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

“…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. …”
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Discovery, Biological Evaluation and Binding Mode Investigation of Novel Butyrylcholinesterase Inhibitors Through Hybrid Virtual Screening by Lizi Li, Puchen Zhao, Can Yang, Qin Yin, Na Wang, Yan Liu, Yanfang Li

“…This study employed a quantitative structure–activity relationship (QSAR) model based on ECFP4 molecular fingerprints with several machine learning algorithms (XGBoost, RF, SVM, KNN), among which the XGBoost model showed the best performance (AUC = 0.9740). …”
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Predictive Models for Educational Purposes: A Systematic Review by Ahlam Almalawi, Ben Soh, Alice Li, Halima Samra

“…The findings show that ML algorithms consistently outperform traditional models due to their capacity to handle large, non-linear datasets and continuously enhance predictive accuracy as new patterns emerge. …”
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Development and Internal Validation of a Machine Learning-Based Colorectal Cancer Risk Prediction Model by Deborah Jael Herrera, Daiane Maria Seibert, Karen Feyen, Marlon van Loo, Guido Van Hal, Wessel van de Veerdonk

“…<b>Methods:</b> We analyzed data from 154,887 adults, aged 55–74 years, who participated in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. A risk prediction model was built using the Light Gradient Boosting Machine (LightGBM) algorithm. …”
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A neural network model enables worm tracking in challenging conditions and increases signal-to-noise ratio in phenotypic screens. by Weheliye H Weheliye, Javier Rodriguez, Luigi Feriani, Avelino Javer, Virginie Uhlmann, André E X Brown

“…Model-based tracking and deep learning approaches have addressed these issues to an extent, but there is still significant room for improvement in tracking crawling worms. …”
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Predictive model for determining the indications for automated 3D ultrasound for screening patients at low risk of developing breast tumors by A. E. Garanina, A. V. Kholin

“…To develop indications for 3D ultrasound based on predictive screening models for patients with a low risk of developing breast tumors based on the identification of the most significant risk factors.Patients and methods. …”
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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

“…Using 10 classical machine learning algorithms, we developed screening models. Evaluation metrics determined the best model. …”
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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

“…Abstract Traditional virtual screening methods need to explore expanse and vast chemical spaces and need to be based on existing chemical libraries. …”
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Sensitive Multispectral Variable Screening Method and Yield Prediction Models for Sugarcane Based on Gray Relational Analysis and Correlation Analysis by Shimin Zhang, Huojuan Qin, Xiuhua Li, Muqing Zhang, Wei Yao, Xuegang Lyu, Hongtao Jiang

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Defects Detection in Screen-Printed Circuits Based on an Enhanced YOLOv8n Algorithm by Xinyu Zhang, Jia Wang, Dan Jiang, Yang Li, Xuewei Wang, Han Zhang

“…To address these challenges, a self-made SPC defect data set and an enhanced CAAB-YOLOv8n detection algorithm were developed. …”
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Semiparametric Transformation Models with a Change Point for Interval-Censored Failure Time Data by Junyao Ren, Shishun Zhao, Dianliang Deng, Tianshu You, Hui Huang

“…Model parameters are estimated via the EM algorithm, with the change point identified through a profile likelihood approach using grid search. …”
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Cell‐free epigenomes enhanced fragmentomics‐based model for early detection of lung cancer by Yadong Wang, Qiang Guo, Zhicheng Huang, Liyang Song, Fei Zhao, Tiantian Gu, Zhe Feng, Haibo Wang, Bowen Li, Daoyun Wang, Bin Zhou, Chao Guo, Yuan Xu, Yang Song, Zhibo Zheng, Zhongxing Bing, Haochen Li, Xiaoqing Yu, Ka Luk Fung, Heqing Xu, Jianhong Shi, Meng Chen, Shuai Hong, Haoxuan Jin, Shiyuan Tong, Sibo Zhu, Chen Zhu, Jinlei Song, Jing Liu, Shanqing Li, Hefei Li, Xueguang Sun, Naixin Liang

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Research on formant estimation algorithm for high order optimal LPC root value screening by Hua LONG, Shumeng SU

“…In terms of the robustness of the algorithm and the overall performance comparison of different methods,the proposed algorithm can extract the formant robustly from order 9 to 22, and the model algorithm shows the optimal performance when the formant is extracted from order 18. …”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

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LumaCam: a novel class of position-sensitive event mode particle detectors using scintillator screens by Alexander Wolfertz, Alex Gustschin, Michael Schulz, Alexander M. Long, Anton Khaplanov, Tsviki Y. Hirsh, Andrei Nomerotski, Manuel Morgano, Anton Tremsin, Giacomo Mauri, G. Jeff Sykora, Adrian Losko

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A cost-utility analysis of newborn screening for spinal muscular atrophy in Canada by Alex Pace, Weston Roda, Corrina Poon, Hugh J. McMillan, Maryam Oskoui, Alex MacKenzie, Pranesh Chakraborty, Jeff Round

“…Methods A decision analytic model was developed, which combined a decision tree for the screening algorithm and a Markov model for long-term health outcomes. …”
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A recurrent neural network and parallel hidden Markov model algorithm to segment and detect heart murmurs in phonocardiograms. by Andrew McDonald, Mark J F Gales, Anurag Agarwal

“…These properties make the algorithm a promising tool for screening of abnormal heart murmurs.…”
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Bone scintigraphy based on deep learning model and modified growth optimizer by Omnia Magdy, Mohamed Abd Elaziz, Abdelghani Dahou, Ahmed A. Ewees, Ahmed Elgarayhi, Mohammed Sallah

“…The results and statistical analysis revealed that the proposed GOAOA algorithm as an FS technique outperforms the other FS algorithms employed in this study.…”
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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

“…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. …”
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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

“…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. …”
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