Showing 1 - 20 results of 163 for search '(( binary b bayesian optimization algorithm ) OR ( primary data effect detection algorithm ))', query time: 0.58s Refine Results
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    Four major algorithms used for signal detection. by Ruibo Li (2063167)

    Published 2024
    “…<div><p>Background</p><p>Due to the limitations of clinical trials, some delayed and rare adverse events (AEs) may remain undetected, and safety information can be supplemented through post-market data analysis. This study aims to comprehensively analyze the AEs associated with Relugolix (Orgovyx<sup>®</sup>) using data from the FAERS database, and gain a better understanding of the potential risks and side effects of Relugolix (Orgovyx<sup>®</sup>) therapy.…”
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    MLP vs classification algorithms. by Mohd Mustaqeem (19106494)

    Published 2024
    “…Software defects are the primary concern, and software defect prediction (SDP) plays a crucial role in detecting faulty modules early and planning effective testing to reduce maintenance costs. …”
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    Data concatenation. by Zhen Wang (72451)

    Published 2023
    “…The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. …”
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    Experimental data comparison table. by Zhen Wang (72451)

    Published 2023
    “…The algorithm’s effectiveness is verified by the pool experiment echo data, which shows that the filter can improve the detection of echo signals by about 10 dB. …”
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    Network Intrusion Detection Datasets by Ogobuchi Daniel Okey (15854591)

    Published 2023
    “…The primary focus of this project is to develop an effective Intrusion Detection System (IDS) using the aforementioned algorithm. …”
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    Related studies on IDS using deep learning. by Arshad Hashmi (13835488)

    Published 2024
    “…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”
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    The architecture of the BI-LSTM model. by Arshad Hashmi (13835488)

    Published 2024
    “…This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. …”