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Titlebook: Intelligent and Fuzzy Systems; Intelligent Industri Cengiz Kahraman,Sezi Cevik Onar,Irem Ucal Sari Conference proceedings 2024 The Editor(s

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發(fā)表于 2025-3-21 17:01:52 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Intelligent and Fuzzy Systems
副標(biāo)題Intelligent Industri
編輯Cengiz Kahraman,Sezi Cevik Onar,Irem Ucal Sari
視頻videohttp://file.papertrans.cn/477/476220/476220.mp4
概述Presents recent research in Intelligent and Fuzzy Techniques.Covers proceedings of the Intelligent and Fuzzy Techniques INFUS 2024 Conference.Provides interdisciplinary research between fuzzy and inte
叢書名稱Lecture Notes in Networks and Systems
圖書封面Titlebook: Intelligent and Fuzzy Systems; Intelligent Industri Cengiz Kahraman,Sezi Cevik Onar,Irem Ucal Sari Conference proceedings 2024 The Editor(s
描述.This book presents recent research in intelligent and fuzzy techniques on Intelligent Industrial Informatics and Efficient Networks. This cutting-edge field integrates advanced technologies, such as artificial intelligence, machine learning and data analytics, into industrial processes, revolutionizing the way industries operate. The book presents the examples of the implementation of smart sensors and IoT devices, which facilitate real-time data collection and communication. High-speed, low-latency networks ensure that information flows effortlessly between devices, enabling timely responses and enabling the coordination of complex manufacturing processes. This network architecture supports the integration of edge computing, where data processing occurs closer to the source, reducing latency and enabling faster decision-making. The readers can benefit from this book for maintaining a leadership position among competitors in both manufacturing and service companies. The intended readers are intelligent and fuzzy systems researchers, lecturers, M.Sc. and Ph.D. students studying intelligent and fuzzy techniques. The book covers fuzzy logic theory and applications, heuristics and met
出版日期Conference proceedings 2024
關(guān)鍵詞Intelligent Techniques; Fuzzy Techniques; Big Data Analytics; Decision Making; INFUS 2024
版次1
doihttps://doi.org/10.1007/978-3-031-67195-1
isbn_softcover978-3-031-67194-4
isbn_ebook978-3-031-67195-1Series ISSN 2367-3370 Series E-ISSN 2367-3389
issn_series 2367-3370
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

書目名稱Intelligent and Fuzzy Systems影響因子(影響力)




書目名稱Intelligent and Fuzzy Systems影響因子(影響力)學(xué)科排名




書目名稱Intelligent and Fuzzy Systems網(wǎng)絡(luò)公開度




書目名稱Intelligent and Fuzzy Systems網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Intelligent and Fuzzy Systems被引頻次




書目名稱Intelligent and Fuzzy Systems被引頻次學(xué)科排名




書目名稱Intelligent and Fuzzy Systems年度引用




書目名稱Intelligent and Fuzzy Systems年度引用學(xué)科排名




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發(fā)表于 2025-3-21 22:24:24 | 只看該作者
Cengiz Kahraman,Sezi Cevik Onar,Irem Ucal SariPresents recent research in Intelligent and Fuzzy Techniques.Covers proceedings of the Intelligent and Fuzzy Techniques INFUS 2024 Conference.Provides interdisciplinary research between fuzzy and inte
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發(fā)表于 2025-3-22 01:35:10 | 只看該作者
Lecture Notes in Networks and Systemshttp://image.papertrans.cn/i/image/476220.jpg
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發(fā)表于 2025-3-22 05:07:18 | 只看該作者
978-3-031-67194-4The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
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發(fā)表于 2025-3-22 09:10:35 | 只看該作者
Intelligent and Fuzzy Systems978-3-031-67195-1Series ISSN 2367-3370 Series E-ISSN 2367-3389
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發(fā)表于 2025-3-22 15:51:37 | 只看該作者
A Bibliometric and Trend Analysis on Impact of Fuzzy Logic in Industry 4.0,er the influence of technology. Fuzzy logic can be used as an important tool in managing the uncertainties and complexities inherent in this paradigm shift. This study aims to reveal the role of fuzzy logic in the transition to Industry 4.0. Despite its critical role, studies explaining the effect o
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發(fā)表于 2025-3-23 00:41:49 | 只看該作者
Fuzzy Analysis of Ground Humidity Sensor Readings on a Crop Area Managed Using IoT-Technology,lytics of data accumulated in IoT-platforms is implemented using predictive models of time series based on various methods of statistical analysis and machine learning. Another approach to time series forecasting is based on a fuzzy paradigm, which assumes a weakly structured data generated in the I
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發(fā)表于 2025-3-23 04:49:20 | 只看該作者
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