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Data Science and Communication: Proceedings of ICTDsC 2023 (Studies in Autonomic, Data-driven and Industrial Computing)
by João Manuel R. S. Tavares Joel J. P. C. Rodrigues Debajyoti Misra Debasmriti BhattacherjeeThe book presents selected papers from the International Conference on Data Science and Communication (ICTDsC 2023) organized by the Department of Electronics and Communication Engineering and Department of Engineering Science and Humanities (DESH) Siliguri Institute of Technology, India during 23 – 24 March 2023 in Siliguri, India. The book covers state-of-the-art research insights on artificial intelligence, machine learning, big data, data analytics, cyber security and forensic, network and mobile security, advanced computing, cloud computing, quantum computing, electronics system, Internet of Things, robotics and automations, blockchain and software technology, and digital technologies for future.
Data Science and Digital Business
by Fausto Pedro García Márquez Benjamin LevThis book combines the analytic principles of digital business and data science with business practice and big data. The interdisciplinary, contributed volume provides an interface between the main disciplines of engineering and technology and business administration. Written for managers, engineers and researchers who want to understand big data and develop new skills that are necessary in the digital business, it not only discusses the latest research, but also presents case studies demonstrating the successful application of data in the digital business.
Data Science and Digital Transformation in the Fourth Industrial Revolution (Studies in Computational Intelligence #929)
by Jongbae Kim Roger LeeThis edited book presents scientific results of the International Semi-Virtual Workshop on Data Science and Digital Transformation in the Fourth Industrial Revolution (DSDT 2020) which was held on October 15, 2020, at Soongsil University, Seoul, Korea. The aim of this workshop was to bring together researchers and scientists, businessmen and entrepreneurs, teachers, engineers, computer users, and students to discuss the numerous fields of computer science and to share their experiences and exchange new ideas and information in a meaningful way. Research results about all aspects (theory, applications and tools) of computer and information science, and to discuss the practical challenges encountered along the way and the solutions adopted to solve them. The workshop organizers selected the best papers from those papers accepted for presentation at the workshop. The papers were chosen based on review scores submitted by members of the program committee and underwent further rigorous rounds of review. From this second round of review, 17 of the conference’s most promising papers are then published in this Springer (SCI) book and not the conference proceedings. We impatiently await the important contributions that we know these authors will bring to the field of computer and information science.
Data Science and Emerging Technologies: Proceedings of DaSET 2023 (Lecture Notes on Data Engineering and Communications Technologies #191)
by Michael W. Berry Yap Bee Wah Dhiya Al-Jumeily OBEThe book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2023), held online at UNITAR International University, Malaysia during December 4–5, 2023. This book presents current research and applications of data science and emerging technologies. The topics covered are artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.
Data Science and Emerging Technologies: Proceedings of DaSET 2022 (Lecture Notes on Data Engineering and Communications Technologies #165)
by Yap Bee Wah Michael W. Berry Azlinah Mohamed Dhiya Al-JumeilyThe book presents selected papers from International Conference on Data Science and Emerging Technologies (DaSET 2022), held online at UNITAR International University, Malaysia, during December 20–21, 2022. This book aims to present current research and applications of data science and emerging technologies. The deployment of data science and emerging technology contributes to the achievement of the Sustainable Development Goals for social inclusion, environmental sustainability, and economic prosperity. Data science and emerging technologies such as artificial intelligence and blockchain are useful for various domains such as marketing, health care, finance, banking, environmental, and agriculture. An important grand challenge in data science is to determine how developments in computational and social-behavioral sciences can be combined to improve well-being, emergency response, sustainability, and civic engagement in a well-informed, data-driven society. The topics of this book include, but not limited to: artificial intelligence, big data technology, machine and deep learning, data mining, optimization algorithms, blockchain, Internet of Things (IoT), cloud computing, computer vision, cybersecurity, augmented and virtual reality, cryptography, and statistical learning.
Data Science and Information Security: First International Artificial Intelligence Conference, IAIC 2023, Nanjing, China, November 25–27, 2023, Revised Selected Papers, Part II (Communications in Computer and Information Science #2059)
by Hai Jin Yi Pan Jianfeng LuThis 3-volume set, CCIS 2058-2060 constitutes the First International Conference, on Artificial Intelligence, IAIC 2023, held in Nanjing, China, in November 2023. The 85 full papers presented were carefully reviewed and selected from 428 submissions. The papers are clustered in parts on: Artificial Intelligence and Machine Learning; Data Security and information Security; Computer Networks and IoT. The papers present recent research and developments in artificial intelligence and its applications in machine learning, natural language processing, computer vision, robotics, and ethical considerations.
