Machine learning analysis of FOSL2 and RHoBTB1 as central immunological regulators in knee osteoarthritis synovium by Kun Gao, Zhenyu Huang, Zhouwei Liao, Yanfei Wang, Dayu Chen

“…We employed several machine learning algorithms, including least absolute shrinkage and selection operator and support vector machine–recursive feature elimination, to screen for key genes. Then, we validated the key genes using an external dataset (GSE51588) and an in vitro knee osteoarthritis animal model. …”
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Biomimetic Responsive Space Divider for Co-Working Environments: An Interactive Design Approach by Ayman Assem

“…Every cell of the space divider is associated with a unique color palette informed by the principles of color psychology, creating an optimal environment for various performance tasks within the interior space.The fabricated prototype employed a complex methodology, combining digital modeling with physical prototyping. …”
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Identifying the Geographical Origin of Wolfberry Using Near-Infrared Spectroscopy and Stacking-Orthogonal Linear Discriminant Analysis by Shijie Song, Xiaohong Wu, Mingyu Li, Bin Wu

“…When Principal Component Analysis (PCA), PCA + Linear Discriminant Analysis (LDA), and OLDA were used for feature extraction, Stacking-OLDA achieved the highest average identification accuracy of 99%. …”
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PARS: A Position-Based Attention for Rumor Detection Using Feedback From Source News by Hanieh Asadi G., Ali Hamzeh, Niloofar Mozafari

“…Despite numerous studies in this field, designing effective feature representations still remains a central challenge, especially for models aiming to capture the dynamics of rumor propagation. …”
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SpaMask: Dual masking graph autoencoder with contrastive learning for spatial transcriptomics. by Wenwen Min, Donghai Fang, Jinyu Chen, Shihua Zhang

“…Unlike previous GNNs, SpaMask masks a portion of spot nodes and spot-to-spot edges to enhance its performance and robustness. SpaMask combines Masked Graph Autoencoders (MGAE) and Masked Graph Contrastive Learning (MGCL) modules, with MGAE using node masking to leverage spatial neighbors for improved clustering accuracy, while MGCL applies edge masking to create a contrastive loss framework that tightens embeddings of adjacent nodes based on spatial proximity and feature similarity. …”
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Epidemiology of Lumbar Puncture and the Validity of Meningeal Signs in Predicting Meningitis in Children: A Cross-sectional Study by Mehjabin, Shiji Joseph, Arun Ravi, Kalyani Pillai

“…Introduction: Predictive models have been developed to estimate the likelihood of bacterial meningitis in patients with clinical features suggestive of Central Nervous System (CNS) involvement. …”
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Seed multispectral imaging combined with machine learning algorithms for distinguishing different varieties of lettuce (Lactuca sativa L.) by Jinpeng Wei, Zhangyan Dai, Qi Zhang, Le Yang, Zhaoqi Zeng, Yuliang Zhou, Jun Liu, Bingxian Chen

“…The LDA model based on morphological and spectral fusion feature data performed best, and the average classification accuracy was 92.7 %. …”
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Elucidating the Prognostic and Therapeutic Implications of Insulin Resistance Genes in Breast Cancer: A Machine Learning-Powered Analysis by Lengyun Wei, Dashuai Li, Hongjin Chen, Yajing Pu, Qun Wang, Jintao Li, Meng Zhou, Chenfeng Liu, Pengpeng Long

“…Furthermore, we used machine learning methods to perform feature selection and reduction, which generated a clinically applicable scoring system consisting of the seven hub genes for predicting clinical outcomes in BC patients. …”
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Optimal Design for a Novel Compliant XY Platform Integrated with a Hybrid Double Symmetric Amplifier Comprising One-Lever and Scott–Russell Mechanisms Arranged in a Perpendicular S... by Minh Phung Dang, Anh Kiet Luong, Hieu Giang Le, Chi Thien Tran

“…The PRBM and Lagrangian methods were used to construct an analytical model, while finite element analysis was used to validate the theoretical solution. …”
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Predicting adverse drug reactions in oncology: A critical review of machine learning approaches and future directions by Abid Yahya, Phatsimo Lobelo, Afiya Eram, Sana Althaf Hussain, Irfan Anjum Badruddin, Lory Liza D. Bulay-og, Dionel O. Albina

