Assessing groundwater drought in Iran using GRACE data and machine learning by Ali Kashani, Hamid R. Safavi

“…Following the application of the Seasonal Autoregressive Integrated Moving Average (SARIMA) model to fill GWSA time series gaps, this study models and forecasts GWSA trends through 2030 using historical data and SSP2 scenario projections of the canESM5 climate model. …”
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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

“…This approach was coupled with widely employed ensemble algorithms, namely the random forest (RF) and the extreme gradient boost (XGBoost). The results suggested that our novelty, SMOTEENN-ENC, integrated with the XGBoost algorithm demonstrated superiority and stability in the predictive performance when applied to skewed fat-tailed distributed datasets with inherent diverse features.…”
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Enhancing Concrete Workability Prediction Through Ensemble Learning Models: Emphasis on Slump and Material Factors by Jiangsong Jiang, Chunhong Xin, Sifei Wu, Wenbing Chen, Hui Li, Zhaolun Ran

“…This study advances concrete workability prediction by integrating ensemble learning models like Random Forest (RF), Extreme Gradient Boosting (XGBoost), adaptive boosting (AdaBoost), and gradient boosted regression trees (GBRTs), and XGBoost showing superior accuracy. …”
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Ensemble-based multiclass lung cancer classification using hybrid CNN-SVD feature extraction and selection method. by Md Sabbir Hossain, Niloy Basak, Md Aslam Mollah, Md Nahiduzzaman, Mominul Ahsan, Julfikar Haider

“…The extracted features were then processed by a set of ML algorithms along with a voting ensemble approach. Additionally, Gradient-weighted Class Activation Mapping (Grad-CAM) was integrated as an explainable AI (XAI) technique for enhancing model transparency by highlighting key influencing regions in the CT scans, which improved interpretability and ensured reliable and trustworthy results for clinical applications. …”
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Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. …”
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Identification of developmental and reproductive toxicity of biocides in consumer products using ToxCast bioassays data and machine learning models by Donghyeon Kim, Siyeol Ahn, Jinhee Choi

“…Initially, we compiled 201 bioassays linked to DART-related mechanisms using the Integrated Chemical Environment (ICE) database of the National Toxicology Program of (NTP). …”
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Investigating Spatial Heterogeneity Patterns and Coupling Coordination Effects of the Cultural Ecosystem Service Supply and Demand: A Case Study of Taiyuan City, China by Xin Huang, Cheng Li, Jie Zhao, Shuang Chen, Minghui Gao, Haodong Liu

“…These outcomes address methodological gaps in coupled social–ecological system research while informing practical spatial governance strategies.…”
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Influence of hatch spacing on molten pool evolution, defects generation and mechanical properties of SLM fabricated diamond/CuSn20 composites by Yangli Xu, Yu Sun, Guoqin Huang, Tingting Li, Haoqing Li, Weihong Wu, Xipeng Xu, Hidetoshi Saitoh

“…Simultaneously, the temperature gradient generated by the second laser pass induces remelting of the overlapping. (2) At reduced hatch spacing, thermal damage preferentially occurs in diamond grits, whereas enlarged hatch spacings promote pore formation, unmelted zones, and interfacial gaps in overlapping regions. (3) Specimens fabricated at an 80 μm hatch spacing exhibit the highest compressive strength. …”
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A recurrent multimodal sparse transformer framework for gastrointestinal disease classification by V. Sharmila, S. Geetha

“…However, existing diagnostic frameworks often face limitations due to modality imbalance, feature redundancy, and cross-modal inconsistencies, particularly when dealing with heterogeneous data such as medical text and endoscopic images. To bridge these gaps, this study proposes a novel recurrent multimodal principal gradient K-proximal sparse transformer (RMP-GKPS-transformer) framework for comprehensive GI disease classification. …”
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Advancing Knowledge on Machine Learning Algorithms for Predicting Childhood Vaccination Defaulters in Ghana: A Comparative Performance Analysis by Eliezer Ofori Odei-Lartey, Stephaney Gyaase, Dominic Asamoah, Thomas Gyan, Kwaku Poku Asante, Michael Asante

