Showing 1 - 20 results of 30 for search '(( binary data effect detection algorithm ) OR ( binary arm bayesian optimization algorithm ))', query time: 0.52s Refine Results
  1. 1
  2. 2
  3. 3
  4. 4

    Joint Network Reconstruction and Community Detection from Rich but Noisy Data by Jie Hu (130301)

    Published 2023
    “…In this article, we propose a novel framework, called the group-based binary mixture (GBM) modeling approach, to simultaneously conduct network reconstruction and community detection from such rich but noisy data. …”
  5. 5

    GSE96058 information. by Sepideh Zununi Vahed (9861298)

    Published 2024
    “…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”
  6. 6

    The performance of classifiers. by Sepideh Zununi Vahed (9861298)

    Published 2024
    “…</p><p>Results</p><p>In this study, five main steps were followed for the analysis of mRNA expression data: reading, preprocessing, feature selection, classification, and SHAP algorithm. …”
  7. 7

    Quantile Regression and Homogeneity Identification of a Semiparametric Panel Data Model by Rui Li (4631)

    Published 2024
    “…<p>In this article, we delve into the quantile regression and homogeneity detection of a varying index coefficient panel data model, which incorporates fixed individual effects and exhibits nonlinear time trends. …”
  8. 8

    Analysis of geo-spatiotemporal data using machine learning algorithms and reliability enhancement for urbanization decision support by Kwame O. Hackman (9289505)

    Published 2020
    “…Two classification algorithms – random forest (RF) and support vector machines (SVM) – were used to produce binary (built-up / non-built up) maps for all years within the temporal span. …”
  9. 9
  10. 10

    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. …”
  11. 11

    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. …”
  12. 12

    Comparison of accuracy and DR on UNSW-NB15. 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. …”
  13. 13

    Comparison of DR and FPR of UNSW-NB15. 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. …”
  14. 14

    DataSheet_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

    Published 2023
    “…However, extracting useful information from PAM data often requires substantial human effort, along with effective estimates of the detection range of the acoustic units, which can be challenging to obtain. …”
  15. 15

    Data_Sheet_1_DINTD: Detection and Inference of Tandem Duplications From Short Sequencing Reads.docx by Jinxin Dong (9229511)

    Published 2020
    “…A 2D binary search tree is used to search the neighbor points effectively. …”
  16. 16
  17. 17

    Modeling Pregnancy Outcomes Through Sequentially Nested Regression Models by Xuan Bi (3096897)

    Published 2022
    “…However, the separate models may lose power in detecting the treatment effects and influential variables for live birth, due to decreased sample sizes and unbalanced event counts. …”
  18. 18

    Table_2_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

    Published 2023
    “…However, extracting useful information from PAM data often requires substantial human effort, along with effective estimates of the detection range of the acoustic units, which can be challenging to obtain. …”
  19. 19

    Table_3_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

    Published 2023
    “…However, extracting useful information from PAM data often requires substantial human effort, along with effective estimates of the detection range of the acoustic units, which can be challenging to obtain. …”
  20. 20

    Table_1_Automated detection and detection range of primate duets: a case study of the red titi monkey (Plecturocebus discolor) using passive acoustic monitoring.pdf by Silvy M. van Kuijk (16881867)

    Published 2023
    “…However, extracting useful information from PAM data often requires substantial human effort, along with effective estimates of the detection range of the acoustic units, which can be challenging to obtain. …”