Concise Survey of Computer Methods PDF Download
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Author: Peter Naur
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 406
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Book Description
Author: Peter Naur
Publisher:
ISBN:
Category : Computers
Languages : en
Pages : 406
Get Book Here
Book Description
Author: United States. National Bureau of Standards
Publisher:
ISBN:
Category : Industrial safety
Languages : en
Pages : 112
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Book Description
Author:
Publisher:
ISBN:
Category : Standardization
Languages : en
Pages : 120
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Book Description
Author: Carlo Ghezzi
Publisher: Springer Science & Business Media
ISBN: 9783540516354
Category : Computers
Languages : en
Pages : 506
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Book Description
The book is concerned with the broad topic of software engineering. It comprises the proceedings of the European Software Engineering Conference (ESEC) held at the University of Warwick in the United Kingdom in September 1989 and its primary purpose is to summarise the state of the art in software engineering as represented by the papers at that conference. The material covers both submitted papers and a number of invited papers given at the conference. The topics covered include: metrics and measurement, software process modelling, formal methods including their use in industry, software configuration management, software development environments, and requirements engineering. The book is most likely to be of interest to researchers and professionals working in the field of software development. The primary value of the book is that it gives an up-to-date treatment of its subject material and includes some interesting discussions of the transfer of research ideas into industrial practice.
Author: Jay Liebowitz
Publisher: CRC Press
ISBN: 1000094650
Category : Computers
Languages : en
Pages : 242
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Book Description
Analytics and artificial intelligence (AI), what are they good for? The bandwagon keeps answering, absolutely everything! Analytics and artificial intelligence have captured the attention of everyone from top executives to the person in the street. While these disciplines have a relatively long history, within the last ten or so years they have exploded into corporate business and public consciousness. Organizations have rushed to embrace data-driven decision making. Companies everywhere are turning out products boasting that "artificial intelligence is included." We are indeed living in exciting times. The question we need to ask is, do we really know how to get business value from these exciting tools? Unfortunately, both the analytics and AI communities have not done a great job in collaborating and communicating with each other to build the necessary synergies. This book bridges the gap between these two critical fields. The book begins by explaining the commonalities and differences in the fields of data science, artificial intelligence, and autonomy by giving a historical perspective for each of these fields, followed by exploration of common technologies and current trends in each field. The book also readers introduces to applications of deep learning in industry with an overview of deep learning and its key architectures, as well as a survey and discussion of the main applications of deep learning. The book also presents case studies to illustrate applications of AI and analytics. These include a case study from the healthcare industry and an investigation of a digital transformation enabled by AI and analytics transforming a product-oriented company into one delivering solutions and services. The book concludes with a proposed AI-informed data analytics life cycle to be applied to unstructured data.
Author: S. P. Agrawal
Publisher: Concept Publishing Company
ISBN: 9788170224952
Category : Communication
Languages : en
Pages : 276
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Book Description
Author: Alfred Z. Spector
Publisher: Cambridge University Press
ISBN: 1009272195
Category : Computers
Languages : en
Pages : 333
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Book Description
Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
Author: Harry Yang
Publisher: CRC Press
ISBN: 100065267X
Category : Business & Economics
Languages : en
Pages : 335
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Book Description
The confluence of big data, artificial intelligence (AI), and machine learning (ML) has led to a paradigm shift in how innovative medicines are developed and healthcare delivered. To fully capitalize on these technological advances, it is essential to systematically harness data from diverse sources and leverage digital technologies and advanced analytics to enable data-driven decisions. Data science stands at a unique moment of opportunity to lead such a transformative change. Intended to be a single source of information, Data Science, AI, and Machine Learning in Drug Research and Development covers a wide range of topics on the changing landscape of drug R & D, emerging applications of big data, AI and ML in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations. Features Provides a comprehensive review of challenges and opportunities as related to the applications of big data, AI, and ML in the entire spectrum of drug R & D Discusses regulatory developments in leveraging big data and advanced analytics in drug review and approval Offers a balanced approach to data science organization build Presents real-world examples of AI-powered solutions to a host of issues in the lifecycle of drug development Affords sufficient context for each problem and provides a detailed description of solutions suitable for practitioners with limited data science expertise
Author: Anno Bunnik
Publisher: Springer
ISBN: 1349948853
Category : Political Science
Languages : en
Pages : 148
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Book Description
This book brings together an impressive range of academic and intelligence professional perspectives to interrogate the social, ethical and security upheavals in a world increasingly driven by data. Written in a clear and accessible style, it offers fresh insights to the deep reaching implications of Big Data for communication, privacy and organisational decision-making. It seeks to demystify developments around Big Data before evaluating their current and likely future implications for areas as diverse as corporate innovation, law enforcement, data science, journalism, and food security. The contributors call for a rethinking of the legal, ethical and philosophical frameworks that inform the responsibilities and behaviours of state, corporate, institutional and individual actors in a more networked, data-centric society. In doing so, the book addresses the real world risks, opportunities and potentialities of Big Data.
Author: Jugal K. Kalita
Publisher: Elsevier
ISBN: 0323972632
Category : Mathematics
Languages : en
Pages : 336
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Book Description
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors' research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included. - Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning - Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning - Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis - Covers computer program code for implementing descriptive and predictive algorithms