“…The findings demonstrate that robust machine learning frameworks, combined with temporal and contextual feature engineering, can improve defaulter risk prediction accuracy. Integrating such models into routine immunization programs could enable data-driven targeting of high-risk groups, supporting policymakers in strategies to close vaccination coverage gaps.…”
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Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters by Loukas Katikas, Sofia Reizopoulou, Paraskevi Drakopoulou, Vassiliki Vassilopoulou

“…., the Common Fisheries Policy) and more integrated policies (e.g., marine spatial planning). …”
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Efficient Trajectory Prediction Using Check-In Patterns in Location-Based Social Network by Eman M. Bahgat, Alshaimaa Abo-alian, Sherine Rady, Tarek F. Gharib

“…The proposed AHLTP integrated with the machine-learning models classifies the data effectively, with the KNN attaining the highest accuracy at 98%, followed by gradient-boosted trees at 96% and deep learning at 92%. …”
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Spatial Patterns and Characteristics of Urban–Rural Agricultural Landscapes: A Case Study of Bengaluru, India by Jayan Wijesingha, Thomas Astor, Sunil Nautiyal, Michael Wachendorf

“…This study developed a workflow to address this information gap and determine the spatial patterns and characteristics of agricultural landscapes along an urban–rural gradient. …”
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First principles study of dielectric properties of ferroelectric perovskite oxides with extended Hubbard interactions by Min Chul Choi, Wooil Yang, Young-Woo Son, Se Young Park

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Machine Learning Applications in Gray, Blue, and Green Hydrogen Production: A Comprehensive Review by Xuejia Du, Shihui Gao, Gang Yang

“…ML algorithms such as artificial neural networks (ANNs), random forest (RF), and gradient boosting regression (GBR) have been widely applied to predict hydrogen yield, optimize operational conditions, reduce emissions, and improve process efficiency. …”
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Establishing strength prediction models for low-carbon rubberized cementitious mortar using advanced AI tools by Fu Limei, Xu Feng

“…Among the tested algorithms, including bagging, gradient boosting, and AdaBoost, the bagging model achieved the highest accuracy (R 2 = 0.975). …”
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Applications of Machine Learning Algorithms in Geriatrics by Adrian Stancu, Cosmina-Mihaela Rosca, Emilian Marian Iovanovici

“…The most studied algorithms in research articles are Random Forest, Extreme Gradient Boosting, and support vector machines. They are preferred due to their performance in processing incomplete clinical data. …”
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Advancing AI Interpretability in Medical Imaging: A Comparative Analysis of Pixel-Level Interpretability and Grad-CAM Models by Mohammad Ennab, Hamid Mcheick

“…The primary objective is to evaluate PLI’s performance against Gradient-Weighted Class Activation Mapping (Grad-CAM) and achieve fine-grained interpretability and improved localization precision. …”
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An improved machine-learning model for lightning-ignited wildfire prediction in Texas by Qi Zhang, Cong Gao, Chunming Shi

“…Using this dataset, we developed an eXtreme gradient boosting-based machine learning model that integrates meteorological, soil, vegetative, lightning, topographic, and human activity variables to predict LIW probability. …”
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Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis by Mahreen Kiran, Ying Xie, Nasreen Anjum, Graham Ball, Barbara Pierscionek, Duncan Russell

“…Additionally, literature analysis highlights advances in integrating real-world datasets, emerging trends like federated learning, and explainability tools such as SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations).ConclusionFuture work should address gaps in generalizability, interdisciplinary T2DM prediction research, and psychosocial integration, while also focusing on clinically actionable solutions and real-world applicability to combat the growing diabetes epidemic effectively.…”
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