Search alternatives:
mode » made (Expand Search)
model » madel (Expand Search)
Showing 61 - 80 results of 1,414 for search '(((((mode OR model) OR model) OR (model OR model)) OR model) OR more) screening algorithm', query time: 0.28s Refine Results
  1. 61

    Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

    Published 2025-02-01
    “…Using 10 classical machine learning algorithms, we developed screening models. Evaluation metrics determined the best model. …”
    Get full text
    Article
  2. 62

    Screening of multi deep learning-based de novo molecular generation models and their application for specific target molecular generation by Yishu Wang, Mengyao Guo, Xiaomin Chen, Dongmei Ai

    Published 2025-02-01
    “…Abstract Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. …”
    Get full text
    Article
  3. 63
  4. 64

    Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

    Published 2024-10-01
    “…However, with the increase in fracturing, drilling, and sand-washing operations, the erosion of coiled tubing walls caused by solid particles has become one of the main failure modes. To accurately predict the erosion rate of coiled tubing, this study studied the influence law of erosion rate through experiments, screened the main influencing factors of erosion rate by grey relational analysis (GRA), and established a back-propagation neural network (BPNN) model optimized by the sparrow search algorithm (SSA) to predict the erosion rate. …”
    Get full text
    Article
  5. 65

    Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

    Published 2024-12-01
    “…Multiple candidate predictors were screened out by using the importance scores. Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
    Get full text
    Article
  6. 66

    Lightweight defect detection algorithm of tunnel lining based on knowledge distillation by Anfu Zhu, Jiaxiao Xie, Bin Wang, Heng Guo, Zilong Guo, Jie Wang, Lei Xu, SiXin Zhu, Zhanping Yang

    Published 2024-11-01
    “…Aiming at the problems of complex detection model, poor real-time performance and low accuracy of the current tunnel lining defect detection methods, the study proposes a lightweight defect detection algorithm of tunnel lining based on knowledge distillation. …”
    Get full text
    Article
  7. 67

    Efficient Design Optimization Assisted by Sequential Surrogate Models by Emiliano Iuliano

    Published 2019-01-01
    “…The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill criteria. …”
    Get full text
    Article
  8. 68

    QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

    Published 2025-05-01
    “…Different machine learning algorithms were applied to build regression models, and the goodness-of-fit of each model was assessed using the statistical parameters of R squared (R<sup>2</sup>), the Root-Mean-Squared Error, and the Mean Absolute Error. …”
    Get full text
    Article
  9. 69

    Rapid screening of fumonisins in maize using near-infrared spectroscopy (NIRS) and machine learning algorithms by Bruna Carbas, Pedro Sampaio, Sílvia Cruz Barros, Andreia Freitas, Ana Sanches Silva, Carla Brites

    Published 2025-04-01
    “…Similarly, ANN models showed good predictive performance, particularly for FB1 + FB2, with R = 0.99, and the root means square error (RMSE) of 131 μg/kg for calibration; and R = 0.95, RMSE = 656 μg/kg for validation.These findings underscore the efficacy of NIR spectroscopy as a rapid, non-destructive tool for fumonisin screening in maize, with chemometric algorithms enhancing model accuracy, offering a valuable method for ensuring food safety.…”
    Get full text
    Article
  10. 70
  11. 71

    Machine Learning Model for Early Detection of COVID-19 by Heart Rhythm Abnormalities by M. S. Mezhov, V. O. Kozitsin, Iu. D. Katser

    Published 2023-07-01
    “…The work aims at creating a mathematical model based on machine learning algorithms to automate the process of detecting covid abnormalities in the heart rhythm. …”
    Get full text
    Article
  12. 72

    Development of machine learning models to predict the risk of fungal infection following flexible ureteroscopy lithotripsy by Haofang Zhang, Changbao Xu, Chenge Hu, Yunlai Xue, Daoke Yao, Yifan Hu, Ankang Wu, Miao Dai, Hang Ye

    Published 2025-04-01
    “…Our study aimed to construct a machine learning algorithm predictive model to predict the risk of fungal infection following F-URL. …”
    Get full text
    Article
  13. 73
  14. 74

    DKK3 and SERPINB5 as novel serum biomarkers for gastric cancer: facilitating the development of risk prediction models for gastric cancer by Yan-Yu Liu, Yan-Yu Liu, Yan-Fang Fu, Yan-Fang Fu, Wan-Yu Yang, Wan-Yu Yang, Zheng Li, Zheng Li, Qian Lu, Qian Lu, Xin Su, Xin Su, Jin Shi, Si-Qi Wu, Di Liang, Yu-Tong He, Yu-Tong He

    Published 2025-03-01
    “…The existing gastric cancer (GC) risk prediction models based on biomarkers are limited. This study aims to identify new promising biomarkers for GC to develop a risk prediction model for effective assessment, screening, and early diagnosis. …”
    Get full text
    Article
  15. 75

    Construction of risk prediction model of sentinel lymph node metastasis in breast cancer patients based on machine learning algorithm by Qianmei Yang, Cuifang Liu, Yongyue Wang, Guifang Dong, Jinghuan Sun

    Published 2025-05-01
    “…Subsequently, five ML algorithms, namely LOGIT, LASSO, XGBOOST, RANDOM FOREST model and GBM model were employed to train and develop an ML model. …”
    Get full text
    Article
  16. 76

    Design of low-carbon planning model for vehicle path based on adaptive multi-strategy ant colony optimization algorithm by Qi Guo, Rui Li, Changjiang Zheng, Gwanggil Jeon

    Published 2025-01-01
    “…At the same time, the global search capability of the model is augmented via an ant colony optimization algorithm to ascertain the final optimized path. …”
    Get full text
    Article
  17. 77

    Development and validation of a biomarker-based prediction model for metastasis in patients with colorectal cancer: Application of machine learning algorithms by Erfan Ayubi, Sajjad Farashi, Leili Tapak, Saeid Afshar

    Published 2025-01-01
    “…Subsequently, the prediction model was developed and internally validated using five machine learning (ML) algorithms including lasso and elastic-net regularized generalized linear model (glmnet), k-nearest neighbors (kNN), support vector machine (SVM) with Radial Basis Function Kernel, random forest (RF), and eXtreme Gradient Boosting (XGBoost). …”
    Get full text
    Article
  18. 78

    Does advancement in marker-less pose-estimation mean more quality research? A systematic review by Shivam Bhola, Shivam Bhola, Hyun-Bin Kim, Hyeon Su Kim, BonSang Gu, Jun-Il Yoo, Jun-Il Yoo

    Published 2025-08-01
    “…Publication frequency trend has accelerated in recent years, with more than half of these studies published after 2021. …”
    Get full text
    Article
  19. 79

    RETRACTED ARTICLE: Screening and identification of susceptibility genes for cervical cancer via bioinformatics analysis and the construction of an mitophagy-related genes diagnosti... by Zhang Zhang, Fangfang Chen, Xiaoxiao Deng

    Published 2024-09-01
    “…Abstract Purpose This study aims to utilize bioinformatics methods to systematically screen and identify susceptibility genes for cervical cancer, as well as to construct and validate an mitophagy-related genes (MRGs) diagnostic model. …”
    Get full text
    Article
  20. 80

    Medical laboratory data-based models: opportunities, obstacles, and solutions by Jiaojiao Meng, Moxin Wu, Fangmin Shi, Ying Xie, Hui Wang, You Guo

    Published 2025-07-01
    “…Abstract Medical Laboratory Data (MLD) models, which combine artificial intelligence with big medical data, have great potential in disease screening, diagnosis, personalized medicine, and health management. …”
    Get full text
    Article