Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models by Jean Marie Vianney Ntamwiza, Hannibal Bwire

“…Specifically, it compared the performance of traditional statistical models—logistic regression, support vector machine (SVM), Naïve Bayes, and k-Nearest Neighbours (KNN)—with ensemble models including eXtreme Gradient Boosting (XGBoost), Light GBM, Random Forest, and stacking classifiers. …”
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Role of Artificial Intelligence and Machine Learning to Create Predictors, Enhance Molecular Understanding, and Implement Purposeful Programs for Myocardial Recovery by Frederick M. Lang, Benjamin C. Lee, Dor Lotan, Mert. R. Sabuncu, Veli K. Topkara

“…By identifying novel patterns in high-dimensional data, artificial intelligence (AI) and machine learning (ML) algorithms can enhance the identification of key predictors and molecular drivers of myocardial recovery. …”
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Social media interaction and built environment effects on urban walking experience: A machine learning analysis of Shanghai Citywalk. by Xingrui Chen, Yu Sun, Filzani Illia Binti Ibrahim, Myzatul Aishah Binti Kamarazaly, Siti Norzaini Binti Zainal Abidin, Suqiu Tang

“…Additionally, spatial autocorrelation analysis of emotional scores reveals spatial clustering patterns, underscoring the critical role of interactions between virtual social behavior and physical spatial elements in emotional generation. …”
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A Novel Hybrid Model for Loan Default Prediction in Maritime Finance Based on Topological Data Analysis and Machine Learning by Mohammad Amin Kheneifar, Babak Amiri

“…This study proposes a novel framework integrating topological data analysis (TDA) and machine learning (ML) to enhance default prediction in maritime finance. …”
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A Comparative study on the impact of urbanisation on microclimate in Cairo (Egypt) and London (UK) using remote sensing and Machine Learning by L. Sabobeh, T. Ali, M. Md. Mortula

“…Several machine learning (ML) algorithms were compared, with Support Vector Machine (SVM) ultimately selected for its superior performance. …”
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Identification and verification of immune and oxidative stress-related diagnostic indicators for malignant lung nodules through WGCNA and machine learning by Zhou An, Meichun Zeng, Xianhua Wang

“…Immune infiltration analysis revealed distinct patterns of immune cell infiltration in malignant LNs compared to those in benign controls. …”
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Adaptive AI-enhanced computation offloading with machine learning for QoE optimization and energy-efficient mobile edge systems by Dinesh Kumar Nishad, Vandna Rani Verma, Pushkar Rajput, Sandeep Gupta, Anurag Dwivedi, Dharti Raj Shah

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Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery by Ratovoson Robert Andriambololonaharisoamalala, Petra Helmholz, Dimitri Bulatov, Ivana Ivanova, Yongze Song, Susannah Soon, Eriita Jones

“…We proposed a hybrid model named “geospatial machine learning” (GeoML) to address these challenges, combining random forest and kriging downscaling techniques. …”
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Integrative analysis of RNA-Seq data and machine learning approaches to identify Biomarkers for Rhizoctonia solani resistance in sugar beet by Bahman Panahi, Mahdi Hassani, Nahid Hosseinzaeh Gharajeh

“…In this study, we employed RNA-Seq analysis alongside machine learning techniques to identify key biomarkers associated with resistance to R. solani. …”
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Developing an Urban Landscape Fumigation Service Robot: A Machine-Learned, Gen-AI-Based Design Trade Study by Prithvi Krishna Chittoor, Bhanu Priya Dandumahanti, Prabakaran Veerajagadheswar, S. M. Bhagya P. Samarakoon, M. A. Viraj J. Muthugala, Mohan Rajesh Elara

“…Generative AI (Gen-AI) revolutionizes design by leveraging machine learning to generate innovative solutions. …”
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A hybrid approach using support vector machine rule-based system: detecting cyber threats in internet of things by M. Wasim Abbas Ashraf, Arvind R. Singh, A. Pandian, Rajkumar Singh Rathore, Mohit Bajaj, Ievgen Zaitsev

