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Data Engineering and Intelligent Computing: Proceedings of ICICC 2020 (Advances in Intelligent Systems and Computing #1)
by Suresh Chandra Satapathy Vikrant Bhateja V. N. Manjunath Aradhya Carlos M. Travieso-GonzálezThis book features a collection of high-quality, peer-reviewed papers presented at the Fourth International Conference on Intelligent Computing and Communication (ICICC 2020) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 18–20 September 2020. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
A Data Engineering Approach to Wave Scattering Analysis with Applications in Radar, Sonar, Medical Diagnostics, Structural Flaw Detection and Intelligent Robotics
by Mark K. HindersComprehensive resource exploring how recent advancements in computational capabilities open doors to new applications in wave scattering A Data Engineering Approach to Wave Scattering Analysis: with Applications in Radar, Sonar, Medical Diagnostics, Structural Flaw Detection and Intelligent Robotics applies scattering analysis to many applications including radar, sonar, medical diagnosis, intelligent robotics, and more, enabling readers to implement new and better measurements with both novel instrumentation and artificial intelligence that automates the interpretation of various (and multiple) imaging data streams. Composed of 10 chapters, this book brings together separate scientific topics that share a common basis of knowledge and their unchanged mathematical techniques to ensure successful results. Through periodic exercises, this book reinforces the importance of revisiting derivations and reproducing established results. It also delves into the individuals who shaped scientific methods and technologies, exploring 81 notable names and providing insights into their professional journeys. Classic results from scattering are included in each chapter, and rather than simply pasting in plots from classic papers, these results have largely been reproduced for a more coherent reader experience. Written by an established academic in the field, A Data Engineering Approach to Wave Scattering Analysis: with Applications in Radar, Sonar, Medical Diagnostics, Structural Flaw Detection and Intelligent Robotics includes information on various topics: Field equations, covering strain as a dimensionless measure of deformation, generalized Hooke’s Law, and elastic and acoustic wavesReflection and refraction, covering reflection from a free surface and surface waves as well as the wave model of acoustic microscopyGuided waves, covering torsional modes, longitudinal waves, and flexural waves in rods, as well as data engineering for lamb wave tomographyInverse scattering, covering wavelet transforms and fingerprinting as well as applications of wavelet fingerprints such as roof fall detection A Data Engineering Approach to Wave Scattering Analysis: with Applications in Radar, Sonar, Medical Diagnostics, Structural Flaw Detection and Intelligent Robotics is an essential up-to-date reference on the subject for researchers interested in radar, sonar, medical imaging, structural health monitoring, manufacturing process control, and autonomous vehicles, as well as upper-level undergraduates and graduate students in related programs of study.
Data Engineering in Medical Imaging: Second MICCAI Workshop, DEMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings (Lecture Notes in Computer Science #15265)
by Binod Bhattarai Sharib Ali Anita Rau Razvan Caramalau Anh Nguyen Prashnna Gyawali Ana Namburete Danail StoyanovThis book constitutes the proceedings of the Second MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 10, 2024. The 18 papers presented in this book were carefully reviewed and selected. These papers focus on the application of various Data engineering techniques in the field of Medical Imaging.
Data Envelopment Analysis: A Handbook of Models and Methods (International Series in Operations Research & Management Science #221)
by Joe ZhuThis handbook compiles state-of-the-art empirical studies and applications using Data Envelopment Analysis (DEA). It includes a collection of 18 chapters written by DEA experts. Chapter 1 examines the performance of CEOs of U. S. banks and thrifts. Chapter 2 describes the network operational structure of transportation organizations and the relative network data envelopment analysis model. Chapter 3 demonstrates how to use different types of DEA models to compute total-factor energy efficiency scores with an application to energy efficiency. In chapter 4, the authors explore the impact of incorporating customers' willingness to pay for service quality in benchmarking models on cost efficiency of distribution networks, and chapter 5 provides a brief review of previous applications of DEA to the professional baseball industry, followed by two detailed applications to Major League Baseball. Chapter 6 examines efficiency and productivity of U. S. property-liability (P-L) insurers using DEA, while chapter 7 presents a two-stage network DEA model that decomposes the overall efficiency of a decision-making unit into two components. Chapter 8 presents a review of the literature of DEA models for the perfoemance assessment of mutual funds, and chapter 9 discusses the management strategies formulation of the international tourist hotel industry in Taiwan. Chapter 10 presents a novel use of the two-stage network DEA to evaluate sustainable product design performances. In chapter 11 authors highlight limitations of some DEA environmental efficiency models, and chapter 12 reviews applications of DEA in secondary and tertiary education. Chapter 13 measures the relative performance of New York State school districts in the 2011-2012 academic year. Chapter 14 provides an introductory prelude to chapters 15 and 16, which both provide detailed applications of DEA in marketing. Chapter 17 then shows how to decompose a new total factor productivity index that satisfies all economically-relevant axioms from index theory with an application to U. S. agriculture. Finally, chapter 18 presents a unique study that conducts a DEA research front analysis, applying a network clustering method to group the DEA literature over the period 2000 to 2014.
