The following chapter reviews some of the most important trends in computational criminology and quantitatively characterizing publication patterns at the intersection of AI and criminology through a network science approach. This book also looks to the future proposing two goals and four pathways to increase the positive societal impact of algorithmic systems in research on crime. The sixth chapter provides a survey of the methods emerging from the integration of machine learning and causal inference showcasing their promise for answering a range of critical questions. With its transdisciplinary approach Machine Learning for Criminology and Crime Research is important reading for scholars and students in criminology criminal justice sociology and economics as well as AI data sciences and statistics and computer science. | Machine Learning for Criminology and Crime Research At the Crossroads

GBP 130.00

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Handbook on Federated Learning Advances Applications and Opportunities

Mobile wearable and self-driving telephones are just a few examples of modern distributed networks that generate enormous amount of information every day. Due to the growing computing capacity of these devices as well as concerns over the transfer of private information it has become important to process the part of the data locally by moving the learning methods and computing to the border of devices. Federated learning has developed as a model of education in these situations. Federated learning (FL) is an expert form of decentralized machine learning (ML). It is essential in areas like privacy large-scale machine education and distribution. It is also based on the current stage of ICT and new hardware technology and is the next generation of artificial intelligence (AI). In FL central ML model is built with all the data available in a centralised environment in the traditional machine learning. It works without problems when the predictions can be served by a central server. Users require fast responses in mobile computing but the model processing happens at the sight of the server thus taking too long. The model can be placed in the end-user device but continuous learning is a challenge to overcome as models are programmed in a complete dataset and the end-user device lacks access to the entire data package. Another challenge with traditional machine learning is that user data is aggregated at a central location where it violates local privacy policies laws and make the data more vulnerable to data violation. This book provides a comprehensive approach in federated learning for various aspects. | Handbook on Federated Learning Advances Applications and Opportunities

GBP 120.00

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Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Techniques and Applications

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating updating and changing knowledge in intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable there is lack of formal models or the knowledge about the application domain is inadequately defined. In the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the IoT concept the need for AI (artificial intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision uncertainties and approximations to get a rapid solution. However recent advancements in representation of intelligent IoTsystems generate a more intelligent and robust system providing a human interpretable low-cost and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics time series biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval brain image segmentation among others. • Discusses deep learning IoT machine learning and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy robustness and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems | Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Techniques and Applications

GBP 140.00

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Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

The text discusses the latest data-driven physics-based and hybrid approaches employed in each stage of industrial prognostics and reliability estimation. It will be a useful text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering electrical engineering and computer science. The book Discusses basic as well as advance research in the field of prognostics Explores integration of data collection fault detection degradation modeling and reliability prediction in one volume Covers prognostics and health management (PHM) of engineering systems Discusses latest approaches in the field of prognostics based on machine learning The text deals with tools and techniques used to predict/ extrapolate/ forecast the process behavior based on current health state assessment and future operating conditions with the help of Machine learning. It will serve as a useful reference text for senior undergraduate graduate students and academic researchers in areas such as industrial and production engineering manufacturing science electrical engineering and computer science. | Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

GBP 110.00

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Neural Networks Machine Learning and Image Processing Mathematical Modeling and Applications

The text comprehensively discusses the latest mathematical modelling techniques and their applications in various areas such as fuzzy modelling signal processing neural network machine learning image processing and their numerical analysis. It further covers image processing techniques like Viola-Jones Method for face detection and fuzzy approach for person video emotion. It will serve as an ideal reference text for graduate students and academic researchers in the fields of mechanical engineering electronics communication engineering computer engineering and mathematics. This book: Discusses applications of neural networks machine learning image processing and mathematical modeling. Provides simulations techniques in machine learning and image processing-based problems. Highlights artificial intelligence and machine learning techniques in the detection of diseases. Introduces mathematical modeling techniques such as wavelet transform modeling using differential equations and numerical techniques for multi-dimensional data. Includes real-life problems for better understanding. The book presents mathematical modeling techniques such as wavelet transform differential equations and numerical techniques for multi-dimensional data. It will serve as an ideal reference text for graduate students and academic researchers in diverse engineering fields such as mechanical electronics and communication and computer. | Neural Networks Machine Learning and Image Processing Mathematical Modeling and Applications

GBP 110.00

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Video Based Machine Learning for Traffic Intersections

