Alpine Meadow Fractional Vegetation Cover Estimation Using UAV-Aided Sentinel-2 Imagery by Kai Du, Yi Shao, Naixin Yao, Hongyan Yu, Shaozhong Ma, Xufeng Mao, Litao Wang, Jianjun Wang

“…The performance of these estimates was evaluated against reference FVC values derived from centimeter-level UAV data. …”
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RoofMapNet: Utilizing geometric primitives for depicting planar building roof structure from high-resolution remote sensing imagery by Jiaqi Wang, Guanzhou Chen, Xiaodong Zhang, Tong Wang, Xiaoliang Tan, Qingyuan Yang, Wenlin Zhou, Kun Zhu

“…In this paper, we introduce an end-to-end network called RoofMapNet, specifically designed for extracting roof structures. …”
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A hybrid model for detecting motion artifacts in ballistocardiogram signals by Yuelong Jiang, Han Zhang, Qizheng Zeng

“…The model was designed to address the randomness and complexity of motion artifacts by integrating deep learning capabilities with manual feature judgment. The data used for this study were collected from patients with sleep apnea using piezoelectric sensors, and the model’s performance was evaluated using a set of predefined metrics. …”
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A Novel LSTM Architecture for Automatic Modulation Recognition: Comparative Analysis With Conventional Machine Learning and RNN-Based Approaches by Sam Ansari, Soliman Mahmoud, Sohaib Majzoub, Eqab Almajali, Anwar Jarndal, Talal Bonny

“…Experimental results demonstrate that the model achieves a recognition accuracy of 99.87% at an SNR of -5 dB, outperforming several conventional machine learning techniques, including multi-layer perceptron (MLP), radial basis function (RBF) networks, adaptive neuro-fuzzy inference systems (ANFIS), decision trees (DT), naïve Bayes (NB), support vector machines (SVM), probabilistic neural networks (PNN), k-nearest neighbors (KNN), and ensemble learning models, as well as recurrent neural networks (RNNs). …”
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Acute Respiratory Infections Identification With Cough Sounds and Overlapping Patch Modulated Vision Transformers by P. V. V. Kishore, D. Anil Kumar, Pasupuleti Sasikiran, Kaja Krishna Mohan, P. Praveen Kumar, Mogadala Vinod Kumar

“…Learning models trained on Mel frequency spectrum(MFCC) features of cough sounds represented as images have recorded an average binary classification accuracy of 68%. …”
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Distance Learning Technologies as a Modern Resource of Implementing Active and Interactive Methods of Education for the Organization of Independent Work of Students by V. V. Maliataki, K. A. Kirichek, A. A. Vendina

“…To resolve this problem, the authors of the article carried out the analysis of pedagogical literature, practical experience in organizing student learning, as well as self-analysis of the use of distance learning technologies in the process of teaching bachelors in the direction of preparation “Pedagogical Education”, followed by a generalization and systematization of the identified features. The study made it possible to distinguish three areas of educational and informational interaction within the framework of organizing and supporting students' independent work, namely, cloud technologies, social networks and distance learning technologies, as well as identify the strengths and weaknesses of each of the areas. …”
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Finger Knuckle Print classification: Leveraging vision Mamba for low complexity and high accuracy by Chiron Bang, Ali Salem Altaher, Ahmed Altaher, Hawraa Moamin, Thamer Alshammari, Basmh Alkanjr, Hasan Altaher, Mohammed G. Al-Jassani, Hanqi Zhuang

“…Seventeen pretrained deep neural networks were evaluated for their effectiveness in classifying FKPs, with experimental results consistently demonstrating the superior performance of the ViM model. …”
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A study on the effectiveness of machine learning models for hepatitis prediction by Popy Khatun, Shafeel Umam, Rubaiya Binte Razzak, Iffat Binta Shamsuddin, Nahid Salma

“…Model performance was evaluated using key metrics including accuracy, sensitivity, specificity, precision, and F1 score. …”
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Magnetic Resonance Imaging Brain Segmentation Using Bi-Directional Convolutional Long Short-Term Memory U-Net With Densely Connected Convolutions by Meshari D. Alanazi, Amna Maraoui, Imen Werda, Ahmed Ben Atitallah, Turki M. Alanazi, Mohammed Albekairi, Anis Sahbani, Amr Yousef

“…The dual difficulties of high computing needs in 3D convolutional networks and the necessity of more precise brain tumor diagnosis are addressed by this work. …”
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Integrating Random Forest With Boundary Enhancement for Mapping Crop Planting Structure at the Parcel Level From Remote Sensing Images by Junyang Xie, Yan Li, Hao Wu, Ziwei Wu, Ruina Zhao, Anqi Lin, Marcos Adami, Guoqiang Li, Jian Zhang

“…Comparative experiments with different convolutional neural networks further highlighted FPENet's exceptional capability. …”
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EFFResNet-ViT: A Fusion-Based Convolutional and Vision Transformer Model for Explainable Medical Image Classification by Tahir Hussain, Hayaru Shouno, Abid Hussain, Dostdar Hussain, Muhammad Ismail, Tatheer Hussain Mir, Fang Rong Hsu, Taukir Alam, Shabnur Anonna Akhy

