Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms by Yiwei Chen, Kejun Pan, Xiaobo Lu, Erxiding Maimaiti, Maimaitiaili Wubuli

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Personalized prediction of psoriasis relapse post-biologic discontinuation: a machine learning-driven population cohort study by Shan Huang, Yanping Bai, Ruozhou Qi, Hongda Yu, Xingwu Duan

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Optimizing concrete strength: How nanomaterials and AI redefine mix design by Dan Huang, Guangshuai Han, Ziyang Tang

“…However, the complex interactions between nanomaterials, SCMs, and cement make concrete mix design a challenging, iterative, and labor-intensive process, often relying on trial-and-error experimentation. Machine learning (ML) offers an opportunity to better understand the influence of input parameters and to accelerate the optimization of mix designs through data-driven insights. …”
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Optimizing Air Pollution Forecasting Across Temporal Scales: A Case Study in Salamanca, Mexico by Francisco-Javier Moreno-Vazquez, Felipe Trujillo-Romero, Amanda Enriqueta Violante Gavira

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Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm by Dike Fitriansyah Putra, Mohd Zaidi Jaafar, Ku Muhd Na’im Khalif, Apri Siswanto, Ichsan Lukman, Ahmad Kurniawan

“…This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. …”
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Hybrid Machine Learning for IoT-Enabled Smart Buildings by Robert-Alexandru Craciun, Simona Iuliana Caramihai, Ștefan Mocanu, Radu Nicolae Pietraru, Mihnea Alexandru Moisescu

“…The paper begins with a comprehensive review of existing hybrid machine learning models for IDS, highlighting both their strengths and limitations. …”
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Intelligent energy management of microgrids using machine learning: Leveraging random forest models for solar and wind power by Hasanur Zaman Anonto, Md Ismail Hossain, Abu Shufian, Md. Shaoran Sayem, S M Tanvir Hassan Shovon, Protik Parvez Sheikh, Sadman Shahriar Alam

“…In upcoming studies, the model needs to be stretched further by using multi-year datasets and highly optimized solutions in the implementation of the model to foster the scalability and flexibility of smart-grid systems towards new developments in energy requirements.…”
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Analysis and modeling of COVID-19 epidemic dynamics in Saudi Arabia using SIR-PSO and machine learning approaches by Rafat Zrieq, Sahbi Boubaker, Souad Kamel, Mohamed Alzain, Fahad D. Algahtani

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Study of Factors Influencing Thermal Comfort at Tram Stations in Guangzhou Based on Machine Learning by Xin Chen, Huanchen Zhao, Beini Wang, Bo Xia

“…We collected microclimatic data from tram stations in Guangzhou, along with passenger comfort feedback, to construct a comprehensive dataset encompassing environmental parameters, individual perceptions, and design characteristics. A variety of ML models, including Extreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Random Forest (RF), and K-Nearest Neighbors (KNNs), were trained and validated, with SHAP analysis facilitating the ranking of significant factors. …”
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Machine learning algorithms to predict epidural-related maternal fever: a retrospective study by Xiaohui Guo, Xiaohui Guo, Xiaohui Guo, Haixia Zhang, Haixia Zhang, Haixia Zhang, Hongliang Mei, Hongliang Mei, Hongliang Mei

“…Consequently, the LR model was selected as the prediction model. Furthermore, the LR-based nomogram identified eight significant predictors of ERMF, including neutrophil percentage, first stage of labor, amniotic fluid contamination during membrane rupture, artificial rupture of membranes, chorioamnionitis, post-analgesic antimicrobials, pre-analgesic oxytocin, post-analgesic oxytocin, and dinoprostone suppositories.ConclusionOptimally applying logistic regression models can enable rapid and straightforward identification of ERMF risk and the implementation of rational therapeutic measures, in contrast to machine learning models.…”
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Design and Experiment of a Vertical Cotton Stalk Crushing and Returning Machine with Large and Small Dual-Blade Discs by Xiaohu Guo, Bin Li, Yang Liu, Shiguo Wang, Zhong Tang, Yuncheng Dong, Xiangxin Liu

“…To address the problems of low crushing efficiency and uneven distribution in traditional straw crushing and returning machines for cotton stalk return operations in Xinjiang, a vertical straw crushing and returning machine with large and small dual-blade discs was designed, adapted to Xinjiang’s cotton planting model. …”
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Electromagnetic Analysis and Multi-Objective Design Optimization of a WFSM with Hybrid GOES-NOES Core by Kyeong-Tae Yu, Hwi-Rang Ban, Seong-Won Kim, Jun-Beom Park, Jang-Young Choi, Kyung-Hun Shin

“…This study presents a design and optimization methodology to enhance the power density and efficiency of wound field synchronous machines (WFSMs) by selectively applying grain-oriented electrical steel (GOES). …”
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Estimation and validation of solubility of recombinant protein in E. coli strains via various advanced machine learning models by Wael A. Mahdi, Adel Alhowyan, Ahmad J. Obaidullah

“…Abstract This study presents a comprehensive approach to predicting solubility of recombinant protein in four E. coli samples by employing machine learning techniques and optimization algorithms. …”
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Enhanced Classification of Ear Disease Images Using Metaheuristic Feature Selection by Murat Ekinci, Furkancan Demircan, Zafer Cömert, Eyup Gedikli

“…Furthermore, the Support Vector Machine (SVM) model achieved an accuracy of 92% using a feature map comprising features selected by a range of optimization algorithms. …”
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Multi-Scale Contextual Coding for Human-Machine Vision of Volumetric Medical Images by Jietao Chen, Weijie Chen, Qianjian Xing, Feng Yu

“…Different from the existing 3D convolutional compression algorithms oriented only for human vision, this paper proposes a Multi-scale Contextual Autoencoder (MCAE) architecture that recurrently incorporates anatomical inter-slice context to optimize the compression of the current slice for both human and machine vision. …”
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Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy by Qianchang Wang, Zhe Wang, Fangfeng Liu, Zhengjian Wang, Qingqiang Ni, Hong Chang

“…However, the heterogeneity of its risk factors and the clinical utility of predictive models remain to be fully elucidated. This study aims to systematically analyze the risk factors for CR-POPF and develop an optimized predictive model using machine learning algorithms, providing an evidence-based approach for individualized risk assessment in patients undergoing LPD. …”
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Machine learning techniques in monitoring and controlling friction stir welding process: a critical review by Bhardwaj Kulkarni, Saurabh Tayde, Yashwant Chapke, Swapnil Vyavahare, Avinash Badadhe

“…This review article critically explores how machine learning can optimize process parameters, identify and rectify defects, predict and manage tool failures, and control corrosion rates to achieve high-quality FSW joints.Using causative variable model accuracy in predicting tool failure increases from 87 to 98.1% as compared to raw FSW process variables.…”
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Enhanced Prediction of California Bearing Ratio (CBR) Values in Geotechnical Engineering Using Decision Tree Algorithm and Meta-Heuristic Optimizations by Linda Davies, Dominik Jánošík

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A convolutional neural network model and algorithm driven prototype for sustainable tilling and fertilizer optimization by Sajeev Magesh

“…The machine learning model utilizes a camera-captured field image to determine existing tilling intensity on a 7-point scale. …”
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Adaptive and Scalable Database Management with Machine Learning Integration: A PostgreSQL Case Study by Maryam Abbasi, Marco V. Bernardo, Paulo Váz, José Silva, Pedro Martins

“…This study is motivated by the need for autonomous tuning mechanisms to manage large-scale, heterogeneous workloads while answering key research questions, such as the following: (1) How can machine learning models be integrated into a DBMS to improve query optimization and workload management? …”
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