%{search_type} search results

• Introduction.- Part I: Government services.- Intelligent chat bots for online customer service.- Improved services delivery using AI.- Intelligent policing: physical crime and cybercrime prevention and detection.- Part II: Healthcare Services.- Breast cancer diagnosis.- IoT Pharmaceutical management.- Biomedical Text conversion algorithms.- Part III: Education.- Machine learning in teaching and assessment of students.- Prediction of student performance.- Training the next generation of AI students.- Part IV: Tourism and hospitality.- Landmark recognition.- Recommender system for tourists.- Part V: AI Ethics.- Ethics of facial recognition.- Ethics of robotic services.- Bias issues and misapplication of machine learning.- Conclusion.
• (source: Nielsen Book Data)

This book provides valuable information on effective, state-of-the-art techniques and approaches for governments, students, researchers, practitioners, entrepreneurs and teachers in the field of artificial intelligence (AI). The book explains the data and AI, types and properties of data, the relation between AI algorithms and data, what makes data AI ready, steps of data pre-processing, data quality, data storage and data platforms. Therefore, this book will be interested by AI practitioners, academics, researchers, and lecturers in computer science, artificial intelligence, machine learning and data sciences.
(source: Nielsen Book Data)

The AI hype is winding down, so where are we now? How far along are most companies in terms of AI adoption? Do they have working applications in production? What tools are they using? What are the biggest bottlenecks? How much has changed since last year, and what does that tell us about the future of AI? If you're curious about the state of AI today, or just want to see how your organization compares to others, check out this new O'Reilly report.

• AI-Driven Information and Communication Technologies, Services, and Applications for Next-Generation Healthcare System / Vijayakumar Ponnusamy, A Vasuki, J Christopher Clement, P Eswaran
• Pneumatic Position Servo System Using Multi-Variable Multi-Objective Genetic Algorithm-Based Fractional-Order PID Controller / D Magdalin Mary, V Vanitha, G Sophia Jasmine
• Improved Weighted Distance Hop Hyperbolic Prediction-Based Reliable Data Dissemination (IWDH-HP-RDD) Mechanism for Smart Vehicular Environments / Sengathir Janakiraman, M Deva Priya, A Christy Jeba Malar
• Remaining Useful Life Prediction of Small and Large Signal Analog Circuits Using Filtering Algorithms / M Sathiyanathan, K Anandhakumar, S Jaganathan, C S Subashkumar
• AI in Healthcare / S Menaga, J Paruvathavardhini
• Introduction of Artificial Intelligence / R Vishalakshi, S Mangai
• Artificial Intelligence in Healthcare: Algorithms and Decision Support Systems / S Palanivel Rajan, M Paranthaman
• Smart Homes and Smart Cities / C N Marimuthu, G Arthy
• Application of AI in Healthcare / V Priya, S Prabu
• Battery Life and Electric Vehicle Range Prediction / S Ravikrishna, Kumar C S Subash, M Sundaram
• AI-Driven Healthcare Analysis / N Kasthuri, T Meeradevi
• A Novel Technique for Continuous Monitoring of Fuel Adulteration / P Rajalakshmy, R Varun, Hency Jose P Subha, K Rajasekaran
• Improved Merkle Hash and Trapdoor Function-Based Secure Mutual Authentication (IMH-TF-SMA) Mechanism for Securing Smart Home Environment / M Deva Priya, Sengathir Janakiraman, A Christy Jeba Malar
• Smart Sensing Technology / S Palanivel Rajan, T Abirami.

