Forecasting water quality indices using generalized ridge model, regularized weighted kernel ridge model, and optimized multivariate variational mode decomposition by Marjan Kordani, Mohsen Bagheritabar, Iman Ahmadianfar, Arvin Samadi-Koucheksaraee

“…Abstract Permeability index (PI) and magnesium absorption ratio (MAR) are both primary irrigation water quality indicators (IWQI) used to evaluate the efficacy of agricultural water supplies. …”
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A quantitative LC-MS/MS method for residues analysis of 62 plant growth regulators and the investigation of their effects on the quality of traditional Chinese medicinal: Codonopsi... by Hongxu Zhou, Hongxu Zhou, Yue Ren, Yangli Wang, Jing Su, Xiangmin Zhou, Siyu Huang, Rui Yan, Jun Zeng, Min Chen, En Zhang, Xiaohu Chen

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Application of Extra-Trees Regression and Tree-Structured Parzen Estimators Optimization Algorithm to Predict Blast-Induced Mean Fragmentation Size in Open-Pit Mines by Madalitso Mame, Shuai Huang, Chuanqi Li, Jian Zhou

“…Moreover, the block size (XB, m) and modulus of elasticity (E, GPa) parameters are identified as the most influential parameters for predicting the MFS distribution of rock. …”
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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…Using a dataset collected from the literature, detailed analyses were conducted using Ridge Regression (RR), Artificial Neural Network (ANN), Random Forest (RF), and Extreme Gradient Boosting (XGB). Model performances were evaluated using metrics including Root Mean Square Errors (RMSE), Mean Absolute Error (MAE), R-squared (R2), Normalized Mean Bias Error (NMBE), and Mean Absolute Percentage Error (MAPE). …”
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A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients... by Syed Waseem Abbas Sherazi, Jang-Whan Bae, Jong Yun Lee

“…We generated each ML-based model with the best hyper-parameters, evaluated by 5-fold stratified cross-validation, and then verified by test dataset. …”
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Optimizing Land Use Classification Using Google Earth Engine: A Comparative Analysis of Machine Learning Algorithms by M. Sultan, N. Saleous, S. Issa, B. Dahy, M. Sami

“…Various parameters influence algorithm performance. Algorithm performance is evaluated based on overall accuracy and kappa coefficient metrics along with user and producer accuracy. …”
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Estimating Subsurface Thermohaline Structure in the Tropical Western Pacific Using DO-ResNet Model by Xianmei Zhou, Shanliang Zhu, Wentao Jia, Hengkai Yao

“…In the model experiment, Argo data were used to train and validate the model, and the root mean square error (RMSE), normalized root mean square error (NRMSE), and coefficient of determination (R²) were employed to evaluate the model’s performance. The results showed that the sea surface parameters selected in this study have a positive effect on the estimation process, and the average RMSE and R² values for estimating ST (SS) by the proposed model are 0.34 °C (0.05 psu) and 0.91 (0.95), respectively. …”
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Objective Detection of Newborn Infant Acute Procedural Pain Using EEG and Machine Learning Algorithms by Jean‐Michel Roué, Amir Avnit, Behnood Gholami, Wassim M. Haddad, Kanwaljeet J. S. Anand

“…An optimal model, having the highest F‐1 score, was obtained and evaluated on the independent testing dataset. A gradient boosting model with 12 features showed optimal performance, with 90% area under the receiver operating characteristic curve suggesting high specificity (0.90) and precision (0.90). …”
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Fault Diagnosis in Power Generators: A Comparative Analysis of Machine Learning Models by Quetzalli Amaya-Sanchez, Marco Julio del Moral Argumedo, Alberto Alfonso Aguilar-Lasserre, Oscar Alfonso Reyes Martinez, Gustavo Arroyo-Figueroa

“…ML models were evaluated with class imbalance and multi-classification metrics, a correspondence analysis, and model performance by class (fault type). …”
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Machine learning-based detection of medical service anomalies: Kazakhstan’s health insurance data by Maksut Kulzhanov, Alexander Wagner, Abylkair Skakov, Iliyas Mukhamejan, Saya Zhorabek, Ainur B. Qumar

“…An automated AI system was developed and tested using nine ML models, including XGBoost, Random Forest, Decision Tree, Gradient Boosting, etc. The dataset comprised 329,584 real records, including demographic and socio-economic parameters. …”
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AI driven prediction of early age compressive strength in ultra high performance fiber reinforced concrete by Mohamed Abdellatief, Wafa Hamla, Hassan Hamouda

