Machine learning-based state of charge estimation: A comparison between CatBoost model and C-BLSTM-AE model by Abderrahim Zilali, Mehdi Adda, Khaled Ziane, Maxime Berger

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Disaggregating IMERG satellite precipitation over Czech Republic: an innovative approach using hybrid Extreme Gradient Boosting based on Fuzzy Spatial-Temporal Multivariate Cluster... by Ujjwal Singh, Sadaf Nasreen, Gaurav Tripathi, Pragya Mehrishi, Rajani Kumar Pradhan, Poppová Bestakova, Vivek Vikram Singh, K C Gouda, Laxmi Kant Sharma, Kiran Jalem, Petr Maca, Rama Rao Nidamanuri, Akhilesh Singh Raghubanshi, Yannis Markonis, Rakovec Oldřich, Martin Hanel

“…This study presents a robust non-parametric framework for disaggregating coarse-resolution satellite precipitation data to finer scales, using a hybrid model that integrates Extreme Gradient Boosting (XGBoost) with multivariate spatio-temporal fuzzy clustering. …”
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Water quality index modelling and its application on artificial intelligence (AI) in conjunction with machine learning (ML) methodologies for mapping surface water potential zones... by Abhijeet Das

“…Again, in this appraisal, we utilized three models- Cat Boost (Cat B), AdaBoost (AB), and Gradient Boosting (GB)- to estimate the specified river catchment’s suitability for surface water irrigation. …”
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Artificial Intelligence-Based Models for Estimating and Extrapolating Soiling Effects on Photovoltaic Systems in Spain by Carlos Sánchez-García, Jesús Polo, Joaquín Alonso-Montesinos

“…In this context, four machine learning models were developed using meteorological and air quality data from the Solar Energy Research Center (CIESOL). A Gradient-Boosting model (LightGBM) and a neural network achieved RMSE values of 0.68% and 0.88% of soiling loss, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> values of 0.86 and 0.76 between measured and estimated values, respectively, on their test sets. …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Regression and classification models were developed using gradient boosting, random forest, and CatBoost algorithms. …”
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Development of a machine learning model for predicting renal damage in children with closed spinal dysraphism by Yu He, Wan-liang Guo, Ming-chang Zhang

“…We developed four machine learning models (logistic regression, support vector machine, decision tree, and extreme gradient boosting [XGBoost]), and compared their predictive performances. …”
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Early Breast Cancer Prediction Using Thermal Images and Hybrid Feature Extraction-Based System by Doaa Youssef, Hanan Atef, Shaimaa Gamal, Jala El-Azab, Tawfik Ismail

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Machine learning models to predict onset of dementia: A label learning approach by Vijay S. Nori, Christopher A. Hane, William H. Crown, Rhoda Au, William J. Burke, Darshak M. Sanghavi, Paul Bleicher

“…Training cohorts were matched on age, gender, index year, and utilization, and fit with a gradient boosting machine, lightGBM. Results Incident 2‐year model quality on a held‐out test set had a sensitivity of 47% and area‐under‐the‐curve of 87%. …”
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A machine learning tool for identifying metastatic colorectal cancer in primary care by Eliya Abedi, Marcela Ewing, Elinor Nemlander, Jan Hasselström, Annika Sjövall, Axel C. Carlsson, Andreas Rosenblad

“…As new treatments become available for metastatic CRC (MCRC), it is important to accurately identify these patients.Aim To develop a predictive model for identifying MCRC in primary health care patients using diagnostic data analysed with machine learning.Design and setting A case-control study utilising data on primary health care visits for 146 patients >18 years old diagnosed with MCRC in the Västra Götaland Region, Sweden during 2011, and 577 sex-, age, and primary health care centre-matched controls.Method Stochastic gradient boosting was used to construct a model for predicting the presence of MCRC based on diagnostic codes from primary health care consultations during the year before index (diagnosis) date and number of consultations. …”
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Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators by Osama Elsherbiny, Salah El-Hendawy, Salah Elsayed, Abdallah Elshawadfy Elwakeel, Abdullah Alebidi, Xianlu Yue, Wael Mohamed Elmessery, Hoda Galal

“…This study proposes an innovative approach combining Visible/Near-Infrared (VIS/NIR) spectroscopy with machine learning techniques—specifically, decision trees (DT) and gradient boosting regression (GBR)—to facilitate a rapid, non-destructive, and cost-effective prediction of key grape ripening indicators such as anthocyanin (An), total acidity (TA), total soluble solids (TSS), and the TSS/TA ratio. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

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Exploring the Sources of Centrality in the Turkish Domestic Airport Network by Keziban Seçkin Codal, Samet Güner

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Advancing crop recommendation system with supervised machine learning and explainable artificial intelligence by Sourabh Shastri, Sachin Kumar, Vibhakar Mansotra, Rohit Salgotra

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Improving transaction safety via anti-fraud protection based on blockchain by Yong Ren, Yan Ren, Hongwei Tian, Wei Song, Yanhong Yang

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Modeling of biodiesel production using optimization designs from literature: aiming to reduce the laboratory workload by Iver Bergh Hvidsten, Kristian Hovde Liland, Oliver Tomic, Jorge Mario Marchetti

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Machine learning modeling for thermochemical biohydrogen production from biomass by Yingju Chang, Wei Wang, Jo-Shu Chang, Duu-Jong Lee

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Variance-based sensitivity analysis of climate variability impact on crop yield using machine learning: A case study in Jordan by Yingqiang Xu, Abeer Albalawneh, Maysoon Al-Zoubi, Hiba Baroud

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Optimizing corn yield prediction: Integrating multi-temporal UAS data and machine learning by Huihui Zhang, Yuting Zhou, Shengfang Ma, Kevin Yemoto

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Machine learning algorithms to predict stroke in China based on causal inference of time series analysis by Qizhi Zheng, Ayang Zhao, Xinzhu Wang, Yanhong Bai, Zikun Wang, Xiuying Wang, Xianzhang Zeng, Guanghui Dong

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Comparative Evaluation of Machine Learning Models for Mobile Phone Price Prediction: Assessing Accuracy, Robustness, and Generalization Performance by Saima Anwar Lashari, Muhammad Muntazir Khan, Abdullah Khan, Sana Salahuddin, Muhammad Noman Ata

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