SMART SYSTEMS FOR INDUSTRIAL APPLICATIONS The prime objective of this book is to provide an insight into the role and advancements of artificial intelligence in electrical systems and future challenges. The book covers a broad range of topics about AI from a multidisciplinary point of view, starting with its history and continuing on to theories about artificial vs. human intelligence, concepts, and regulations concerning AI, human-machine distribution of power and control, delegation of decisions, the social and economic impact of AI, etc. The prominent role that AI plays in society by connecting people through technologies is highlighted in this book. It also covers key aspects of various AI applications in electrical systems in order to enable growth in electrical engineering. The impact that AI has on social and economic factors is also examined from various perspectives. Moreover, many intriguing aspects of AI techniques in different domains are covered such as e-learning, healthcare, smart grid, virtual assistance, etc. Audience The book will be of interest to researchers and postgraduate students in artificial intelligence, electrical and electronic engineering, as well as those engineers working in the application areas such as healthcare, energy systems, education, and others.
(source: Nielsen Book Data)

• Fraud Detection in Online Transactions Using Machine Learning Approaches - A Review.- Artificial Intelligence Techniques for Predicting TYPE 2 DIABETES.- Braille cell Segmentation and Removal of Unwanted Dots using Canny Edge Detector.- IoT Based Data Storage for Cloud Computing Applications.- An Introduction To Sparse Sampling On Audio Signal By Exploring Different Basis Matrix.- A Home Security Camera System Based on Cloud and SNS.- Cyberbullying Detection Using Sentiment and Personality.- Sparse Reflectance Map Based Fabric Characterization.- Human Resource Working Prediction Based on Logistic Regression.- Study on Automatic Speech Therapy System for Patients.- An IoT based Congestion Control Framework for Intelligent Traffic Management System.- Multi Join Query Optimization using Modified ACO with GA.- Character Recognition of Tulu script using Convolutional Neural Network.
• (source: Nielsen Book Data)

This book presents selected peer-reviewed papers from the International Conference on Artificial Intelligence and Data Engineering (AIDE 2019). The topics covered are broadly divided into four groups: artificial intelligence, machine vision and robotics, ambient intelligence, and data engineering. The book discusses recent technological advances in the emerging fields of artificial intelligence, machine learning, robotics, virtual reality, augmented reality, bioinformatics, intelligent systems, cognitive systems, computational intelligence, neural networks, evolutionary computation, speech processing, Internet of Things, big data challenges, data mining, information retrieval, and natural language processing. Given its scope, this book can be useful for students, researchers, and professionals interested in the growing applications of artificial intelligence and data engineering.
(source: Nielsen Book Data)

• Introduction.- Neuro-Fuzzy Approach and its Application in Recommender Systems.- Novel Explainable Recommenders Based on Neuro-Fuzzy.- Explainable Recommender for Investment Advisers.- Summary and Final Remarks.
• (source: Nielsen Book Data)

The book proposes techniques, with an emphasis on the financial sector, which will make recommendation systems both accurate and explainable. The vast majority of AI models work like black box models. However, in many applications, e.g., medical diagnosis or venture capital investment recommendations, it is essential to explain the rationale behind AI systems decisions or recommendations. Therefore, the development of artificial intelligence cannot ignore the need for interpretable, transparent, and explainable models. First, the main idea of the explainable recommenders is outlined within the background of neuro-fuzzy systems. In turn, various novel recommenders are proposed, each characterized by achieving high accuracy with a reasonable number of interpretable fuzzy rules. The main part of the book is devoted to a very challenging problem of stock market recommendations. An original concept of the explainable recommender, based on patterns from previous transactions, is developed; it recommends stocks that fit the strategy of investors, and its recommendations are explainable for investment advisers.
(source: Nielsen Book Data)

• Analysis of Breast Cancer Detection Techniques Using Rapid Miner.- Software Cost Estimation Using LSTM - RNN.- Artificial Neural Network (ANN) to Design Microstrip Transmission Line.- Classifying Breast Cancer based on Machine learning.- Comparison of Various Statistical Techniques Used in Meta-Analysis.- Stress Prediction Model Using Machine Learning.- Finger Vein Recognition Using Deep Learning.- Cyber Security Safeguards among Parents/Guardians in the Facet of Arba Minch, Ethiopia.- Secure Communication: Using Double Compound-Combination Hybrid Synchronization.- Fractional Inverse Matrix Projective Combination Synchronization with Application in Secure Communication.- Cryptosystem Based on hybrid Chaotic Structured Phase Mask and Hybrid Mask Using Gyrator Transform.- PE Files based Malware Detection Using Machine Learning.
• (source: Nielsen Book Data)

