Hybrid incompressible SPH–machine learning approach for simulating natural convection in a porous intricate domain by Noura Alsedais, Abdelraheem M. Aly

“…The integration of machine learning with ISPH modeling offers a computationally efficient framework for optimizing NEPCM-based cooling and storage technologies in electronics, solar energy systems, and industrial heat exchangers.…”
Get full text

Machine learning-based prediction of volatile compounds profiles in Saccharomyces cerevisiae fermentation simulating canned meat by Lin Du, Shujie Wang, Yongyan Chen, Zhongxu Zhu, Hai-Xi Sun, Tsan-Yu Chiu

“…A two-stage model was developed to quantify the importance of volatile compounds and predict meat aroma and the gradient-boosted decision trees (GBDT) model demonstrated optimal performance. …”
Get full text

Daily reference evapotranspiration prediction in Iran: A machine learning approach with ERA5-land data by Ali Asghar Zolfaghari, Maryam Raeesi, Giuseppe Longo-Minnolo, Simona Consoli, Miles Dyck

“…This study evaluates the potential of ERA5-Land reanalysis data, in combination with a Random Forest (RF) machine learning model, to predict daily and 8-day ET₀ across these diverse climatic conditions. …”
Get full text

Predicting the Relative Density of Stainless Steel and Aluminum Alloys Manufactured by L-PBF Using Machine Learning by José Luis Mullo, Iván La Fé-Perdomo, Jorge Ramos-Grez, Ángel F. Moreira Romero, Alejandra Ramírez-Albán, Mélany Yarad-Jácome, Germán Omar Barrionuevo

“…LazyPredict was employed to select the algorithm that best models the variability of the inherent data. Ensemble boosting regressors offer higher accuracy, providing hyperparameter fitting and optimization advantages. …”
Get full text

Hierarchical Information-Extreme Machine Learning of Hand Prosthesis Control System Based on Decursive Data Structure by Anatolii Dovbysh, Vladyslav Piatachenko, Mykyta Myronenko, Mykyta Suprunenko, Julius Simonovskiy

“…The modified Kullback–Leibler information measure was the optimization criterion for machine learning parameters. …”
Get full text

Sustainable Energy and Exergy Analysis in Offshore Wind Farms Using Machine Learning: A Systematic Review by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Abdul Hameed Kalifullah, Arife Tugsan Isiacik Colak, Md Redzuan Zoolfakar

“…This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. …”
Get full text

Real-time prediction of HFNC treatment failure in acute hypoxemic respiratory failure using machine learning by Xiaojie Li, Chunliang Jiang, Qingyan Xie, Huiquan Wang, Jiameng Xu, Guanjun Liu, Panpan Chang, Guang Zhang

“…To address this, we developed a machine learning-based predictive model using temporal data from AHRF patients, aimed at facilitating quicker development of individualized treatment plans and intervention strategies for healthcare professionals. …”
Get full text

Guided Particle Swarm Optimization for Feature Selection: Application to Cancer Genome Data by Simone A. Ludwig

“…Feature selection is a crucial step in the data preprocessing stage of machine learning. It involves selecting a subset of relevant features for use in model construction. …”
Get full text

Predicting high-need high-cost pediatric hospitalized patients in China based on machine learning methods by Peng Zhang, Bifan Zhu, Xing Chen, Linan Wang

“…There is an urgent need to establish a specific, valid, and reliable prediction model using machine-learning-based methods to identify potential HNHC pediatric patients and implement proactive interventions before high costs arise. …”
Get full text

Automated machine learning for predicting perioperative ischemia stroke in endovascularly treated ruptured intracranial aneurysm patients by Yuhang Peng, Ke Bi, Xiaolin Zhang, Ning Huang, Xiang Ji, Weifu Chen, Ying Ma, Yuan Cheng, Yongxiang Jiang, Jianhe Yue

