Prediction of alkali-silica reaction expansion of concrete using explainable machine learning methods by Yasitha Alahakoon, Hirushan Sajindra, Ashen Krishantha, Janaka Alawatugoda, Imesh U. Ekanayake, Upaka Rathnayake

“…This research holds significant value for the construction industry, as accurately predicting ASR expansion can lead to optimized material usage and improved structural performance.…”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…These findings suggest that the proposed method can effectively predict tool wear in real-world CNC machining, contributing to improved production efficiency, reduced tool replacement frequency, and lower maintenance costs, thereby providing valuable insights for industrial applications.…”
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Hardware-software simulator for modeling indoor temperature and humidity processes by О.M. Yevseienko

“…This makes it a reliable tool for forecasting and optimizing the operation of the object, which is important for further improvement of the control system.…”
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Experimental and machine learning based analysis of pervious concrete enhanced with fly ash and silica fume by Siva Shanmukha Anjaneya Babu Padavala, Siva Avudaiappan, Venkatesh Noolu

“…Pervious concrete (PC) has quickly gained attention as an eco-friendly solution to urban stormwater management, offering improved drainage performance while decreasing environmental impact. …”
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An Intelligent LoRaWAN-Based IoT Device for Monitoring and Control Solutions in Smart Farming Through Anomaly Detection Integrated With Unsupervised Machine Learning by Maram Fahaad Almufareh, Mamoona Humayun, Zulfiqar Ahmad, Asfandyar Khan

“…Predominantly, the study also indicates precision in the temperature variation prediction model through the use of the predictive model based on the linear regression and random forest algorithms. This improves smart farming practices developed for precision agriculture in terms of efficiency, productivity, and sustainability.…”
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A deep contrastive learning-based image retrieval system for automatic detection of infectious cattle diseases by Veerayuth Kittichai, Morakot Kaewthamasorn, Apinya Arnuphaprasert, Rangsan Jomtarak, Kaung Myat Naing, Teerawat Tongloy, Santhad Chuwongin, Siridech Boonsang

“…The findings may aid in the early diagnosis of anaplasmosis infections in remote areas without access to veterinary care or costly molecular diagnostic tools.…”
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A self-configuration framework for balancing services in the fog of things by Edson Mota, Jurandir Barbosa, Gustavo B. Figueiredo, Maycon Peixoto, Cássio Prazeres

“…The experimental results demonstrate significant improvements in network performance, response time, and load balancing. …”
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Deep Residual Transfer Ensemble Model for mRNA Gene-Expression-Based Breast Cancer by Job Prasanth Kumar Chinta Kunta, Vijayalakshmi A. Lepakshi

“…Being consensus-driven solution, it improved reliability of breast cancer prediction results. …”
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Developing a Machine Learning Model for Predicting 30-Day Major Adverse Cardiac and Cerebrovascular Events in Patients Undergoing Noncardiac Surgery: Retrospective Study by Ju-Seung Kwun, Houng-Beom Ahn, Si-Hyuck Kang, Sooyoung Yoo, Seok Kim, Wongeun Song, Junho Hyun, Ji Seon Oh, Gakyoung Baek, Jung-Won Suh

“…ConclusionsOur prediction models outperformed the widely used Revised Cardiac Risk Index in predicting MACCE within 30 days after noncardiac surgery, demonstrating superior calibration and generalizability across institutions. Its use can optimize preoperative evaluations, minimize unnecessary testing, and streamline perioperative care, significantly improving patient outcomes and resource use. …”
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Benchmarking Federated Few-Shot Learning for Video-Based Action Recognition by Nguyen Anh Tu, Nartay Aikyn, Nursultan Makhanov, Assanali Abu, Kok-Seng Wong, Min-Ho Lee

“…Additionally, we explore three meta-learning paradigms and three FL algorithms to investigate their effectiveness and suggest the optimal choices for performance improvement. …”
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Linguistic Markers of Pain Communication on X (Formerly Twitter) in US States With High and Low Opioid Mortality: Machine Learning and Semantic Network Analysis by ShinYe Kim, Winson Fu Zun Yang, Zishan Jiwani, Emily Hamm, Shreya Singh

“…MethodsWe collected 1,438,644 pain-related tweets posted between January and December 2021 using tweepy and snscrape. …”
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