Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning by Fei Li, Haolan Song, Yifan Wang

“…Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. …”
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Evaluation of liver fibrosis in patients with metabolic dysfunction-associated steatotic liver disease using ultrasound controlled attenuation parameter combined with clinical feat... by LIU Chunyu, TANG Jingkuan, ZHAO Wei

“…Features were selected using the Boruta algorithm, and a predictive model combining CAP and clinical features was constructed. …”
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On Fuzzy Soft Expert Sets by Hilal Donmez, Serdar Enginoglu

“…Furthermore the results of this algorithm and Maji et al's algorithm without reduction are equivalent. …”
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Clinical performance validation and four diagnostic strategy assessments of high-sensitivity troponin I assays by Junyi Wu, Yaotong Hua, Yilin Ge, Ke Chen, Siyu Chen, Jiashu Yang, Hui Yuan

“…Both the hs-cTnI 0/1 h Algorithm and hs-cTnI 0/2 h Algorithm showed high diagnostic performance, with the 0/2 h algorithm providing the best overall. 89.0% accuracy, 93.3% sensitivity, and 73.68% F1-score. …”
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Flexural strengthening of corroded steel beams with CFRP by using the end anchorage: Experimental, numerical, and machine learning methods by Amin Shabani Ammari, Younes Nouri, Habib Ghasemi Jouneghani, Seyed Amin Hosseini, Arash Rayegani, Mehrdad Ebrahimi, Pooria Heydari

“…The ML prediction showed that the CatBoost algorithm had the highest accuracy, with an R2 score of 0.954. …”
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Time-delayed vibration control and parameter optimization of semi-active vehicle suspension designed for random road excitation by Yong Guo, Yong-Jing Li, Yan-Bo Wei

“…Under the influence of road excitations, the acquisition of optimal time-delayed feedback control parameters is deemed crucial for the improvement of the vibration reduction effect of semi-active vehicle suspension systems. …”
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Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer by Peng Zhang, Meizhong Qin, Fen Li, Kunpeng Hu, He Huang, Cuicui Li

“…Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value. …”
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Improving fluoroprobe sensor performance through machine learning by D. Lafer, A. Sukenik, T. Zohary, O. Tal

“…Phytoplankton species biomass data were collected concurrently using standard inverted microscope counts. The SVR algorithm outperformed the other algorithms in predicting phytoplankton abundance and composition in Lake Kinneret, based on the FP measurements. …”
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MolNexTR: a generalized deep learning model for molecular image recognition by Yufan Chen, Ching Ting Leung, Yong Huang, Jianwei Sun, Hao Chen, Hanyu Gao

“…In addition, it employs a series of novel augmentation algorithms to significantly enhance the robustness and performance of the model.…”
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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays by Sina Hasibi Taheri, Javad Mohammadpour, Ali Lalbakhsh, Slawomir Koziel, Stanislaw Szczepanski

“…Decision Tree-based models are chosen to provide accurate prediction while minimizing the dataset preparation time. …”
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Artificial intelligence for children with attention deficit/hyperactivity disorder: a scoping review by Bo Sun, Bo Sun, Fei Cai, Huiman Huang, Bo Li, Bing Wei

“…Artificial intelligence provides advanced models and algorithms for better diagnosis, prediction and classification of attention deficit/hyperactivity disorder. …”
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Personalizing the Marketing with Artificial Intelligence (AI) by Rozhko Viktor I., Pletnova Yelyzaveta S.

“…Another major challenge is the technical limitations of AI. While AI algorithms are capable of analyzing vast amounts of data, they cannot always properly adapt to a changing market environment or predict consumer behavior in non-standard situations. …”
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Use of four next-generation sequencing platforms to determine HIV-1 coreceptor tropism. by John Archer, Jan Weber, Kenneth Henry, Dane Winner, Richard Gibson, Lawrence Lee, Ellen Paxinos, Eric J Arts, David L Robertson, Larry Mimms, Miguel E Quiñones-Mateu

“…In this study, HIV-1 co-receptor usage was predicted for twelve patients scheduled to start a maraviroc-based antiretroviral regimen. …”
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Research on the Evaluation of the Node Cities of China Railway Express Based on Machine Learning by Chenglin Ma, Mengwei Zhou, Wenchao Kang, Haolong Wang, Jiajia Feng

“…The Random Forest model outperformed comparative algorithms with 99.5% prediction accuracy (8.33% higher than conventional classification models), particularly in handling multi-dimensional interactions between urban development factors. …”
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Machine learning in stream and river water temperature modeling: a review and metrics for evaluation by C. R. Corona, T. S. Hogue, T. S. Hogue

“…The aim of this work is threefold: first, to provide a concise review of the use of ML algorithms in SWT modeling and prediction; second, to review ML performance evaluation metrics as they pertain to SWT modeling and prediction to find the commonly used metrics and suggest guidelines for easier comparison of ML performance across SWT studies; and, third, to examine how ML use in SWT modeling has enhanced our understanding of spatial and temporal patterns of SWT and examine where progress is still needed.…”
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Machine Learning (ML) Based Repair-Count and Periodic Maintenance Policy for Multipurpose CNC Machinery by Alifin Fakhri Ikhwanul, Winarno, Fasa Nadia, Darajatun Rizki Achmad, Kusnadi, Safariyani Eva

“…Several censored data were utilized to predict failure times using machine learning algorithms, such as process temperature, air temperature, rotational speed, and torque. …”
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Clinical Phenotype Identification and Validation of Patients with Sepsis in the Intensive Care Unit by GONG Chao, YU Na, CHEN Haoran

“…Then, supervised machine learning algorithms (lightweight gradient boosting machine) were used for the prediction of the patient's phenotypes, and were further combined with SHAP (Shapely Additive eXplanations) for the identification of important features. …”
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Machine learning for clustering and classification of early knee osteoarthritis using single-leg standing kinematics by Ui-Jae Hwang, Kyu Sung Chung, Sung-Min Ha

“…This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict EOA. (1) To identify distinct groups based on SLS kinematic patterns using unsupervised learning algorithms, (2) to develop supervised learning models to predict EOA status, and (3) to identify the most influential kinematic variables associated with EOA. …”
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Synthetic Data Generation for AI-Informed End-of-Line Testing for Lithium-Ion Battery Production by Tessa Krause, Daniel Nusko, Johannes Rittmann, Luciana Pitta Bauermann, Moritz Kroll, Carlo Holly

“…To make these predictions, we implement artificial intelligence algorithms to extract information from the measurements. …”
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Reducing False Alarm in Target Sidelobes Using Time-Varying Trajectories in Range-Doppler Diagram With Conditional 3D-DCNN by Jiyeon Choi, Daegun Oh, Youngwook Kim

“…The proposed method was validated using frequency-modulated continuous wave radar to detect low-RCS targets and demonstrated a reduction in FAs in clutter environments. Verification with 200 simulation datasets showed a 99.8 % reduction in FAs excluding the sidelobe region along the range axis compared to CFAR and a 95.22 % reduction compared to 3D-DCNN, while all FAs in the Doppler sidelobe region were eliminated (100 % reduction). …”
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