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Titlebook: Artificial Intelligence and Soft Computing; 14th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings

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發(fā)表于 2025-3-21 19:29:19 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
期刊全稱Artificial Intelligence and Soft Computing
期刊簡稱14th International C
影響因子2023Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad
視頻videohttp://file.papertrans.cn/163/162298/162298.mp4
發(fā)行地址Includes supplementary material:
學(xué)科分類Lecture Notes in Computer Science
圖書封面Titlebook: Artificial Intelligence and Soft Computing; 14th International C Leszek Rutkowski,Marcin Korytkowski,Jacek M. Zurad Conference proceedings
影響因子The two-volume set LNAI 9119 and LNAI 9120 constitutes the refereed proceedings of the 14th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2015, held in Zakopane, Poland in June 2015. The 142 revised full papers presented in the volumes, were carefully reviewed and selected from 322 submissions. These proceedings present both traditional artificial intelligence methods and soft computing techniques. The goal is to bring together scientists representing both areas of research. The first volume covers topics as follows neural networks and their applications, fuzzy systems and their applications, evolutionary algorithms and their applications, classification and estimation, computer vision, image and speech analysis and the workshop: large-scale visual recognition and machine learning. The second volume has the focus on the following subjects: data mining, bioinformatics, biometrics and medical applications, concurrent and parallel processing, agent systems, robotics and control, artificial intelligence in modeling and simulation and various problems of artificial intelligence.
Pindex Conference proceedings 2015
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發(fā)表于 2025-3-22 00:15:08 | 只看該作者
Theoretische Ans?tze im überblickpotentially important biomarkers. In this study, a segmentation approach is adopted to locate the potential biomarker regions from the possible m/z range. Illustration is through real prostate cancer proteomic mass spectra data.
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Gesundheitskompetenz als soziale Praxis,g flat images; however, they are much more complex. In this paper, we present subsequent results of our research on a new representation of characteristic points for the 3D face. As a comparative methods SOM, FCM and PCA are applied. We discuss the usefulness of these methods with the new representation of characteristic points.
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發(fā)表于 2025-3-22 07:23:07 | 只看該作者
https://doi.org/10.1007/978-3-319-19369-4Artificial neural networks; Bioinformatics; Clustering and classification; Data mining; Distributed comp
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Segmentation Based Feature Selection on Classifying Proteomic Spectral Datapotentially important biomarkers. In this study, a segmentation approach is adopted to locate the potential biomarker regions from the possible m/z range. Illustration is through real prostate cancer proteomic mass spectra data.
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發(fā)表于 2025-3-22 17:36:01 | 只看該作者
SOM vs FCM vs PCA in 3D Face Recognitiong flat images; however, they are much more complex. In this paper, we present subsequent results of our research on a new representation of characteristic points for the 3D face. As a comparative methods SOM, FCM and PCA are applied. We discuss the usefulness of these methods with the new representation of characteristic points.
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Leszek Rutkowski,Marcin Korytkowski,Jacek M. ZuradIncludes supplementary material:
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Lecture Notes in Computer Sciencehttp://image.papertrans.cn/b/image/162298.jpg
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Sergej Seitz,Anke Grane?,Georg Stengerques and models. One of the important subjects is to reveal and indicate heterogeneous of non-trivial features of a large database. Original techniques of modelling, data mining, pattern recognition, machine learning in such fields like commercial behaviour of Internet users, social networks analysi
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