Machine learning framework to estimate ridership loss in public transport during external crises: case study of bus network in Stockholm by Mahsa Movaghar, Erik Jenelius, David Hunter

“…And then introduces an approach to use Machine Learning algorithms and extract hidden patterns for predicting financial loss during any crisis, which is a novel perspective and application. …”
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Wear Prediction and Mechanism Study of Tunnel Boring Machine Disc Cutter Breaking in Hard–Soft Rock Considering Thermal Effect by Xiongfei Lyu, Youliang Chen, Shaoming Liao, Tomas Manuel Fernandez-Steeger

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Flow dynamics in a vertical pipe with internal fins exposed to sunlight – A machine learning based evaluation of thermal signature by Assmaa Abd-Elmonem, Zill E Shams, Mariam Imtiaz, Kashif Ali, Sohail Ahmad, Wasim Jamshed, Fayza Abdel Aziz ElSeabee, Neissrien Alhubieshi, Syed M. Hussain, Hijaz Ahmad

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Cost-effectiveness of the 3E model in diabetes management: a machine learning approach to assess long-term economic impact by Supriya Raghav, Santosh Kumar, Hamid Ashraf, Poonam Khanna

“…Machine learning models, including random forest and K-means clustering, were used to identify key factors influencing treatment costs and to segment patient subgroups that were most responsive to the intervention. …”
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A Review of the Industry 4.0 to 5.0 Transition: Exploring the Intersection, Challenges, and Opportunities of Technology and Human–Machine Collaboration by Md Tariqul Islam, Kamelia Sepanloo, Seonho Woo, Seung Ho Woo, Young-Jun Son

“…Learning from historic patterns will enable us to navigate this era of change and mitigate any uncertainties in the future.…”
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Heart Rate Variability-Based Stress Detection and Fall Risk Monitoring During Daily Activities: A Machine Learning Approach by Ines Belhaj Messaoud, Ornwipa Thamsuwan

“…K-means clustering identified three distinct physiological states based on HRV features, such as the high-frequency band power and the root mean square of successive differences between normal heartbeats, suggesting patterns that may reflect stress levels. In the second phase, integrating the cluster labels obtained from the first phase together with HRV features into machine learning models for fall risk classification, we found that Gradient Boosting performed the best, achieving an accuracy of 95.45%, a precision of 93.10% and a recall of 85.71%. …”
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Comparison of Classical Arima Forecasting Methods to the Machine Learning LSTM Method: a Case Study on DAX® 50 ESG Index by Rosinus, Manuel

“…Originality/Value: The findings suggest that while the LSTM's ability to capture nonlinear patterns offers a forecasting edge, the improvement is incremental in a highly liquid and efficient market. …”
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Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine by Hojin Moon, Lauren Tran, Andrew Lee, Taeksoo Kwon, Minho Lee

“…A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately. …”
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Comparing Machine Learning-Based Crime Hotspots Versus Police Districts: What’s the Best Approach for Crime Forecasting? by Eugenio Cesario, Paolo Lindia, Andrea Vinci

“…However, traditional spatial partitioning approaches, which divide cities into predefined police districts based on geographic and operational considerations, often fail to account for variations in crime patterns. In contrast, machine learning-based approaches could dynamically adapt to areas with differing crime frequencies and densities, making them particularly effective in cities characterized by diverse population distributions and crime activity levels. …”
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Enhancing Power Generation Forecasting in Smart Grids Using Hybrid Autoencoder Long Short-Term Memory Machine Learning Model by Ahsan Zafar, Yanbo Che, Muneer Ahmed, Muhammad Sarfraz, Ashfaq Ahmad, Mohammad Alibakhshikenari

“…Results highlight the superior accuracy of the hybrid AE-LSTM model compared to the LSTM model as well as Bi-LSTM model, attributed to its capability to capture intricate temporal patterns and correlations within the data. This research underscores the significant potential of machine learning techniques, particularly the hybrid AE-LSTM approach, in facilitating the seamless integration of renewable energy resources into smart grids, contributing to more efficient and environmentally conscious power systems. …”
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Ensemble Machine Learning, Deep Learning, and Time Series Forecasting: Improving Prediction Accuracy for Hourly Concentrations of Ambient Air Pollutants by Valentino Petrić, Hussain Hussain, Kristina Časni, Milana Vuckovic, Andreas Schopper, Željka Ujević Andrijić, Simonas Kecorius, Leizel Madueno, Roman Kern, Mario Lovrić