This book gathers high-quality papers presented at the International Conference on Artificial Intelligence and Applications (ICAIA 2020), held at Maharaja Surajmal Institute of Technology, New Delhi, India, on 6-7 February 2020. The book covers areas such as artificial neural networks, fuzzy systems, computational optimization technologies and machine learning. .
(source: Nielsen Book Data)

• Part 1. Application of artificial intelligence in Information Systems
• Towards Industry 4.0: Functional and Technological Basis for ERP 4.0 Systems
• Statistic-Based Method for Budgetary Control Limits Setting--Renewed Approach in the Context of Industry 4.0
• Supporting Investment Decisions Based on Cognitive Technology
• Intelligent Support of Accounting IT Systems in Modern Enterprises
• A conceptual framework of Intelligent System for environmental life cycle costing
• Part 2: Cyber security in Information Systems
• Prospects and Limitations of the Use of Blockchain-options for the Supply of Electricity
• Digital transformation of public administration through blockchain technology
• The cyber threats analysis for web applications security in Industry 4.0
• Part 3: High performance data processing
• Schemes for verification of resources in the cloud: comparison of the Cloud technology providers
• Business process as a service---a systematic literature review
• Data lake: strategic challeges for small and medium sized enterprises
• Agile approach to develop to develop data lake based systems
• Part 4: Advanced business processes in Industry 4.0
• On quality assessment of websites offering modbile applications
• Conceptual Foundations for the TBDA Implementation Methodology in Organizations
• Supporting time management in the process of new product development: the source of problems and directions of research, the problem of creative work.

This book discusses various aspects of Industry 4.0 from the perspective of information system evolution. Industry 4.0 refers to a new phase in the industrial revolution that relies heavily on interconnectivity, automation, machine learning, real-time data, the Internet of Things and blockchain technology. The interdisciplinary book addresses a number of topics related to modern information technologies, and presents innovative concepts, methods, models and tools for the development of information systems to support Industry 4.0. Focusing on artificial intelligence, collective knowledge processing and blockchain technology, it appeals to a wide readership, including researchers, students, business managers and professionals, software developers, as well as IT and management specialists. .
(source: Nielsen Book Data)

Since its 2015 release, TensorFlow has become a de facto standard among enterprise AI technologies. This comprehensive report introduces the new features of TensorFlow 2.x and Keras to developers and data scientists with machine learning skills. Many companies consider TensorFlow 2.x to be a major step in building a one-stop shop for deep learning tasks they need to perform. Romeo Kienzler, chief data scientist at the IBM Center for Open Source Data and AI Technologies, and Jerome Nilmeier, a data scientist and developer at Spark Technology Center, explain how your company can benefit from the new TensorFlow functionality. After reading this report, you can determine objectively whether adopting or upgrading to TensorFlow 2.x is worth your while. You'll examine: Two key TensorFlow APIs: Tensor-based API and Keras The eager execution mode for natural Python programming without TensorFlow sessions tf.function and AutoGraph for creating TensorFlow code in pure Python that the TensorFlow execution engine can consume How Keras is now tightly integrated with the TensorFlow backend under the hood The TensorBoard visualization framework, which contains rich capabilities How TensorFlow accomplishes parallel neural network training and scoring TensorFlow 2.x features including API cleanup, improved model export, and TensorFlow Serving.

