Timeseries Fault Classification in Power Transmission Lines by Non-Intrusive Feature Extraction and Selection Using Supervised Machine Learning by Rab Nawaz, Hani A. Albalawi, Syed Basit Ali Bukhari, Khawaja Khalid Mehmood, Muhammad Sajid

“…Performing specific operations on data in sequence of steps provided flexibility and adaptability in processing the data, making it easy to train, evaluate, and validate the learning algorithms. …”
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Machine learning insights on activities of daily living disorders in Chinese older adults by Huanting Zhang, Wenhao Zhou, Jianan He, Xingyou Liu, Jie Shen

“…Nine machine learning algorithms, including neural networks and an ensemble model, were employed with a 2/3 training and 1/3 testing split. …”
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Predicting adolescent psychopathology from early life factors: A machine learning tutorial by Faizaan Siddique, Brian K. Lee

“…Conclusion: Our results suggest that inclusion of prenatal, family history, and sociodemographic factors in ML models can generate moderately accurate predictions of adolescent psychopathology. Issues associated with model overfitting, hyperparameter tuning, and system seed setting should be considered throughout model training, testing, and validation. …”
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Ethical and Legal Challenges of Implementing AI in Science and Math Education in Central Asia by Dilfuza M.Makhmudova, Xilola R. Sharipova, Nosirjon K. Hojiyev, Azamat E.Ergashev, Yulduzknon Kh.Satvaldieva, Khosiyat U.Mamatkulova, Egambergan M. Khudoynazarov

“…Additionally, 64% of respondents expressed serious concerns about student data privacy, while 71% supported the need for formal AI ethics training. Qualitative interviews (N = 18) uncovered recurring themes such as lack of legal frameworks, teacher autonomy dilemmas, and algorithmic bias in grading systems. …”
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Role of mass spectrometry-based serum proteomics signatures in predicting clinical outcomes and toxicity in patients with cancer treated with immunotherapy by Young Kwang Chae, Emma Yu, Na Hyun Kim, Min Jeong Kim, Leeseul Kim, Hyung-Gyo Cho, Yeonggyeong Park, Yoonhee Choi, Seung Pyo Daniel Hong

“…Using machine learning algorithms, serum proteomic tests were developed through training data sets from advanced non-small cell lung cancer (Host Immune Classifier, Primary Immune Response) and malignant melanoma patients (PerspectIV test). …”
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Study on a Traditional Chinese Medicine constitution recognition model using tongue image characteristics and deep learning: a prospective dual-center investigation by Yongyue Liu, Linmiao Fan, Mei Zhao, Dongshen Wei, Menglan Zhao, Yihang Dong, Xiaoqing Zhang

“…Eight machine learning algorithms were employed to construct and evaluate the efficacy of the models. …”
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Predicting Nottingham grade in breast cancer digital pathology using a foundation model by Jun Seo Kim, Jeong Hoon Lee, Yousung Yeon, Doyeon An, Seok Jun Kim, Myung-Giun Noh, Suehyun Lee

“…The predicted grades demonstrated statistically significant association with 5-year overall survival (p < 0.05). …”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

“…Abstract Cholesterol metabolism-related genes (CMRGs) have been associated with osteoarthritis (OA), but their specific regulatory mechanisms remain unclear. …”
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Costs of testing sick children in primary care with pulse oximetry: Evidence from four countries, both with and without electronic clinical decision support. by Susan Horton, Ulrich Adombi, Fenella Beynon, Mira Emmanuel-Fabula, Tara Herrick, Sandeep Kumar, Suzan Makawia, Mercy Mugo, Michael Onah, Michael Ruffo, Shally Awasthi, Maymouna Ba, Leah F Bohle, Silvia Cicconi, Hélène Langet, Papa Moctar Faye, Honorati Masanja, Andolo Miheso, Deusdedit Mjungu, James Machoki M'Imunya, Ousmane Ndiaye, Kovid Sharma, Valérie D'Acremont, Kaspar Wyss, TIMCI Collaborator Group

“…Adding clinical decision support algorithms (CDSA) can improve adherence to Integrated Management of Childhood Illness guidelines. …”
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AI Scribes in Health Care: Balancing Transformative Potential With Responsible Integration by Tiffany I Leung, Andrew J Coristine, Arriel Benis

