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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Single-index logistic model for high-dimensional group testing data by Changfu Yang, Wenxin Zhou, Wenjun Xiong, Junjian Zhang, Juan Ding

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Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening by Zhuo Zhao, Xiaoxu Liu, Yonghui Guan, Chunfang Li, Zheng Wang

“…A data preprocessing algorithm was used to screen relevant cfRNA indicators for PE. …”
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A Rapid Intelligent Screening of a Three-Band Index for Estimating Soil Copper Content by Shiyao Liu, Shichao Cui, Rengui Wang, Minming Han, Jingtao Kou

“…This strategy drastically reduces the time spent screening and is proven to have similar model accuracy, as compared to the individual group lifting method. …”
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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

“…Publication frequency trend has accelerated in recent years, with more than half of these studies published after 2021. …”
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ALGEBRAIC MODELS OF STRIP LINES IN A MULTILAYER DIELECTRIC MEDIUM by A. N. Kovalenko, A. N. Zhukov

“…The use of the Chebyshev basis and the improvement of the series convergence made it possible to develop an effective algorithm for calculating the basic electrodynamic parameters of the strip lines - the propagation constants and the wave impedances of the natural waves. …”
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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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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

“…Here we train a version of the DeepTangle algorithm developed for swimming worms using a combination of data derived from Tierpsy tracker and hand-annotated data for more difficult cases. …”
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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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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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Validation of three models (Tolcher, Levine, and Burke) for predicting term cesarean section in Chinese population by Fangcan Sun, Minhong Shen, Bing Han, Youguo Chen, Fangfang Wu

“…A predicted probability for CS was calculated for women in the dataset by the algorithm of each model. The performance of the model was evaluated for discrimination. …”
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Credit risk identification of high-risk online lending enterprises based on neural network model by Mao-guang WANG, Zi-jun ZHU

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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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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

“…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. …”
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Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective... by Seok Jun Park, Seungwon Yang, Suhyun Lee, Sung Hwan Joo, Taemin Park, Dong Hyun Kim, Hyeonji Kim, Soyun Park, Jung-Tae Kim, Won Gun Kwack, Sung Wook Kang, Yun-Kyoung Song, Jae Myung Cha, Sang Youl Rhee, Eun Kyoung Chung

“…Our model might enhance early diagnostic screening of irHAEs induced by PD-1/PD-L1 inhibitors, contributing to minimizing the risk of severe irHAEs and improving the effectiveness of cancer immunotherapy.…”
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Toward Next-Generation Biologically Plausible Single Neuron Modeling: An Evolutionary Dendritic Neuron Model by Chongyuan Wang, Huiyi Liu

“…The Dendritic Neuron Model (DNM) offers a more realistic alternative by simulating nonlinear and compartmentalized processing within dendritic branches, enabling efficient and transparent learning. …”
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