A scalable framework for soil property mapping tested across a highly diverse tropical data-scarce regionZENODO by Rodrigo de Q. Miranda, Rodolfo L.B. Nóbrega, Anne Verhoef, Estevão L.R. da Silva, Jadson F. da Silva, José C. de Araújo Filho, Magna S.B. de Moura, Alexandre H.C. Barros, Alzira G.S.S. Souza, Wanhong Yang, Hui Shao, Raghavan Srinivasan, Feras Ziadat, Suzana M.G.L. Montenegro, Maria do S.B. Araújo, Josiclêda D. Galvíncio

“…We developed a machine learning framework that integrates the Soil-Landscape Estimation and Evaluation Program (SLEEP) with gradient boosting to predict soil properties at regional scales and multiple depths. …”
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Huff-n-puff technology and parameter optimization of large displacement water injection in tight oil reservoir by ZHANG Yi, NING Chongru, CHEN Yazhou, JI Yulong, ZHAO Liyang, WANG Aifang, HUANG Jingjing, YU Kaiyi

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Forestry climate adaptation with HarvesterSeasons service—a gradient boosting model to forecast soil water index SWI from a comprehensive set of predictors in Destination Earth by Mikko Strahlendorff, Anni Kröger, Golda Prakasam, Miriam Kosmale, Mikko Moisander, Heikki Ovaskainen, Asko Poikela

“…The Copernicus Global Land Monitoring Service’s Soil Water Index (SWI) satellite-based observations from 2015 to 2023 at 10,000 locations in Europe were used as the predictand (target parameter) to train an artificial intelligence (AI) model to predict soil wetness with XGBoost (eXtreme Gradient Boosting) and LightGBM (Light Gradient Boosting Machine) implementations of gradient boosting algorithms. …”
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Use machine learning to predict treatment outcome of early childhood caries by Yafei Wu, Maoni Jia, Ya Fang, Duangporn Duangthip, Chun Hung Chu, Sherry Shiqian Gao

“…Model performance was evaluated using discrimination and calibration metrics including accuracy, recall, precision, F1 score, area under the receiver operating characteristic curve (AUROC) and Brier score. …”
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Optimized intelligent learning for groundwater quality prediction in diverse aquifers of arid and semi-arid regions of India by Imran Khan, Sarwar Nizam, Apoorva Bamal, Abdul Majed Sajib, Mir Talas Mahammad Diganta, Mohd Azfar Shaida, S.M. Ashekuzzaman, Stephen Nash, Agnieszka I. Olbert, Md Galal Uddin

“…This study employs the eXtreme Gradient Boosting (XGB) algorithm, demonstrating strong predictive capabilities within the RMS-WQI model across diverse aquifers of Rajasthan. …”
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OPTICALS: A Novel Framework for Optimizing Predictive Trading Indicators in Cryptocurrency Using Advanced Learning Simulations by Hasib Shamshad, Fasee Ullah, Syed Adeel Ali Shah, Muhammad Faheem, Beena Shamshad

“…Models underwent rigorous evaluation, including multiple simulations and hyperparameter tuning. …”
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Research and application of VoLTE video call quality based on machine learning by Qizhu ZHONG

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Group-Specific SVM With Bilevel Programming Methods for Parameter Optimization and Explainable AI in Urban Quality of Life Prediction by Gonzalo Rios-Vasquez, Hanns De La Fuente-Mella, Jose Ceroni-Diaz

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Estimation of Genetic Parameters for Body Weight and Its Stability in Huaxi Cows from Xinjiang Region by Ye Feng, Wenjuan Zhao, Xubin Lu, Xue Gao, Qian Zhang, Bin Zhang, Bao Wang, Fagang Zhong, Mengli Han, Zhi Chen

“…In this study, we analyzed data from 2992 cows to comprehensively evaluate the adult weight (WEI), a key growth and body-size indicator, in West China cattle, aiming to estimate the related phenotypic and genetic parameters. …”
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Machine learning-based model for acute asthma exacerbation detection using routine blood parameters by Youpeng Chen, Junquan Sun, Yabang Chen, Enzhong Li, Jiancai Lu, Huanhua Tang, Yifei Xie, Jiana Zhang, Lesi Peng, Haojie Wu, Zhangkai J. Cheng, Baoqing Sun

“…This study aimed to develop and validate a diagnostic model for AAE using routine blood parameters through machine learning techniques. Methods: We developed a machine learning-based diagnostic model using routine blood test parameters. …”
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Exploring the Impact of Magnetic Water on the Physiological and Functional Parameters of Maize as a Vital Industrial Crop by Hamid Bakhtiari, Mohsen Okhovat

