Predictive model establishment for forward-head posture disorder in primary-school-aged children based on multiple machine learning algorithms by Hongjun Tao, Yang Wen, Rongfang Yu, Yining Xu, Fangliang Yu

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Machine learning algorithms predict breast cancer incidence risk: a data-driven retrospective study based on biochemical biomarkers by Qianqian Guo, Peng Wu, Junhao He, Ge Zhang, Wu Zhou, Qianjun Chen

“…Results The data were divided into a training cohort of 17,360 cases and a testing cohort of 8,551 cases. …”
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Construction of a risk prediction model for postoperative deep vein thrombosis in colorectal cancer patients based on machine learning algorithms by Xin Liu, Xingming Shu, Yejiang Zhou, Yifan Jiang

“…We employed the Synthetic Minority Oversampling Technique to address imbalanced data and split the dataset into training and validation sets in a 7:3 ratio. Feature selection was performed using Random Forest (RF), XGBoost, and Least Absolute Shrinkage and Selection Operator algorithms (LASSO). …”
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Exploration of the Ignition Delay Time of RP-3 Fuel Using the Artificial Bee Colony Algorithm in a Machine Learning Framework by Wenbo Liu, Zhirui Liu, Hongan Ma

“…Ignition delay time (IDT) is a critical parameter for evaluating the autoignition characteristics of aviation fuels. …”
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Development of a prognostic model for breast cancer patients based on intratumoral tumor-infiltrating lymphocytes using machine learning algorithms by Xinyi Wu, Chun Li

“…Results Our study constructed a pioneering prognostic model based on iTIL-centric signature via a machine learning framework that evaluated 101 algorithm combinations. This model revealed significant differences in the immune landscape among stratified patient cohorts, and demonstrated robust predictive capabilities across multiple datasets. …”
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Skeletal Muscle Segmentation at the Level of the Third Lumbar Vertebra (L3) in Low-Dose Computed Tomography: A Lightweight Algorithm by Xuzhi Zhao, Yi Du, Haizhen Yue

“…The performance of the proposed algorithm was evaluated in terms of the Dice similarity coefficient (DSC), precision, recall, 95th percentile of the Hausdorff distance (HD95), and average surface distance (ASD). …”
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Development and validation of a risk prediction model for kinesiophobia in postoperative lung cancer patients: an interpretable machine learning algorithm study by Chuang Li, Youbei Lin, Xuyang Xiao, Xinru Guo, Jinrui Fei, Yanyan Lu, Junling Zhao, Lan Zhang

“…This study demonstrates that machine learning models—particularly the RF algorithm—hold substantial promise for predicting kinesiophobia in postoperative lung cancer patients. …”
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Identification of novel metabolism-related biomarkers of Kawasaki disease by integrating single-cell RNA sequencing analysis and machine learning algorithms by Chenhui Feng, Zhimiao Wei, Xiaohui Li, Xiaohui Li

“…Through differential expressed genes (DEG) analysis, high-dimensional Weighted Correlation Network Analysis (hdWGCNA) and machine learning algorithms, we identified signature genes associated with both BAM and FAM. …”
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Government-Expert Joint Intervention with Treatment Algorithm and Improved Hypertension Management and Reduced Stroke Mortality in a Primary-Care Setting by Mulalibieke Heizhati, Nanfang Li, Delian Zhang, Suofeiya Abulikemu, Guijuan Chang, Jing Hong, Nuerguli Maimaiti, Junli Hu, Lei Wang, Gulinuer Duiyimuhan

“…Primary-care providers were trained on treatment algorithm and physicians for specialized management. …”
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Advanced reference crop evapotranspiration prediction: a novel framework combining neural nets, bee optimization algorithm, and mode decomposition by Ahmed Elbeltagi, Okan Mert Katipoğlu, Veysi Kartal, Ali Danandeh Mehr, Sabri Berhail, Elsayed Ahmed Elsadek

