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Rasım Çekik

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Articles

Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach

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By Rasım Çekik, Abdullah Turan
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Effective Text Classification Through Supervised Rough Set-Based Term Weighting

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By Rasım Çekik
| MDPI
5.5. Accuracy Analysis This section presents a comprehensive comparison of the proposed term weighting methods with established approaches, namely TF-IDF, ICF, ICSDF, TRR, IGM, SIGM, IGMimp, and SIGMimp. The performance evaluation is conducted on various term dimensions: 750, 1500, 2500, 3750, 4500, 5750, 6500, and 7750. The Distinguishing Feature Selector (DFS) [31] algorithm is utilized for term selection within the dimension selection process.
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