Graph reinforcement learning-driven source-load cooperative scheduling optimization for textile production by Tianhao Tan, Tao Wu, Jie Li, Yuyuan Lan, Jinsong Bao

“…This research proposes a graph reinforcement learning-driven source-load collaborative optimization method. …”
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Optimizing resource allocation in industrial IoT with federated machine learning and edge computing integration by Ala'a R. Al-Shamasneh, Faten Khalid Karim, Yu Wang

“…The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. …”
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Online learning to accelerate nonlinear PDE solvers: Applied to multiphase porous media flow by Vinicius L.S. Silva, Pablo Salinas, Claire E. Heaney, Matthew D. Jackson, Christopher C. Pain

“…Furthermore, this work performs a sensitivity study in the dimensionless parameters (machine learning features), assess the efficacy of various machine learning models, demonstrate a decrease in nonlinear iterations using our method in more intricate, realistic three-dimensional models, and fully couple a machine learning model into an open-source multiphase flow simulator achieving up to 85% reduction in computational time.…”
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Rapid mapping of landslides using satellite SAR imagery: A progressive learning approach by Nikhil Prakash, Andrea Manconi, Alessandro Cesare Mondini

“…The choice of active learning prioritizes speed over accuracy. However, we note only a modest reduction in performance (∼10% drop in F1 and MCC scores), with our method allowing a preliminary landslide inventory to be completed within a single day. …”
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Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook by Pengcheng Xue, Rui Qiu, Chuchuan Peng, Zehang Peng, Kui Ding, Rui Long, Liang Ma, Qifeng Zheng

“…Abstract The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. …”
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Advanced Handover Optimization (AHO) using deep reinforcement learning in 5G Networks by T. Senthil Kumar, Mardeni Roslee, J. Jayapradha, Yasir Ullah, Chilakala Sudhamani, Sufian Mousa Ibrahim Mitani, Anwar Faizd Osman, Fatimah Zaharah Ali

“…The proposed model contributes a scalable, learning based optimization strategy applicable to systems of future generation wireless communication.…”
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Learning on Bandwidth Constrained Multi-Source Data With MIMO-Inspired DPP MAP Inference by Xiwen Chen, Huayu Li, Rahul Amin, Abolfazl Razi

“…Extensive experiments verify the superiority of our scalable method over the most commonly used data selection methods, including GreeDi, Greedymax, random selection, and stratified sampling by a substantial gain of at least 12% reduction in Relative Diversity Error (RDE). This enhanced diversity translates to a substantial improvement in the performance of various downstream learning tasks, including multi-level classification (2%-4% gain in accuracy), object detection (2% gain in mAP), and multiple-instance learning (1.3% gain in AUC).…”
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Post COVID-19 Effect on Medical Staff and Doctors' Productivity Analysed by Machine Learning by Maitham G. Yousif, Khalid Hashim, Salman Rawaf

“…This study employs machine learning to analyze the post-COVID-19 impact on the productivity of medical staff and doctors across various specialties. …”
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Mechanical properties and machine learning analysis of concrete incorporating waste glass as coarse aggregate by Bhukya Govardhan Naik, G. Nakkeeran, Dipankar Roy, G. Uday Kiran, Kalyani Gurram, Gade Venkata Ramanjaneyulu, George Uwadiegwu Alaneme, Mutiu Shola Bakare

“…The experimental results demonstrate a gradual decrease in mechanical properties as WGCA content increases, showing a peak reduction of 10% in compressive strength, 13% in tensile strength, and 12% in flexural strength at a 15% replacement level. …”
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Learning curve in robotic rectal cancer surgery: A national two-centre study by Anislav Ventsislavov Gabarski, Paulina T. Vladova, Martin P. Karamanliev, Nikolai Ramadanov, Aleksandar K. Zlatarov, Turgay T. Kalinov

