Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. …”
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How Long Until Agricultural Carbon Peaks in the Three Gorges Reservoir? Insights from 18 Districts and Counties by Danqing Li, Yunqi Wang, Huifang Liu, Cheng Li, Jinhua Cheng, Xiaoming Zhang, Peng Li, Lintao Wang, Renfang Chang

“…Pathway scenario prediction: We construct three developmental scenarios (low-carbon transition, business-as-usual, and high-resource dependency) integrated with regional planning parameters. This framework enables the identification of optimal peaking chronologies for each county and proposes gradient peaking strategies through spatial zoning, thereby resolving fragmented carbon governance in agrarian counties. …”
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Altitudinal Variations in Coniferous Vegetation and Soil Carbon Storage in Kalam Temperate Forest, Pakistan by Bilal Muhammad, Umer Hayat, Lakshmi Gopakumar, Shuangjiang Xiong, Jamshid Ali, Muhammad Tariq Badshah, Saif Ullah, Arif UR Rehman, Qun Yin, Zhongkui Jia

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A machine learning based estimation method of beach slopes at a national scale: a case study of New Zealand by Hao Xu, Nan Xu, Chi Zhang, Shanhang Chi, Yuan Li, Wenyu Li, Yifu Ou, Jiaqi Yao, Han-Su Zhang, Fan Mo, Hui Lu

“…We developed robust coastal slope estimation models for sandy beaches by integrating 12 environmental factors with high-precision LiDAR-derived slope data, employing four machine learning regression techniques: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Category Boosting (CatBoost). …”
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Chi2 weighted ensemble: A multi-layer ensemble approach for skin lesion classification using a novel framework - optimized RegNet synergy with Attention-Triplet. by Anwar Hossain Efat

“…A significant gap in current research is the lack of techniques for optimal weight allocation in model predictions. …”
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Leveraging LIME for Trustworthy Apple Quality Assessment by İsmail Kırbaş, Ahmet Çifci

“…This study explores the integration of machine learning (ML) models and explainable artificial ıntelligence (XAI) techniques to enhance the accuracy and transparency of apple quality assessment. …”
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Towards a geography of plastic fragmentation by Maciej Liro, Anna Zielonka

“…We propose a research agenda that includes mapping fragmentation hotspots, conducting field experiments across environmental gradients, developing integrative modeling approaches, and leveraging spatial management strategies to mitigate secondary microplastic release. …”
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SOC Estimation of Lithium-Ion Batteries Utilizing EIS Technology with SHAP–ASO–LightGBM by Panpan Hu, Chun Yin Li, Chi Chung Lee

“…This paper proposes a novel machine learning-based approach for SOC estimation by integrating Electrochemical Impedance Spectroscopy (EIS) with the SHapley Additive exPlanations (SHAP) method, Atom Search Optimization (ASO), and Light Gradient Boosting Machine (LightGBM). …”
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Numerical modeling and analysis of plasmonic flying head for rotary near-field lithography technology by Yueqiang Hu, Yonggang Meng

“…The linewidth has a strong correlation with the near-field gap, and the manufacturing uniformity is directly influenced by the dynamic performance. …”
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Linear and Tree‐Based Intelligent Investigation of Cross‐Domain Housing Features to Enhance Energy Efficiency by Hafiz Muhammad Shakeel, Shamaila Iram, Hafiz Muhammad Athar Farid, Richard Hill

“…These approaches overlook the potential insights gained from integrating data across different domains. This research addresses this gap using a cross‐domain dataset that includes building characteristics, energy usage, and environmental factors. …”
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Natural barriers facing female cyclists and how to overcome them: A cross national examination of bikesharing schemes by Richard Bean, Dorina Pojani, Jonathan Corcoran

“…For the analysis, we spatially integrate gender for more than 200 million bikesharing trips with fine-grained weather, gradient, and sunset/sunrise data. …”
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XGBoost Algorithm for Cervical Cancer Risk Prediction: Multi-dimensional Feature Analysis by Sudi Suryadi, Masrizal

“…Future research directions include prospective validation across diverse populations, integration of longitudinal data, and further exploration of explainable AI techniques to bridge the gap between algorithmic predictions and clinical implementation.…”
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Analyzing Dispersion Characteristics of Fine Particulate Matter in High-Density Urban Areas: A Study Using CFD Simulation and Machine Learning by Daeun Lee, Caryl Anne M. Barquilla, Jeongwoo Lee

“…The resulting dataset trained five ML models with Extreme Gradient Boosting (XGBoost), achieving the highest accuracy (91–95%). …”
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Eye-Guided Multimodal Fusion: Toward an Adaptive Learning Framework Using Explainable Artificial Intelligence by Sahar Moradizeyveh, Ambreen Hanif, Sidong Liu, Yuankai Qi, Amin Beheshti, Antonio Di Ieva

“…We validate the system’s interpretability using Gradient-weighted Class Activation Mapping (Grad-CAM) and assess both classification performance and explanation alignment with expert annotations. …”
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Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI by Sajedeh Talebi, Neda Abdolvand

“…Targeting Reddit’s anonymity-driven subreddits, the dataset fills a platform-specific gap. Integrating LLMs, LIME, and graph analysis, we develop scalable tools for real-time moderation to foster inclusive online spaces. …”
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A stacked ensemble model for traffic conflict prediction using emerging sensor data by Bowen Cai, Léah Camarcat, Nicolette Formosa, Mohammed Quddus

“…Employing machine learning approaches to handle the extensive and disaggregated data, a novel stacked ensemble learning model is proposed. This model integrates a Random Forest (RF), three-layer Deep Neural Networks (DNN), Support Vector Machine Radial (SVM-R), and a Gradient Boosting Model (GBM) meta layer to enhance prediction accuracy. …”
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SC-CoSF: Self-Correcting Collaborative and Co-Training for Image Fusion and Semantic Segmentation by Dongrui Yang, Lihong Qiao, Yucheng Shu

“…End-to-end joint training enables gradient propagation across all task branches via shared parameters, exploiting inter-task consistency for superior performance. …”
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Multi-Agent Deep Reinforcement Learning Cooperative Control Model for Autonomous Vehicle Merging into Platoon in Highway by Jiajia Chen, Bingqing Zhu, Mengyu Zhang, Xiang Ling, Xiaobo Ruan, Yifan Deng, Ning Guo

“…To enhance training efficiency, we develop a dual-layer multi-agent maximum Q-value proximal policy optimization (MAMQPPO) method, which extends the multi-agent PPO algorithm (a policy gradient method ensuring stable policy updates) by incorporating maximum Q-value action selection for platoon gap control and discrete command generation. …”
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Machine Learning Ensemble Methods for Co-Seismic Landslide Susceptibility: Insights from the 2015 Nepal Earthquake by Tulasi Ram Bhattarai, Netra Prakash Bhandary

“…Despite substantial advances in landslide susceptibility mapping, existing studies often overlook the compound role of post-seismic rainfall and lack robust spatial validation. To address this gap, we validated an ensemble machine learning framework for co-seismic landslide susceptibility modeling by integrating seismic, geomorphological, hydrological, and anthropogenic variables, including cumulative post-seismic rainfall. …”
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Preparation of land subsidence susceptibility map using machine learning methods based on decision tree (case study: Isfahan–Borkhar) by Negar Ghasemi, Iman Khosravi, Ali Bahrami

“…This study aims to bridge the existing research gap by employing a multi-factor approach using machine learning algorithms including Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) to map and analyze the subsidence susceptibility of the Isfahan–Borkhar region. …”
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