• Chapter 1 Introduction.-
• Chapter 2 AI and Authoritarian Governance.-
• Chapter 3 Securitization of AI.-
• Chapter 4 China's AI politics: Top-down vs Bottom-up.-
• Chapter 5 Concluding Remarks.
• (source: Nielsen Book Data)

"This book provides the first book-lengthy study focusing on Artificial Intelligence (AI) with Chinese characteristics, in line with China's open ambition of becoming an AI superpower by 2030. China's unique domestic politics has developed distinct characteristics for its AI approach. By analysing national strategy, security and governance aspects of AI in China, this book argues that China's AI approach is sophisticated and multifaceted, and it has brought about both considerable benefits and challenges to China. First, many characterize China's AI approach as a nationally concerted top-down geopolitical strategy to advance Beijing's unified objective. This book argues that this view is mistaken. It shows that China's AI politics is largely shaped by economically rather than geopolitically motivated domestic stakeholders. In addition, China's national AI plan is an upgrade of existing local AI initiatives to the national level, reflecting a bottom-up development. Thus, China's AI strategy is more of a political manifesto rather than a concrete policy plan. The second part of the book discusses how the Chinese central government has been securitizing AI in order to mobilize local states, market actors, intellectuals and the general public. This security discourse is built on China's historical anxieties about technology, regime security needs and the growing tension caused by great power competition. Despite its help in convincing domestic actors, however, this securitization trend may undermine key AI objectives. The third part of the book studies the Chinese governance approach to the use of AI. It argues that China's bold AI practices are part of its broad and incoherent adaptation strategy to governance by digital means. AI is part of a digital technology package that the Chinese authoritarian regime has actively employed not only to improve public services but also to strengthen its authoritarian governance. While China's AI progress benefits from its unique political and social environment, its ambitious AI plan contains considerable risks. China's approach is gambling on its success in (a) delivering a booming AI economy, (b) ensuring a smooth social transformation to the age of AI, and (c) proving ideological superiority of its authoritarian and communist values. This book suggests that a more accurate understanding of AI with Chinese characteristics is essential in order to inform the debate regarding what lessons can be learnt from China's AI approach and how to respond to China's rise as the AI leader if not superpower.".
(source: Nielsen Book Data)

• 1-Dimensional Convolution Neural Network Classification Technique for Gene Expression Data.- Classification of Sequences with Deep Artificial Neural Networks: Representation and Architectural Issues.- A Deep Learning Model for MicroRNA-Target Binding.- Recurrent Neural Networks Architectures for Accidental Fall Detection on Wearable Embedded Devices.- Medical Image Retrieval System using Deep Learning Techniques.- Medical Image Fusion using Deep Learning.- Deep Learning for Histopathological Image Analysis.- Innovative Deep Learning Approach for Biomedical Data Instantiation and Visualization.- Convolutional Neural Networks in Advanced Biomedical Imaging Applications.- Deep Learning for Lung Disease Detection from Chest X-Rays Images.- Deep Learning in Multi-Omics Data Integration in Cancer Diagnostic.- Using Deep Learning with Canadian Primary Care Data for Disease Diagnosis.- Brain Tumor Segmentation and Surveillance with Deep Artificial Neural Networks.
• (source: Nielsen Book Data)

This book is the first overview on Deep Learning (DL) for biomedical data analysis. It surveys the most recent techniques and approaches in this field, with both a broad coverage and enough depth to be of practical use to working professionals. This book offers enough fundamental and technical information on these techniques, approaches and the related problems without overcrowding the reader's head. It presents the results of the latest investigations in the field of DL for biomedical data analysis. The techniques and approaches presented in this book deal with the most important and/or the newest topics encountered in this field. They combine fundamental theory of Artificial Intelligence (AI), Machine Learning (ML) and DL with practical applications in Biology and Medicine. Certainly, the list of topics covered in this book is not exhaustive but these topics will shed light on the implications of the presented techniques and approaches on other topics in biomedical data analysis. The book finds a balance between theoretical and practical coverage of a wide range of issues in the field of biomedical data analysis, thanks to DL. The few published books on DL for biomedical data analysis either focus on specific topics or lack technical depth. The chapters presented in this book were selected for quality and relevance. The book also presents experiments that provide qualitative and quantitative overviews in the field of biomedical data analysis. The reader will require some familiarity with AI, ML and DL and will learn about techniques and approaches that deal with the most important and/or the newest topics encountered in the field of DL for biomedical data analysis. He/she will discover both the fundamentals behind DL techniques and approaches, and their applications on biomedical data. This book can also serve as a reference book for graduate courses in Bioinformatics, AI, ML and DL. The book aims not only at professional researchers and practitioners but also graduate students, senior undergraduate students and young researchers. This book will certainly show the way to new techniques and approaches to make new discoveries.
(source: Nielsen Book Data)

