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Samsung Media Empire and Family A power web

Samsung Media Empire and Family A power web

This book analyses media conglomerates owning multiple media holdings under centralized ownership within and across media markets. It argues that Asian capitalists utilize both a market-oriented ideology and family connections to build their media empires thereby creating cultural conglomerates that exercise corporate censorship over media markets. It focuses on family-controlled media conglomerates in Korea specifically the international business giant Samsung and its related media companies Cheil Jedang and JoongAng Ilbo all of which are controlled by the single Lee family. Utilizing the theoretical approach of political economy of communication the book examines how and why the Lee family exercise corporate censorship over Korean society. Offering an essential take on Asia’s political economy of communication in order to understand the workings of Asian media empires this book will appeal to students and scholars of Korean Studies Korean Business and Mass Communications. | Samsung Media Empire and Family A power web

GBP 44.99
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Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry

Today raw data on any industry is widely available. With the help of artificial intelligence (AI) and machine learning (ML) this data can be used to gain meaningful insights. In addition as data is the new raw material for today’s world AI and ML will be applied in every industrial sector. Industry 4. 0 mainly focuses on the automation of things. From that perspective the oil and gas industry is one of the largest industries in terms of economy and energy. Applications of Artificial Intelligence (AI) and Machine Learning (ML) in the Petroleum Industry analyzes the use of AI and ML in the oil and gas industry across all three sectors namely upstream midstream and downstream. It covers every aspect of the petroleum industry as related to the application of AI and ML ranging from exploration data management extraction processing real-time data analysis monitoring cloud-based connectivity system and conditions analysis to the final delivery of the product to the end customer while taking into account the incorporation of the safety measures for a better operation and the efficient and effective execution of operations. This book explores the variety of applications that can be integrated to support the existing petroleum and adjacent sectors to solve industry problems. It will serve as a useful guide for professionals working in the petroleum industry industrial engineers AI and ML experts and researchers as well as students.

GBP 82.99
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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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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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Machine Learning in Translation

Machine Learning in Translation

Machine Learning in Translation introduces machine learning (ML) theories and technologies that are most relevant to translation processes approaching the topic from a human perspective and emphasizing that ML and ML-driven technologies are tools for humans. Providing an exploration of the common ground between human and machine learning and of the nature of translation that leverages this new dimension this book helps linguists translators and localizers better find their added value in a ML-driven translation environment. Part One explores how humans and machines approach the problem of translation in their own particular ways in terms of word embeddings chunking of larger meaning units and prediction in translation based upon the broader context. Part Two introduces key tasks including machine translation translation quality assessment and quality estimation and other Natural Language Processing (NLP) tasks in translation. Part Three focuses on the role of data in both human and machine learning processes. It proposes that a translator’s unique value lies in the capability to create manage and leverage language data in different ML tasks in the translation process. It outlines new knowledge and skills that need to be incorporated into traditional translation education in the machine learning era. The book concludes with a discussion of human-centered machine learning in translation stressing the need to empower translators with ML knowledge through communication with ML users developers and programmers and with opportunities for continuous learning. This accessible guide is designed for current and future users of ML technologies in localization workflows including students on courses in translation and localization language technology and related areas. It supports the professional development of translation practitioners so that they can fully utilize ML technologies and design their own human-centered ML-driven translation workflows and NLP tasks.

GBP 34.99
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Machine Learning in Signal Processing Applications Challenges and the Road Ahead

Machine Learning in Signal Processing Applications Challenges and the Road Ahead

Machine Learning in Signal Processing: Applications Challenges and the Road Ahead offers a comprehensive approach toward research orientation for familiarizing signal processing (SP) concepts to machine learning (ML). ML as the driving force of the wave of artificial intelligence (AI) provides powerful solutions to many real-world technical and scientific challenges. This book will present the most recent and exciting advances in signal processing for ML. The focus is on understanding the contributions of signal processing and ML and its aim to solve some of the biggest challenges in AI and ML. FEATURES Focuses on addressing the missing connection between signal processing and ML Provides a one-stop guide reference for readers Oriented toward material and flow with regards to general introduction and technical aspects Comprehensively elaborates on the material with examples and diagrams This book is a complete resource designed exclusively for advanced undergraduate students post-graduate students research scholars faculties and academicians of computer science and engineering computer science and applications and electronics and telecommunication engineering. | Machine Learning in Signal Processing Applications Challenges and the Road Ahead

