Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III

This book PDF is perfect for those who love Computers genre, written by A.J. Tallón-Ballesteros and published by IOS Press which was released on 03 December 2021 with total hardcover pages 738. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III books below.

Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III
Author : A.J. Tallón-Ballesteros
File Size : 52,9 Mb
Publisher : IOS Press
Language : English
Release Date : 03 December 2021
ISBN : 9781643682259
Pages : 738 pages
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Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III by A.J. Tallón-Ballesteros Book PDF Summary

It is data that guides the path of applications, and Big Data technologies are enabling new paths which can deal with information in a reasonable time to arrive at an approximate solution, rather than a more exact result in an unacceptably long time. This can be particularly important when dealing with an urgent issue such as that of the COVID-19 pandemic. This book presents the proceedings of two conferences: MMBD 2021 and MLIS 2021. The MMBD conference deals with two main subjects; those of Big Data and Modern Management. The MLIS conference aims to provide a platform for knowledge exchange of the most recent scientific and technological advances in the field of machine learning and intelligent systems. Both conferences were originally scheduled to be held from 8-11 November 2021, in Quanzhou, China and Xiamen, China respectively. Both conferences were ultimately held fully online on the same dates, hosted by Huaqiao University in Quanzhou and Xiamen respectively. The book is in two parts, and contains a total of 78 papers (54 from MMBD2021 and 24 from MLIS2021) selected after rigorous review from a total of some 300 submissions. The reviewers bore in mind the breadth and depth of the research topics that fall within the scope of MMBD and MLIS, and selected the 78 most promising and FAIA mainstream-relevant contributions for inclusion in this two-part volume. All the papers present original ideas or results of general significance supported by clear reasoning, compelling evidence and rigorous methods.

Modern Management Based on Big Data II and Machine Learning and Intelligent Systems III

It is data that guides the path of applications, and Big Data technologies are enabling new paths which can deal with information in a reasonable time to arrive at an approximate solution, rather than a more exact result in an unacceptably long time. This can be particularly important when dealing

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Modern Management based on Big Data I

The management of any modern organisation involves data, but the volume of information has become almost impossible for even the most up-to-date computer system to handle. Fortunately, big-data technologies are now enabling new ways of dealing with the flood of information, making an approximate solution possible in a reasonable time-frame,

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Modern Management Based on Big Data IV

The concept of Big Data has become increasingly familiar in recent years, and it is already an indispensible tool in the management of everything from supply chains and transport to health and education. This book presents the proceedings of MMBD 2023, the 4th International Conference on Modern Management based on Big

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Modern Management Based on Big Data III

Data is the basic ingredient of all Big Data applications, and Big Data technologies are constantly deploying new strategies to maximise efficiency and reduce the time taken to process information. This book presents the proceedings of MMBD2022, the third edition of the conference series Modern Management based on Big Data (

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Effectively documenting data services is a crucial issue in any organization, not only for governing data but also for interoperation purposes. Indeed, in order to fully realize the promises and benefits of a data-driven society, data-driven approaches need to be resilient, transparent, and fully accountable. This book, Abstraction in Ontology-based

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Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI

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Artificial intelligence often has to deal with uncertain scenarios, such as a partially observed environment or noisy observations. Traditional probabilistic models, while being very principled approaches in these contexts, are incapable of dealing with both algebraic and logical constraints. Existing hybrid continuous/discrete models are typically limited in expressivity, or

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