“…Magnetized water as an environmentally friendly method boosts crop yield and quality. In this study, we investigate the effects of magnetized water treatment on vegetation growth responses and physiological parameters and vegetation growth responses of maize plants and physiological parameters. …”
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Prediction of Metabolic Parameters of Diabetic Patients Depending on Body Weight Variation Using Machine Learning Techniques by Oana Vîrgolici, Daniela Lixandru, Andrada Mihai, Diana Simona Ștefan, Cristian Guja, Horia Vîrgolici, Bogdana Virgolici

“…Several machine learning models, namely linear regression, polynomial regression, Gradient Boosting, and Extreme Gradient Boosting, were employed to predict changes in medical parameters as a function of body weight variation. …”
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A comparative analysis of emotion recognition from EEG signals using temporal features and hyperparameter-tuned machine learning techniques by Rabita Hasan, Sheikh Md. Rabiul Islam

“…To categorize emotional states, we used four classifiers: K-Nearest Neighbors (KNN), Support Vector Machine (SVM), XGBoost and Gradient Boosting. Differential entropy and Higuchi's fractal dimension are two important time-domain parameters that we extracted after applying a segmentation technique to capture the temporal interdependence of EEG data. …”
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Machine learning-based prediction of physical parameters in heterogeneous carbonate reservoirs using well log data by Fuyong Wang, Xianmu Hou

“…Six machine learning algorithms are utilized: support vector machine (SVM), backpropagation (BP) neural network, gaussian process regression (GPR), extreme gradient boosting (XGBoost), K-nearest neighbor (KNN), and random forest (RF). …”
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Using machine learning models based on cardiac magnetic resonance parameters to predict the prognostic in children with myocarditis by Dongliang Hu, Manman Cui, Xueke Zhang, Yuanyuan Wu, Yan Liu, Duchang Zhai, Wanliang Guo, Shenghong Ju, Guohua Fan, Wu Cai

“…Four models were established, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost) model. The performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC). …”
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Improving Diagnostic Performance for Head and Neck Tumors with Simple Diffusion Kurtosis Imaging and Machine Learning Bi-Parameter Analysis by Suzuka Yoshida, Masahiro Kuroda, Yoshihide Nakamura, Yuka Fukumura, Yuki Nakamitsu, Wlla E. Al-Hammad, Kazuhiro Kuroda, Yudai Shimizu, Yoshinori Tanabe, Masataka Oita, Irfan Sugianto, Majd Barham, Nouha Tekiki, Nurul N. Kamaruddin, Miki Hisatomi, Yoshinobu Yanagi, Junichi Asaumi

“…Five ML algorithms were used to create models for the prediction of tumor status (benign or malignant), which were evaluated through ROC analysis. <b>Results:</b> Bi-parameter analysis with gradient boosting achieved the best diagnostic performance, with an AUC of 0.81. …”
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Enhancing solar power efficiency with hybrid GEP ANFIS MPPT under dynamic weather conditions by Mutiu Shola Bakare, Abubakar Abdulkarim, Aliyu Nuhu Shuaibu, Mundu Mustafa Muhamad

“…The study introduces a novel approach, combining ANFIS with Gene Expression Programming (GEP), aimed at optimizing the reference maximum power output using solar irradiance and temperature as input parameters. The integration was tested on a boost converter via Matlab/Simulink simulations, which reveals the GEP-ANFIS double diode model’s exceptional 99.84% efficiency under high solar irradiation. …”
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Exploring the Potential Imaging Biomarkers for Parkinson’s Disease Using Machine Learning Approach by Illia Mushta, Sulev Koks, Anton Popov, Oleksandr Lysenko

“…Using 13 DATSCAN-derived parameters and patient handedness from 1309 individuals in the Parkinson’s Progression Markers Initiative (PPMI) database, we trained an AdaBoost classifier, achieving an accuracy of 98.88% and an area under the receiver operating characteristic (ROC) curve of 99.81%. …”
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Adaptive Neuro-Fuzzy Inference System-Genetic Algorithm approach for global maximum power point tracking in PV systems under different shading conditions by Nivine Guler, Zied Ben Hazem, Ali Gunes, Firas Saidi

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Interpretable noninvasive diagnosis of tuberculous pleural effusion using LGBM and SHAP: development and clinical application of a machine learning model by Bihua Yao, Xingyu Yu, Liannv Qiu, Er-min Gu, Siyu Mao, Lei Jiang, Jijun Tong, Jianguo Wu

“…The model was built upon 18 routine laboratory parameters, including pleural fluid and serum biomarkers, with multiple machine learning (ML) algorithms evaluated. …”
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