GBP 150.00
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Engineering Mathematics and Artificial Intelligence Foundations Methods and Applications

Engineering Mathematics and Artificial Intelligence Foundations Methods and Applications

The fields of Artificial Intelligence (AI) and Machine Learning (ML) have grown dramatically in recent years with an increasingly impressive spectrum of successful applications. This book represents a key reference for anybody interested in the intersection between mathematics and AI/ML and provides an overview of the current research streams. Engineering Mathematics and Artificial Intelligence: Foundations Methods and Applications discusses the theory behind ML and shows how mathematics can be used in AI. The book illustrates how to improve existing algorithms by using advanced mathematics and offers cutting-edge AI technologies. The book goes on to discuss how ML can support mathematical modeling and how to simulate data by using artificial neural networks. Future integration between ML and complex mathematical techniques is also highlighted within the book. This book is written for researchers practitioners engineers and AI consultants. | Engineering Mathematics and Artificial Intelligence Foundations Methods and Applications

GBP 150.00
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Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower enhance and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector this book demonstrates the depth breadth complexity and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics drug discovery and development medical imaging automation robotic surgery electronic smart records creation outbreak prediction medical image analysis and radiation treatments. This book aims to endow different communities with the innovative advances in theory analytical results case studies numerical simulation modeling and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers scientists healthcare professionals programmers and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science Ravenshaw University Cuttack Odisha India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology Sharda University Greater Noida India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University Turkey.

GBP 115.00
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Machine Learning Theory to Applications

Machine Learning Theory to Applications

The book reviews core concepts of machine learning (ML) while focusing on modern applications. It is aimed at those who want to advance their understanding of ML by providing technical and practical insights. It does not use complicated mathematics to explain how to benefit from ML algorithms. Unlike the existing literature this work provides the core concepts with emphasis on fresh ideas and real application scenarios. It starts with the basic concepts of ML and extends the concepts to the different deep learning algorithms. The book provides an introduction and main elements of evaluation tools with Python and walks you through the recent applications of ML in self-driving cars cognitive decision making communication networks security and signal processing. The concept of generative networks is also presented and focuses on GANs as a tool to improve the performance of existing algorithms. In summary this book provides a comprehensive technological path from fundamental theories to the categorization of existing algorithms covers state-of-the-art practical evaluation tools and methods to empower you to use synthetic data to improve the performance of applications. | Machine Learning Theory to Applications

GBP 52.99
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AI Machine Learning and Deep Learning A Security Perspective

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
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Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition

Maximum Likelihood Estimation with Stata Fifth Edition is the essential reference and guide for researchers in all disciplines who wish to write maximum likelihood (ML) estimators in Stata. Beyond providing comprehensive coverage of Stata’s commands for writing ML estimators the book presents an overview of the underpinnings of maximum likelihood and how to think about ML estimation. The fifth edition includes a new second chapter that demonstrates the easy-to-use mlexp command. This command allows you to directly specify a likelihood function and perform estimation without any programming. The core of the book focuses on Stata's ml command. It shows you how to take full advantage of ml’s noteworthy features: Linear constraints Four optimization algorithms (Newton–Raphson DFP BFGS and BHHH) Observed information matrix (OIM) variance estimator Outer product of gradients (OPG) variance estimator Huber/White/sandwich robust variance estimator Cluster–robust variance estimator Complete and automatic support for survey data analysis Direct support of evaluator functions written in Mata When appropriate options are used many of these features are provided automatically by ml and require no special programming or intervention by the researcher writing the estimator. In later chapters you will learn how to take advantage of Mata Stata's matrix programming language. For ease of programming and potential speed improvements you can write your likelihood-evaluator program in Mata and continue to use ml to control the maximization process. A new chapter in the fifth edition shows how you can use the moptimize() suite of Mata functions if you want to implement your maximum likelihood estimator entirely within Mata. In the final chapter the authors illustrate the major steps required to get from log-likelihood function to fully operational estimation command. This is done using several different models: logit and probit linear regression Weibull regression the Cox proportional hazards model random-effects regression and seemingly unrelated regression. This edition adds a new example of a bivariate Poisson model a model that is not available otherwise in Stata. The authors provide extensive advice for developing your own estimation commands. With a little care and the help of this book users will be able to write their own estimation commands-commands that look and behave just like the official estimation commands in Stata. Whether you want to fit a special ML estimator for your own research or wish to write a general-purpose ML estimator for others to use you need this book.

