Criteria for predicting the initiation of rolling contact fatigue damage in the railway wheels and rails by V. I. Sakalo, A. V. Sakalo

“…The methods of modeling allow predict the possibility of initiation of the fatigue cracks during operation with the sufficient accuracy in the short time. …”
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Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction by Kuo-Yang Huang, Ying-Lin Hsu, Che-Liang Chung, Huang-Chi Chen, Ming-Hwarng Horng, Ching-Hsiung Lin, Ching-Sen Liu, Jia-Lang Xu

“…This study presents an edge computing-based framework that incorporates machine learning algorithms to predict ventilator extubation success using real-time data collected directly from ventilators. …”
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Optimising Insider Threat Prediction: Exploring BiLSTM Networks and Sequential Features by Phavithra Manoharan, Wei Hong, Jiao Yin, Hua Wang, Yanchun Zhang, Wenjie Ye

“…Moreover, we explore the performance of different predictive lengths on the ground truth of the day and different embedded lengths for the sequential features. …”
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An interpretable stacking ensemble model for high-entropy alloy mechanical property prediction by Songpeng Zhao, Zeyuan Li, Changshuai Yin, Zhaofu Zhang, Teng Long, Jingjing Yang, Ruyue Cao, Yuzheng Guo

“…However, accurately predicting their mechanical behavior remains challenging because of the vast compositional design space and complex multi-element interactions. …”
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Driver Takeover Performance Prediction Based on LSTM-BiLSTM-ATTENTION Model by Lijie Chen, Daofei Li, Tao Wang, Jun Chen, Quan Yuan

“…In this regard, this study proposes a hybrid LSTM-BiLSTM-ATTENTION algorithm for driver takeover performance prediction. …”
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Traffic accident severity prediction based on an enhanced MSCPO-XGBoost hybrid model by Fei Chen, Xiang Qun Liu, Jian Jun Yang, Xu Kang Liu, Jing Hui Ma, Jia Chen, Hua Yu Xiao

“…This study proposes a novel severity prediction framework based on a Modified Stochastic Crested Porcupine Optimizer (MSCPO) combined with the XGBoost algorithm. …”
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Hourly and Day Ahead Power Prediction of Building Integrated Semitransparent Photovoltaic System by S. Kaliappan, R. Saravanakumar, Alagar Karthick, P. Marish Kumar, V. Venkatesh, V. Mohanavel, S. Rajkumar

“…The building integrated semitransparent photovoltaic (BISTPV) system is an emerging technology which replaces the conventional building material envelopes and roof. The performance prediction of the BISTPV system places a vital role in the reduction of the energy consumption in the building. …”
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Machine learning–based feature prediction of convergence zones in ocean front environments by Weishuai Xu, Lei Zhang, Hua Wang

“…This study aimed to address this gap by developing a high-resolution ocean front-based model for convergence zone prediction. Out of 24 machine learning algorithms tested through K-fold cross-validation, the multilayer perceptron–random forest hybrid demonstrated the highest accuracy, showing its superiority in predicting the convergence zone within a complex ocean front environment. …”
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Using Electrocardiogram Signal Features and Heart Rate Variability to Predict Epileptic Attacks by Ying Jiang, Yuan Feng, Danni Lu, Lin Yang, Qun Zhang, Haiyan Yang, Ning Li

“…From a practical point of view, due to the ease of obtaining the heart rate variability signal, the proposed algorithm is more promising than the algorithms that use brain signal processing to predict epilepsy.…”
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Predicting clozapine-induced adverse drug reaction biomarkers using machine learning by John-Paul Cooper, Pierre Chue, Arno G. Siraki, Lusine Tonoyan

“…We addressed the class imbalance (337 agranulocytosis-positive cases vs. 9058 agranulocytosis-negative cases) through systematically evaluating resampling techniques and selecting appropriate performance metrics for rare event prediction. Five ML algorithms were evaluated on a hold-out test set. …”
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Machine learning approach to predict prognosis and immunotherapy responses in colorectal cancer patients by Zhen Liu, Dou Yu, Pengyan Xia, Shuo Wang

