Predicting Quail Egg Quality Using Machine Learning Algorithms by BI Yildiz, K Eskioğlu, D Özdemir, M Akşit

“…Notably, Random Forest and Gradient Boosting algorithms achieved accuracies exceeding 97%, while predictions based only on external parameters exhibited lower accuracy but presented a promising starting point for non-invasive evaluations. …”
Get full text

Artificial intelligence-driven modeling of biodiesel production from fats, oils, and grease (FOG) with process optimization via particle swarm optimization by Badril Azhar, Muhammad Ikhsan Taipabu, Cries Avian, Karthickeyan Viswanathan, Wei Wu, Raymond Lau

“…To enhance process efficiency, adjustments to reaction temperature, time, and methanol-to-oil ratios are proposed, resulting in lower energy consumption and material costs. A ML model evaluation, using various algorithms, identify XGBoost, Extra Trees, Gradient Boosting, LGBM, and Random Forest demonstrate the best performer for predicting process parameters, achieving an R2 value of nearly to 1. …”
Get full text

Data-driven machine learning approaches for simultaneous prediction of peak particle velocity and frequency induced by rock blasting in mining by Yewuhalashet Fissha, Prashanth Ragam, Hajime Ikeda, N. Kushal Kumar, Tsuyoshi Adachi, P.S. Paul, Youhei Kawamura

“…Assessing the severity of blasting vibrations necessitates a thorough evaluation of Peak Particle Velocity (PPV) and frequency, which are essential parameters for measuring vibration velocity. …”
Get full text

Data-Driven Optimised XGBoost for Predicting the Performance of Axial Load Bearing Capacity of Fully Cementitious Grouted Rock Bolting Systems by Behshad Jodeiri Shokri, Ali Mirzaghorbanali, Kevin McDougall, Warna Karunasena, Hadi Nourizadeh, Shima Entezam, Shahab Hosseini, Naj Aziz

“…These models were subsequently evaluated for their ability to predict peak load values. …”
Get full text

Impacts of carbon nanotubes and nano-graphene oxide on fatigue cracking in HMA exposed to runoff with varying acidity by Javad Zarrinfam, Gholam Hossein Hamedi, Alireza Azarhoosh

“…Conversely, CNTs and NGO substantially reduced G*Sinδ while boosting fracture energy and toughness. In other words, these two nanomaterials reduced the fatigue parameter of the bitumen under dry, acidic, and alkaline moisture conditions, demonstrating their outstanding performance in enhancing the fatigue resistance of the modified bitumen against intermediate temperature cracking under various moisture exposures. …”
Get full text

An effectiveness of machine learning models for estimate the financial cost of assistive services to disability care in the Kingdom of Saudi Arabia by Obaid Algahtani, Mohammed M. A. Almazah, Farouq Alshormani

“…The performance validation of the EMLM-EFCASDC method portrayed the least RMSLE value of 0.1154 on existing approaches in terms of diverse evaluation measures.…”
Get full text

An Optimal Control Strategy for DC Bus Voltage Regulation in Photovoltaic System with Battery Energy Storage by Muhamad Zalani Daud, Azah Mohamed, M. A. Hannan

“…For the grid side VSC (G-VSC), two control methods, namely, the voltage-mode and current-mode controls, are applied. For control parameter optimization, the simplex optimization technique is applied for the G-VSC voltage- and current-mode controls, including the BES DC/DC buck-boost converter controllers. …”
Get full text

Prognostic Value of Radiological and Laboratory Biomarkers for Assessing Risk of Adverse Outcome in Patients with COVID-19 by А. D. Strutynskaya, M. А. Karnaushkina, L. I. Dvoretskiy, I. Е. Tyurin

“…The study included 162 patients with COVID-19 stratified according to the presence or absence of deterioration during hospitalization. We evaluated chest computed tomography (CT) data, assessed empirically and using a semi-quantitative scale, blood cell counts and parameters of biochemical blood test. …”
Get full text

Solid Oxide Fuel Cell Voltage Prediction by a Data-Driven Approach by Hristo Ivanov Beloev, Stanislav Radikovich Saitov, Antonina Andreevna Filimonova, Natalia Dmitrievna Chichirova, Egor Sergeevich Mayorov, Oleg Evgenievich Babikov, Iliya Krastev Iliev