GBP 59.99
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Deep Learning A Comprehensive Guide

Deep Learning A Comprehensive Guide

Deep Learning: A Comprehensive Guide provides comprehensive coverage of Deep Learning (DL) and Machine Learning (ML) concepts. DL and ML are the most sought-after domains requiring a deep understanding – and this book gives no less than that. This book enables the reader to build innovative and useful applications based on ML and DL. Starting with the basics of neural networks and continuing through the architecture of various types of CNNs RNNs LSTM and more till the end of the book each and every topic is given the utmost care and shaped professionally and comprehensively. Key Features Includes the smooth transition from ML concepts to DL concepts Line-by-line explanations have been provided for all the coding-based examples Includes a lot of real-time examples and interview questions that will prepare the reader to take up a job in ML/DL right away Even a person with a non-computer-science background can benefit from this book by following the theory examples case studies and code snippets Every chapter starts with the objective and ends with a set of quiz questions to test the reader’s understanding Includes references to the related YouTube videos that provide additional guidance AI is a domain for everyone. This book is targeted toward everyone irrespective of their field of specialization. Graduates and researchers in deep learning will find this book useful. | Deep Learning A Comprehensive Guide

GBP 130.00
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The AI Wave in Defence Innovation Assessing Military Artificial Intelligence Strategies Capabilities and Trajectories

The AI Wave in Defence Innovation Assessing Military Artificial Intelligence Strategies Capabilities and Trajectories

An international and interdisciplinary perspective on the adoption and governance of artificial intelligence (AI) and machine learning (ML) in defence and military innovation by major and middle powers. Advancements in AI and ML pose pressing questions related to evolving conceptions of military power compliance with international humanitarian law peace promotion strategic stability arms control future operational environments and technology races. To navigate the breadth of this AI and international security agenda the contributors to this book include experts on AI technology governance and defence innovation to assess military AI strategic perspectives from major and middle AI powers alike. These include views of how the United States China Japan South Korea the European Union and Russia see AI/ML as a technology with the potential to reshape military affairs and power structures in the broader international system. This diverse set of views aims to help elucidate key similarities and differences between AI powers in the evolving strategic context. A valuable read for scholars of security studies public policy and STS studies with an interest in the impacts of AI and ML technologies. | The AI Wave in Defence Innovation Assessing Military Artificial Intelligence Strategies Capabilities and Trajectories

GBP 34.99
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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
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Machine Learning Animated

Machine Learning Animated

The release of ChatGPT has kicked off an arms race in Machine Learning (ML) however ML has also been described as a black box and very hard to understand. Machine Learning Animated eases you into basic ML concepts and summarizes the learning process in three words: initialize adjust and repeat. This is illustrated step by step with animation to show how machines learn: from initial parameter values to adjusting each step to the final converged parameters and predictions. This book teaches readers to create their own neural networks with dense and convolutional layers and use them to make binary and multi-category classifications. Readers will learn how to build deep learning game strategies and combine this with reinforcement learning witnessing AI achieve super-human performance in Atari games such as Breakout Space Invaders Seaquest and Beam Rider. Written in a clear and concise style illustrated with animations and images this book is particularly appealing to readers with no background in computer science mathematics or statistics. Access the book's repository at: https://github. com/markhliu/MLA

GBP 74.99
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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
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AI for Physics

AI for Physics

Written in accessible language without mathematical formulas this short book provides an overview of the wide and varied applications of artificial intelligence (AI) across the spectrum of physical sciences. Focusing in particular on AI's ability to extract patterns from data known as machine learning (ML) the book includes a chapter on important machine learning algorithms and their respective applications in physics. It then explores the use of ML across a number of important sub-fields in more detail ranging from particle molecular and condensed matter physics to astrophysics cosmology and the theory of everything. The book covers such applications as the search for new particles and the detection of gravitational waves from the merging of black holes and concludes by discussing what the future may hold. | AI for Physics

