Showing 1 - 20 results of 4,968 for search 'data set detection', query time: 0.18s Refine Results
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    Adaptive Fault Detection for Complex Dynamic Processes Based on JIT Updated Data Set by Jinna Li, Yuan Li, Haibin Yu, Yanhong Xie, Cheng Zhang

    Published 2012-01-01
    “…Mahalanobis distance, representing the correlation among samples, is used to simplify and update the raw data set, which is the first merit in this paper. …”
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    Developing an Immune Negative Selection Algorithm for Intrusion Detection in NSL-KDD data Set by Baghdad Science Journal

    Published 2016-06-01
    “…Practical Experiments showed the process to achieve a high rate of detection in the system designer using data NSL-KDD with 12 field without vulnerability to change the radius of the detector or change the number of reagents were obtained as the ratio between detection (0.984, 0.998, 0.999) and the ratio between a false alarm (0.003, 0.002, 0.001). …”
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    Protocol-Based Deep Intrusion Detection for DoS and DDoS Attacks Using UNSW-NB15 and Bot-IoT Data-Sets by Muhammad Zeeshan, Qaiser Riaz, Muhammad Ahmad Bilal, Muhammad K. Shahzad, Hajira Jabeen, Syed Ali Haider, Azizur Rahim

    Published 2022-01-01
    “…In this paper, we propose a Protocol Based Deep Intrusion Detection (PB-DID) architecture, in which we created a data-set of packets from IoT traffic by comparing features from the UNSWNB15 and Bot-IoT data-sets based on flow and Transmission Control Protocol (TCP). …”
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    Impact of Data Distribution and Bootstrap Setting on Anomaly Detection Using Isolation Forest in Process Quality Control by Hyunyul Choi, Kihyo Jung

    Published 2025-07-01
    “…This study investigates the impact of data distribution and bootstrap resampling on the anomaly detection performance of the Isolation Forest (iForest) algorithm in statistical process control. …”
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    Deep Learning for Anomaly Detection in CNC Machine Vibration Data: A RoughLSTM-Based Approach by Rasım Çekik, Abdullah Turan

    Published 2025-03-01
    “…This study proposes rough long short-term memory (RoughLSTM), a novel hybrid model integrating rough set theory (RST) with LSTM to enhance anomaly detection in CNC machine vibration data. …”
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    Class-consistent technology-based outlier detection for incomplete real-valued data based on rough set theory and granular computing by Li Zhaowen, He Hongxuan, Wang Pei

    Published 2025-05-01
    “…This article studies outlier detection for incomplete real-valued data based on class-consistent technology, rough set theory, and granular computing. …”
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