Showing 201 - 220 results of 440 for search '(( collected control set algorithm ) OR ( conducted control set algorithm ))', query time: 0.32s Refine Results
  1. 201

    Verifying the Effects of the Grey Level Co-Occurrence Matrix and Topographic–Hydrologic Features on Automatic Gully Extraction in Dexiang Town, Bayan County, China by Zhuo Chen, Tao Liu

    Published 2025-07-01
    “…Erosion gullies can reduce arable land area and decrease agricultural machinery efficiency; therefore, automatic gully extraction on a regional scale should be one of the preconditions of gully control and land management. The purpose of this study is to compare the effects of the grey level co-occurrence matrix (GLCM) and topographic–hydrologic features on automatic gully extraction and guide future practices in adjacent regions. …”
    Get full text
    Article
  2. 202
  3. 203

    Evolving rifampicin and isoniazid mono-resistance in a high multidrug-resistant and extensively drug-resistant tuberculosis region: a retrospective data analysis by Koleka Mlisana, Nomonde Ritta Mvelase, Yusentha Balakrishna, Keeren Lutchminarain

    Published 2019-11-01
    “…This study assessed the changes in resistance levels in culture confirmed Mycobacterium tuberculosis (MTB) in the highest burdened province of South Africa during a period where major changes in diagnostic algorithm were implemented.Setting This study was conducted at the central academic laboratory of the KwaZulu-Natal province of South Africa.Participants We analysed data for all MTB cultures performed in the KwaZulu-Natal province between 2011 and 2014. …”
    Get full text
    Article
  4. 204

    Combined Prediction of Dust Concentration in Opencast Mine Based on RF-GA-LSSVM by Shuangshuang Xiao, Jin Liu, Yajie Ma, Yonggui Zhang

    Published 2024-09-01
    “…Next, the data are split into a training set and a test set at a 7:3 ratio, and the genetic algorithm (GA) is applied to optimize the least squares support vector machine (LSSVM) model for predicting dust concentration in opencast mines. …”
    Get full text
    Article
  5. 205

    A blood-based screening tool for Alzheimer's disease that spans serum and plasma: findings from TARC and ADNI. by Sid E O'Bryant, Guanghua Xiao, Robert Barber, Ryan Huebinger, Kirk Wilhelmsen, Melissa Edwards, Neill Graff-Radford, Rachelle Doody, Ramon Diaz-Arrastia, Texas Alzheimer's Research & Care Consortium, Alzheimer's Disease Neuroimaging Initiative

    Published 2011-01-01
    “…<h4>Objective</h4>To generate and cross-validate a blood-based screener for AD that yields acceptable accuracy across both serum and plasma.<h4>Design, setting, participants</h4>Analysis of serum biomarker proteins were conducted on 197 Alzheimer's disease (AD) participants and 199 control participants from the Texas Alzheimer's Research Consortium (TARC) with further analysis conducted on plasma proteins from 112 AD and 52 control participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI). …”
    Get full text
    Article
  6. 206

    A diagnostic model for polycystic ovary syndrome based on machine learning by Cheng Tong, Yue Wu, Zhenchao Zhuang, Ying Yu

    Published 2025-03-01
    “…The data of 10 case groups and 10 control groups were randomly selected as validation set data, and the rest of the data were included in the model construction. …”
    Get full text
    Article
  7. 207

    The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

    Published 2025-05-01
    “…Additionally, single-gene gene set enrichment analysis (GSEA) was conducted, and correlations between feature genes and differential immune cells were analyzed. …”
    Get full text
    Article
  8. 208
  9. 209

    Hard-coded backdoor detection method based on semantic conflict by Anxiang HU, Da XIAO, Shichen GUO, Shengli LIU

    Published 2023-02-01
    “…The current router security issues focus on the mining and utilization of memory-type vulnerabilities, but there is low interest in detecting backdoors.Hard-coded backdoor is one of the most common backdoors, which is simple and convenient to set up and can be implemented with only a small amount of code.However, it is difficult to be discovered and often causes serious safety hazard and economic loss.The triggering process of hard-coded backdoor is inseparable from string comparison functions.Therefore, the detection of hard-coded backdoors relies on string comparison functions, which are mainly divided into static analysis method and symbolic execution method.The former has a high degree of automation, but has a high false positive rate and poor detection results.The latter has a high accuracy rate, but cannot automate large-scale detection of firmware, and faces the problem of path explosion or even unable to constrain solution.Aiming at the above problems, a hard-coded backdoor detection algorithm based on string text semantic conflict (Stect) was proposed since static analysis and the think of stain analysis.Stect started from the commonly used string comparison functions, combined with the characteristics of MIPS and ARM architectures, and extracted a set of paths with the same start and end nodes using function call relationships, control flow graphs, and branching selection dependent strings.If the strings in the successfully verified set of paths have semantic conflict, it means that there is a hard-coded backdoor in the router firmware.In order to evaluate the detection effect of Stect, 1 074 collected device images were tested and compared with other backdoor detection methods.Experimental results show that Stect has a better detection effect compared with existing backdoor detection methods including Costin and Stringer: 8 hard-coded backdoor images detected from image data set, and the recall rate reached 88.89%.…”
    Get full text
    Article
  10. 210
  11. 211

    Predicting the Activity Level of the Great Gerbil (Rhombomys opimus) via Machine Learning by Fan Jiang, Peng Peng, Zhenting Xu, Yu Xu, Ding Yang, Shouquan Chai, Shuai Yuan, Limin Hua, Dawei Wang, Xuanye Wen

    Published 2025-05-01
    “…Second, principal component analysis was used to reduce the dimensionality of the 92 sets of collected data to six principal components, thus eliminating the correlation between the indicators. …”
    Get full text
    Article
  12. 212

    APPLICATION OF IMPROVED GWO-SVM IN WIND TURBINE GEARBOX FAULT DIAGNOSIS by HU Xuan, LI Chun, YE KeHua, ZHANG WanFu

    Published 2021-01-01
    “…The improved gray wolf algorithm is used to optimize the support vector machine to diagnose the gearbox fault feature set after dimensionality reduction. …”
    Get full text
    Article
  13. 213
  14. 214
  15. 215

    Pengembangan Deep Learning untuk Sistem Deteksi Dini Komplikasi Kaki Diabetik Menggunakan Citra Termogram by Medycha Emhandyksa, Indah Soesanti, Rina Susilowati

    Published 2023-12-01
    “…In this study, four deep convolutional neural network models were designed with Occam's razor principle through hyperparameter settings on the algorithm structure aspect in the form of number of layers and optimization aspect in the form of optimizer type. …”
    Get full text
    Article
  16. 216
  17. 217

    Selection and validation of reference genes for quantitative real-time PCR in the green microalgae Tetraselmis chui. by Sonia Torres, Carmen Lama, Lalia Mantecón, Emmanouil Flemetakis, Carlos Infante

    Published 2021-01-01
    “…Samples from standard indoor cultures under highly controlled conditions (IND) were also collected to complement the other data. …”
    Get full text
    Article
  18. 218
  19. 219
  20. 220

    The relationship between immune cell infiltration and necroptosis gene expression in sepsis: an analysis using single-cell transcriptomic data by Shouyi Wang

    Published 2025-08-01
    “…Immune cell infiltration differences between sepsis (SE) and healthy control (HC) groups were quantified using the single-sample Gene Set Enrichment Analysis (ssGSEA) algorithm. …”
    Get full text
    Article