Modeling the Impact of Hydrogen Embrittlement on the Fracture Toughness of Low-Carbon Steel Using a Machine Learning Approach by Michael Gyaabeng, Ramadan Ahmed, Nayem Ahmed, Catalin Teodoriu, Deepak Devegowda

“…The selected models were further evaluated for their predictive accuracy and reliability, and the best model was used to perform parametric studies to investigate the impact of relevant parameters on FT. …”
Get full text

Random Forest-Based Prediction of the Optimal Solid Ink Density in Offset Lithography by Laihu Peng, Hao Fan, Yubao Qi, Jianqiang Li

“…The experimental data show that the relevant evaluation metrics MAE, RMSE, MSE, and R² of the model are within the reliable range. …”
Get full text

Machine and deep learning models for predicting high pressure density of heterocyclic thiophenic compounds based on critical properties by Amir Hossein Sheikhshoaei, Ali Khoshsima

“…The critical properties including critical temperature (Tc), critical pressure (Pc), critical volume (Vc), and acentric factor (ω), together with boiling point (Tb), and molecular weight (Mw) were used as input parameters. Models employed include Decision Tree (DT), Adaptive Boosting Decision Tree (AdaBoost-DT), Light Gradient Boosting Machine (LightGBM), Gradient Boosting (GBoost), TabNet, and Deep Neural Network (DNN). …”
Get full text

Dynamic flood risk prediction in Houston: a multi-model machine learning approach by Shuchi Mishra, Aproorv Bajpai, Agradeep Mohanta, Biplab Banerjee, Shrishti Rajput, Sudipta Kundu

“…In assessing flood susceptibility in Houston, key geographical parameters such as drainage density, slope, distance from rivers and roads, LULC, and rainfall data were analyzed using machine learning models, including Decision Trees, Random Forest, Gradient Boosting, SVM, and ANN. …”
Get full text

Improving the quality of payment fraud detection by using a combined approach of transaction analysis by Світлана Гавриленко, Олексій Абдуллін

“…The feature importance within each subclass is evaluated by the gradient boosting algorithm. The results of the experiment showed a different influence of features on subclass belonging, which allows for a more detailed analysis of the data to identify hidden structures in the data. …”
Get full text

Algorithmic Classification of Psychiatric Disorder–Related Spontaneous Communication Using Large Language Model Embeddings: Algorithm Development and Validation by Ryan Allen Shewcraft, John Schwarz, Mariann Micsinai Balan

“…Performance was evaluated using metrics such as precision, recall, F1- ResultsThe 10-fold cross-validated Extreme Gradient Boosting classifier achieved a support-weighted average precision, recall, F1 ConclusionsThis study introduces an innovative use of LLMs in psychiatry, showcasing their potential to objectively examine language use for distinguishing between different psychiatric disorders. …”
Get full text

Machine learning model for random forest acute oral toxicity prediction by A.M. Elsayad, M.M. Zeghid, K.A. Elsayad, A.N. Khan, ِA.K.M. Baareh, A. Sadiq, S.A. Mukhtar, H.F. Ali, S. Abd El-kader

“…A diverse set of two-dimensional molecular descriptors generated via rational discovery kit were used as input features, and model preprocessing involved normalization, validation, and feature selection. Hyper-parameter tuning was conducted using Bayesian optimization and cross-validation, while the performance of random forests was evaluated in comparison to gradient boosting, extreme gradient boosting, artificial neural networks, and the generalized linear model.FINDINGS: The random forests models, particularly those utilizing under sampling and cost-sensitive learning, demonstrated superior performance, achieving sensitivity of 0.81, Specificity of 0.85, accuracy of 0.85, and an area under the receiver operating characteristic curve of 0.89 on an independent test set. …”
Get full text

Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models by Hongxia Hao, Jiemin Chen, Yifei Yan, Yifei Yan, Qi Zhang, Qi Zhang, Zhilu Zhou, Wentao Xia

“…Various ophthalmic parameters were input into the above algorithms for model training, and the performance of the algorithms was analyzed from the difference between the prediction value and the ground truth. …”
Get full text

Advanced hybrid machine learning models with explainable AI for predicting residual friction angle in clay soils by Mawuko Luke Yaw Ankah, Shalom Adjei-Yeboah, Yao Yevenyo Ziggah, Edmund Nana Asare

“…This study explores three advanced hybrid machine learning models: Gradient Boosting Neural Network (GrowNet), Reinforcement Learning Gradient Boosting Machine (RL-GBM), and a Stacking Ensemble to predict the residual friction angle of clay soils, addressing a critical gap in current predictive methodologies. …”
Get full text