Data Science and Innovations for Intelligent Systems: Computational Excellence and Society 5.0 (Demystifying Technologies for Computational Excellence)
by Kavita TanejaData science is an emerging field and innovations in it need to be explored for the success of society 5.0. This book not only focuses on the practical applications of data science to achieve computational excellence, but also digs deep into the issues and implications of intelligent systems. This book highlights innovations in data science to achieve computational excellence that can optimize performance of smart applications. The book focuses on methodologies, framework, design issues, tools, architectures, and technologies necessary to develop and understand data science and its emerging applications in the present era. This book will be useful for the research community, start-up entrepreneurs, academicians, and data centered industries and professors that are interested in exploring innovations in varied applications and areas of data science.
Data Science and Intelligent Applications: Proceedings of ICDSIA 2020 (Lecture Notes on Data Engineering and Communications Technologies #52)
by Ketan Kotecha Vincenzo Piuri Hetalkumar N. Shah Rajan PatelThis book includes selected papers from the International Conference on Data Science and Intelligent Applications (ICDSIA 2020), hosted by Gandhinagar Institute of Technology (GIT), Gujarat, India, on January 24–25, 2020. The proceedings present original and high-quality contributions on theory and practice concerning emerging technologies in the areas of data science and intelligent applications. The conference provides a forum for researchers from academia and industry to present and share their ideas, views and results, while also helping them approach the challenges of technological advancements from different viewpoints. The contributions cover a broad range of topics, including: collective intelligence, intelligent systems, IoT, fuzzy systems, Bayesian networks, ant colony optimization, data privacy and security, data mining, data warehousing, big data analytics, cloud computing, natural language processing, swarm intelligence, speech processing, machine learning and deep learning, and intelligent applications and systems. Helping strengthen the links between academia and industry, the book offers a valuable resource for instructors, students, industry practitioners, engineers, managers, researchers, and scientists alike.
Data Science and Intelligent Systems: Proceedings of 5th Computational Methods in Systems and Software 2021, Vol. 2 (Lecture Notes in Networks and Systems #231)
by Radek Silhavy Petr Silhavy Zdenka ProkopovaThis book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included.The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results
Data Science and Internet of Things: Research and Applications at the Intersection of DS and IoT (Internet of Things)
by Giancarlo Fortino Antonio Liotta Raffaele Gravina Alessandro LongheuThis book focuses on the combination of IoT and data science, in particular how methods, algorithms, and tools from data science can effectively support IoT. The authors show how data science methodologies, techniques and tools, can translate data into information, enabling the effectiveness and usefulness of new services offered by IoT stakeholders. The authors posit that if IoT is indeed the infrastructure of the future, data structure is the key that can lead to a significant improvement of human life. The book aims to present innovative IoT applications as well as ongoing research that exploit modern data science approaches. Readers are offered issues and challenges in a cross-disciplinary scenario that involves both IoT and data science fields. The book features contributions from academics, researchers, and professionals from both fields.
Data Science and Machine Learning: 21st Australasian Conference, AusDM 2023, Auckland, New Zealand, December 11–13, 2023, Proceedings (Communications in Computer and Information Science #1943)
by Diana Benavides-Prado Sarah Erfani Philippe Fournier-Viger Yee Ling Boo Yun Sing KohThis book constitutes the proceedings of the 21st Australasian Conference on Data Science and Machine Learning, AusDM 2023, held in Auckland, New Zealand, during December 11–13, 2023.The 20 full papers presented in this book were carefully reviewed and selected from 50 submissions. The papers are organized in the following topical sections: research track and application track. They deal with topics around data science and machine learning in everyday life.
Data Science and Machine Learning Applications in Subsurface Engineering
by Daniel Asante OtchereThis book covers unsupervised learning, supervised learning, clustering approaches, feature engineering, explainable AI and multioutput regression models for subsurface engineering problems. Processing voluminous and complex data sets are the primary focus of the field of machine learning (ML). ML aims to develop data-driven methods and computational algorithms that can learn to identify complex and non-linear patterns to understand and predict the relationships between variables by analysing extensive data. Although ML models provide the final output for predictions, several steps need to be performed to achieve accurate predictions. These steps, data pre-processing, feature selection, feature engineering and outlier removal, are all contained in this book. New models are also developed using existing ML architecture and learning theories to improve the performance of traditional ML models and handle small and big data without manual adjustments. This research-oriented book will help subsurface engineers, geophysicists, and geoscientists become familiar with data science and ML advances relevant to subsurface engineering. Additionally, it demonstrates the use of data-driven approaches for salt identification, seismic interpretation, estimating enhanced oil recovery factor, predicting pore fluid types, petrophysical property prediction, estimating pressure drop in pipelines, bubble point pressure prediction, enhancing drilling mud loss, smart well completion and synthetic well log predictions.
