Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study by Yue Liu, Jingshu Gao, Hang Ge, Hang Ge, Jiaxing Feng, Yu Wang, Xiaoke Wu, Xiaoke Wu

“…The mean-filling method was used to address missing data, and Lasso-Logistic regression was combined with machine learning models to identify the most significant predictors of live births. …”
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Smooth Guided Adversarial Fully Test-Time Adaptation by Dong Li, Panfei Yang

“…Fully test-time adaptation (FTTA) refers to a specific type of domain adaptation that involves adjusting a pre-trained machine learning model to work with a new target domain, without accessing any data from the source domain. …”
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Risk Model for Predicting Gaps in Surgical Oncology Care Among Patients With Stage I-III Rectal Cancer From KwaZulu-Natal, South Africa by Yoshan Moodley, Willie Brink, Jacqueline van Wyk, Shakeel Kader, Steven D. Wexner, Alfred I. Neugut, Ravi P. Kiran

“…A supervised logistic regression machine learning algorithm was used to train and test an appropriate risk model, which was translated into a nomogram. …”
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Research on collaborative scheduling strategies of multi-agent agricultural machinery groups by Ziyi Wang, Fan Zhang, Shiji Ma, Hailong Wang, Shunyao Zhang, Xiaozhong Gao

“…Abstract Addressing the challenges of high scheduling costs and low efficiency in the collaborative operations of agricultural machinery across multiple dispatch centers, this paper develops a scheduling model designed to minimize total costs. It introduces a Multi-Center and Multi-Machine Path Planning Algorithm Based on Deep Reinforcement Learning (MCMPP-DRL). …”
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A digitizing craftsmanship: a comparative study of traditional vs. SDF-based shoe last making using 3D scanning and computer numerical control technologies by G. Surya, Anbarasan P., B. Suresh

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Quantized Convolutional Neural Networks Robustness under Perturbation [version 1; peer review: 2 approved] by Guy Kember, Issam Hammad, Jack Langille

“…Contemporary machine learning models are increasingly becoming restricted by size and subsequent operations per forward pass, demanding increasing compute requirements. …”
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Globalized parameter tuning of microwave passives by dimensionality-reduced surrogates and multi-fidelity simulations by Slawomir Koziel, Anna Pietrenko-Dabrowska

“…This study introduces an alternative approach for rapid global optimization of microwave passive components using artificial intelligence (AI) techniques, specifically, machine learning (ML). The core elements of our methodology include reduction of the problem dimensionality using a rapid global sensitivity analysis, multi-fidelity EM simulations, and a two-stage search process. …”
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Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer by I-Chen Wu, Yen-Chun Chen, Riya Karmakar, Arvind Mukundan, Gahiga Gabriel, Chih-Chiang Wang, Hsiang-Chen Wang

“…Future research should emphasize dimensionality reduction techniques, the integration of multiple machine learning models, and the development of extensive spectral libraries to enhance HSI’s clinical utility in HNC diagnostics. …”
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Climate change promotes shifts of summer maize yield and water productivity in the Weihe River Basin: A regionalization study based on a distributed crop model by Wenxin Xie, Hui Ran, Anni Deng, Kunhao Jiang, Han Ru, Ning Yao, Jianqiang He, Tehseen Javed, Xiaotao Hu

“…Machine learning quantified the relative importance of key meteorological factors influencing spatial variations in yield and WP. …”
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Screening benzimidazole derivatives for atypical antipsychotic activity by K. Yu. Kalitin, O. Yu. Mukha, V. B. Voynov

“…The antipsychotic activity of the most promising compound was assessed in vivo using tests with apomorphine in rats and mice.Results. Machine learning models were developed and tested to predict the antipsychotic activity of benzimidazole derivatives. …”
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AI-powered estimation of tree covered area and number of trees over the Mediterranean island of Cyprus by Anna Zenonos, Sizhuo Li, Martin Brandt, Jean Sciare, Philippe Ciais