Video Based Machine Learning for Traffic Intersections describes the development of computer vision and machine learning-based applications for Intelligent Transportation Systems (ITS) and the challenges encountered during their deployment. This book presents several novel approaches including a two-stream convolutional network architecture for vehicle detection tracking and near-miss detection; an unsupervised approach to detect near-misses in fisheye intersection videos using a deep learning model combined with a camera calibration and spline-based mapping method; and algorithms that utilize video analysis and signal timing data to accurately detect and categorize events based on the phase and type of conflict in pedestrian-vehicle and vehicle-vehicle interactions. The book makes use of a real-time trajectory prediction approach combined with aligned Google Maps information to estimate vehicle travel time across multiple intersections. Novel visualization software designed by the authors to serve traffic practitioners is used to analyze the efficiency and safety of intersections. The software offers two modes: a streaming mode and a historical mode both of which are useful to traffic engineers who need to quickly analyze trajectories to better understand traffic behavior at an intersection. Overall this book presents a comprehensive overview of the application of computer vision and machine learning to solve transportation-related problems. Video Based Machine Learning for Traffic Intersections demonstrates how these techniques can be used to improve safety efficiency and traffic flow as well as identify potential conflicts and issues before they occur. The range of novel approaches and techniques presented offers a glimpse of the exciting possibilities that lie ahead for ITS research and development. Key Features: Describes the development and challenges associated with Intelligent Transportation Systems (ITS) Provides novel visualization software designed to serve traffic practitioners in analyzing the efficiency and safety of an intersection Has the potential to proactively identify potential conflict situations and develop an early warning system for real-time vehicle-vehicle and pedestrian-vehicle conflicts

GBP 99.99

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AI Machine Learning and Deep Learning A Security Perspective

Today Artificial Intelligence (AI) and Machine Learning/ Deep Learning (ML/DL) have become the hottest areas in information technology. In our society many intelligent devices rely on AI/ML/DL algorithms/tools for smart operations. Although AI/ML/DL algorithms and tools have been used in many internet applications and electronic devices they are also vulnerable to various attacks and threats. AI parameters may be distorted by the internal attacker; the DL input samples may be polluted by adversaries; the ML model may be misled by changing the classification boundary among many other attacks and threats. Such attacks can make AI products dangerous to use. While this discussion focuses on security issues in AI/ML/DL-based systems (i. e. securing the intelligent systems themselves) AI/ML/DL models and algorithms can actually also be used for cyber security (i. e. the use of AI to achieve security). Since AI/ML/DL security is a newly emergent field many researchers and industry professionals cannot yet obtain a detailed comprehensive understanding of this area. This book aims to provide a complete picture of the challenges and solutions to related security issues in various applications. It explains how different attacks can occur in advanced AI tools and the challenges of overcoming those attacks. Then the book describes many sets of promising solutions to achieve AI security and privacy. The features of this book have seven aspects: This is the first book to explain various practical attacks and countermeasures to AI systems Both quantitative math models and practical security implementations are provided It covers both securing the AI system itself and using AI to achieve security It covers all the advanced AI attacks and threats with detailed attack models It provides multiple solution spaces to the security and privacy issues in AI tools The differences among ML and DL security and privacy issues are explained Many practical security applications are covered | AI Machine Learning and Deep Learning A Security Perspective

GBP 99.99

1

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Machine Learning Concepts Techniques and Applications

Applied Machine Learning for Smart Data Analysis

Machine Learning Theory and Practice

Machine Learning and Deep Learning Techniques for Medical Image Recognition

Stochastic Optimization for Large-scale Machine Learning

Machine Learning for Healthcare Handling and Managing Data

Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques

Machine Learning for Decision Sciences with Case Studies in Python

Statistical Machine Learning A Unified Framework

Green Machine Learning Protocols for Future Communication Networks

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

VLSI and Hardware Implementations using Modern Machine Learning Methods

Machine Learning for Sustainable Manufacturing in Industry 4.0 Concept Concerns and Applications

Artificial Intelligence Trends for Data Analytics Using Machine Learning and Deep Learning Approaches

Marketing Analytics A Machine Learning Approach

Handbook of Machine Learning for Computational Optimization Applications and Case Studies

Uncertainty Analysis in Rainfall-Runoff Modelling - Application of Machine Learning Techniques UNESCO-IHE PhD Thesis

Recent Trends in Computational Sciences Proceedings of the Fourth Annual International Conference on Data Science Machine Learning and Bloc

Machine Learning for Criminology and Crime Research At the Crossroads

Handbook on Federated Learning Advances Applications and Opportunities

Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Techniques and Applications

Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

Neural Networks Machine Learning and Image Processing Mathematical Modeling and Applications

Video Based Machine Learning for Traffic Intersections

AI Machine Learning and Deep Learning A Security Perspective

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