Data Science and Medical Informatics in Healthcare Technologies (SpringerBriefs in Applied Sciences and Technology)
by Nguyen Thi Dieu Linh Zhongyu (Joan) LuThis book highlights a timely and accurate insight at the endeavour of the bioinformatics and genomics clinicians from industry and academia to address the societal needs. The contents of the book unearth the lacuna between the medication and treatment in the current preventive medicinal and pharmaceutical system. It contains chapters prepared by experts in life sciences along with data scientists for examining the circumstances of health care system for the next decade. It also highlights the automated processes for analyzing data in clinical trial research, specifically for drug development. Additionally, the data science solutions provided in this book help pharmaceutical companies to improve on what had historically been manual, costly and laborious process for cross-referencing research in clinical trials on drug development, while laying the groundwork for use with a full range of other drugs for the conditions ranging from tuberculosis, to diabetes, to heart attacks and many others.
Data Science and Network Engineering: Proceedings of ICDSNE 2023 (Lecture Notes in Networks and Systems #791)
by Suyel Namasudra Munesh Chandra Trivedi Ruben Gonzalez Crespo Pascal LorenzThis book includes research papers presented at the International Conference on Data Science and Network Engineering (ICDSNE 2023) organized by the Department of Computer Science and Engineering, National Institute of Technology Agartala, Tripura, India, during July 21–22, 2023. It includes research works from researchers, academicians, business executives, and industry professionals for solving real-life problems by using the advancements and applications of data science and network engineering. This book covers many advanced topics, such as artificial intelligence (AI), machine learning (ML), deep learning (DL), computer networks, blockchain, security and privacy, Internet of things (IoT), cloud computing, big data, supply chain management, and many more. Different sections of this book are highly beneficial for the researchers, who are working in the field of data science and network engineering.
Data Science and Security: Proceedings of IDSCS 2020 (Lecture Notes in Networks and Systems #132)
by Durgesh Kumar Mishra Aynur Unal Dharm Singh Jat Samiksha ShuklaThis book presents best selected papers presented at the International Conference on Data Science for Computational Security (IDSCS 2020), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during 13–14 March 2020. The proceeding will be targeting the current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing and cloud computing.
Data Science and Security: Proceedings of IDSCS 2021 (Lecture Notes in Networks and Systems #290)
by Durgesh Kumar Mishra Aynur Unal Samiksha Shukla Joseph Varghese Kureethara Dong Seog HanThis book presents the best-selected papers presented at the International Conference on Data Science, Computation and Security (IDSCS-2021), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during April 16–17, 2021. The proceeding is targeting the current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing, and cloud computing.
Data Science and Security: Proceedings of IDSCS 2022 (Lecture Notes in Networks and Systems #462)
by Samiksha Shukla Xiao-Zhi Gao Joseph Varghese Kureethara Durgesh MishraThis book presents best selected papers presented at the International Conference on Data Science for Computational Security (IDSCS 2022), organized by the Department of Data Science, CHRIST (Deemed to be University), Pune Lavasa Campus, India, during 11 – 12 February 2022. The book proposes new technologies and discusses future solutions and applications of data science, data analytics and security. The book targets current research works in the areas of data science, data security, data analytics, artificial intelligence, machine learning, computer vision, algorithms design, computer networking, data mining, big data, text mining, knowledge representation, soft computing and cloud computing.
Data Science-Based Full-Lifespan Management of Lithium-Ion Battery: Manufacturing, Operation and Reutilization (Green Energy and Technology)
by Kailong Liu Yujie Wang Xin LaiThis open access book comprehensively consolidates studies in the rapidly emerging field of battery management. The primary focus is to overview the new and emerging data science technologies for full-lifespan management of Li-ion batteries, which are categorized into three groups, namely (i) battery manufacturing management, (ii) battery operation management, and (iii) battery reutilization management. The key challenges, future trends as well as promising data-science technologies to further improve this research field are discussed. As battery full-lifespan (manufacturing, operation, and reutilization) management is a hot research topic in both energy and AI fields and none specific book has focused on systematically describing this particular from a data science perspective before, this book can attract the attention of academics, scientists, engineers, and practitioners. It is useful as a reference book for students and graduates working in related fields. Specifically, the audience could not only get the basics of battery manufacturing, operation, and reutilization but also the information of related data-science technologies. The step-by-step guidance, comprehensive introduction, and case studies to the topic make it accessible to audiences of different levels, from graduates to experienced engineers.