“…Strong performance has been demonstrated by ensemble techniques like Random Forest and Gradient Boosting, while deep neural networks allow for sophisticated feature extraction, albeit with interpretability issues. …”
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Machine learning-based fatigue lifetime prediction of structural steels by Konstantinos Arvanitis, Pantelis Nikolakopoulos, Dimitrios Pavlou, Mina Farmanbar

“…The implementation of these methodologies for fatigue lifetime prediction yields substantial outcomes. All models exhibit satisfactory performance, with XGB Regression demonstrating superior effectiveness. …”
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Development of a UR5 Cobot Vision System with MLP Neural Network for Object Classification and Sorting by Szymon Kluziak, Piotr Kohut

“…Object classification was conducted using contour similarity with Hu moments, SIFT-based descriptors with FLANN matching, and MLP-based neural models trained on preprocessed images. Conducted performance evaluations encompassed accuracy metrics for used identification methods (MLP classifier, contour similarity, and feature descriptor matching) and the effectiveness of the vision system in controlling the cobot for sorting tasks. …”
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Predicting 30-day readmissions in pneumonia patients using machine learning and residential greenness by Seohyun Choi, Young Jae Kim, Seon Min Lee, Kwang Gi Kim

“…The models demonstrated sufficient performance, and residential greenness was significant in predicting readmission. …”
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RESOCIALIZATION OF ADOLESCENTS ON PROBATION: PSYCHOSOCIAL APPROACH WITH ELEMENTS OF COACHING by Inesa Harkusha, Serhii Dubinskyi

“…In the context of modern approaches and individualized models to the resocialization of minors, the introduction of coaching approaches is becoming increasingly relevant. …”
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In depth transcriptomic profiling defines a landscape of dysfunctional immune responses in patients with VEXAS syndrome by Hiroki Mizumaki, Shouguo Gao, Zhijie Wu, Fernanda Gutierrez-Rodrigues, Massimiliano Bissa, Xingmin Feng, Emma M. Groarke, Haoran Li, Lemlem Alemu, Diego Quinones Raffo, Ivana Darden, Sachiko Kajigaya, Peter C. Grayson, Genoveffa Franchini, Neal S. Young, Bhavisha A. Patel

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M18K: A Multi-Purpose Real-World Dataset for Mushroom Detection, 3D Pose Estimation, and Growth Monitoring by Abdollah Zakeri, Mulham Fawakherji, Jiming Kang, Bikram Koirala, Venkatesh Balan, Weihang Zhu, Driss Benhaddou, Fatima A. Merchant

“…This paper details the dataset’s characteristics, presents detailed statistics on mushroom growth and yield, evaluates algorithmic performance, and, for broader applicability, makes all resources publicly available, including images, code, and trained models, via our GitHub repository. …”
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Machine‐learning based spatiotemporal prediction of soil moisture in a grassland hillslope by Timo Houben, Pia Ebeling, Swamini Khurana, Julia Sabine Schmid, Johannes Boog

“…The day of the year emerged as an important feature to predict SM across models and can thus serve as a proxy for seasonal hydroclimatic variability. …”
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Extraction of kaolin and tribo informative analysis of the Al-kaolin composite through machine learning approaches by V. S. S. Venkatesh, Guttikonda Manohar, Pandu Ranga Vundavilli, M. M. Mahapatra, Ashish Goyal, Abhijit Bhowmik

“…To further understand and predict the behaviour of the composites, a systematic dataset was collected, and various machine learning (ML) models were trained and tested for predictive modelling of wear rate and COF. …”
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Advancing Textile Damage Segmentation: A Novel RGBT Dataset and Thermal Frequency Normalization by Farshid Rayhan, Jitesh Joshi, Guangyu Ren, Lucie Hernandez, Bruna Petreca, Sharon Baurley, Nadia Berthouze, Youngjun Cho

“…We evaluate our dataset alongside six existing RGBT datasets using state-of-the-art (SOTA) models. Experimental results demonstrate the superior performance of the SOTA models with ThermoFreq, highlighting its effectiveness in addressing noise challenges inherent in RGBT semantic segmentation across diverse environmental conditions. …”
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Temporal Coupling of Brain Signals and Fine Motor Output Using Affordable EEG by Adam Gyula Nemes, Adam Gyulai, Adam Szarvas, Erno Nemeth, Kristof Tajti, Viktor Toth, Gyorgy Eigner

“…A real-time demonstration validated our approach in a virtual environment, where the highest-performing model successfully translated EEG signals into predicted finger movements. …”
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