“…Identifying known attack signatures and patterns using rule-based approaches improves detection efficiency without retraining by adapting pre-trained models to new IoT contexts. …”
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End-Region Losses in High-Power Electrical Machines: Impact of Material Thickness on Eddy Current Losses in Clamping Structures by Walid Mohand Oussaid, Abdelmounaïm Tounzi, Raphaël Romary, Abdelkader Benabou, Walid Boughanmi, Daniel Laloy

“…High-power electrical machines often utilize clamping structures composed of various materials with specific geometric dimensions to secure the stator laminations. …”
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Prediction of Voice Therapy Outcomes Using Machine Learning Approaches and SHAP Analysis: A K-VRQOL-Based Analysis by Ji Hye Park, Ah Ra Jung, Ji-Na Lee, Ji-Yeoun Lee

“…Multiple regression analysis and four machine learning algorithms—random forest (RF), gradient boosting (GB), light gradient boosting machine (LightGBM), and extreme gradient boosting (XGBoost)—are applied to predict changes in K-VRQOL scores across the total, physical, and emotional domains. …”
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Motion Gait Recognition of Lower Limb Exoskeleton Based on Particle Swarm Optimization-Based Extreme Learning Machine Algorithm by Ting Liu, Kai Liu, Wuyi Luo, Jiange Kou, Haoran Zhan, Guangkai Yu, Qing Guo, Yan Shi

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Non-Invasive Glucose Monitoring Using Optical Sensors and Machine Learning: A Predictive Model for Nutritional and Health Assessment by Heru Agus Santoso, Nur Setiawati Dewi, Susilo, Arga Dwi Pambudi, Hanif Pandu Suhito, Iman Dehzangi

“…CNN-AHM combines spatial feature extraction with attention-based prioritization of relevant signal patterns, enhancing both accuracy and interpretability. …”
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Machine learning derived development and validation of extracellular matrix related signature for predicting prognosis in adolescents and young adults glioma by Pancheng Wu, Yi Zheng, Wei Wu, Beichen Zhang, Yichang Wang, Mingjing Zhou, Ziyi Liu, Zhao Wang, Maode Wang, Jia Wang

“…Extracellular matrix is involved in the development of tumors, while their prognostic significance in AYAs glioma remains unclear. By an integrated machine learning workflow and circuit training and validation procedure, we developed a machine learning-derived prognostic signature (MLDPS) based on 1,026 extracellular matrix-related genes and 3 AYAs glioma cohorts. …”
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Fine-grained analysis and mapping of urban flood susceptibility with interpretable machine learning: A case study of Hefei, China by Ziyao Xing, Guijia Lyu, Yu Yao, Zhe Liu, Xiaodong Zhang

“…This paper proposes a novel approach combining interpretable machine learning and spatial autocorrelation. An ensemble learning model assesses susceptibility by incorporating terrain, urban construction, and precipitation factors. …”
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Integrating Machine Learning Workflow into Numerical Simulation for Optimizing Oil Recovery in Sand-Shale Sequences and Highly Heterogeneous Reservoir by Dung Bui, Abdul-Muaizz Koray, Emmanuel Appiah Kubi, Adewale Amosu, William Ampomah

“…A machine learning-based proxy model was built for the prediction of cumulative oil production and then optimized with hybrid machine learning techniques. …”
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Comparative evaluation of machine learning models for extreme river water level forecasting in Bangladesh: Implications for flood and drought resilience by Md Touhidul Islam, Sujan Chandra Roy, Nusrat Jahan, Al-Mahmud, Md Mazharul Islam, Abdullah Al Ferdaus, Kazunori Fujisawa, A.K.M. Adham

“…This study compares nine machine learning (ML) models for predicting monthly maximum and minimum water levels at three key stations along the Old Brahmaputra River using a 34-year dataset (1990–2024). …”
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A flexible, channel‐minimized and large‐scale touch position sensor combining contact electrification effect and screen‐printed stagger electrode patterns by Zhihao Hu, Yuanchao Ren, Xindan Hui, Lirong Tang, Jie Chen, Hengyu Guo

“…Abstract The large‐scale touch position sensor as a key human–machine interface toolkit holds immense significance in smart city and home construction. …”
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