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Applied Machine Learning for Smart Data Analysis

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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Stochastic Optimization for Large-scale Machine Learning

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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Green Machine Learning Protocols for Future Communication Networks

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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Machine Learning for Decision Sciences with Case Studies in Python

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

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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Handbook of Machine Learning for Computational Optimization Applications and Case Studies

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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Machine Learning and Deep Learning Techniques for Medical Image Recognition

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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Video Based Machine Learning for Traffic Intersections

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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Human-Machine Interaction and IoT Applications for a Smarter World

Human-Machine Interaction and IoT Applications for a Smarter World

Human-Machine Interaction and IoT Applications for a Smarter World explores the futuristic trends at the cutting edge of study and research on Human-Machine Interaction (HMI) which is also known as Human-Computer Interface (HCI) and the Internet of Things (IoT) by featuring applications in a proficient adaptable and manageable way. It covers the mainstays of the IoT world through a thorough description of the present advancements systems and structures. This book: Discusses algorithms and design methodologies for the implementation of HMI based IoT systems. Covers real-time utility of IoT-based devices and systems. Provides human-machine interactive technologies and smart applications using IoT. Covers cyber-physical systems and IoT in HMI using a blend of theoretical knowledge with a practical approach. It also covers important concepts including smart grid and energy consumption monitoring smart vehicular and transportation systems smart home automation automatic identification systems supervisory control and data acquisition systems designing and integrating heterogeneous Human-Machine interactions virtual and augmented reality natural language processing computer vision and automatic speech recognition. This text will be useful for senior undergraduate graduate students and academic researchers in areas including electrical electronics and communications engineering as well as computer science.

GBP 130.00
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Intelligent Prognostics for Engineering Systems with Machine Learning Techniques

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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VLSI and Hardware Implementations using Modern Machine Learning Methods

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

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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Neural Networks Machine Learning and Image Processing Mathematical Modeling and Applications

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

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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Data Science for Engineers

Data Science for Engineers

With tremendous improvement in computational power and availability of rich data almost all engineering disciplines use data science at some level. This textbook presents material on data science comprehensively and in a structured manner. It provides conceptual understanding of the fields of data science machine learning and artificial intelligence with enough level of mathematical details necessary for the readers. This will help readers understand major thematic ideas in data science machine learning and artificial intelligence and implement first-level data science solutions to practical engineering problems. The book- Provides a systematic approach for understanding data science techniques Explain why machine learning techniques are able to cross-cut several disciplines. Covers topics including statistics linear algebra and optimization from a data science perspective. Provides multiple examples to explain the underlying ideas in machine learning algorithms Describes several contemporary machine learning algorithms The textbook is primarily written for undergraduate and senior undergraduate students in different engineering disciplines including chemical engineering mechanical engineering electrical engineering electronics and communications engineering for courses on data science machine learning and artificial intelligence. | Data Science for Engineers

GBP 89.99
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Machine Learning in 2D Materials Science

Machine Learning in 2D Materials Science

Data science and machine learning (ML) methods are increasingly being used to transform the way research is being conducted in materials science to enable new discoveries and design new materials. For any materials science researcher or student it may be daunting to figure out if ML techniques are useful for them or if so which ones are applicable in their individual contexts and how to study the effectiveness of these methods systematically. KEY FEATURES Provides broad coverage of data science and ML fundamentals to materials science researchers so that they can confidently leverage these techniques in their research projects Offers introductory material in topics such as ML data integration and 2D materials Provides in-depth coverage of current ML methods for validating 2D materials using both experimental and simulation data researching and discovering new 2D materials and enhancing ML methods with physical properties of materials Discusses customized ML methods for 2D materials data and applications and high-throughput data acquisition Describes several case studies illustrating how ML approaches are currently leading innovations in the discovery development manufacturing and deployment of 2D materials needed for strengthening industrial products Gives future trends in ML for 2D materials explainable AI and dealing with extremely large and small diverse datasets Aimed at materials science researchers this book allows readers to quickly yet thoroughly learn the ML and AI concepts needed to ascertain the applicability of ML methods in their research. | Machine Learning in 2D Materials Science