Data Science Careers, Training, and Hiring: A Comprehensive Guide to the Data Ecosystem: How to Build a Successful Data Science Career, Program, or Unit (SpringerBriefs in Computer Science)
by Renata Rawlings-GossThis book is an information packed overview of how to structure a data science career, a data science degree program, and how to hire a data science team, including resources and insights from the authors experience with national and international large-scale data projects as well as industry, academic and government partnerships, education, and workforce. Outlined here are tips and insights into navigating the data ecosystem as it currently stands, including career skills, current training programs, as well as practical hiring help and resources. Also, threaded through the book is the outline of a data ecosystem, as it could ultimately emerge, and how career seekers, training programs, and hiring managers can steer their careers, degree programs, and organizations to align with the broader future of data science. Instead of riding the current wave, the author ultimately seeks to help professionals, programs, and organizations alike prepare a sustainable plan for growth in this ever-changing world of data. The book is divided into three sections, the first “Building Data Careers”, is from the perspective of a potential career seeker interested in a career in data, the second “Building Data Programs” is from the perspective of a newly forming data science degree or training program, and the third “Building Data Talent and Workforce” is from the perspective of a Data and Analytics Hiring Manager. Each is a detailed introduction to the topic with practical steps and professional recommendations. The reason for presenting the book from different points of view is that, in the fast-paced data landscape, it is helpful to each group to more thoroughly understand the desires and challenges of the other. It will, for example, help the career seekers to understand best practices for hiring managers to better position themselves for jobs. It will be invaluable for data training programs to gain the perspective of career seekers, who they want to help and attract as students. Also, hiring managers will not only need data talent to hire, but workforce pipelines that can only come from partnerships with universities, data training programs, and educational experts. The interplay gives a broader perspective from which to build.
Data Science for Civil Engineering: A Beginner's Guide
by Rakesh K. Jain Prashant Shantaram Dhotre Deepak Tatyasaheb Mane Parikshit Narendra MahalleThis book explains use of data science-based techniques for modeling and providing optimal solutions to complex problems in civil engineering. It discusses civil engineering problems like air, water and land pollution, climate crisis, transportation infrastructures, traffic and travel modes, mobility services, and so forth. Divided into two sections, the first one deals with the basics of data science and essential mathematics while the second section covers pertinent applications in structural and environmental engineering, construction management, and transportation. Features: Details information on essential mathematics required to implement civil engineering applications using data science techniques. Discusses broad background of data science and its fundamentals. Focusses on structural engineering, transportation systems, water resource management, geomatics, and environmental engineering. Includes python programming libraries to solve complex problems. Addresses various real-world applications of data science based civil engineering use cases. This book aims at senior undergraduate students in Civil Engineering and Applied Data Science.
Data Science for Engineers
by Raghunathan Rengaswamy Resmi SureshWith tremendous improvement in computational power and availability of rich data, almost all engineering disciplines use data science at some level. This textbook presents material on data science comprehensively, and in a structured manner. It provides conceptual understanding of the fields of data science, machine learning, and artificial intelligence, with enough level of mathematical details necessary for the readers. This will help readers understand major thematic ideas in data science, machine learning and artificial intelligence, and implement first-level data science solutions to practical engineering problems. <p><p> The book- <p><p> Provides a systematic approach for understanding data science techniques. Explain why machine learning techniques are able to cross-cut several disciplines. Covers topics including statistics, linear algebra and optimization from a data science perspective. Provides multiple examples to explain the underlying ideas in machine learning algorithms. Describes several contemporary machine learning algorithms. <p><p> The textbook is primarily written for undergraduate and senior undergraduate students in different engineering disciplines including chemical engineering, mechanical engineering, electrical engineering, electronics and communications engineering for courses on data science, machine learning and artificial intelligence.
Data Science for Financial Econometrics (Studies in Computational Intelligence #898)
by Nguyen Ngoc Thach Vladik Kreinovich Nguyen Duc TrungThis book offers an overview of state-of-the-art econometric techniques, with a special emphasis on financial econometrics. There is a major need for such techniques, since the traditional way of designing mathematical models – based on researchers’ insights – can no longer keep pace with the ever-increasing data flow. To catch up, many application areas have begun relying on data science, i.e., on techniques for extracting models from data, such as data mining, machine learning, and innovative statistics. In terms of capitalizing on data science, many application areas are way ahead of economics. To close this gap, the book provides examples of how data science techniques can be used in economics. Corresponding techniques range from almost traditional statistics to promising novel ideas such as quantum econometrics. Given its scope, the book will appeal to students and researchers interested in state-of-the-art developments, and to practitioners interested in using data science techniques.
