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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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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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Machine Learning for Neuroscience A Systematic Approach

Machine Learning for Neuroscience A Systematic Approach

This book addresses the growing need for machine learning and data mining in neuroscience. The book offers a basic overview of the neuroscience machine learning and the required math and programming necessary to develop reliable working models. The material is presented in a easy to follow user-friendly manner and is replete with fully working machine learning code. Machine Learning for Neuroscience: A Systematic Approach tackles the needs of neuroscience researchers and practitioners that have very little training relevant to machine learning. The first section of the book provides an overview of necessary topics in order to delve into machine learning including basic linear algebra and Python programming. The second section provides an overview of neuroscience and is directed to the computer science oriented readers. The section covers neuroanatomy and physiology cellular neuroscience neurological disorders and computational neuroscience. The third section of the book then delves into how to apply machine learning and data mining to neuroscience and provides coverage of artificial neural networks (ANN) clustering and anomaly detection. The book contains fully working code examples with downloadable working code. It also contains lab assignments and quizzes making it appropriate for use as a textbook. The primary audience is neuroscience researchers who need to delve into machine learning programmers assigned neuroscience related machine learning projects and students studying methods in computational neuroscience. | Machine Learning for Neuroscience A Systematic Approach

GBP 82.99
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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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Machine Learning in Healthcare Fundamentals and Recent Applications

Machine Learning in Healthcare Fundamentals and Recent Applications

Artificial intelligence (AI) and machine learning (ML) techniques play an important role in our daily lives by enhancing predictions and decision-making for the public in several fields such as financial services real estate business consumer goods social media etc. Despite several studies that have proved the efficacy of AI/ML tools in providing improved healthcare solutions it has not gained the trust of health-care practitioners and medical scientists. This is due to poor reporting of the technology variability in medical data small datasets and lack of standard guidelines for application of AI. Therefore the development of new AI/ML tools for various domains of medicine is an ongoing field of research. Machine Learning in Healthcare: Fundamentals and Recent Applications discusses how to build various ML algorithms and how they can be applied to improve healthcare systems. Healthcare applications of AI are innumerable: medical data analysis early detection and diagnosis of disease providing objective-based evidence to reduce human errors curtailing inter- and intra-observer errors risk identification and interventions for healthcare management real-time health monitoring assisting clinicians and patients for selecting appropriate medications and evaluating drug responses. Extensive demonstrations and discussion on the various principles of machine learning and its application in healthcare is provided along with solved examples and exercises. This text is ideal for readers interested in machine learning without any background knowledge and looking to implement machine-learning models for healthcare systems. | Machine Learning in Healthcare Fundamentals and Recent Applications

GBP 82.99
1

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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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 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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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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Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

Machine learning is an exciting topic with a myriad of applications. However most textbooks are targeted towards computer science students. This however creates a complication for scientists across the physical sciences that also want to understand the main concepts of machine learning and look ahead to applica- tions and advancements in their fields. This textbook bridges this gap providing an introduction to the mathematical foundations for the main algorithms used in machine learning for those from the physical sciences without a formal background in computer science. It demon- strates how machine learning can be used to solve problems in physics and engineering targeting senior undergraduate and graduate students in physics and electrical engineering alongside advanced researchers. All codes are available on the author's website: C•Lab (nau. edu) They are also available on GitHub: https://github. com/StxGuy/MachineLearning Key Features: Includes detailed algorithms. Supplemented by codes in Julia: a high-performing language and one that is easy to read for those in the natural sciences. All algorithms are presented with a good mathematical background. | Machine Learning for the Physical Sciences Fundamentals and Prototyping with Julia

GBP 64.99
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Artificial Intelligence and Machine Learning for Business for Non-Engineers

Artificial Intelligence and Machine Learning for Business for Non-Engineers

The next big area within the information and communication technology field is Artificial Intelligence (AI). The industry is moving to automate networks cloud-based systems (e. g. Salesforce) databases (e. g. Oracle) AWS machine learning (e. g. Amazon Lex) and creating infrastructure that has the ability to adapt in real-time to changes and learn what to anticipate in the future. It is an area of technology that is coming faster and penetrating more areas of business than any other in our history. AI will be used from the C-suite to the distribution warehouse floor. Replete with case studies this book provides a working knowledge of AI’s current and future capabilities and the impact it will have on every business. It covers everything from healthcare to warehousing banking finance and education. It is essential reading for anyone involved in industry. | Artificial Intelligence and Machine Learning for Business for Non-Engineers

GBP 56.99
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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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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
1

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
1

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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Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

In machine learning applications practitioners must take into account the cost associated with the algorithm. These costs include: Cost of acquiring training dataCost of data annotation/labeling and cleaningComputational cost for model fitting validation and testingCost of collecting features/attributes for test dataCost of user feedback collectionCost of incorrect prediction/classificationCost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process. The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles. Spurring further research on several open problems this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

GBP 59.99
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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
1

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
1

Feature Engineering for Machine Learning and Data Analytics

Feature Engineering for Machine Learning and Data Analytics

Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering including feature generation feature extraction feature transformation feature selection and feature analysis and evaluation. The book presents key concepts methods examples and applications as well as chapters on feature engineering for major data types such as texts images sequences time series graphs streaming data software engineering data Twitter data and social media data. It also contains generic feature generation approaches as well as methods for generating tried-and-tested hand-crafted domain-specific features. The first chapter defines the concepts of features and feature engineering offers an overview of the book and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering namely feature selection feature transformation based feature engineering deep learning based feature engineering and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection software management and Twitter-based applications respectively. This book can be used as a reference for data analysts big data scientists data preprocessing workers project managers project developers prediction modelers professors researchers graduate students and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering or as a supplement for courses on machine learning data mining and big data analytics.

GBP 44.99
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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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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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Applied Genetic Programming and Machine Learning

Applied Genetic Programming and Machine Learning

What do financial data prediction day-trading rule development and bio-marker selection have in common? They are just a few of the tasks that could potentially be resolved with genetic programming and machine learning techniques. Written by leaders in this field Applied Genetic Programming and Machine Learning delineates the extension of Genetic Programming (GP) for practical applications. Reflecting rapidly developing concepts and emerging paradigms this book outlines how to use machine learning techniques make learning operators that efficiently sample a search space navigate the search process through the design of objective fitness functions and examine the search performance of the evolutionary system. It provides a methodology for integrating GP and machine learning techniques establishing a robust evolutionary framework for addressing tasks from areas such as chaotic time-series prediction system identification financial forecasting classification and data mining. The book provides a starting point for the research of extended GP frameworks with the integration of several machine learning schemes. Drawing on empirical studies taken from fields such as system identification finanical engineering and bio-informatics it demonstrates how the proposed methodology can be useful in practical inductive problem solving.

GBP 59.99
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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
1