ATT-CR: Adaptive Triangular Transformer for Cloud Removal by Yang Wu, Ye Deng, Pengna Li, Wenli Huang, Kangyi Wu, Xiaomeng Xin, Jinjun Wang

“…However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the attention computation brings disturbances in subsequent layers, leading to suboptimal performance. To address these challenges, we propose the adaptive triangular transformer for cloud removal (ATT-CR), a model that effectively reduces computational costs and mitigates interference from cloudy pixels. …”
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YOLO-SRMX: A Lightweight Model for Real-Time Object Detection on Unmanned Aerial Vehicles by Shimin Weng, Han Wang, Jiashu Wang, Changming Xu, Ende Zhang

“…Furthermore, the lightweight GhostConv is incorporated to reduce model complexity. By synthesizing these principles, a novel composite receptive field lightweight convolution, DRFAConvP, is proposed to further optimize multi-scale feature fusion efficiency and promote model lightweighting. …”
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A Transformer-Based Multi-Scale Deep Learning Model for Lung Cancer Surgery Optimization by Pengfei Zhu, Tingmin Wang, Fan Yang, Meng Wang, Yunjie Zhang

“…By using the self-attention mechanism in Transformers and multi-scale feature extraction, the model expertly explores different data modalities. …”
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Modeling of Thermal Conductivity in a Medium with Phase Transition with a Moving Boundary of Phase Change by Mizonov V.E., Tikhonov A.I., Basova E.V., Mitrofanov A.V.

“…The objective was achieved by solving the following tasks: the construction of the process mathematical model based on its cell representation and with the use of the Markov chain theory mathematical apparatus, performing numerical experiments with the developed model, demonstrating its operability and the possibility to achieve the set goal. …”
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Development of a Model for Soil Salinity Segmentation Based on Remote Sensing Data and Climate Parameters by Gulzira Abdikerimova, Dana Khamitova, Akmaral Kassymova, Assyl Bissengaliyeva, Gulsara Nurova, Murat Aitimov, Yerlan Alimzhanovich Shynbergenov, Moldir Yessenova, Roza Bekbayeva

“…The approach includes the sequential application of unsupervised learning algorithms (K-Means, hierarchical clustering, DBSCAN), the XGBoost model, and a multitasking neural network that performs simultaneous classification and regression. …”
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Utilizing metagenomic profiling and machine learning model to identify bacterial biomarkers for major depressive disorder by Xuan Wang, Di Cao, Hanlin Zhang, Wei Chen, Jiaxin Sun, Huimin Hu

“…We then applied a Support Vector Machine Recursive Feature Elimination (SVM-RFE) machine learning model to find potential microbial markers.ResultsOur results showed that the alpha diversity of the intestinal flora did not differ significantly in major depressive disorder compared to healthy populations. …”
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K0-K¯0 Mixing in the Minimal Flavor-Violating Two-Higgs-Doublet Models by Natthawin Cho, Xin-Qiang Li, Fang Su, Xin Zhang

“…The two-Higgs-doublet model (2HDM), as one of the simplest extensions of the Standard Model (SM), is obtained by adding another scalar doublet to the SM and is featured by a pair of charged Higgs, which could affect many low-energy processes. …”
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Representational models: A common framework for understanding encoding, pattern-component, and representational-similarity analysis. by Jörn Diedrichsen, Nikolaus Kriegeskorte

“…However, the other two approaches-when conducted appropriately-can perform similarly. In encoding analysis, the linear model needs to be appropriately regularized, which effectively imposes a prior on the activity profiles. …”
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A novel routing protocol for content-based publish/subscribe model in mobile sensor networks by Lin Lin, Jinfu Chen, Patrick Kwaku Kudjo, Omari Michael

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Extension of an ICU-based noninvasive model to predict latent shock in the emergency department: an exploratory study by Mingzheng Wu, Shaoping Li, Haibo Yu, Cheng Jiang, Shuai Dai, Shan Jiang, Yan Zhao

“…The modeling and validation data revealed similar non-invasive feature distributions. …”
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Diagnostic Models for Differentiating COVID-19-Related Acute Ischemic Stroke Using Machine Learning Methods by Eylem Gul Ates, Gokcen Coban, Jale Karakaya

“…Various feature selection algorithms were applied to identify the most relevant features, which were then used to train and evaluate machine learning classification models. …”
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Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens, Zamir Libohova

“…The RF, SVMRadial, and KNN models performed best, followed by the models from the Neural Network group (NNET). …”
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Adaptasi Model CNN Terlatih pada Aplikasi Bergerak untuk Klasifikasi Citra Termal Payudara by Roslidar Roslidar, Muhammad Rizky Syahputra, Rusdha Muharar, Fitri Arnia

“…The shuffle node on ShuffleNet cannot be fully exported using ExportToOnnx, so it needs to be re-defined with a placeholder named “MATLAB PLACEHOLDER”. In addition to the model conversion process, this article describes the user interaction process with the application using UML diagrams and application feature menu designs. …”
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Development and evaluation of machine learning models for individualized prediction of myopia control efficacy treated with overnight orthokeratology by Lan Zhang, Mingjun Gao, Yiru Wang, Siqi Zhang, Huailin Zhu, Qi Zhao

“…LASSO regression identified predictive factors correlated with controlling myopia. The final features are input into the machine learning model for prediction model construction to predict 1-year axial length elongation. …”
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Bioprinting of GelMA-Based Hydrogels to Aid in Creation of Biomimetic 3D Models for Glioblastoma by Kaitlyn Ann Rose Schroyer, Kylie Marie Schmitz, Gunjeeta Raheja, Bin Su, Justin D. Lathia, Liqun Ning

“…The resulting 3D GBM models were structurally reliable and reproducible, featuring integrated inner channels and possessing tunable properties to emulate the characteristics of the GBM ECM. …”
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Comparative analysis of machine learning models for predicting water quality index in Dhaka’s rivers of Bangladesh by Mosaraf Hosan Nishat, Md. Habibur Rahman Bejoy Khan, Tahmeed Ahmed, Syed Nahin Hossain, Amimul Ahsan, M. M. El-Sergany, Md. Shafiquzzaman, Monzur Alam Imteaz, Mohammad T. Alresheedi

“…Among the evaluated ML models, ANN and Random Forest Regressor performed the best. …”
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Development of a prognostic immune cell-based model for ovarian cancer using multiplex immunofluorescence by Sai Li, Boyang Jiang, Hongying Zhou, Sifu Yang, Liu Yang, Yupeng Hong

“…The LASSO Cox regression model was used for feature selection, and Kaplan-Meier survival analysis was performed to evaluate the prognostic significance of immune cell markers. …”
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A PET-CT radiomics model for immunotherapy response and prognosis prediction in patients with metastatic colorectal cancer by Wenbiao Chen, Peng Zhu, Yeda Chen, Guoping Sun

“…The model combines radiomic features, immune scores and clinical features, showing high prediction accuracy and clinical application value. …”
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Machine learning-derived cumulative health measure for assessing disease impact in dairy cattle by Afef Marzougui, Craig S. McConnel, Amber Adams-Progar, Tyler D. Biggs, Stephen P. Ficklin, Sindhuja Sankaran

“…The selected features were combined into a summary disability weight – cumulative health measure – for each disease computed through weighted sums of feature importance from classification models. …”
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c-Triadem: A constrained, explainable deep learning model to identify novel biomarkers in Alzheimer's disease. by Sherlyn Jemimah, Ferial Abuhantash, Aamna AlShehhi

“…We demonstrate that our model's performance is superior to previous models with an AUC of 97% and accuracy of 89%. …”
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