Artificial intelligence has the potential to provide productive, efficient, and innovative solutions to everyday problems. But it comes with risks. Multiple examples of alleged bias in AI have been reported in recent years, and many people were already affected by the time those issues surfaced. This could have been avoided if humans had visibility into every stage of the system life cycle. In this report, Danny Farah and Amit Paka explain the importance of establishing an efficient Model Performance Management (MPM) system in your organizationâ??s machine learning workflow. Youâ??ll learn how MPM enables CxOs, IT leaders, and AI/ML leaders to gain visibility into every stage of the system life cycle. That includes training ML models to help your system make decisions. This report covers: MPM and Explainability: Explore a data-centric framework for producing high-quality ML and AI models and systems Explainable AI (XAI): Generate explanations from ML models so humans can explain and interpret the overarching AI system The ML Life Cycle: Follow an ML model on its journey from conception to production MPM in the ML Life Cycle: Learn how MPM can provide full visibility into issues that arise when training, deploying, and monitoring models MPM and Responsible AI: Explore ways to ensure that your AI systems are built with responsibility in mind.

• Introduction.- Fairness and proxy features.- Bias in data.- Explainability.- Remove bias from ML model.- Remove bias from ML output.- Accountability in AI.- Data & Model privacy.- Conclusion.
• (source: Nielsen Book Data)

This book is written for software product teams that use AI to add intelligent models to their products or are planning to use it. As AI adoption grows, it is becoming important that all AI driven products can demonstrate they are not introducing any bias to the AI-based decisions they are making, as well as reducing any pre-existing bias or discrimination. The responsibility to ensure that the AI models are ethical and make responsible decisions does not lie with the data scientists alone. The product owners and the business analysts are as important in ensuring bias-free AI as the data scientists on the team. This book addresses the part that these roles play in building a fair, explainable and accountable model, along with ensuring model and data privacy. Each chapter covers the fundamentals for the topic and then goes deep into the subject matter - providing the details that enable the business analysts and the data scientists to implement these fundamentals. AI research is one of the most active and growing areas of computer science and statistics. This book includes an overview of the many techniques that draw from the research or are created by combining different research outputs. Some of the techniques from relevant and popular libraries are covered, but deliberately not drawn very heavily from as they are already well documented, and new research is likely to replace some of it.
(source: Nielsen Book Data)

• Foreword
• Preface Acknowledgments Introduction
• 1 A Primer on Modern AI
• 2 Fair and Impartial
• 3 Robust and Reliable
• 4 Transparent 5 Explainable
• 6 Secure 7 Safe
• 8 Privacy
• 9 Accountable
• 10 Responsible 11Trustworthy AI in Practice
• 12 Looking Forward Index.
• (source: Nielsen Book Data)

An essential resource on artificial intelligence ethics for business leaders In Trustworthy AI, award-winning executive Beena Ammanath offers a practical approach for enterprise leaders to manage business risk in a world where AI is everywhere by understanding the qualities of trustworthy AI and the essential considerations for its ethical use within the organization and in the marketplace. The author draws from her extensive experience across different industries and sectors in data, analytics and AI, the latest research and case studies, and the pressing questions and concerns business leaders have about the ethics of AI. Filled with deep insights and actionable steps for enabling trust across the entire AI lifecycle, the book presents: In-depth investigations of the key characteristics of trustworthy AI, including transparency, fairness, reliability, privacy, safety, robustness, and more A close look at the potential pitfalls, challenges, and stakeholder concerns that impact trust in AI application Best practices, mechanisms, and governance considerations for embedding AI ethics in business processes and decision making Written to inform executives, managers, and other business leaders, Trustworthy AI breaks new ground as an essential resource for all organizations using AI. .
(source: Nielsen Book Data)