“…Furthermore, this IRRS model outperformed the Tumor Immune Dysfunction and Exclusion (TIDE) tool in predicting immunotherapy response. Therefore, by integrating patient clinical and transcriptomic data and applying machine learning algorithms, we developed a predictive model with enhanced accuracy and clinical utility for risk stratification and immunotherapy response prediction in CRC patients.…”
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Optimizing Energy Forecasting Using ANN and RF Models for HVAC and Heating Predictions by Khaled M. Salem, Javier M. Rey-Hernández, A. O. Elgharib, Francisco J. Rey-Martínez

“…Our approach systematically evaluates and compares the predictive performance of Artificial Neural Networks (ANNs) and Random Forests (RFs) for energy demand forecasting, leveraging each algorithm’s unique characteristics to assess their suitability for this application. …”
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Harvester Maintenance Prediction Tool: Machine Learning Model Based on Mechanical Features by Rodrigo Oliveira Almeida, Richardson Barbosa Gomes da Silva, Danilo Simões

“…Along with the data from the experimental research, we will make available the complete file containing the predictive model, as well as the software, both developed in the Python language.…”
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HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors by Viet-Khoa Tran-Nguyen, Ulrick Fineddie Randriharimanamizara, Olivier Taboureau

“…Multiple structure-based artificial intelligence (AI) binary classifiers for predicting hERG inhibitors were developed, employing, as descriptors, protein–ligand extended connectivity (PLEC) fingerprints fed into random forest, extreme gradient boosting, and deep neural network (DNN) algorithms. …”
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Study on the temperature prediction model of residual coal in goaf based on ACO-KELM by ZHAI Xiaowei, WANG Chen, HAO Le, LI Xintian, HOU Qinyuan, MA Teng

“…ACO was employed to optimize the regularization coefficients and kernel parameters in the KELM model, thereby obtaining the best-performing hyperparameter combination and generating the optimal KELM model. Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. …”
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Integrating spatiotemperporal features into fault prediction using a multi-dimensional method by Chun-Yi Lin, Yu-Chuan Tseng, Wu-Sung Yao

“…This study proposes a method to validate multidimensional fault prediction models. It integrates vibration and current data, analyzes spatiotemporal characteristics, and uses support vector machines and random forest algorithms to analyze fault characteristics. …”
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Prediction of the Ultimate Impact Response of Concrete Strengthened with Polyurethane Grout as the Repair Material by Sadi I. Haruna, Yasser E. Ibrahim, Sani I. Abba

“…The findings highlight the potential of LSTM models for the accurate and reliable prediction of the ultimate strength of composite U-shaped specimens.…”
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Water quality prediction using LSTM with combined normalizer for efficient water management by N. Mahesh, J. Jagan Babu, K. Nithya, S.A. Arunmozhi

“…In recent research, deep learning algorithms have been extensively used for water quality prediction due to their robust ability to map highly nonlinear connections while maintaining acceptable computational efficiency. …”
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Predicting the Acceptance of Informal Learning Technologies: A Case of the TikTok Application by Ahmed Al-Azawei, Ali Alowayr

“…Moreover, previous literature focused on the use of structural equation modeling (SEM) to predict technology acceptance, whereas the application of data mining algorithms is rare in this direction of research. …”
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Comprehensive Machine Learning Model for Cervical Cancer Prediction and Risk Factor Identification by null Mahendra, Mila Desi Anasanti

“…Our findings showed that selecting fewer features, such as half or even a quarter of the variables, still yielded strong results, emphasizing the importance of careful feature selection in cervical cancer prediction. The RF algorithm achieved the highest accuracy, with 99% using the full feature set and 98% with a reduced set of five features. …”
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