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Proceedings of the Future Technologies Conference (FTC) 2024.. Volume 1

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Proceedings of the Future Technologies Conference (FTC) 2024./ edited by Kohei Arai.
其他作者:	Arai, Kohei.
團體作者:	Future Technologies Conference
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	x, 640 p. :ill. (some col.), digital ;24 cm.
內容註:	A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams -- Cloud Versus Local: Performance Evaluation of Multi-Node Hadoop Clusters Using HiBench Benchmarks -- The Serial Data Processing Unit: High Performance with Parallelism in Network Processing -- Leveraging Artificial Intelligence Models Using Synthetic Data -- Tausi: A Holistic Artificial Intelligence Approach to Credit Scoring Using Informal Data for a Sustainable Micro-Lending African Economy -- Quickest Detection of Failures in Autoregressive Stochastic Systems in Short Periods -- Towards the Design of Explanation-aware Decision Support Systems -- AI to Detect Firms' Competitive Performance: An Application of Auto Contractive Map to the Hospitality Industry -- Sustainable Practices in AI and Big Data -- A Multi-Layered Approach to Evaluating Speech Translation Performance of Meetings.
Contained By:	Springer Nature eBook
標題:	Computer science - Congresses. - Technological innovations -
電子資源:	https://doi.org/10.1007/978-3-031-73110-5
ISBN:	9783031731105

Proceedings of the Future Technologies Conference (FTC) 2024.. Volume 1
Proceedings of the Future Technologies Conference (FTC) 2024.Volume 1[electronic resource] /edited by Kohei Arai. - Cham :Springer Nature Switzerland :2024. - x, 640 p. :ill. (some col.), digital ;24 cm. - Lecture notes in networks and systems,11542367-3389 ;. - Lecture notes in networks and systems ;1154..

A Conditioned Unsupervised Regression Framework Attuned to the Dynamic Nature of Data Streams -- Cloud Versus Local: Performance Evaluation of Multi-Node Hadoop Clusters Using HiBench Benchmarks -- The Serial Data Processing Unit: High Performance with Parallelism in Network Processing -- Leveraging Artificial Intelligence Models Using Synthetic Data -- Tausi: A Holistic Artificial Intelligence Approach to Credit Scoring Using Informal Data for a Sustainable Micro-Lending African Economy -- Quickest Detection of Failures in Autoregressive Stochastic Systems in Short Periods -- Towards the Design of Explanation-aware Decision Support Systems -- AI to Detect Firms' Competitive Performance: An Application of Auto Contractive Map to the Hospitality Industry -- Sustainable Practices in AI and Big Data -- A Multi-Layered Approach to Evaluating Speech Translation Performance of Meetings.

This book covers proceedings of the Future Technologies Conference (FTC) 2024 which showcase a collection of thoroughly researched studies presented at the ninth Future Technologies Conference, held in London, the UK. This premier annual event highlights groundbreaking research in artificial intelligence, computer vision, data science, computing, ambient intelligence, and related fields. With 476 submissions, FTC 2024 gathers visionary minds to explore innovative solutions to today's most pressing challenges. The 173 selected papers represent cutting-edge advancements that foster vital conversations and future collaborations in the realm of information technologies. The authors extend their deepest gratitude to all contributors, reviewers, and participants for making FTC 2024 an unparalleled success. The authors hope this volume inspires and informs its readers, encouraging continued exploration and innovation in future technologies.

ISBN: 9783031731105

Standard No.: 10.1007/978-3-031-73110-5doiSubjects--Topical Terms:

3380968
Computer science
--Technological innovations--Congresses.

LC Class. No.: QA75.5 / .F8823 2024

Dewey Class. No.: 004

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