• Conceptual Frameworks.- Agent-Based Approaches.- Logic-Based Approaches.- Machine Learning Approaches. .
• (source: Nielsen Book Data)

This book constitutes selected papers presented at the First International Workshop on Deceptive AI, DeceptECAI 2020, held in conjunction with the 24th European Conference on Artificial Intelligence, ECAI 2020, in Santiago de Compostela, Spain, in August 2020, and Second International Workshop on Deceptive AI, DeceptAI 2021, held in conjunction with the 30th International Joint Conference on Artificial Intelligence, IJCAI 2021, in Montreal, Canada, in August 2021. Due to the COVID-19 pandemic both conferences were held in a virtual mode. The 12 papers presented were thoroughly reviewed and selected from the 16 submissions. They present recent developments in the growing area of research in the interface between deception and AI. .
(source: Nielsen Book Data)

• Chapter 1: AI in our Society Chapter goal: Reviews the place of AI within our society, discuss the various challenges that it AI faces, and introduces the foundational concepts of our sustainable AI framework 1.1 The Need for Artificial Intelligence 1.2 Challenges of Artificial Intelligence 1.3 Sustainable Artificial Intelligence Chapter 2 Ethics of the Data Science Practice Chapter goal: Reviews the human factor pillar of artificial intelligence, the relevance of ethics in AI and the source of ethical hazards in AI 2.1 Introduction 2.2 Ethics and their relevance to AI 2.3 Ethical nature of AI inferencing capability 2.4 Data - The business asset 2.5 AI regulatory outlook 2.6 Conclusion Chapter 3 Overview of the Sustainable Artificial Intelligence Framework (SAIF) Chapter goal: Summarises the SAIF framework for the development and deployment of AI applications Chapter 4 Intra-organizational understanding of AI: Towards Transparency Chapter goal: Discusses the need for understanding AI at the organization's level and introduces concepts of AI governance 4.1 Introduction 4.2 Data Science Development Process 4.3 AI development process Controls 4.4 Governance 4.4.1 Expectations from AI governance 4.4.2 People and Values 4.4.3 Assessment of AI governance arrangements 4.5 Conclusion Chapter 5 AI Performance Measurement: Think business values and objectives Chapter goal: Summarises performance metrics for evaluating AI systems and introduces a framework to account for the human factor of AI 5.1 Introduction 5.2 AI performance metrics overview 5.2.1 Supervised problems 5.2.2 Unsupervised problems 5.3 Beyond traditional AI performance metrics 5.3.1 Soft performance metrics 5.3.2 From AI performance metrics to business objectives 5.4 Conclusion
• Chapter 6 SAIF in Action Chapter goal: This chapter illustrates how SAIF would work in practice through use cases
• Chapter 7 Alternatives avenues for regulating AI systems Chapter goal: Draws from experiences in academic, Telecom/Utility, and healthcare sectors to explore and examine the need for industry specific regulations.
• Chapter 8 AI decision-making - from expectations to reality: The use case of healthcare Chapter goal: Explores the use of artificial intelligence in the healthcare, its practical limitations an implications
• Chapter 9 Conclusions and discussion Chapter goal: Presents concluding remarks and discuss current lack of standards 9.1 Conclusions 9.2 Need for standards and definitions.
• (source: Nielsen Book Data)