Data Envelopment Analysis with R (Studies in Fuzziness and Soft Computing #386)
by Farhad Hosseinzadeh Lotfi Ali Ebrahimnejad Mohsen Vaez-Ghasemi Zohreh MoghaddasThis book introduces readers to the use of R codes for optimization problems. First, it provides the necessary background to understand data envelopment analysis (DEA), with a special emphasis on fuzzy DEA. It then describes DEA models, including fuzzy DEA models, and shows how to use them to solve optimization problems with R. Further, it discusses the main advantages of R in optimization problems, and provides R codes based on real-world data sets throughout. Offering a comprehensive review of DEA and fuzzy DEA models and the corresponding R codes, this practice-oriented reference guide is intended for masters and Ph.D. students in various disciplines, as well as practitioners and researchers.
Data Feminism (Strong Ideas)
by Catherine D'Ignazio Lauren F. KleinA new way of thinking about data science and data ethics that is informed by the ideas of intersectional feminism. Today, data science is a form of power. It has been used to expose injustice, improve health outcomes, and topple governments. But it has also been used to discriminate, police, and surveil. This potential for good, on the one hand, and harm, on the other, makes it essential to ask: Data science by whom? Data science for whom? Data science with whose interests in mind? The narratives around big data and data science are overwhelmingly white, male, and techno-heroic. In Data Feminism, Catherine D'Ignazio and Lauren Klein present a new way of thinking about data science and data ethics—one that is informed by intersectional feminist thought.Illustrating data feminism in action, D'Ignazio and Klein show how challenges to the male/female binary can help challenge other hierarchical (and empirically wrong) classification systems. They explain how, for example, an understanding of emotion can expand our ideas about effective data visualization, and how the concept of invisible labor can expose the significant human efforts required by our automated systems. And they show why the data never, ever “speak for themselves.”Data Feminism offers strategies for data scientists seeking to learn how feminism can help them work toward justice, and for feminists who want to focus their efforts on the growing field of data science. But Data Feminism is about much more than gender. It is about power, about who has it and who doesn't, and about how those differentials of power can be challenged and changed.
Data Fusion and Data Mining for Power System Monitoring
by Arturo Román MessinaData Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications. Features Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics Applications to a wide range of power networks are provided including distribution and transmission networks Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies Includes applications to massive spatiotemporal data from simulations and actual events
Data Fusion Mathematics: Theory and Practice
by Jitendra R. RaolFills the Existing Gap of Mathematics for Data FusionData fusion (DF) combines large amounts of information from a variety of sources and fuses this data algorithmically, logically and, if required intelligently, using artificial intelligence (AI). Also, known as sensor data fusion (SDF), the DF fusion system is an important component for use in va
Data Governance: A Guide
by Dimitrios SargiotisThis book is a comprehensive resource designed to demystify the complex world of data governance for professionals across various sectors. This guide provides in-depth insights, methodologies, and best practices to help organizations manage their data effectively and securely. It covers essential topics such as data quality, privacy, security, and management ensuring that readers gain a holistic understanding of how to establish and maintain a robust data governance framework. Through a blend of theoretical knowledge and practical applications, this book addresses the challenges and benefits of data governance, equipping readers with the tools needed to navigate the evolving data landscape. In addition to foundational principles, this book explores real-world case studies that illustrate the tangible benefits and common pitfalls of implementing data governance. Emerging trends and technologies, including artificial intelligence, machine learning, and blockchain are also examined to prepare readers for future developments in the field. Whether you are a seasoned data management professional or new to the discipline, this book serves as an invaluable resource for mastering the intricacies of data governance and leveraging data as a strategic asset for organizational success. This resourceful guide targets data management professionals, IT managers, Compliance officers, Data Stewards, Data Owners Data Governance Managers and more. Business leaders, business executives academic researchers, students focused on computer science in data-related fields will also find this book a useful resource.