Collecting Performance Prediction for the Rubber Collector in Horizontal Wellbore Based on AutoML by Shaohua Li, Yang Li, Longlin Chen, Xianbin Wang, Weihang Kong

“…The experimental findings indicate that, during parameter optimization for rubber collectors with varying eccentricities, priority should be given to the aforementioned factors. …”
Get full text

Machine learning prediction of anxiety symptoms in social anxiety disorder: utilizing multimodal data from virtual reality sessions by Jin-Hyun Park, Yu-Bin Shin, Dooyoung Jung, Ji-Won Hur, Seung Pil Pack, Heon-Jeong Lee, Hwamin Lee, Chul-Hyun Cho, Chul-Hyun Cho

“…The best parameters were explored through grid search or random search, and the models were validated using stratified cross-validation and leave-one-out cross-validation.ResultsThe CatBoost, using multimodal features, exhibited high performance, particularly for the Social Phobia Scale with an area under the receiver operating characteristics curve (AUROC) of 0.852. …”
Get full text

Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…Five different machine learning regression models, namely linear regression (LR), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were employed and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) along with R2 for predicting the daily ground-level PM10 concentration using AOD, land cover data, and meteorological parameters. …”
Get full text

Sustainable energy: Advancing wind power forecasting with grey wolf optimization and GRU models by Zainab Al-Ibraheemi, Samaher Al-Janabi

“…By employing GWO, essential features were identified by grouping the dataset into intervals and analyzing their frequencies. Performance evaluation was conducted using various compression measures, including Rate DC-Miss, Rate DC-MEF, and Rate DC-BDG, compared with other models such as extreme gradient boosting, space-time graph neural networks, and deep learning models. …”
Get full text

Data-Driven Approach for Intelligent Classification of Tunnel Surrounding Rock Using Integrated Fractal and Machine Learning Methods by Junjie Ma, Tianbin Li, Roohollah Shirani Faradonbeh, Mostafa Sharifzadeh, Jianfeng Wang, Yuyang Huang, Chunchi Ma, Feng Peng, Hang Zhang

“…This study compiled a database containing 246 railway and highway tunnel cases based on these parameters. Then, four SRC models were constructed, integrating Bayesian optimization (BO) with support vector machine (SVM), random forest (RF), adaptive boosting (AdaBoost), and gradient boosting decision tree (GBDT) algorithms. …”
Get full text

QSAR Models for Predicting the Antioxidant Potential of Chemical Substances by Sofia Ghironi, Edoardo Luca Viganò, Gianluca Selvestrel, Emilio Benfenati

“…The Extra Trees model outperformed the other models in both internal and external validations, achieving the highest R² of 0.77 and the lowest RMSE on the test set. Gradient Boosting and eXtreme Gradient Boosting also achieved promising results with R² values of 0.76 and 0.75, respectively. …”
Get full text

AI-Based Prediction of Warpage in Organic Substrates by Jingyi Zhao, Meiying Su, Rui Ma

“…Utilizing this dataset, the network architectures and hyperparameters of Multi-Layer Perceptron (MLP), Extreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM) algorithms were optimized, and their performance was evaluated in terms of loss convergence, learning rate adaptability, training efficiency, and robustness. …”
Get full text

Advanced generalized machine learning models for predicting hydrogen–brine interfacial tension in underground hydrogen storage systems by Ahmed Farid Ibrahim

“…Several ML models, including Random Forests (RF), Gradient Boosting Regressor (GBR), Extreme Gradient Boosting Regressor (XGBoost), Artificial Neural Networks (ANN), Decision Trees (DT), and Linear Regression (LR), were trained and evaluated. …”
Get full text

Deep learning models to predict CO2 solubility in imidazolium-based ionic liquids by Amir Hossein Sheikhshoaei, Ali Sanati, Ali Khoshsima

“…The models evaluated include Bayesian Neural Networks (BNN), Deep Neural Networks (DNN), Gradient Boosting Neural Networks (GrowNet), Tabular Neural Networks (TabNet), Random Forest (RF), and Support Vector Regression (SVR). …”
Get full text

Integration of multi agent reinforcement learning with golden jackal optimization for predicting average localization error in wireless sensor networks by K. Lakshmi Prabha, Hanan Abdullah Mengash, Hamed Alqahtani, Randa Allafi

“…Varying network densities and the interdependence of parameters such as anchor ratio, transmission range, and node density increase the Average Localization Error (ALE) in WSNs. …”
Get full text

Solar Irradiance Prediction Method for PV Power Supply System of Mobile Sprinkler Machine Using WOA-XGBoost Model by Dan Li, Jiwei Qu, Delan Zhu, Zheyu Qin

“…The prediction accuracy and stability of the proposed method are then evaluated for different input parameters through training and testing. …”
Get full text