“…This study examines three ML models: artificial neural network (ANN), random forest (RF), and extreme gradient boosting (XGB). The training dataset consisted of experimental results from SOFC laboratory experiments, comprising 32,843 records with 47 control parameters. …”
Get full text

RSTHFS: A Rough Set Theory-Based Hybrid Feature Selection Method for Phishing Website Classification by Jahanggir Hossain Setu, Nabarun Halder, Ashraful Islam, M. Ashraful Amin

“…Performance was further assessed using three advanced classifiers: Light Gradient-Boosting Machine (LightGBM), Random Forest (RF), and Categorical Boosting (CatBoost), with CatBoost emerging as the most efficient, achieving the highest accuracy. …”
Get full text

Critical Factors Governing the Frictional Coefficient in Mg Alloys—Learn From Machine Learning by Negar Bagherieh, Moslem Noori, Dongyang Li, Meisam Nouri

“…After preprocessing, the data is partitioned into train and test datasets where the train dataset is used for model training and hyperparameter tuning, K‐fold cross‐validation, and the test dataset is used for evaluating the best trained models. The results indicate that light gradient boosting (LGBM) accurately predicts COF of magnesium alloys using the processing procedure, heat treatment, alloy composition, and tribology variables with an R‐squared value of 0.89. …”
Get full text

Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
Get full text

Enhancing classification of active and non-active lesions in multiple sclerosis: machine learning models and feature selection techniques by Atefeh Rostami, Mostafa Robatjazi, Amir Dareyni, Ali Ramezan Ghorbani, Omid Ganji, Mahdiye Siyami, Amir Reza Raoofi

“…Models’ performances in test data set were evaluated by metric parameters of accuracy, precision, sensitivity, specificity, AUC, and F1 score. …”
Get full text

CO2 adsorption on NaOH and acid modified montmorillonite: Response surface methodology and machine learning modeling by Pardis Mehrmohammadi, Amir Ahmadvand, Ahad Ghaemi

“…Several ML models were evaluated, including Decision Tree Regressor, Random Forest Regressor, AdaBoost Regressor, Gradient Boosting Regressor, Extreme Gradient Boosting Regressor, Support Vector Machine, Multilayer Perceptron (MLP), Radial Basis Function, and Kernel Ridge Regressor. …”
Get full text

Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care by Lin Wang, Shao-Bin Duan, Ping Yan, Xiao-Qin Luo, Ning-Ya Zhang

“…Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. …”
Get full text

Contribution to the research of Anthropometric measurements and comparison of body proportions in the student population in Bangladesh by Md Eanamul Haque Nizam, Emeritus Darko Ujevic, Ayub Nabi Khan

“…An improved regression model utilizing gradient boosting was employed to predict key anthropometric measurements. …”
Get full text

Research on Support Vector Regression Short-Time Traffic Flow Prediction Model for Secondary Roads Based on Associated Road Analysis by Ganglong Duan, Yutong Du, Yanying Shang, Hongquan Xue, Ruochen Zhang

“…Simulation and Evaluation: Third, we conduct simulation experiments using real traffic data from Xi’an city. …”
Get full text

Predicting the impact of climate change on the re-emergence of malaria cases in China using LSTMSeq2Seq deep learning model: a modelling and prediction analysis study by Eric Kamana, Jijun Zhao, Di Bai

“…We trained and tested the extreme gradient boosting (XGBoost), gated recurrent unit, LSTM, LSTMSeq2Seq models using monthly malaria cases and corresponding meteorological data in 31 provinces of China. …”
Get full text

Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…The model dynamically allocates weights to top-performing algorithms based on their 10-fold cross-validated RMSE, ensuring robust and adaptive prediction performance.Five models—Linear Regression, Ridge Regression, Decision Tree, Random Forest, and Gradient Boosting—were evaluated over ten folds. Based on their average RMSE values, the top three models (Gradient Boosting: 8.25, Ridge Regression: 8.30, Linear Regression: 8.31) were selected. …”
Get full text

Machine learning models for performance estimation of solar still in a humid sub-tropical region by Farooque Azam, Naiem Akhtar, Shahid Husain

“…Five machine learning models, namely, k-nearest neighbors, support vector machines, random forest, multilayer perceptron, and extreme gradient boosting, were employed to predict hourly yield. Their performance was evaluated using statistical indicators such as mean bias error, mean absolute percentage error, correlation coefficient, t-statistics, and maximum absolute relative error. …”
Get full text