TEEMLEAP—A New Testbed for Exploring Machine Learning in Atmospheric Prediction for Research and Education by J. Wilhelm, J. Quinting, M. Burba, S. Hollborn, U. Ehret, I. Pena Sánchez, S. Lerch, J. Meyer, B. Verfürth, P. Knippertz

“…Abstract In the past 5 years, data‐driven prediction models and Machine Learning (ML) techniques have revolutionized weather forecasting. …”
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Structure optimization of jacquard lifting knife on NURBS curve by YANG ZhenNing, LI YuLiang

“…Finally, aiming at the minimum quality, the primal-dual infeasible-interior-point algorithm was used to optimize the lifting knife structure. The optimization result shows that compared with the optimized initial value model, the mass of lifting knife decreases by about 1.7% and the maximum deflection decreases by about 26.67%.…”
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Prediction model of water inrush risk level of coal seam floor based on KPCA-DBO-SVM by Wei Wang, Huangrui Wang, Xuping Li, Yun Qi, Xinchao Cui, Chenhao Bai

“…Secondly, Kernel Principal Component Analysis (KPCA) is used to reduce the dimension of high-dimensional features of the influencing factors, Then, results of feature extraction are input into the DBO-SVM model. Penalty parameters and kernel parameters of Support Vector Machine (SVM) are optimized by Dung Beetle Optimization algorithm (DBO). …”
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Multi-omics and AI-driven immune subtyping to optimize neoantigen-based vaccines for colorectal cancer by Karthick Vasudevan, Dhanushkumar T, Sripad Rama Hebbar, Prasanna Kumar Selvam, Majji Rambabu, Krishnan Anbarasu, Karunakaran Rohini

“…Neoantigen-based vaccines could potentially boost tumor recognition and improve survival for patients in immune-cold subtypes. Machine learning models like LightGBM, XGBoost, and XGBRF predicted optimal immune targets for vaccine design, validated through SHAP analysis. …”
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Hybrid Machine Learning in Hydrological Runoff Forecasting: An Exploration of Extreme Gradient-Boosting and Categorical Gradient Boosting Optimization in the Russian River Basin by Reza Seifi Majdar, Ali Rahnamaei, Vahid Babazadeh

“…These models are known for handling complex, non-linear relationships and offering high accuracy with efficient computation. …”
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Machine-learning based optimizing the neutronic and thermal-hydraulic performance in a VVER-1000 mixed-core as well as fuel burnup assessment by A. Koraniany, G.R. Ansarifar

“…By integrating this neural network with a genetic algorithm, optimization was carried out to identify the optimal fuel placement and enrichment for loading the UTVS fuel assembly into the targeted reactor core. …”
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A novel dynamic scheduling model for application in multimode approach by Zineb Elqabli, Oulaid Kamach, Abdelhakim Khatab, Youness Chater

“…A novel job dynamic scheduling optimization model is proposed to enhance the multimode system resiliency and robustness to unexpected events. …”
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Machine learning approaches for forecasting compressive strength of high-strength concrete by Mohammed Shaaban, Mohamed Amin, S. Selim, Islam M. Riad

“…This research proposes a machine learning (ML) model using the Python programming language to predict the compressive strength of HSC. …”
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An Innovative Smart Irrigation Using Embedded and Regression-Based Machine Learning Technologies for Improving Water Security and Sustainability by Abdennabi Morchid, Abdennacer Elbasri, Zahra Oughannou, Hassan Qjidaa, Rachid El Alami, Badre Bossoufi, Saleh Mobayen, Pawel Skruch

“…The use of embedded systems and machine learning offers a solution for optimizing irrigation according to local conditions and actual crop needs while contributing to food security and environmental sustainability. …”
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Optimized ensemble learning for non-destructive avocado ripeness classification by Panudech Tipauksorn, Prasert Luekhong, Minoru Okada, Jutturit Thongpron, Chokemongkol Nadee, Krisda Yingkayun

“…Four algorithms were used to optimize the model weight distribution: Bayesian Optimisation, Differential Evolution, Particle Swarm Optimisation, and Grid Search. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…However, the effects of immunosuppressants exposure was not considered in previous ML models. Thus, the purpose of this study was to develop and optimize models by Cox regression and machine learning algorithms to predict the risk of aGVHD in which cyclosporin A exposure and common clinical factors were included as variables. …”
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Synergizing neural networks with multi-objective thermal exchange optimization and PROMETHEE decision-making to improve PCM-based photovoltaic thermal systems by Yongxin Li, Ali Basem, As'ad Alizadeh, Pradeep Kumar Singh, Saurav Dixit, Hanaa Kadhim Abdulaali, Rifaqat Ali, Pancham Cajla, Husam Rajab, Kaouther Ghachem

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Evaluation of a Machine Learning Model Based on Laboratory Parameters for the Prediction of Influenza A and B in Chongqing, China: Multicenter Model Development and Validation Stud... by Weiwei Hu, Yulong Liu, Jian Dong, Xuelian Peng, Chunyan Yang, Honglin Wang, Yong Chen, Shan Shi, Jin Li

“…ObjectiveThis study aimed to develop a machine learning model using 24 routine blood parameters to predict influenza A and B infections and validate a deployable diagnostic tool for low-resource clinical settings. …”
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Energy Management and Edge-Driven Trading in Fractal-Structured Microgrids: A Machine Learning Approach by Mostafa Pasandideh, Jason Kurz, Mark Apperley

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Research on trajectory planning method for food sorting robot based on machine vision and improved BOA by CUI Wei, YUAN Rui, SUN Litao, WANG Wei

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Leveraging Machine Learning for Enhanced Bug Triaging in Open-Source Software Projects by Nitanta Adhikari, Rabindra Bista, Joao Carlos Ferreira

“…Using a dataset of over 122,000 issues from the microsoft/vscode GitHub repository, we evaluate several machine learning models including Bidirectional LSTM, CNN-LSTM, Random Forest, and Multinomial Naive Bayes. …”
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Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction by Jingjing Chen, Dan Zhang, Chengxiu Zhu, Lin Lin, Kejun Ye, Ying Hua, Mengjia Peng

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

“…Leveraging ML, the framework offers a promising approach to optimizing medication prescriptions and improving patient outcomes.…”
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Dynamic weighted ensemble model for predictive optimization in green sand casting: Advancing industry 4.0 manufacturing by Rajesh V․ Rajkolhe, Dr. Sanjay S․ Bhagwat, Dr. Priyanka V․ Deshmukh

“…To overcome the limitations of individual machine learning models and static ensemble strategies, a novel Dynamic Weighted Ensemble (DWE) model is introduced. …”
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Personalized Human Thermal Sensation Prediction Based on Bayesian-Optimized Random Forest by Hao Yang, Maoyu Ran

“…More accurate personalized thermal sensation prediction models were then constructed using various machine learning algorithms, followed by a comparative analysis of their performance. …”
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