Data Grab: The New Colonialism of Big Tech and How to Fight Back
by Ulises A. Mejias Nick CouldryA compelling argument that the extractive practices of today’s tech giants are the continuation of colonialism—and a crucial guide to collective resistance. Large technology companies like Meta, Amazon, and Alphabet have unprecedented access to our daily lives, collecting information when we check our email, count our steps, shop online, and commute to and from work. Current events are concerning—both the changing owners (and names) of billion-dollar tech companies and regulatory concerns about artificial intelligence underscore the sweeping nature of Big Tech’s surveillance and the influence such companies hold over the people who use their apps and platforms. As trusted tech experts Ulises A. Mejias and Nick Couldry show in this eye-opening and convincing book, this vast accumulation of data is not the accidental stockpile of a fast-growing industry. Just as nations stole territories for ill-gotten minerals and crops, wealth, and dominance, tech companies steal personal data important to our lives. It’s only within the framework of colonialism, Mejias and Couldry argue, that we can comprehend the full scope of this heist. Like the land grabs of the past, today’s data grab converts our data into raw material for the generation of corporate profit against our own interests. Like historical colonialism, today’s tech corporations have engineered an extractive form of doing business that builds a new social and economic order, leads to job precarity, and degrades the environment. These methods deepen global inequality, consolidating corporate wealth in the Global North and engineering discriminatory algorithms. Promising convenience, connection, and scientific progress, tech companies enrich themselves by encouraging us to relinquish details about our personal interactions, our taste in movies or music, and even our health and medical records. Do we have any other choice? Data Grab affirms that we do. To defy this new form of colonialism we will need to learn from previous forms of resistance and work together to imagine entirely new ones. Mejias and Couldry share the stories of voters, workers, activists, and marginalized communities who have successfully opposed unscrupulous tech practices. An incisive discussion of the digital media that’s transformed our world, Data Grab is a must-read for anyone concerned about privacy, self-determination, and justice in the internet age.
Data Integration in the Life Sciences: 13th International Conference, Dils 2018, Hannover, Germany, November 20-21, 2018, Proceedings (Lecture Notes in Computer Science #11371)
by Sören Auer Maria-Esther VidalThis book constitutes revised selected papers from the 13th International Conference on Data Integration in the Life Sciences, DILS 2018, held in Hannover, Germany, in November 2018. The 5 full, 8 short, 3 poster and 4 demo papers presented in this volume were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections named: big biomedical data integration and management; data exploration in the life sciences; biomedical data analytics; and big biomedical applications.
Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2021 (Algorithms for Intelligent Systems)
by Robert Bestak I. Jeena Jacob Selvanayaki Kolandapalayam ShanmugamThe book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 16–17, 2021. This book discusses new cognitive informatics tools, algorithms, and methods that mimic the mechanisms of the human brain which leads to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning, and cognitive science to study and develop a deeper understanding of the information processing systems.
Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2023 (Algorithms for Intelligent Systems)
by I. Jeena Jacob Selwyn Piramuthu Przemyslaw Falkowski-GilskiThe book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2023), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during June 27–28, 2023. This book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems.
Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2022 (Algorithms for Intelligent Systems)
by I. Jeena Jacob Selvanayaki Kolandapalayam Shanmugam Ivan IzoninThe book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2021), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during July 6–7, 2022. This book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems.
Data Intelligence and Cognitive Informatics: Proceedings of ICDICI 2020 (Algorithms for Intelligent Systems)
by I. Jeena Jacob Selvanayaki Kolandapalayam Shanmugam Selwyn Piramuthu Przemyslaw Falkowski-GilskiThis book discusses new cognitive informatics tools, algorithms and methods that mimic the mechanisms of the human brain which lead to an impending revolution in understating a large amount of data generated by various smart applications. The book is a collection of peer-reviewed best selected research papers presented at the International Conference on Data Intelligence and Cognitive Informatics (ICDICI 2020), organized by SCAD College of Engineering and Technology, Tirunelveli, India, during 8–9 July 2020. The book includes novel work in data intelligence domain which combines with the increasing efforts of artificial intelligence, machine learning, deep learning and cognitive science to study and develop a deeper understanding of the information processing systems.