“…Artificial Intelligence is a powerful tool that can enable the development of tree monitoring systems by applying machine learning models to high-resolution image data. …”
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Evidence Based Gait Analysis Interpretation Tools (EB-GAIT) treatment recommendation and outcome prediction models to support decision-making based on clinical gait analysis data. by Michael H Schwartz, Andrew G Georgiadis

“…This paper introduces Evidence-Based Gait Analysis Interpretation Tools (EB-GAIT), a novel framework leveraging machine learning to support treatment decisions. The core of EB-GAIT consists of two key components: (1) treatment recommendation models, which are models that estimate the probability of specific surgeries based on historical standard-of-practice (SOP), and (2) treatment outcome models, which predict changes in patient characteristics following treatment or natural history. …”
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Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review by Arun Nair, Wilson Ong, Aric Lee, Naomi Wenxin Leow, Andrew Makmur, Yong Han Ting, You Jun Lee, Shao Jin Ong, Jonathan Jiong Hao Tan, Naresh Kumar, James Thomas Patrick Decourcy Hallinan

“…A few studies also explored machine learning-based automation software and, more recently, large language models. …”
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Flash Flood Regionalization for the Hengduan Mountains Region, China, Combining GNN and SHAP Methods by Yifan Li, Chendi Zhang, Peng Cui, Marwan Hassan, Zhongjie Duan, Suman Bhattacharyya, Shunyu Yao, Yang Zhao

“…The performances of two classic machine learning methods (K-means and Self-organizing feature map) and three GNN methods (Deep Graph Infomax (DGI), Deep Modularity Networks (DMoN), and Dilation shrink Network (Dink-Net)) were compared for flash-flood regionalization, and the Dink-Net model outperformed the others. …”
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Enhanced safety assessment on tunnel excavation via refined rock mass parameter identification by Hongwei Huang, Tongjun Yang, Jiayao Chen, Zhongkai Huang, Chen Wu, Jianhong Man

“…The integration of contact measurement data and surrounding environmental parameters leads to a proposal for rock mass quality prediction, utilizing integrated machine learning techniques. Subsequently, a 3D model is established by incorporating tunnel face features and environmental data. …”
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Response Relationship Between Runoff and Meteorological Drought and Flood Characteristics in Jialing River Basin by LI Wen-hui, ZHANG Yang, CAO Hui, XING Long, REN Yu-feng, ZHAI Shao-jun, MA Yi-ming, LI Wen-da

“…The findings indicate that emerging machine learning models, such as support vector machines and random forests, can effectively simulate the complex mechanisms through which meteorological drought and flood events affect runoff in the river basin. …”
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Cosmology with One Galaxy: Autoencoding the Galaxy Properties Manifold by Amanda Lue, Shy Genel, Marc Huertas-Company, Francisco Villaescusa-Navarro, Matthew Ho

“…Previous studies have demonstrated that machine learning can be used to infer the cosmological parameter Ω _m from the internal properties of even a single randomly selected simulated galaxy. …”
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Advances in the pilot point inverse method: Où En Sommes-Nous maintenant? by White, Jeremy, Lavenue, Marsh

“…The paper ends with newly developed applications of the PPM, given modern machine learning capabilities, and some foreshadowing as to where the PPM might evolve.…”
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Balancing Efficiency and Efficacy: A Contextual Bandit-Driven Framework for Multi-Tier Cyber Threat Detection by Ibrahim Mutambik, Abdullah Almuqrin

“…In response to the rising volume and sophistication of cyber intrusions, data-oriented methods have emerged as critical defensive measures. While machine learning—including neural network-based solutions—has demonstrated strong capabilities in identifying malicious activities, several fundamental challenges remain. …”
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Deep Pseudogene Categorization and Genome-Wide Transcription Prediction Using GANP-Based Feature Selection and TabNet Interpretability by Zeeshan Ahmed, Kashif Munir, Muhammad Usama Tanveer, Syed Rizwan Hassan, Ateeq Ur Rehman, Habib Hamam

“…Tested on a large-scale curated transcriptomic dataset, our framework achieves an accuracy of 96%, surpassing traditional machine learning models. Visualization tools such as t-SNE, heatmaps, and SHAP plots further enhance model interpretability. …”
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