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A multilevel boost DC-DC converter with MPPT algorithm for the rooftop PV system of the urban railway Nhon - Hanoi Station by Tran Hung Cuong, An Thi Hoai Thu Anh

“…The process of evaluating the effectiveness of the proposed converter configuration and the algorithm is performed on Matlab/Simulink software, and the output parameters of the conversion process are compared with a conventional Boost DC-DC converter based on published analyses.…”
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Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography by Luke Oakden-Rayner, Catherine M Jones, John Lambert, Jarrel Seah, Cyril Tang, Quinlan D Buchlak, Michael Robert Milne, Xavier Holt, Hassan Ahmad, Nazanin Esmaili, Peter Brotchie

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Human-Centred Design Meets AI-Driven Algorithms: Comparative Analysis of Political Campaign Branding in the Harris–Trump Presidential Campaigns by Hedda Martina Šola, Fayyaz Hussain Qureshi, Sarwar Khawaja

“…Metrics including total attention, engagement, start attention, end attention, and percentage seen were evaluated across 13–14 areas of interest (AOIs) for each design. …”
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Introducing HeliEns: A Novel Hybrid Ensemble Learning Algorithm for Early Diagnosis of <i>Helicobacter pylori</i> Infection by Sultan Noman Qasem

“…The development of HeliEns involved rigorous data preprocessing steps, including data cleaning, encoding of categorical variables, and feature scaling, to ensure the dataset’s suitability for quantum machine learning algorithms. Individual models (QKNN, QNB, and QLR) were trained and evaluated using metrics such as accuracy, precision, recall, and F1-score. …”
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Comparative Performance of Autoencoders and Traditional Machine Learning Algorithms in Clinical Data Analysis for Predicting Post-Staged GKRS Tumor Dynamics by Simona Ruxandra Volovăț, Tudor Ovidiu Popa, Dragoș Rusu, Lăcrămioara Ochiuz, Decebal Vasincu, Maricel Agop, Călin Gheorghe Buzea, Cristian Constantin Volovăț

“…Traditional ML models, such as Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Extra Trees, Random Forest, and XGBoost, were trained and evaluated. The study further explored the impact of incorporating features derived from autoencoders, particularly focusing on the effect of compression in the bottleneck layer on model performance. …”
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An electricity price optimization model considering time-of-use and active distribution network efficiency improvements by Yan Li, Yaheng Su, Qixin Zhao, Bala Wuda, Kaibo Qu, Lei Tang

“…This model combines an improved Particle Swarm Optimization algorithm, Quantum-behaved Particle Swarm Optimization, and the Shuffle Frog Leaping Algorithm. …”
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RETRACTED: Intelligent power grid energy supply forecasting and economic operation management using the snake optimizer algorithm with Bigur-attention model by Lingling Cui, Jiacheng Liao

“…The research process includes data preprocessing, model training, and model evaluation. Data preprocessing ensures data quality and suitability. …”
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Estimating ocean currents from the joint reconstruction of absolute dynamic topography and sea surface temperature through deep learning algorithms by D. Ciani, C. Fanelli, B. Buongiorno Nardelli

“…This modification allows us to evaluate the potential enhancement in the ADT and SST mapping while integrating dynamical constraints through tailored, physics-informed loss functions. …”
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Evaluating performance and generalizability of Learning from Demonstration for the harvesting of apples &amp; pears by Robert van de Ven, Ard T. Nieuwenhuizen, Eldert J. van Henten, Gert Kootstra

“…We performed apple and pear harvesting with the LfD-method trained on five, ten, twenty and forty demonstrations to evaluate the effect of increasing the number of demonstrations. …”
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Empirical Evaluation on GPU, Overclocking, and LoRA for Deep Learning on Embedded Systems by Evandro Raphaloski, Mariana Caravanti De Souza, Edson Takashi Matsubara

“…What distinguishes our study is its multi-platform, multi-strategy evaluation, which combines hardware tuning with software training strategies. …”
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