“…Although robotic-assisted surgery benefits from technical advantages that shorten the learning curve, it nonetheless presents a steep and extended learning curve. …”
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Photodiagnosis with deep learning: A GAN and autoencoder-based approach for diabetic retinopathy detection by Kerem GENCER, Gülcan GENCER, Tuğçe Horozoğlu CERAN, Aynur ER BİLİR, Mustafa DOĞAN

“…This study proposes a novel framework integrating Generative Adversarial Networks (GANs) for data augmentation, denoising autoencoders for noise reduction, and transfer learning with EfficientNetB0 to enhance the performance of DR classification models. …”
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Utilizing Machine Learning Techniques for Cancer Prediction and Classification based on Gene Expression Data by Mariwan Mahmood Hama Aziz, Sozan Abdullah Mahmood

“…Lately, several studies have delved into cancer classification by leveraging data mining techniques, machine learning algorithms, and statistical methods to thoroughly analyze high-dimensional datasets. …”
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Chondrogenic Cancer Grading by Combining Machine and Deep Learning with Raman Spectra of Histopathological Tissues by Gianmarco Lazzini, Mario D’Acunto

“…In particular, in the last years several studies have demonstrated how the diagnostic performances of RS can be significantly improved by employing machine learning (ML) algorithms for the interpretation of Raman-based data. …”
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Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…To overcome these limitations, we propose EOSNN, a neural network based physics informed deep learning method that jointly learns multiple EOS surfaces from diverse data sources, including static and dynamic compression and ab initio calculations. …”
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Nuclear morphometrics coupled with machine learning identifies dynamic states of senescence across age by Sahil A. Mapkar, Sarah A. Bliss, Edgar E. Perez Carbajal, Sean H. Murray, Zhiru Li, Anna K. Wilson, Vikrant Piprode, You Jin Lee, Thorsten Kirsch, Katerina S. Petroff, Fengyuan Liu, Michael N. Wosczyna

“…We developed a machine learning-based approach that uses nuclear morphometrics to identify senescent cells at single-cell resolution. …”
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Machine Learning Reveals Microbial Taxa Associated with a Swim across the Pacific Ocean by Garry Lewis, Sebastian Reczek, Osayenmwen Omozusi, Taylor Hogue, Marc D. Cook, Jarrad Hampton-Marcell

“…<b>Results:</b> Our findings show a significant reduction in percent fat mass (Pearson; <i>p</i> < 0.01, R = −0.89) and daily swim distance (Spearman; <i>p</i> < 0.01, R = −0.30). …”
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Scalable and robust machine learning framework for HIV classification using clinical and laboratory data by Qian Sui, Gaoxu Li, Yaqi Peng, Jiasheng Zhang, Yibo Zhang, Riyang Zhao

“…A comprehensive two-step feature selection process is employed, resulting in a reduction from 22 original features to 12 critical variables. …”
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Convolutional neural network-based deep learning for landslide susceptibility mapping in the Bakhtegan watershed by Li Feng, Maosheng Zhang, Yimin Mao, Hao Liu, Chuanbo Yang, Ying Dong, Yaser A. Nanehkaran

“…This study demonstrates that deep learning, particularly CNNs, offers a powerful and scalable solution for landslide susceptibility assessment. …”
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SHM System for Composite Material Based on Lamb Waves and Using Machine Learning on Hardware by Gracieth Cavalcanti Batista, Carl-Mikael Zetterling, Johnny Öberg, Osamu Saotome

“…The system employs machine learning (ML), specifically support vector machines (SVM), to classify damage while addressing outlier challenges with the Mahalanobis distance during the classification phase. …”
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Deep Learning-Based Adaptive Downsampling of Hyperspectral Bands for Soil Organic Carbon Estimation by Mohammad Rahman, Shyh Wei Teng, Manzur Murshed, Manoranjan Paul, David Brennan

“…The selected bands also exhibit automatic exclusion of noisy wavelengths, confirming the model&#x2019;s ability to learn scientifically aligned features.…”
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