GBP 110.00
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Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques

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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Artificial Intelligence and Machine Learning in Business Management Concepts Challenges and Case Studies

Artificial Intelligence and Machine Learning in Business Management Concepts Challenges and Case Studies

Artificial Intelligence and Machine Learning in Business Management The focus of this book is to introduce artificial intelligence (AI) and machine learning (ML) technologies into the context of business management. The book gives insights into the implementation and impact of AI and ML to business leaders managers technology developers and implementers. With the maturing use of AI or ML in the field of business intelligence this book examines several projects with innovative uses of AI beyond data organization and access. It follows the Predictive Modeling Toolkit for providing new insight on how to use improved AI tools in the field of business. It explores cultural heritage values and risk assessments for mitigation and conservation and discusses on-shore and off-shore technological capabilities with spatial tools for addressing marketing and retail strategies and insurance and healthcare systems. Taking a multidisciplinary approach for using AI this book provides a single comprehensive reference resource for undergraduate graduate business professionals and related disciplines. | Artificial Intelligence and Machine Learning in Business Management Concepts Challenges and Case Studies

GBP 105.00
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Recent Trends in Computational Sciences Proceedings of the Fourth Annual International Conference on Data Science Machine Learning and Bloc

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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Medical Imaging Artificial Intelligence Image Recognition and Machine Learning Techniques

Medical Imaging Artificial Intelligence Image Recognition and Machine Learning Techniques

Winner of the Outstanding Academic Title recognition by Choice for the 2020 OAT Awards. The Choice OAT Award represents the highest caliber of scholarly titles that have been reviewed by Choice and conveys the extraordinary recognition of the academic community. The book discusses varied topics pertaining to advanced or up-to-date techniques in medical imaging using artificial intelligence (AI) image recognition (IR) and machine learning (ML) algorithms/techniques. Further coverage includes analysis of chest radiographs (chest x-rays) via stacked generalization models TB type detection using slice separation approach brain tumor image segmentation via deep learning mammogram mass separation epileptic seizures breast ultrasound images knee joint x-ray images bone fracture detection and labeling and diabetic retinopathy. It also reviews 3D imaging in biomedical applications and pathological medical imaging. | Medical Imaging Artificial Intelligence Image Recognition and Machine Learning Techniques

GBP 130.00
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Machine Learning-Based Modelling in Atomic Layer Deposition Processes

Machine Learning-Based Modelling in Atomic Layer Deposition Processes

While thin film technology has benefited greatly from artificial intelligence (AI) and machine learning (ML) techniques there is still much to be learned from a full-scale exploration of these technologies in atomic layer deposition (ALD). This book provides in-depth information regarding the application of ML-based modeling techniques in thin film technology as a standalone approach and integrated with the classical simulation and modeling methods. It is the first of its kind to present detailed information regarding approaches in ML-based modeling optimization and prediction of the behaviors and characteristics of ALD for improved process quality control and discovery of new materials. As such this book fills significant knowledge gaps in the existing resources as it provides extensive information on ML and its applications in film thin technology. Offers an in-depth overview of the fundamentals of thin film technology state-of-the-art computational simulation approaches in ALD ML techniques algorithms applications and challenges. Establishes the need for and significance of ML applications in ALD while introducing integration approaches for ML techniques with computation simulation approaches. Explores the application of key techniques in ML such as predictive analysis classification techniques feature engineering image processing capability and microstructural analysis of deep learning algorithms and generative model benefits in ALD. Helps readers gain a holistic understanding of the exciting applications of ML-based solutions to ALD problems and apply them to real-world issues. Aimed at materials scientists and engineers this book fills significant knowledge gaps in existing resources as it provides extensive information on ML and its applications in film thin technology. It also opens space for future intensive research and intriguing opportunities for ML-enhanced ALD processes which scale from academic to industrial applications. | Machine Learning-Based Modelling in Atomic Layer Deposition Processes

