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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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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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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Ensemble learning for prediction of inorganic scale formation: A case study in Oman by Mohammed Talib Said Al Harrasi, Alireza Kazemi, Reza Yousefzadeh

“…Owing to the intricate nature of scale formation, developing a closed-form mathematical formulation for its prediction is difficult. Thereby, the ability of six machine learning algorithms and a Power Law Ensemble Model (PLEM) to predict inorganic scale formation in carbonate formations is examined in this study. …”
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Development and validation of a modified SOFA score for mortality prediction in candidemia patients by Xiaofei Liu, Ranran Ding, Guangming Yang, Yuling Qiao, Zhen Ma, Yaping Feng, Feng Qu, Qiang Meng

“…The mSOFA_3 model demonstrated superior predictive performance across multiple machine learning algorithms, with the logistic regression-based model achieving the highest AUC of 0.826 in the internal validation cohort and 0.813 in the test cohort. …”
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Apple Trajectory Prediction in Orchards: A YOLOv8-EK-IPF Approach by Jinxing Niu, Zhengyi Liu, Shuo Wang, Jiaxi Huang, Junlong Zhao

“…The algorithm first employs spatial partitioning according to the cyclical motion patterns of apples to constrain the prediction results. …”
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Solar Irradiance Prediction for Zaria Town Using Different Machine Learning Models by Ibrahim Abdulwahab, Sulaiman Haruna Sulaiman, Umar Musa, Ibrahim Abdullahi Shehu, Abdullahi Kakumi Musa, Ismaila Mahmud, Mohammed Musa, Abdullahi Abubakar, Abdulrahman Olaniyan

“… The research is set to predict solar irradiation using various machine learning algorithms. …”
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Prediction of Total Organic Carbon Content in Shale Based on PCA-PSO-XGBoost by Yingjie Meng, Chengwu Xu, Tingting Li, Tianyong Liu, Lu Tang, Jinyou Zhang

“…In this study, for the shale of the Qingshankou Formation of the Gulong Sag in the Songliao Basin, TOC content prediction models using various machine learning algorithms are established and compared based on measured data, principal component analysis, and the particle swarm optimization algorithm. …”
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Graph-based machine learning model for weight prediction in protein–protein networks by Hajer Akid, Kirsley Chennen, Gabriel Frey, Julie Thompson, Mounir Ben Ayed, Nicolas Lachiche

“…By leveraging data from the yeast Saccharomyces cerevisiae obtained from the STRING database, we introduce a new model that combines similarity-based algorithms and aggregated confidence score weights for accurate link prediction purposes. …”
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