Data Intensive Industrial Asset Management: IoT-based Algorithms and Implementation
by Tian Zhao Farhad Balali Jessie Nouri Adel NasiriThis book presents a step by step Asset Health Management Optimization Approach Using Internet of Things (IoT). The authors provide a comprehensive study which includes the descriptive, diagnostic, predictive, and prescriptive analysis in detail. The presentation focuses on the challenges of the parameter selection, statistical data analysis, predictive algorithms, big data storage and selection, data pattern recognition, machine learning techniques, asset failure distribution estimation, reliability and availability enhancement, condition based maintenance policy, failure detection, data driven optimization algorithm, and a multi-objective optimization approach, all of which can significantly enhance the reliability and availability of the system.
Data-ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else
by Steve Lohr“Lohr uses his Pulitzer Prize-winning reporting skills to dig into and explain the power, pervasiveness, and potential downside of big data.” —Library JournalIn Data-ism, New York Times reporter Steve Lohr explains how big-data technology is ushering in a revolution in proportions that promise to be the basis of the next wave of efficiency and innovation across the economy. But more is at work here than technology. Big data is also the vehicle for a point of view, or philosophy, about how decisions will be—and perhaps should be—made in the future. Lohr investigates the benefits of data while also examining its dark side.Data-ism is about this next phase, in which vast Internet-scale data sets are used for discovery and prediction in virtually every field. It shows how this new revolution will change decision making—by relying more on data and analysis, and less on intuition and experience—and transform the nature of leadership and management. Focusing on young entrepreneurs at the forefront of data science as well as on giant companies such as IBM that are making big bets on data science for the future of their businesses, Data-ism is a field guide to what is ahead, explaining how individuals and institutions will need to exploit, protect, and manage data to stay competitive in the coming years. With rich examples of how the rise of big data is affecting everyday life, Data-ism also raises provocative questions about policy and practice that have wide implications for everyone.The age of data-ism is here. But are we ready to handle its consequences, good and bad?
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 2 (Advances in Intelligent Systems and Computing #1016)
by Valentina Emilia Balas Amlan Chakrabarti Neha SharmaThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2018, Volume 1 (Advances in Intelligent Systems and Computing #808)
by Valentina Emilia Balas Neha Sharma Amlan ChakrabartiThe book presents the latest, high-quality, technical contributions and research findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It discusses state-of-the-art topics as well as the challenges and solutions for future development. It includes original and previously unpublished international research work highlighting research domains from different perspectives. This book is mainly intended for researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings Of Icdmai 2018, Volume 2 (Advances In Intelligent Systems and Computing #839)
by Amlan Chakrabarti Neha Sharma Valentina Emilia BalasThe volume on Data Management, Analytics and Innovations presents the latest high-quality technical contributions and research results in the areas of data management and smart computing, big data management, artificial intelligence and data analytics along with advances in network technologies. It deals with the state-of-the-art topics and provides challenges and solutions for future development. Original, unpublished research work highlighting specific research domains from all viewpoints are contributed from scientists throughout the globe. This volume is mainly designed for professional audience, composed of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 2 (Lecture Notes in Networks and Systems #998)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2024, Volume 1 (Lecture Notes in Networks and Systems #997)
by Amlan Chakrabarti Neha Sharma Alfred M. Bruckstein Amol C. GojeThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at 8th International Conference on Data Management, Analytics and Innovation (ICDMAI 2024), held during 19–21 January 2024 in Vellore Institute of Technology, Vellore, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry. The book is divided into two volumes.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2019, Volume 1 (Advances in Intelligent Systems and Computing #1042)
by Neha Sharma Amlan Chakrabarti Valentina Emilia BalasThis book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Volume 1 (Lecture Notes on Data Engineering and Communications Technologies #70)
by Neha Sharma Amlan Chakrabarti Valentina Emilia Balas Alfred M. BrucksteinThis book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.
Data Management, Analytics and Innovation: Proceedings of ICDMAI 2021, Volume 2 (Lecture Notes on Data Engineering and Communications Technologies #71)
by Neha Sharma Amlan Chakrabarti Valentina Emilia Balas Alfred M. BrucksteinThis book presents the latest findings in the areas of data management and smart computing, machine learning, big data management, artificial intelligence, and data analytics, along with advances in network technologies. The book is a collection of peer-reviewed research papers presented at Fifth International Conference on Data Management, Analytics and Innovation (ICDMAI 2021), held during January 15–17, 2021, in a virtual mode. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.