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kNNVWC: An efficient k-nearest neighbours approach based on Various-Widths Clustering.

A. Almalawi, A. Fahad, Z. Tari, M. Cheema, and I. Khalil. ICDE, page 1572-1573. IEEE Computer Society, (2016)

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A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks.A. Almalawi, S. Hassan, A. Fahad, and A. Khan. Int. J. Comput. Intell. Syst., 17 (1): 24 (December 2024)An Efficient Network Classification Based on Various-Widths Clustering and Semi-Supervised Stacking.A. Almalawi, and A. Fahad. IEEE Access, (2021)Deep Kalman Neuro Fuzzy-Based Adaptive Broadcasting Scheme for Vehicular Ad Hoc Network: A Context-Aware Approach.F. Ghaleb, B. Al rimy, A. Almalawi, A. Ali, A. Zainal, M. Rassam, S. Shaid, and M. Maarof. IEEE Access, (2020)A Taxonomy of Supervised Learning for IDSs in SCADA Environments.J. Suaboot, A. Fahad, Z. Tari, J. Grundy, A. Mahmood, A. Almalawi, A. Zomaya, and K. Drira. ACM Comput. Surv., 53 (2): 40:1-40:37 (2021)Managing Security of Healthcare Data for a Modern Healthcare System.A. Almalawi, A. Khan, F. Alsolami, Y. Abushark, and A. Alfakeeh. Sensors, 23 (7): 3612 (April 2023)An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems.A. Almalawi, X. Yu, Z. Tari, A. Fahad, and I. Khalil. Comput. Secur., (2014)CluClas: Hybrid clustering-classification approach for accurate and efficient network classification.A. Fahad, K. Alharthi, Z. Tari, A. Almalawi, and I. Khalil. LCN, page 168-176. IEEE Computer Society, (2014)SemTra: A semi-supervised approach to traffic flow labeling with minimal human effort.A. Fahad, A. Almalawi, Z. Tari, K. Alharthi, F. Al-Qahtani, and M. Cheriet. Pattern Recognit., (2019)An optimal and stable feature selection approach for traffic classification based on multi-criterion fusion.A. Fahad, Z. Tari, I. Khalil, A. Almalawi, and A. Zomaya. Future Gener. Comput. Syst., (2014)An Efficient Data-Driven Clustering Technique to Detect Attacks in SCADA Systems.A. Almalawi, A. Fahad, Z. Tari, A. Alamri, R. AlGhamdi, and A. Zomaya. IEEE Trans. Inf. Forensics Secur., 11 (5): 893-906 (2016)

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kNNVWC: An efficient k-nearest neighbours approach based on Various-Widths Clustering.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML kNNVWC: An efficient k-nearest neighbours approach based on Various-Widths Clustering.

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kNNVWC: An efficient k-nearest neighbours approach based on Various-Widths Clustering.