Estimation of common breaks in linear panel data models via screening and ranking algorithm by Fuxiao Li, Yanting Xiao, Zhanshou Chen

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Screening of glioma susceptibility SNPs and construction of risk models based on machine learning algorithms by Mingjun Hu, Jie Hao, Jie Wei

“…This study aimed to develop a predictive model for glioma risk by these screened key SNPs in the Chinese Han population. …”
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Development of a cataract screening model using an open dataset and deep machine learning algorithms by S.N. Sakhnov, K.D. Axenov, L.E. Axenova, V.V. Vronskaya, A.O. Martsinkevich, V.V. Myasnikova

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Comparison of Machine Learning Algorithms to Predict Down Syndrome During the Screening of the First Trimester of Pregnancy by Eduardo Alonso, Andoni Beristain, Jorge Burgos, Ibai Gurrutxaga

“…Various machine learning models, including statistical, linear, and ensemble models, were trained using a pseudo-anonymized dataset of 90,532 screening patients. …”
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Experimental investigations and field applications of a tension estimation method for two linked suspenders using only local vibration measurements by Wen-Hwa Wu, Chien-Chou Chen, Meng-Xuan Wu, Gwolong Lai

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MODEL OF ORGANIZATION OF KIDNEY CANCER EARLY DIAGNOSIS by V. G. Moreva, G. N. Alekseeva, P. F. Kiku, L. I. Gurina, K. M. Sabirova, V. N. Rasskazova

“…The model included a population questionnaire to identify risk factors and algorithm of patient routing («roadmap») with suspected kidney cancer for in-depth examination and treatment. …”
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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

“…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.…”
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Refining the Feasibility of Machine‐Learning‐Based Diagnostic Model Utilizing Gut Microbiota Analysis for Colorectal Cancer Screening by Shintaro Okumura, Yusuke Konishi, Taku Kitano, Tomonori Matsumoto, Kazutaka Obama, Satoshi Nagayama, Eiji Hara

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Establishment and assessment of an early screening model for cervical cancer based on single-cell Raman spectroscopy combined with machine learning algorithms by MA Dongmei, ZHAO Wenjie, LIU Shihai, XU Haicang, CAI Duo, JI Yuetong, XU Jian, GUO Cancan, MA Bo, PAN Huazheng

“…Objective To establish an early screening model for cervical cancer based on single-cell Raman spectroscopy (SCRS) combined with machine learning algorithms, and to assess the performance of the model. …”
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APPLICATION OF THE ENTROPY-PROBABILISTIC MODEL OF THE X-RAY TELEVISION INTROSCOPE OPERATOR IN THE ALGORITHM OF THE COMPUTER SIMULATOR DURING TRAINING OF SECURITY SCREENING POINT EMPLOYEES by A. K. Volkov, V. V. Iudaev, A. V. Dormidontov

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Research and application of optimization of physical education training model based on multi-objective differential evolutionary algorithm by Man Wu

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The utility of an artificial intelligence model based on decision tree and evolution algorithm to evaluate steatotic liver disease in a primary care setting by A.C. Goulart, A.P. Alencar, G. Tunes, L.L.T. Bianchi, M.H. Miname, C.M. Padilha, J.M.S. Pescuma, A.L.C.C. Rodrigues, B.B. Henares, M.S. de Almeida, T.A.O. Machado, D.H. Syllos, Y.P. Wang, M. Rienzo

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Multi-Objective Parameter Optimization of Rotary Screen Coating Process for Structural Plates in Spacecraft by Yanhui Guo, Yanpeng Chen, Peibo Li, Xinfu Chi, Yize Sun

Subjects: “…multi-objective optimization algorithm…”
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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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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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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. A CAD module was integrated into the backbone network to improve the model’s ability to detect bar-shaped features. …”
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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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Rapid diagnostic algorithms as a screening tool for tuberculosis: an assessor blinded cross-sectional study. by Franz Ratzinger, Harald Bruckschwaiger, Martin Wischenbart, Bernhard Parschalk, Delmiro Fernandez-Reyes, Heimo Lagler, Alexandra Indra, Wolfgang Graninger, Stefan Winkler, Sanjeev Krishna, Michael Ramharter

“…A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms.…”
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A novel machine-learning algorithm to screen for trisomy 21 in first-trimester singleton pregnancies by James Osborne, Chris Cockcroft, Carolyn Williams

“…This study investigates the use of machine-learning algorithms in the prediction of T21 in first-trimester singleton pregnancies and compares their performance to existing screening models.Methods A total of 86,354 anonymised, first trimester, singleton pregnancy screening cases, including 211 with T21, were used to train and test machine-learning models using adaptive boosting technology. …”
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Precision Medicine in Lung Cancer Screening: A Paradigm Shift in Early Detection—Precision Screening for Lung Cancer by Hsin-Hung Chen, Yun-Ju Wu, Fu-Zong Wu

“…Emerging tools, such as risk prediction models, radiomics, artificial intelligence (AI), and liquid biopsies, enhance the accuracy of screening, allowing for the identification of high-risk individuals who may not meet conventional criteria. …”
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