GBP 150.00
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Deep Learning Machine Learning and IoT in Biomedical and Health Informatics Techniques and Applications

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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Artificial Intelligence and Machine Learning An Intelligent Perspective of Emerging Technologies

Artificial Intelligence and Machine Learning An Intelligent Perspective of Emerging Technologies

This book focuses on artificial intelligence (AI) and machine learning (ML) technologies and how they are progressively being incorporated into a wide range of products including consumer gadgets smart personal assistants cutting-edge medical diagnostic systems and quantum computing systems. This concise reference book offers a broad overview of the most important trends and discusses how these trends and technologies are being created and employed in the applications in which they are being used. Artificial Intelligence and Machine Learning: An Intelligent Perspective of Emerging Technologies offers a broad package involving the incubation of AI and ML with various emerging technologies such as Internet of Things (IoT) healthcare smart cities robotics and more. The book discusses various data collection and data transformation techniques and also maps the legal and ethical issues of data-driven e-healthcare systems while covering possible ways to resolve them. The book explores different techniques on how AI can be used to create better virtual reality experiences and deals with the techniques and possible ways to merge the power of AI and IoT to create smart home appliances. With contributions from experts in the field this reference book is useful to healthcare professionals researchers and students of industrial engineering systems engineering biomedical computer science electronics and communications engineering. | Artificial Intelligence and Machine Learning An Intelligent Perspective of Emerging Technologies

GBP 89.99
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Learning from Data for Aquatic and Geotechnical Environments

Learning from Data for Aquatic and Geotechnical Environments

The book presents machine learning as an approach to building models that learn from data and that can be used to complement the existing modelling practice in aquatic and geotechnical environments. It provides concepts of learning from data and identifies segmentation (clustering) classification regression and control as the learning tasks. A unified methodology based on the concepts of machine learning information theory and statistics is presented that can be followed to build models using data as well as expert knowledge. Several machine learning methods are used to extract features to build data-driven models in geotechnics. A set of regression models are built to predict sediment transport rates and assess harbour sedimentation. Controllers that replicate the control strategy of model-based optimal controllers of water systems are built for situations where fast and accurate decisions are needed. The models built demonstrate excellent performance; they may complement or even replace the existing models and can be used in practice. The performance of the models proves the effectiveness of the methodology and machine learning in general. | Learning from Data for Aquatic and Geotechnical Environments

GBP 175.00
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Artificial Intelligence and Machine Learning in the Thermal Spray Industry Practices Implementation and Challenges

Artificial Intelligence and Machine Learning in the Thermal Spray Industry Practices Implementation and Challenges

This book details the emerging area of the induction of expert systems in thermal spray technology replacing traditional parametric optimization methods like numerical modeling and simulation. It promotes enlightens and hastens the digital transformation of the surface engineering industry by discussing the contribution of expert systems like Machine Learning (ML) and Artificial Intelligence (AI) toward achieving durable Thermal Spray (TS) coatings. Artificial Intelligence and Machine Learning in the Thermal Spray Industry: Practices Implementation and Challenges highlights how AI and ML techniques are used in the TS industry. It sheds light on AI’s versatility revealing its applicability in solving problems related to conventional simulation and numeric modeling techniques. This book combines automated technologies with expert machines to show several advantages including decreased error and greater accuracy in judgment and prediction enhanced efficiency reduced time consumption and lower costs. Specific barriers preventing AI’s successful implementation in the TS industry are also discussed. This book also looks at how training and validating more models with microstructural features of deposited coating will be the center point to grooming this technology in the future. Lastly this book thoroughly analyzes the digital technologies available for modeling and achieving high-performance coatings including giving AI-related models like Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) more attention. This reference book is directed toward professors students practitioners and researchers of higher education institutions working in the fields that deal with the application of AI and ML technology. | Artificial Intelligence and Machine Learning in the Thermal Spray Industry Practices Implementation and Challenges

GBP 100.00
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