“…Based on these features, nine machine learning models were constructed using a training set (75% of participants) and assessed on a test set (25% of participants). …”
Get full text

Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Entity embeddings for each relation from top-performing models (based on MRR) were input into 7 machine learning classifiers separately, creating 1,050 predictive pipelines evaluated through extensive experimentation and hyperparameter optimization. …”
Get full text

Enhancing english oral translation through cross-modal learning and synchronous optimization. by Yan Wang

“…Experimental results reveal that, when compared to the existing bilingual attention neural machine translation (NMT) model and the context-aware NMT model, the model proposed in this study yields an average bilingual evaluation understudy (BLEU) score that is 9.3% and 26.9% higher, respectively. …”
Get full text

Selective Cleaning Enhances Machine Learning Accuracy for Drug Repurposing: Multiscale Discovery of MDM2 Inhibitors by Mohammad Firdaus Akmal, Ming Wah Wong

“…The optimized model was integrated with structure-based virtual screening via molecular docking to prioritize repurposing candidate compounds. …”
Get full text

Application of machine learning algorithms and SHAP explanations to predict fertility preference among reproductive women in Somalia by Jamilu Sani, Salad Halane, Abdiwali Mohamed Ahmed, Mohamed Mustaf Ahmed

“…Seven ML algorithms were evaluated for predictive performance, with Random Forest emerging as the optimal model based on metrics such as accuracy, precision, recall, F1-score, and the Area Under the Receiver Operating Characteristic Curve (AUROC). …”
Get full text

Research on Path Optimization of Vehicle-Drone Joint Distribution considering Customer Priority by Xiaoye Zhou, Yuhao Feng

“…To meet the personalized distribution needs of customers, comprehensively consider customer value, the urgency of customer needs, and the impact of priority distribution to the customer on the enterprise, and based on regional restrictions, put forward vehicle-drone joint distribution path optimization problem considering customer priority. First, the goal is to minimize the sum of total distribution cost and customer priority cost integrating soft time windows and constructing a path optimization model of the vehicle-drone joint distribution. …”
Get full text

Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning by Peng Huang, Pan Yang, Libiao Yang, Futong Xiao, Yanqi Feng, Yuchao Wang

“…Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.…”
Get full text

Machine Learning Based Early Diagnosis of ADHD with SHAP Value Interpretation: A Retrospective Observational Study by Zhang X, Xiao X, Luo Y, Xiao W, Cao Y, Chang Y, Wu D, Xu H, Zhao J, Deng X, Jiang Y, Xie R, Liu Y

“…Feature selection and model construction were performed using various machine learning algorithms.Results: Our results indicated that the Gradient Boosting Machine algorithm is the optimal model.Conclusion: Our machine learning analyses suggest that the Gradient Boosting Machine (GBM) model may be the optimal choice, highlighting blood beta-2 microglobulin levels, red blood cell distribution width, 25-dihydroxyvitamin D3, and the percentage of eosinophils as key predictors of ADHD risk, thereby aiding early diagnosis. …”
Get full text

High-accuracy prediction of vessels’ estimated time of arrival in seaports: A hybrid machine learning approach by Sunny Md. Saber, Kya Zaw Thowai, Muhammad Asifur Rahman, Md. Mehedi Hassan, A.B.M. Mainul Bari, Asif Raihan

“…Compared to existing machine learning algorithms, our stacking model exhibits superior prediction performance. …”
Get full text

Rapid Identification and Typing of Carbapenem‐Resistant Klebsiella pneumoniae Using MALDI‐TOF MS and Machine Learning by Zhiyi Ye, Jin Zhu, Yang Liu, Jun Lu

Get full text

Machine learning of whole-brain resting-state fMRI signatures for individualized grading of frontal gliomas by Yue Hu, Xin Cao, Hongyi Chen, Daoying Geng, Kun Lv

“…Based on these features, the model was established using the SVM had an optimal performance. …”
Get full text