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: Longbing Cao
Publisher: Springer
ISBN: 3319950924
Category : Computers
Languages : en
Pages : 404
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Book Description
This book explores answers to the fundamental questions driving the research, innovation and practices of the latest revolution in scientific, technological and economic development: how does data science transform existing science, technology, industry, economy, profession and education? How does one remain competitive in the data science field? What is responsible for shaping the mindset and skillset of data scientists? Data Science Thinking paints a comprehensive picture of data science as a new scientific paradigm from the scientific evolution perspective, as data science thinking from the scientific-thinking perspective, as a trans-disciplinary science from the disciplinary perspective, and as a new profession and economy from the business perspective.
Author: Timothy T.R. Colburn
Publisher: Springer Science & Business Media
ISBN: 9401117934
Category : Computers
Languages : en
Pages : 454
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Book Description
Among the most important problems confronting computer science is that of developing a paradigm appropriate to the discipline. Proponents of formal methods - such as John McCarthy, C.A.R. Hoare, and Edgar Dijkstra - have advanced the position that computing is a mathematical activity and that computer science should model itself after mathematics. Opponents of formal methods - by contrast, suggest that programming is the activity which is fundamental to computer science and that there are important differences that distinguish it from mathematics, which therefore cannot provide a suitable paradigm. Disagreement over the place of formal methods in computer science has recently arisen in the form of renewed interest in the nature and capacity of program verification as a method for establishing the reliability of software systems. A paper that appeared in Communications of the ACM entitled, `Program Verification: The Very Idea', by James H. Fetzer triggered an extended debate that has been discussed in several journals and that has endured for several years, engaging the interest of computer scientists (both theoretical and applied) and of other thinkers from a wide range of backgrounds who want to understand computer science as a domain of inquiry. The editors of this collection have brought together many of the most interesting and important studies that contribute to answering questions about the nature and the limits of computer science. These include early papers advocating the mathematical paradigm by McCarthy, Naur, R. Floyd, and Hoare (in Part I), others that elaborate the paradigm by Hoare, Meyer, Naur, and Scherlis and Scott (in Part II), challenges, limits and alternatives explored by C. Floyd, Smith, Blum, and Naur (in Part III), and recent work focusing on formal verification by DeMillo, Lipton, and Perlis, Fetzer, Cohn, and Colburn (in Part IV). It provides essential resources for further study. This volume will appeal to scientists, philosophers, and laypersons who want to understand the theoretical foundations of computer science and be appropriately positioned to evaluate the scope and limits of the discipline.
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: Hannah Knox
Publisher: Manchester University Press
ISBN: 152612761X
Category : Social Science
Languages : en
Pages : 236
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Book Description
This edited collection aims to reimagine and extend ethnography for a data-saturated world. The book brings together leading scholars in the social sciences who have been interrogating and collaborating with data scientists working in a range of different settings. The book explores how a repurposed form of ethnography might illuminate the kinds of knowledge that are being produced by data science. It also describes how collaborations between ethnographers and data scientists might lead to new forms of social analysis
