An Elderly Fall Detection Method Based on Federated Learning and Extreme Learning Machine (Fed-ELM) by Zhigang Yu, Jiahui Liu, Mingchuan Yang, Yanmin Cheng, Jie Hu, Xinchi Li

“…However, there are differences in movement patterns between young and elderly individuals due to bone aging, which leads to the degradation of the algorithm performance in the elderly population. …”
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Machine learning algorithms applied to the diagnosis of COVID-19 based on epidemiological, clinical, and laboratory data by Silvia Elaine Cardozo Macedo, Marina de Borba Oliveira Freire, Oscar Schmitt Kremer, Ricardo Bica Noal, Fabiano Sandrini Moraes, Mauro André Barbosa Cunha

“…Epidemiological, clinical, and laboratory data were processed by machine learning algorithms in order to identify patterns. …”
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A Compact Dual-Band Antenna for Brain–Machine Interface and Skin-Implantable Biotelemetry Applications by Emtiaz Ahmed Mainul, Md. Faruque Hossain

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Optimized machine learning algorithms with SHAP analysis for predicting compressive strength in high-performance concrete by Samuel Olaoluwa Abioye, Yusuf Olawale Babatunde, Oluwafikejimi Abigail Abikoye, Aisha Nene Shaibu, Bailey Jonathan Bankole

“…Abstract This research examines the application of eight different machine learning (ML) algorithms for predicting the compressive strength of high-performance concrete (HPC). …”
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High School and Undergraduate Student Volunteers as an Imperfect Solution to Machine Learning Geoscience Research Needs by Sarah E. Esenther, Neiv Gupta, Chanatip Vongkitbuncha, Mason N. Lee, Laurence C. Smith

“…We describe our experiences working with 20 early‐stage students to build a large training data set digitized from satellite images of meltwater drainage patterns on ice sheets. The intent of this Perspective is to share our experience and lessons learned with other machine learning researchers who, like us, may have minimal experience mentoring young volunteer researchers but may seek such partnerships for the first time in response to their machine learning training data set needs. …”
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Effects of Broken Rotor Bars on Three-Phase Induction Machines and Detection Methods – State of Arts by Boudmane Bilal, Rachid Aziz, Mortabit Idriss, Errifai Nidale

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A Machine Learning Framework for Urban Ventilation Corridor Identification Using LBM and Morphological Indices by Bu Yu, Peng Xie

“…The results show that the proposed method can accurately predict spatial wind speed patterns and identify both primary and secondary ventilation corridors. …”
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MLRec: A Machine Learning-Based Recommendation System for High School Students Context of Bangladesh by Momotaz Begum, Mehedi Hasan Shuvo, Jia Uddin

“…Social media and mobile devices, commonly referred to as socimedevices, have become integral to students’ daily lives, influencing both their academic performance and overall well-being. Depending on usage patterns, these technologies can positively or negatively impact students’ education. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

“…Avoiding strenuous driving behaviors (TTF < 400 N, TLF < 400 N), reducing tread temperature (T < 45℃), and minimizing the number of small tread patterns are feasible ways to reduce TWPs.…”
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Comparative Analysis of Machine Learning Techniques for Fault Diagnosis of Rolling Element Bearing with Wear Defects by Devendra Sahu, Ritesh Kumar Dewangan, Surendra Pal Singh Matharu

“…The model was further refined by extracting 14 types of features from the SNR-enhanced vibration data, presenting a comprehensive depiction of fault patterns and finally, machine learning techniques were applied to categorize faults using the aforementioned datasets, facilitating a comparative analysis of results. …”
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Texas rural land market integration: A causal analysis using machine learning applications by Tian Su, Senarath Dharmasena, David Leatham, Charles Gilliland

“…Using quarterly transactional land value data from 1966 to 2017, this study uses cutting-edge machine learning algorithms and probabilistic graphical models to uncover causal interaction patterns of different land markets in Texas. …”
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Spotting Leaders in Organizations with Graph Convolutional Networks, Explainable Artificial Intelligence, and Automated Machine Learning by Yunbo Xie, Jose D. Meisel, Carlos A. Meisel, Juan Jose Betancourt, Jianqi Yan, Roberto Bugiolacchi

“…State-of-the-art performance is obtained using various statistical machine learning methods, graph convolutional networks (GCN), automated machine learning (AutoML), and explainable artificial intelligence (XAI). …”
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Trade-offs between machine learning and deep learning for mental illness detection on social media by Zhanyi Ding, Zhongyan Wang, Yeyubei Zhang, Yuchen Cao, Yunchong Liu, Xiaorui Shen, Yexin Tian, Jianglai Dai

“…Our findings indicate that ML and DL models achieve comparable classification performance on medium-sized datasets, with ML models offering greater interpretability through variable importance scores, while DL models are more robust to complex linguistic patterns. Additionally, ML models require explicit feature engineering, whereas DL models learn hierarchical representations directly from text. …”
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Optimizing Recycled Tunnel Boring Machine (TBM)-Excavated Materials as Aggregates in Shotcrete Mix Design by Wei Zhang, Rusheng Hao, Zhijun Men, Jingjing He, Yong Zhang, Wei Hu

“…Tunnel Boring Machine (TBM) excavation materials were recycled by sieving and separating particles into sizes 5–10 mm (coarse aggregates) and below 5 mm (manufactured sand) to explore their potential as aggregates in shotcrete production, with the aim of reducing environmental harm from waste disposal. …”
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Machine learning-based prediction of optimal antenatal care utilization among reproductive women in Nigeria by Jamilu Sani, Adeyemi Oluwagbemiga, Mohamed Mustaf Ahmed

“…Traditional statistical models often fall short in identifying complex non-linear relationships in population health data. Machine learning (ML) offers a promising alternative that uncovers hidden patterns and improves prediction accuracy. …”
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Elucidating the role of KLRD1 in coronary atherosclerosis: harnessing bioinformatics and machine learning to advance understanding by Huan Liang, Xiao Liang, Man Zheng, Shoudong Wang

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Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter? by Joanna Duda-Goławska, Aleksander Rogowski, Zuzanna Laudańska, Jarosław Żygierewicz, Przemysław Tomalski

“…Many research teams are currently employing supervised machine learning classifiers that utilise hand-crafted features for data segment classification. …”
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Machine learning explores the prognostic and immuno-oncological impact of mitochondrial unfolded protein response in CESC by Keh-Sen Liu, Yen-Hsiang Chang, Hsing-Ju Wu, Hung-Yu Lin

“…Single-cell RNA sequencing revealed ATF5’s distinct expression patterns in stromal cells, particularly in endometrial stromal and smooth muscle cells. …”
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Evaluating machine learning algorithms for energy consumption prediction in electric vehicles: A comparative study by Izhar Hussain, Kok Boon Ching, Chessda Uttraphan, Kim Gaik Tay, Adil Noor

“…This study focuses on their applicability to energy projections to demonstrate how well ensemble and non-linear models may capture intricate patterns in time series. These cutting-edge machine learning techniques might greatly enhance energy demand predictions.…”
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Machine learning-driven multi-targeted drug discovery in colon cancer using biomarker signatures by Tingting Liu, Lifan Zhong, Xizhe Sun, Zhijiang He, Witiao Lv, Liyun Deng, Yanfei Chen

“…The results demonstrated that the proposed system outperformed traditional Machine Learning models, such as Support Vector Machine and Random Forest, in terms of accuracy (98.6%), specificity (0.984), sensitivity (0.979), and F1-score (0.978). …”
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