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. …”
Get full text

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. …”
Get full text

Applying machine learning to decode built environment thresholds for public and active transport distances in the global south by Ali Shkera, Domokos Esztergár-Kiss

“…The study provides data-driven policy recommendations to enhance pedestrian infrastructure, refine transit-oriented development, and promote sustainable multimodal mobility. By integrating advanced ML methods with transportation policy analysis, this research bridges critical methodological and contextual gaps thus offering actionable insights for urban planners in high-density, transit-dependent cities worldwide.…”
Get full text

CFD analysis of air flows and temperatures based on radiant heating in industrial environments by E. S. Aralov, B. M. Kumitsky

“…The findings demonstrate the potential for significant progress in energy savings and improved worker comfort in industrial environments using radiant heating. The integrated research approach fills a critical gap in existing research, highlighting the need and potential for further exploration of sustainable heating technologies in challenging industrial environments.…”
Get full text

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. …”
Get full text

A Comprehensive Review of Machine Learning Models for Optimizing Wind Power Processes by Cosmina-Mihaela Rosca, Adrian Stancu

“…Thus, this paper identifies gaps in the current research, discusses ML algorithms in the context of optimizing wind energy production processes, and identifies future directions for increasing the efficiency of wind turbines through integrated predictive methods.…”
Get full text

Remote Sensing-Based Multilayer Perceptron Model for Grassland Above-Ground Biomass Estimation by Zhiguo Wang, Shuai Ma, Yongguang Zhai, Pingping Huang, Xiangli Yang, Jianhao Cui, Qimuge Eridun

“…This study addresses this gap by developing a Multilayer Perceptron (MLP) model integrating Landsat 9 OLI/TIRS imagery acquired on 15 August 2024, with ground data from 78 sampling points (62 training, 16 testing). …”
Get full text

Small but mighty: Enhancing 3D point clouds semantic segmentation with U-Next framework by Ziyin Zeng, Qingyong Hu, Zhong Xie, Bijun Li, Jian Zhou, Yongyang Xu

“…Specifically, we construct the U-Next by stacking multiple U-Net L1 sub-networks in a dense arrangement to diminish the semantic gap. Concurrently, it integrates feature maps across various scales to proficiently restore intricate fine-grained details. …”
Get full text

Moisture prediction in chicken litter using hyperspectral data and machine learning by Ahmad Tulsi, Abdul Momin, Victoria Ayres

“…This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. …”
Get full text

Quantum algorithms and complexity in healthcare applications: a systematic review with machine learning-optimized analysis by Agostino Marengo, Vito Santamato

“…This study emphasizes the importance of hybrid quantum-classical models and cross-disciplinary research to bridge the gap between cutting-edge quantum computing theory and its practical applications in healthcare.…”
Get full text

A Risk-Optimized Framework for Data-Driven IPO Underperformance Prediction in Complex Financial Systems by Mazin Alahmadi

“…The current research landscape lacks modern models that address the needs of small and imbalanced datasets relevant to emerging markets, as well as the risk preferences of investors. To fill this gap, we present a practical framework utilizing tree-based ensemble learning, including Bagging Classifier (BC), Random Forest (RF), AdaBoost (Ada), Gradient Boosting (GB), XGBoost (XG), Stacking Classifier (SC), and Extra Trees (ET), with Decision Tree (DT) as a base estimator. …”
Get full text

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.…”
Get full text

Triple-layered security system: reliable and secured image communications over 5G and beyond networks by Tarek Srour, Mohsen A. M. El-Bendary, Mostafa Eltokhy, Atef E. Abouelazm

“…This paper presents the proposed vision of 5G and beyond security to build a research gap of existing and related technique that lack the adaptation, boosting gradient and complexity analysis, through design and evaluate the adapted and graded security system. …”
Get full text

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. …”
Get full text

Predicting Predisposition to Tropical Diseases in Female Adults Using Risk Factors: An Explainable-Machine Learning Approach by Kingsley Friday Attai, Constance Amannah, Moses Ekpenyong, Said Baadel, Okure Obot, Daniel Asuquo, Ekerette Attai, Faith-Valentine Uzoka, Emem Dan, Christie Akwaowo, Faith-Michael Uzoka

“…Most studies have focused on vector control measures, such as insecticide-treated nets and time series analysis, often neglecting emerging yet critical risk factors vital for effectively preventing febrile diseases. We address this gap by investigating the use of machine learning (ML) models, specifically extreme gradient boost and random forest, in predicting adult females’ susceptibility to these diseases based on biological, environmental, and socioeconomic factors. …”
Get full text

Desertification Monitoring Using Machine Learning Techniques with Multiple Indicators Derived from Sentinel-2 in Turkmenistan by Arslan Berdyyev, Yousef A. Al-Masnay, Mukhiddin Juliev, Jilili Abuduwaili

“…Despite the fact that desertification has been the subject of numerous studies conducted worldwide, this study is among the first to use a multi-index approach to specifically focus on Turkmenistan. It does this by integrating six important desertification indicators within machine learning models like random forest (RF), eXtreme Gradient Boosting (XGBoost), naïve Bayes (NB), and K-nearest neighbors (KNN). …”
Get full text

A Meta-Learning-Based Ensemble Model for Explainable Alzheimer’s Disease Diagnosis by Fatima Hasan Al-bakri, Wan Mohd Yaakob Wan Bejuri, Mohamed Nasser Al-Andoli, Raja Rina Raja Ikram, Hui Min Khor, Zulkifli Tahir, The Alzheimer’s Disease Neuroimaging Initiative

“…The methodology involves training an ensemble model that integrates Random Forest, Support Vector Machine, XGBoost, and Gradient Boosting classifiers, with meta-logistic regression used for the final decision. …”
Get full text

Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…This study addresses the gap in disease-specific binding affinity prediction by integrating PDAC-derived targets with diverse molecular descriptors and artificial intelligence (AI) models, enabling more accurate therapeutic profiling. …”
Get full text

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. …”
Get full text

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables by Amir Shahcheraghian, Adrian Ilinca

“…The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. …”
Get full text