So far, little effort has been devoted to developing practical approaches on how to develop and deploy AI systems that meet certain standards and principles. This is despite the importance of principles such as privacy, fairness, and social equality taking centre stage in discussions around AI. However, for an organization, failing to meet those standards can give rise to significant lost opportunities. It may further lead to an organization's demise, as the example of Cambridge Analytica demonstrates. It is, however, possible to pursue a practical approach for the design, development, and deployment of sustainable AI systems that incorporates both business and human values and principles. This book discusses the concept of sustainability in the context of artificial intelligence. In order to help businesses achieve this objective, the author introduces the sustainable artificial intelligence framework (SAIF), designed as a reference guide in the development and deployment of AI systems. The SAIF developed in the book is designed to help decision makers such as policy makers, boards, C-suites, managers, and data scientists create AI systems that meet ethical principles. By focusing on four pillars related to the socio-economic and political impact of AI, the SAIF creates an environment through which an organization learns to understand its risk and exposure to any undesired consequences of AI, and the impact of AI on its ability to create value in the short, medium, and long term. What You Will Learn See the relevance of ethics to the practice of data science and AI Examine the elements that enable AI within an organization Discover the challenges of developing AI systems that meet certain human or specific standards Explore the challenges of AI governance Absorb the key factors to consider when evaluating AI systems Who This Book Is For Decision makers such as government officials, members of the C-suite and other business managers, and data scientists as well as any technology expert aspiring to a data-related leadership role. .
(source: Nielsen Book Data)

• Chapter 1: Introduction and Synopsis.-
• Chapter 2: Artificial Intelligence, machine autonomy and emerging needs.-
• Chapter 3: Intelligence: from natural to artificial.-
• Chapter 4: Intelligence and autonomy in artificial agents.-
• Chapter 5: Cognition in context: an evolutionary logic impacting our individual and collective worlds.-
• Chapter 6: Being and appearance, or the omnipresence of algorithms.-
• Chapter 7: A question of epistemological nature: are there limits to AI enabled knowledge.
• (source: Nielsen Book Data)

This book offers the first systematic guide to machine ethics, bridging between computer science, social sciences and philosophy. Based on a dialogue between an AI scientist and a novelist philosopher, the book discusses important findings on which moral values machines can be taught and how. In turn, it investigates what kind of artificial intelligence (AI) people do actually want. What are the main consequences of the integration of AI in people's every-day life? In order to co-exist and collaborate with humans, machines need morality, but which moral values should we teach them? Moreover, how can we implement benevolent AI? These are just some of the questions carefully examined in the book, which offers a comprehensive account of ethical issues concerning AI, on the one hand, and a timely snapshot of the power and potential benefits of this technology on the other. Starting with an introduction to common-sense ethical principles, the book then guides the reader, helping them develop and understand more complex ethical concerns and placing them in a larger, technological context. The book makes these topics accessible to a non-expert audience, while also offering alternative reading pathways to inspire more specialized readers.
(source: Nielsen Book Data)

• Natural Language Processing.- Robotics and Drones.- Knowledge Based Systems.- Innovative Applications of Intelligent Systems.- Industrial Applications.- Networking Applications.- Social Network Analysis.- Financial Applications and Blockchain.- Medical and Health-related Applications.- Anomaly Detection and Automated Diagnosis.- Decision-Support and Agent-Based Systems.- Multimedia Applications.- Machine Learning.- Data Management and Data Clustering.- Pattern Mining.- System Control, Classification, and Fault Diagnosis.
• (source: Nielsen Book Data)

This book constitutes the thoroughly refereed proceedings of the 33rd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2020, held in Kitakyushu, Japan, in September 2020. The 62 full papers and 17 short papers presented were carefully reviewed and selected from 119 submissions. The IEA/AIE 2020 conference will continue the tradition of emphasizing on applications of applied intelligent systems to solve real-life problems in all areas. These areas include are language processing; robotics and drones; knowledge based systems; innovative applications of intelligent systems; industrial applications; networking applications; social network analysis; financial applications and blockchain; medical and health-related applications; anomaly detection and automated diagnosis; decision-support and agent-based systems; multimedia applications; machine learning; data management and data clustering; pattern mining; system control, classification, and fault diagnosis.
(source: Nielsen Book Data)