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Titlebook: Recent Developments and the New Directions of Research, Foundations, and Applications; Selected Papers of t Shahnaz N. Shahbazova,Ali M. Ab

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發(fā)表于 2025-3-21 19:12:47 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱Recent Developments and the New Directions of Research, Foundations, and Applications
副標(biāo)題Selected Papers of t
編輯Shahnaz N. Shahbazova,Ali M. Abbasov,Ildar Z. Baty
視頻videohttp://file.papertrans.cn/824/823111/823111.mp4
概述Presents recent research and applications of Soft Computing.Addresses selected proceedings of the 8th World Conference on Soft Computing.Is dedicated to the 100th anniversary of the birth of Professor
叢書(shū)名稱Studies in Fuzziness and Soft Computing
圖書(shū)封面Titlebook: Recent Developments and the New Directions of Research, Foundations, and Applications; Selected Papers of t Shahnaz N. Shahbazova,Ali M. Ab
描述.This book is a collection of papers presented during the 8th World Conference on Soft Computing in February 2022. The papers cover multiple areas important for soft computing. Some papers are dedicated to fundamental aspects of soft computing, i.e., fuzzy mathematics, type-2 fuzzy sets, evolutionary-based optimization, aggregation, and neural networks. Others emphasize the application of soft computing methods to data analysis, image processing, decision-making, classification, series prediction, economics, control, and modeling..
出版日期Book 2023
關(guān)鍵詞Soft Computing; Fuzzy Mathematics; Type-2 Fuzzy Sets; Evolutionary-based Optimization; Aggregation; Neura
版次1
doihttps://doi.org/10.1007/978-3-031-23476-7
isbn_softcover978-3-031-23478-1
isbn_ebook978-3-031-23476-7Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 20:52:41 | 只看該作者
板凳
發(fā)表于 2025-3-22 01:12:56 | 只看該作者
Studies in Fuzziness and Soft Computinghttp://image.papertrans.cn/r/image/823111.jpg
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發(fā)表于 2025-3-22 05:55:36 | 只看該作者
https://doi.org/10.1007/978-3-031-23476-7Soft Computing; Fuzzy Mathematics; Type-2 Fuzzy Sets; Evolutionary-based Optimization; Aggregation; Neura
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發(fā)表于 2025-3-22 12:34:16 | 只看該作者
Call Center and Robot Call Center How to Reduce the CostThis article explores the application of artificial intelligence in call centers. It is shown how an artificial intelligence-based robot operator can stop the constant increase in call center costs and significantly reduce call center costs. It also describes the process of developing robot operators and their work in real time.
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發(fā)表于 2025-3-22 16:29:40 | 只看該作者
Shahnaz N. Shahbazova,Ali M. Abbasov,Ildar Z. BatyPresents recent research and applications of Soft Computing.Addresses selected proceedings of the 8th World Conference on Soft Computing.Is dedicated to the 100th anniversary of the birth of Professor
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發(fā)表于 2025-3-22 17:47:03 | 只看該作者
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發(fā)表于 2025-3-22 23:49:19 | 只看該作者
The Problem of Parsing and Recognizing Individual Objects of Information on Base of Fuzzy Logican algorithmic model that simulates the human abilities of the motor (unconscious or non-intellectual) behavior. The paper is devoted to the development of a machine intelligence system with a gradual increase in the complexity of the behavioral model of an artificial personality (AP) to experimentally study the issues of artificial intelligence.
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發(fā)表于 2025-3-23 04:00:06 | 只看該作者
How to?Gauge Students’ Ability to?Collaborate?ffort. So, to make sure that students are successful, we also need to gauge their ability to collaborate. In this paper, we describe when it is possible to gauge the students’ ability to collaborate; in situations when such a determination is possible, we explain how exactly we can estimate these abilities.
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發(fā)表于 2025-3-23 07:42:00 | 只看該作者
Skin Cancer Diagnosis Enhancement Through NLP and?DNN-Based Binary Classificationication problem. Our experimentation with different architectures yielded noteworthy results over a specific corpora of medical notes. Using a BoW/TF-IDF representation of the corpus and a 1-dimensional Convolutional Neural Network(CNN1D) architecture, we achieved an F1-measure of 0.95, which is better than state of the art.
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