GBP 22.99
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Data Science AI and Machine Learning in Drug Development

Data Science AI and Machine Learning in Drug Development

The confluence of big data artificial intelligence (AI) and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information Data Science AI and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D emerging applications of big data AI and ML in drug development and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data AI and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise | Data Science AI and Machine Learning in Drug Development

GBP 99.99
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Machine Learning for Factor Investing Python Version

Machine Learning for Factor Investing Python Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out-of-reach. Machine learning for factor investing: Python version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained Python code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise. | Machine Learning for Factor Investing Python Version

GBP 66.99
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Machine Learning for Factor Investing: R Version

Machine Learning for Factor Investing: R Version

Machine learning (ML) is progressively reshaping the fields of quantitative finance and algorithmic trading. ML tools are increasingly adopted by hedge funds and asset managers notably for alpha signal generation and stocks selection. The technicality of the subject can make it hard for non-specialists to join the bandwagon as the jargon and coding requirements may seem out of reach. Machine Learning for Factor Investing: R Version bridges this gap. It provides a comprehensive tour of modern ML-based investment strategies that rely on firm characteristics. The book covers a wide array of subjects which range from economic rationales to rigorous portfolio back-testing and encompass both data processing and model interpretability. Common supervised learning algorithms such as tree models and neural networks are explained in the context of style investing and the reader can also dig into more complex techniques like autoencoder asset returns Bayesian additive trees and causal models. All topics are illustrated with self-contained R code samples and snippets that are applied to a large public dataset that contains over 90 predictors. The material along with the content of the book is available online so that readers can reproduce and enhance the examples at their convenience. If you have even a basic knowledge of quantitative finance this combination of theoretical concepts and practical illustrations will help you learn quickly and deepen your financial and technical expertise.

GBP 66.99
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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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Security and Risk Analysis for Intelligent Cloud Computing Methods Applications and Preventions

Security and Risk Analysis for Intelligent Cloud Computing Methods Applications and Preventions

This edited book is a compilation of scholarly articles on the latest developments in the field of AI Blockchain and ML/DL in cloud security. This book is designed for security and risk assessment professionals and to help undergraduate postgraduate students research scholars academicians and technology professionals who are interested in learning practical approaches to cloud security. It covers practical strategies for assessing the security and privacy of cloud infrastructure and applications and shows how to make cloud infrastructure secure to combat threats and attacks and prevent data breaches. The chapters are designed with a granular framework starting with the security concepts followed by hands-on assessment techniques based on real-world studies. Readers will gain detailed information on cloud computing security that—until now—has been difficult to access. This book: • Covers topics such as AI Blockchain and ML/DL in cloud security. • Presents several case studies revealing how threat actors abuse and exploit cloud environments to spread threats. • Explains the privacy aspects you need to consider in the cloud including how they compare with aspects considered in traditional computing models. • Examines security delivered as a service—a different facet of cloud security. | Security and Risk Analysis for Intelligent Cloud Computing Methods Applications and Preventions

GBP 110.00
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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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Social Media Analytics Strategies and Governance

Social Media Analytics Strategies and Governance

Social media has spread rapidly on the global stage driving consumers’ attention and influence both consciously and subconsciously. Whilst this type of platform may have been initially designed as a tool for open communication and expression it is also being utilized as a digital tool with widescale use cases. The intelligence explosion information overload and disinformation play a significant part regarding individual group and country perceptions. The complex nature of this data explosion created an increasing demand and use of artificial intelligence (AI) and machine learning (ML) to help provide ‘big insights’ to ‘big data’. AI and ML enable the analysis and dissemination of vast amounts of data however the ungoverned pace at which AI and autonomous systems have been deployed has created unforeseen problems. Many algorithms and AI systems have been trained on limited or unverified datasets creating inbuilt and unseen biases. Where these algorithmic tools have been deployed in high impact systems there are documented occurrences of disastrous decision making and outcomes that have negatively impacted people and communities. Little to no work had been conducted in its vulnerability and ability to exploit AI itself. So AI and autonomous systems whilst being a force for societal good could have the potential to create and exacerbate societies greatest challenges. This is a cohesive volume that addresses challenging problems and presents a range of innovative approaches and discussion. | Social Media Analytics Strategies and Governance

GBP 160.00
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