“…These models include support vector regression (SVR), random forest (RF), artificial neural network (ANN), gradient boosting (GB), and Gaussian Process Regression (GPR). …”
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Random Reflectance: A New Hyperspectral Data Preprocessing Method for Improving the Accuracy of Machine Learning Algorithms by Pavel A. Dmitriev, Anastasiya A. Dmitrieva, Boris L. Kozlovsky

“…Furthermore, the efficacy of this method will be evaluated through its application in deep machine learning algorithms.…”
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Assessment of road-cut slope stability using empirical, numerical, and machine learning methodologies by Virat Singh Chauhan, Md. Rehan Sadique, Mohd. Masroor Alam, Mohd. Ahmadullah Farooqi

“…The objective is to evaluate the effectiveness of each approach and to assess the potential of advanced machine learning models as viable validation tools for conventional slope stability assessment outcomes. …”
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Ecological Suitability Assessment Methods of Waste Pile-Up along Railway Routes Based on Machine Learning Algorithms by Cuicui Ji, Zaoyang Huang, Xiangjun Pei, Bin Sun, Lichuan Chen, Dan Liang, Yanfei Kang

“…We tested 3 machine learning methods—random forest (RF), deep neural network (DNN), and extreme gradient boosting (XGBoost)—using 7 key indicators as input parameters. …”
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Enhancing concrete strength for sustainability using a machine learning approach to improve mechanical performance by Amir Khan, Aneel Manan, Muhammad Umar, Mudassir Mehmood, Kennedy C. Onyelowe, Krishna Prakash Arunachalam

“…Three ML models Extreme Gradient Boosting (XGBoost), Decision Tree, and K-Nearest Neighbors (KNN) were developed and evaluated using metrics such as R2, RMSE, MAE, and MAPE. …”
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Comparing the Performance of Automatic Milking Systems through Dynamic Testing Also Helps to Identify Potential Risk Factors for Mastitis by Stefano Milanesi, Dario Donina, Viviana Chierici Guido, Francesca Zaghen, Valerio M. Sora, Alfonso Zecconi

“…Automatic milking systems (AMSs) are revolutionizing the dairy industry by boosting herd efficiency, primarily through an increased milk yield per cow and reduced labor costs. …”
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A meta-analysis on plant growth and heavy metals uptake with the application of 2,4-epibrassinolide in contaminated soils by Kuiju Niu, Hong Xiao, Yong Wang, Ting Cui, Chunxu Zhao

“…EBR application enhanced photosynthesis and the mitigation of oxidative damage by significantly boosting antioxidant enzyme activity, non-enzymatic antioxidants, and metabolites. …”
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Objective assessment of gait and posture symptoms in Parkinson’s disease using wearable sensors and machine learning by Lingyan Ma, Lingyan Ma, Shinuan Lin, Shinuan Lin, Jianing Jin, Jianing Jin, Zhan Wang, Zhan Wang, Xuemei Wang, Xuemei Wang, Zhonglue Chen, Zhonglue Chen, Yun Ling, Yun Ling, Fei Zhang, Fei Zhang, Kang Ren, Kang Ren, Tao Feng, Tao Feng, Tao Feng

“…This study aims to predict the severity of gait and posture symptoms using data collected from wearable sensors during a single laboratory-based walking assessment, providing an objective, efficient, and automated evaluation approach.MethodsSensor-based gait parameters were collected from 225 PD participants (mean age 63.15 ± 10.46 years) through a standardized walking assessment. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…A dataset comprising 598 points was compiled, considering cement, glass powder, aggregates, water, superplasticizer, and curing days as key input parameters. Three standalone ML models—K-Nearest Neighbors (KNN), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained, with RF achieving R² = 0.963 and XGB achieving R² = 0.946 on the test set. …”
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Fertility-Enhancing Potential of P. amygdalas and J. regia Oil Mixture in Wistar Rats: Male/Female Infertility Models Assessment by Sadia Suri Kashif, Sadaf Naeem, Saira Saeed Khan, Shaheen Perveen, Nausheen Alam, Saba Zubair, Javeria Ameer

“…The present study was intended to evaluate the fertility-boosting effect of a mixture constituting P. amygdalas and J. regia oil on male/female infertility models and in two successive generations of rats; F0 (parents) and F1 (offspring). …”
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