Data Science for Nano Image Analysis (International Series in Operations Research & Management Science #308)
by Chiwoo Park Yu DingThis book combines two distinctive topics: data science/image analysis and materials science. The purpose of this book is to show what type of nano material problems can be better solved by which set of data science methods. The majority of material science research is thus far carried out by domain-specific experts in material engineering, chemistry/chemical engineering, and mechanical & aerospace engineering. The book could benefit materials scientists and manufacturing engineers who were not exposed to systematic data science training while in schools, or data scientists in computer science or statistics disciplines who want to work on material image problems or contribute to materials discovery and optimization.This book provides in-depth discussions of how data science and operations research methods can help and improve nano image analysis, automating the otherwise manual and time-consuming operations for material engineering and enhancing decision making for nano material exploration. A broad set of data science methods are covered, including the representations of images, shape analysis, image pattern analysis, and analysis of streaming images, change points detection, graphical methods, and real-time dynamic modeling and object tracking. The data science methods are described in the context of nano image applications, with specific material science case studies.
Data Science for Sensory and Consumer Scientists (Chapman & Hall/CRC Data Science Series)
by Thierry Worch Julien Delarue Vanessa Rios De Souza John EnnisData Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: • Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science. • Examples based in industrial practice used throughout the book
Data Science for Transport: A Self-study Guide With Computer Exercises (Springer Textbooks In Earth Sciences, Geography And Environment Ser.)
by Charles FoxThe quantity, diversity and availability of transport data is increasing rapidly, requiring new skills in the management and interrogation of data and databases. Recent years have seen a new wave of 'big data', 'Data Science', and 'smart cities' changing the world, with the Harvard Business Review describing Data Science as the "sexiest job of the 21st century". Transportation professionals and researchers need to be able to use data and databases in order to establish quantitative, empirical facts, and to validate and challenge their mathematical models, whose axioms have traditionally often been assumed rather than rigorously tested against data. This book takes a highly practical approach to learning about Data Science tools and their application to investigating transport issues. The focus is principally on practical, professional work with real data and tools, including business and ethical issues."Transport modeling practice was developed in a data poor world, and many of our current techniques and skills are building on that sparsity. In a new data rich world, the required tools are different and the ethical questions around data and privacy are definitely different. I am not sure whether current professionals have these skills; and I am certainly not convinced that our current transport modeling tools will survive in a data rich environment. This is an exciting time to be a data scientist in the transport field. We are trying to get to grips with the opportunities that big data sources offer; but at the same time such data skills need to be fused with an understanding of transport, and of transport modeling. Those with these combined skills can be instrumental at providing better, faster, cheaper data for transport decision- making; and ultimately contribute to innovative, efficient, data driven modeling techniques of the future. It is not surprising that this course, this book, has been authored by the Institute for Transport Studies. To do this well, you need a blend of academic rigor and practical pragmatism. There are few educational or research establishments better equipped to do that than ITS Leeds". - Tom van Vuren, Divisional Director, Mott MacDonald"WSP is proud to be a thought leader in the world of transport modelling, planning and economics, and has a wide range of opportunities for people with skills in these areas. The evidence base and forecasts we deliver to effectively implement strategies and schemes are ever more data and technology focused a trend we have helped shape since the 1970's, but with particular disruption and opportunity in recent years. As a result of these trends, and to suitably skill the next generation of transport modellers, we asked the world-leading Institute for Transport Studies, to boost skills in these areas, and they have responded with a new MSc programme which you too can now study via this book." - Leighton Cardwell, Technical Director, WSP."From processing and analysing large datasets, to automation of modelling tasks sometimes requiring different software packages to "talk" to each other, to data visualization, SYSTRA employs a range of techniques and tools to provide our clients with deeper insights and effective solutions. This book does an excellent job in giving you the skills to manage, interrogate and analyse databases, and develop powerful presentations. Another important publication from ITS Leeds." - Fitsum Teklu, Associate Director (Modelling & Appraisal) SYSTRA Ltd"Urban planning has relied for decades on statistical and computational practices that have little to do with mainstream data science. Information is still often used as evidence on the impact of new infrastructure even when it hardly contains any valid evidence. This book is an extremely welcome effort to provide young professionals with the skills needed to analyse how cities and transport networks actually work. The book is also highly relevant to anyone who will later want to build digital solutions to optimise urban travel based on emerging data sources". - Yaron Hollander, author of "T