“…Further, there are concerns about ethical and legal issues, algorithmic bias, the potential for long-term “cognitive debt” from overreliance on AI, and even the potential loss of physician autonomy. …”
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Machine learning based identification of suicidal ideation using non-suicidal predictors in a university mental health clinic by Muhammed Ballı, Asli Ercan Dogan, Sevin Hun Senol, Hale Yapici Eser

“…The final model achieved an AUC of 0.80 on the training data and 0.79 on external validation data. …”
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Optimising test intervals for individuals with type 2 diabetes: A machine learning approach. by Sasja Maria Pedersen, Nicolai Damslund, Trine Kjær, Kim Rose Olsen

“…<h4>Methods</h4>We classify HbA1c test intervals into four categories (3, 6, 9, and 12 months) using three classification algorithms: logistic regression, random forest, and extreme gradient boosting (XGBoost). …”
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Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning by Sizhang Wang, Xiaoyan Wang, Jing Xia, Qiang Mu

“…The average value of the area under the curve for the nomogram models was higher than 0.75 in both the training and testing sets. After that, survival analysis showed that higher expression of CCDC69, PPP1R16B, and IL21R were associated with overall survival of Her2-positive breast cancer patients. …”
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A machine learning model for early detection of sexually transmitted infections by Juma Shija, Judith Leo, Elizabeth Mkoba

“…The dataset was split into a 70%:15%:15% ratio for training, testing, and validation, respectively, and five machine learning algorithms were evaluated: AdaBoost, Support Vector Machine, Random Forest, Decision Tree, and Stochastic Gradient Descent. …”
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Identifying emphysema risk using brominated flame retardants exposure: a machine learning predictive model based on the SHAP methodology by Qihang Xie, Haoran Qu, Jianfeng Li, Rui Zeng, Wenhao Li, Rui Ouyang, Chengxiang Zhang, Siyu Xie, Siyu Xie, Ming Du

“…The participants were divided into a training set (70%) and a testing set (30%). Eight machine learning algorithms, including lightGBM, MLP, DT, KNN, RF, SVM, Enet, and XGBoost, were applied to build and evaluate the model. …”
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ATP6AP1 drives pyroptosis-mediated immune evasion in hepatocellular carcinoma: a machine learning-guided therapeutic target by Lei Tang, Xiyue Wang, Zhengzheng Xia, Jiayu Yan, Shanshan Lin

“…Results Through a rigorous multi-algorithm screening process, ATP6AP1 was found to be a highly reliable biomarker with an area under the curve (AUC) of 0.979. …”
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Innovative cone resistance and sleeve friction prediction from geophysics based on a coupled geo-statistical and machine learning process by A. Bolève, R. Eddies, M. Staring, Y. Benboudiaf, H. Pournaki, M. Nepveaux

“…A sensitivity study is also performed on input features used to train the ML algorithm to better define the optimal combination of input features for the prediction. …”
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A clinical benchmark of public self-supervised pathology foundation models by Gabriele Campanella, Shengjia Chen, Manbir Singh, Ruchika Verma, Silke Muehlstedt, Jennifer Zeng, Aryeh Stock, Matt Croken, Brandon Veremis, Abdulkadir Elmas, Ivan Shujski, Noora Neittaanmäki, Kuan-lin Huang, Ricky Kwan, Jane Houldsworth, Adam J. Schoenfeld, Chad Vanderbilt

“…With the increase in availability of public foundation models of different sizes, trained using different algorithms on different datasets, it becomes important to establish a benchmark to compare the performance of such models on a variety of clinically relevant tasks spanning multiple organs and diseases. …”
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Exploring the Current Applications and Future Implications of Artificial Intelligence in Public Health Dentistry: A Narrative Review by Tanushri Mahendra Dalvi, Rajeshri Kotwadekar, Shrivardhan R Kalghatgi, Priyanka Paul Madhu, Priyanka Methildeotare

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A machine-learning approach for predicting butyrate production by microbial consortia using metabolic network information by Claudia Silva-Andrade, Sergio Hernández, Pedro Saa, Ernesto Perez-Rueda, Daniel Garrido, Alberto J. Martin

“…Genome-scale network reconstructions enable the computation of metabolic interactions and specific associations within microbial consortia underpinning the production of different metabolites. …”
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