Prediction of compressive strength of fly ash-based geopolymers concrete based on machine learning by Hesong Hu, Mingye Jiang, Mengxiong Tang, Huqing Liang, Hao Cui, Chunlin Liu, Chunjie Ji, Yaozeng Wang, Simin Jian, Chaohai Wei, Siqi Song

“…The results suggest that all four machine learning models are proficient in accurately predicting the compressive strength of FA-GPC, with the GBDT model displaying superior predictive performance. …”
Get full text

Artificial intelligence and machine learning in acute respiratory distress syndrome management: recent advances by Songbei Li, Songbei Li, Ruiming Yue, Sen Lu, Jingchao Luo, Xiaoxiao Wu, Zhao Zhang, Mingzong Liu, Yuxin Fan, Yuxin Fan, Yuxuan Zhang, Yuxuan Zhang, Chun Pan, Xiaobo Huang, Hongli He

“…This review highlights the transformative potential of Artificial Intelligence (AI) and Machine Learning (ML) in revolutionizing ARDS management. …”
Get full text

Is 2 h of Hypothermic Machine Perfusion for Pancreas Preservation Effective in Improving Graft Reperfusion? by Benoit Mesnard, MD, Christophe Masset, MD, PhD, Etohan Ogbemudia, MD, PhD, Sarah Bruneau, PhD, Mohamed Elzawahry, MD, Stéphanie Le Bas-Bernardet, PhD, David Minault, Jeremy Hervouet, Diego Cantarovich, MD, PhD, Jérôme Rigaud, MD, PhD, Lionel Badet, MD, PhD, Peter Friend, MD, PhD, Rutger Ploeg, MD, PhD, Gilles Blancho, MD, PhD, James Hunter, MD, PhD, Thomas Prudhomme, MD, Julien Branchereau, MD, PhD

“…A short period of HMP could optimize the pancreas for reperfusion while minimizing the side effects related to perfusion. …”
Get full text

Optimization of Axial Misalignment due to Glass Drilling by Statistical Methods by Sabri Ozturk, Faruk Harmancı

“…According to the results, the suggested model and optimization method could be used for estimating axial misalignment and this investigation is reliable and proper for figuring out the problems met in machining operations. …”
Get full text

Building Safer Social Spaces: Addressing Body Shaming with LLMs and Explainable AI by Sajedeh Talebi, Neda Abdolvand

“…We assess traditional Machine Learning (ML), Deep Learning (DL), and transformer-based Large Language Models (LLMs) for detection, employing accuracy, F1-score, and Area Under the Curve (AUC). …”
Get full text

Bridging Machine Learning and Cosmological Simulations: Using Neural Operators to emulate Chemical Evolution by Pelle van de Bor, John Brennan, John A. Regan, Jonathan Mackey

“…Compared to Grackle, the machine learning models provide computational speedups of up to a factor of six in large-scale simulations, highlighting their potential for reducing computational bottlenecks in astrophysical modeling. …”
Get full text

Cooperative game based bandwidth allocation mechanism live migration of multiple virtual machines by Yong CUI, Yu-song LIN, Run-zhi LI, Zong-min WANG

“…A cooperative game based band-width allocation mechanism in live migration of multip virtual machines was proposed, which models the bandwidth allocation problem as a Nash bargaining game and attains a desirable bandwidth sharing scheme that guarantees Pareto optimality. …”
Get full text

Time Series Analysis of Solar Power Generation Based on Machine Learning for Efficient Monitoring by Umer Farooq, Muhammad Faheem Mushtaq, Zahid Ullah, Muhammad Talha Ejaz, Urooj Akram, Sheraz Aslam

“…Contrarily, the second power plant achieved an accuracy of 0.61% with the gradient boosting classifier and 0.62% with the linear regression models. This study's techniques and insights can help PV plant operators and electricity market stakeholders make informed decisions to optimize the use of generated PV power, minimize waste, plan for system preservation, reduce costs, and facilitate the widespread integration of PV power into the electricity grid.…”
Get full text

Machine Learning‐Based Failure Prediction in Concrete Slabs and Cubes Under Impact Loading by Mohammad Hematibahar, Ahmed Deifalla, Adham E. Ragab, Gebre Tesfaldet

“…Using these models allows engineers to design more resistant and optimal structures against impact loads. …”
Get full text

Economic implications of artificial intelligence-driven recommended systems in healthcare: a focus on neurological disorders by Jing Zhang, Shihui Xiang, Li Li

Get full text

Phenology-Aware Machine Learning Framework for Chlorophyll Estimation in Cotton Using Hyperspectral Reflectance by Chunbo Jiang, Yi Cheng, Yongfu Li, Lei Peng, Gangshang Dong, Ning Lai, Qinglong Geng

“…This study introduces a phenology-aware machine learning framework that combines hyperspectral reflectance data with various regression models to estimate leaf chlorophyll content (LCC) in cotton at six key reproductive stages. …”
Get full text

Machine learning-driven molecular dynamics decodes thermal tuning in graphene foam composites by Pingyang Zhang, Shaodong Zhang, Yihan Qin, Tingting Du, Lei Wei, Xiangyu Li

Get full text

Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Despite recent advances, maintaining Hb levels within the recommended range (10–12 g/dL) remains difficult owing to inter- and intra-patient variability. Machine learning (ML) has shown potential in optimizing anemia management by predicting Hb levels and reducing ESA usage, though clinical implementation remains limited. …”
Get full text

A Comparative Evaluation of Machine Learning Methods for Predicting Student Outcomes in Coding Courses by Zakaria Soufiane Hafdi, Said El Kafhali

Get full text

ADVANCED CLUSTERING TECHNIQUES FOR TIN DEPOSIT CLASSIFICATION IN MALAYSIA: A MACHINE LEARNING APPROACH by Meisam Saleki, Kamran Mostafaei, Zakaria Bin Endut, Mohammad Nabi Kianpour

Get full text

Exposing factors influencing Korean leisure life satisfaction through machine learning techniques by Yong-Kwan Lee, Boohyun Kim, Jinheum Kim

“…Abstract This study examines factors influencing leisure life satisfaction (LLS) through machine learning techniques based on the data from the 2019 National Leisure Activity Survey in Korea. …”
Get full text

Enhancing Medical Diagnostics with Machine Learning: A Study on Ensemble Methods and Transfer Learning by Zhang Jiaming

“…This paper explores the use of machine learning (ML) in medicine, emphasizing how important it is to enhance patient outcomes and diagnostic precision. …”
Get full text

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective. by Md Maeen Molla, Md Sifat Hossain, Md Ayub Ali, Md Raqibul Islam, Mst Papia Sultana, Dulal Chandra Roy

“…Additionally, Machine Learning (ML) models, including Bidirectional Recurrent Neural Networks (BRNN) and Elastic Neural Networks (ENET), were employed for all the indicators.…”
Get full text

Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…Specifically, we hypothesize that automated machine learning (AutoML), which optimizes algorithm selection and hyperparameters, will outperform an unoptimized Random Forest (RF) classifier and the cross-correlation coefficient (R2), a commonly used metric for comparing age distributions. …”
Get full text

Aircraft Skin Machine Learning-Based Defect Detection and Size Estimation in Visual Inspections by Angelos Plastropoulos, Kostas Bardis, George Yazigi, Nicolas P. Avdelidis, Mark Droznika

Get full text