“…The proposed architecture employs a feature fusion strategy to integrate the local feature extraction strengths of CNNs with the global dependency modeling capabilities of transformers. …”
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A Comprehensive Deep Learning System With MGRF Modeling for Predicting Breast Cancer Response to Neoadjuvant Chemotherapy by Ahmed Sharafeldeen, Fatma Taher, Norah Saleh Alghamdi, Eman Alnaghy, Reham Alghandour, Khadiga M. Ali, Sameh Shamaa, Abdelrahman Gamal, Mohammed Ghazal, Sohail Contractor, Ayman El-Baz

“…Afterward, an adaptive rescaling module (ARM) is proposed to adjust spatial resolution and project volumetric inputs into 2D, enabling compatibility with pretrained convolutional networks. Finally, a customized SEResNet architecture, augmented with Squeeze-and-Excitation (SE) blocks, is introduced to extract modality-specific features which are then fused with clinical and molecular subtypes descriptors for final classification. …”
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Bioinformatics Analysis Identifies Lipid Droplet‐Associated Gene Signatures as Promising Prognostic and Diagnostic Models for Endometrial Cancer by Vijayalakshmi N. Ayyagari, Miao Li, Paula Diaz‐Sylvester, Kathleen Groesch, Teresa Wilson, Ejaz M. Shah, Laurent Brard

“…Model performance was evaluated using ROC curves and support vector machine (SVM) classification. …”
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Deep learning models based on multiparametric magnetic resonance imaging and clinical parameters for identifying synchronous liver metastases from rectal cancer by Jing Sun, Pu-Yeh Wu, Fangmin Shen, Xingfa Chen, Jieqiong She, Mingcong Luo, Feifei Feng, Dechun Zheng

“…Clinical factors, including sex, primary tumor site, CEA level, and CA199 level were assessed. A clinical feature (CF) model was first developed by multivariate logistic regression, then two residual network DL models were constructed based on multiparametric MRI of primary cancer with or without CF incorporation. …”
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Predicting antiretroviral therapy adherence status of adult HIV-positive patients using machine-learning Northwest, Ethiopia, 2025 by Kelemua Aschale Yeneakal, Gizaw Hailiye Teferi, Temesgen T. Mihret, Abraham Keffale Mengistu, Sefefe Birhanu Tizie, Maru Meseret Tadele

“…Seven machine learning algorithms: support vector machine, random forest, decision tree, logistic regression, gradient boosting, K-nearest neighbors, and artificial neural network were trained. The model performance was evaluated using ROC-AUC, F1 score, accuracy, precision, and recall. …”
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Multi-Decision Vector Fusion Model for Enhanced Mapping of Aboveground Biomass in Subtropical Forests Integrating Sentinel-1, Sentinel-2, and Airborne LiDAR Data by Wenhao Jiang, Linjing Zhang, Xiaoxue Zhang, Si Gao, Huimin Gao, Lin Sun, Guangjian Yan

“…Finally, eight models were evaluated, and MDVF was implemented to integrate outputs from the top-performing models. …”
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Letter from editor by Carlos A. Vargas

“…One is about the experimental triaxial tests developed to estimate undrained critical shear strength under monolithic loadings; the second one reads about classification of caving depth by feature extraction in cutting images, and the other is a simulation using artificial neural network and adaptative neuro-fuzzy system of compression ratio in a municipal solid waste. …”
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Deep-Learning-Based Land Cover Mapping in Franciacorta Wine Growing Area by Girma Tariku, Isabella Ghiglieno, Andres Sanchez Morchio, Luca Facciano, Celine Birolleau, Anna Simonetto, Ivan Serina, Gianni Gilioli

“…Integrating pre-trained feature extraction networks significantly improves performance across various metrics. …”
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FlyPhoneDB2: A computational framework for analyzing cell-cell communication in Drosophila scRNA-seq data integrating AlphaFold-multimer predictions by Mujeeb Qadiri, Ying Liu, Ah-Ram Kim, Myeonghoon Han, Eric Zhou, Austin Veal, Tzu-Chiao Lu, Hongjie Li, Yanhui Hu, Norbert Perrimon

“…New functionalities have also been introduced, such as the addition of downstream reporter genes to evaluate pathway activity, multi-sample CCC comparison, and enhanced visualizations summarizing communication at a network level.We demonstrate the utility of FlyPhoneDB2 by analyzing whole-body single-nuclei RNA-seq datasets from flies with gut tumors induced by the Yorkie oncogene. …”
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LMVT: A hybrid vision transformer with attention mechanisms for efficient and explainable lung cancer diagnosis by Jesika Debnath, Al Shahriar Uddin Khondakar Pranta, Amira Hossain, Anamul Sakib, Hamdadur Rahman, Rezaul Haque, Md.Redwan Ahmed, Ahmed Wasif Reza, S M Masfequier.Rahman Swapno, Abhishek Appaji

“…To tackle these problems, we introduce Lung MobileVIT (LMVT), a lightweight hybrid model that combines a Convolutional Neural Network (CNN) and a Transformer for multiclass lung cancer classification. …”
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