“…Abstract This study aims to improve the generalisation capabilities of machine learning models for modelling hourly air pollutant concentrations in scenarios where access to high-quality data is limited. …”
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Augmented robustness in home demand prediction: Integrating statistical loss function with enhanced cross-validation in machine learning hyperparameter optimisation by Banafshe Parizad, Ali Jamali, Hamid Khayyam

“…Sustainable forecasting of home energy demand (SFHED) is crucial for promoting energy efficiency, minimizing environmental impact, and optimizing resource allocation. Machine learning (ML) supports SFHED by identifying patterns and forecasting demand. …”
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PLOD3 as a novel oncogene in prognostic and immune infiltration risk model based on multi-machine learning in cervical cancer by Lingling Qiu, Xiuchai Qiu, Xiaoyi Yang

“…To improve prognostic accuracy and therapeutic strategies, we developed a multi-machine learning prognostic model based on metabolic-associated genes. …”
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DeepSeek-AI-enhanced virtual reality training for mass casualty management: Leveraging machine learning for personalized instructional optimization. by Zhe Li, Lei Shi, Mingyu Pei, Wan Chen, Yutao Tang, Guozheng Qiu, Xibin Xu, Liwen Lyu

“…<h4>Objective</h4>This study aimed to evaluate the effectiveness of a virtual reality (VR) training system for mass casualty management, integrating artificial intelligence (AI) and machine learning (ML) to analyze trainee performance and error patterns. …”
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Integrating weighted gene co-expression network analysis and machine learning to elucidate neural characteristics in a mouse model of depression by Jinli Gao, Qinglang Wang, Jie Liu, Siqian Zheng, Jiahong Liu, Zhiyong Gao, Cheng Zhu

“…Notably, Oprm1 exhibited the highest feature importance, contributing to a model accuracy of 94.5%. Gene expression patterns showed strong consistency across the prefrontal cortex (PFC) and nucleus accumbens (NAC).ConclusionThe combined application of machine learning and transcriptomic analysis effectively identified core neurobiological genes in a depression model. …”
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Machine learning driven design and optimization of a compact dual Port CPW fed UWB MIMO antenna for wireless communication by Jayant Kumar Rai, Swati Yadav, Ajay Kumar Dwivedi, Vivek Singh, Pinku Ranjan, Anand Sharma, Somesh Kumar, Stuti Pandey

“…Abstract In this article, a compact dual port Multiple Input Multiple Output (MIMO) Coplanar Waveguide (CPW) fed Ultra-Wideband (UWB) antenna for the next generation wireless communication using Machine Learning (ML) optimization is presented. It is designed on an FR4 epoxy substrate of 16 × 30 mm2 with a thickness of 1.6 mm. …”
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Prognostic risk modeling of endometrial cancer using programmed cell death-related genes: a comprehensive machine learning approach by Tianshu Chen, Yuhan Yang, Zhizhong Huang, Feng Pan, Zhendi Xiao, Kunxue Gong, Wenguang Huang, Liu Xu, Xueqin Liu, Caiyun Fang

“…The model showed strong correlations with clinical characteristics, immune cell infiltration patterns, and potential therapeutic responses. Conclusions This study presents a novel, comprehensive approach to endometrial cancer prognosis, integrating machine learning and molecular insights to provide a more precise risk stratification tool with potential clinical translation.…”
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Comparative analysis of machine learning models and explainable AI for agriculture drought prediction: A case study of the Ta-pieh mountains by Lichang Xu, Shaowei Ning, Xiaoyan Xu, Shenghan Wang, Le Chen, Rujian Long, Shengyi Zhang, Yuliang Zhou, Min Zhang, Bhesh Raj Thapa

“…The analysis examined interactions between key factors and spatial patterns, showing how their contributions varied with drought severity and location. …”
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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. …”
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Development of operational decision support tools for mechanized ski guiding using avalanche terrain modeling, GPS tracking, and machine learning by J. Sykes, J. Sykes, P. Haegeli, R. Atkins, P. Mair, Y. Bühler, Y. Bühler

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