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Showing 1 - 16 results of 16 for search '(( binary based driven optimization algorithm ) OR ( laboratory based ai optimization algorithm ))', query time: 0.47s Refine Results
  1. 1
    Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things

    Optimized Bayesian regularization-back propagation neural network using data-driven intrusion detection system in Internet of Things by Ashok Kumar K (21441108)

    Published 2025
    “…Hence, Binary Black Widow Optimization Algorithm (BBWOA) is proposed in this manuscript to improve the BRBPNN classifier that detects intrusion precisely. …”
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  2. 2
    Sample multi-parameter patient data from the MIMIC-III clinical database: This figure demonstrates the complicated, high-resolution time-series data monitored on a single patient on a visit to an Intensive Care Unit (ICU).

    Sample multi-parameter patient data from the MIMIC-III clinical database: This figure demonstrates the complicated, high-resolution time-series data monitored on a single patient o... by Medhat A. Tawfeek (22522087)

    Published 2025
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  3. 3
    the functioning of BRPSO.

    the functioning of BRPSO. by Hossein Jarrahi (22530251)

    Published 2025
    “…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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  4. 4
    Characteristic of 6- and 10-story SMRF [99,98].

    Characteristic of 6- and 10-story SMRF [99,98]. by Hossein Jarrahi (22530251)

    Published 2025
    “…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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  5. 5
    The RFD’s behavior mechanism (2002).

    The RFD’s behavior mechanism (2002). by Hossein Jarrahi (22530251)

    Published 2025
    “…A sensitivity analysis of key RFD parameters, including frictional moment and rigid beam length, highlights their influence on seismic performance. The optimization problem is formulated based on the seismic energy dissipation concept, employing a modified binary and real-coded particle swarm optimization (BRPSO) algorithm. …”
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  6. 6
    Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation

    Seamless integration of legacy robotic systems into a self-driving laboratory via NIMO: a case study on liquid handler automation by Ryo Tamura (1957942)

    Published 2025
    “…We developed NIMO (formerly NIMS-OS, NIMS Orchestration System), an OS explicitly designed to integrate multiple artificial intelligence (AI) algorithms with diverse exploratory objectives. …”
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  7. 7
    UK Biobank data wrangling statistics and results.

    UK Biobank data wrangling statistics and results. by Simeone Marino (81051)

    Published 2020
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  8. 8
    Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence

    Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence by Yao Xu (247302)

    Published 2024
    “…This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. …”
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  9. 9
    Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence

    Human-Guided Metaverse Synthesis for Quantum Dots: Advancing Nanomaterial Research through Augmented Artificial Intelligence by Yao Xu (247302)

    Published 2024
    “…This study proposes an innovative paradigm for metaverse-based synthesis experiments, aiming to enhance experimental optimization efficiency through human-guided parameter tuning in the metaverse and augmented artificial intelligence (AI) with human expertise. …”
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  10. 10
    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043)

    <b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) by Erola Fenollosa (20977421)

    Published 2025
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  11. 11
    Data_Sheet_1_Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.docx

    Data_Sheet_1_Constructing machine learning models based on non-contrast CT radiomics to predict hemorrhagic transformation after stoke: a two-center study.docx by Yue Zhang (30585)

    Published 2024
    “…Then, five ML models were established and evaluated, and the optimal ML algorithm was used to construct the clinical, radiomics, and clinical-radiomics models. …”
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  12. 12
    Image_1_Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.tif

    Image_1_Establishment of a novel lysosomal signature for the diagnosis of gastric cancer with in-vitro and in-situ validation.tif by Qi Wang (22418)

    Published 2023
    “…</p>Methods<p>To this end, this study, by utilizing the transcriptomic as well as single cell data and integrating 20 mainstream machine-learning (ML) algorithms. We optimized an AI-based predictor for GC diagnosis. …”
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  13. 13
    Table_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.DOCX

    Table_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.DOCX by Carles Rubio Maturana (17448078)

    Published 2023
    “…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”
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  14. 14
    Image_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF

    Image_1_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF by Carles Rubio Maturana (17448078)

    Published 2023
    “…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”
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  15. 15
    Image_2_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF

    Image_2_iMAGING: a novel automated system for malaria diagnosis by using artificial intelligence tools and a universal low-cost robotized microscope.TIFF by Carles Rubio Maturana (17448078)

    Published 2023
    “…</p>Conclusion<p>The coalescence of the fully-automated system via auto-focus and slide movements and the autonomous detection of Plasmodium parasites in digital images with a smartphone software and AI algorithms confers the prototype the optimal features to join the global effort against malaria, neglected tropical diseases and other infectious diseases.…”
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  16. 16
    Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx

    Table 1_Explainable machine learning model for predicting the outcome of acute ischemic stroke after intravenous thrombolysis.docx by Fanhai Bu (22315168)

    Published 2025
    “…The least absolute shrinkage and selection operator (LASSO) regression selected predictors from clinical/neuroimaging/laboratory variables. Eight ML algorithms (including Logistic Regression, Random Forest, Extreme Gradient Boosting, Multilayer Perceptron, Support Vector Machine, Light Gradient Boosting Machine, Decision Tree, and K-Nearest Neighbors) were trained using 10-fold cross-validation and evaluated on test/external sets via the area under the curve (AUC), accuracy, precision, recall and F1-score. …”
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