Telemedicine during COVID- 19: features of legal regulation in the field of administrative liability for errors committed by medical institutions by Raissa Orsayeva, Anna Berestova, Varvara Krasilnikova, Anton Timoshin

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Image preference estimation with a data-driven approach: A comparative study between gaze and image features by Yusuke Sugano, Yasunori Ozaki, Hiroshi Kasai, Keisuke Ogaki, Yoichi Sato

“…A dataset of eye movements is collected while the participants are viewing pairs of natural images, and it is used to train image preference label classifiers. The input feature is defined as a combination of various fixation and saccade event statistics, and the use of the random forest algorithm allows us to quantitatively assess how each of the statistics contributes to the classification task. …”
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Modified Gray-Level Haralick Texture Features for Early Detection of Diabetes Mellitus and High Cholesterol with Iris Image by Rinci Kembang Hapsari, Miswanto Miswanto, Riries Rulaningtyas, Herry Suprajitno, Gan Hong Seng

“…The feature extraction process does early detection based on iris images. …”
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Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors by Fan Yang, Fan Yang, Fan Yang, Fan Yang, Chun-wei Liu, Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei, Mo Yang, Mo Yang, Mo Yang

“…The patient dataset was randomly divided into a training set and a validation set at a ratio of 7:3. …”
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Spatial–Temporal Semantic and Geographic Correlation Network for SAR Image Change Detection With Limited Training Data by Haolin Li, Bin Zou, Lamei Zhang, Jiang Qin

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PaleAle 6.0: Prediction of Protein Relative Solvent Accessibility by Leveraging Pre-Trained Language Models (PLMs) by Wafa Alanazi, Di Meng, Gianluca Pollastri

“…Inspired by the remarkable success of NLP techniques, this study leverages pre-trained language models (PLMs) to enhance RSA prediction. …”
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Fault diagnosis in photovoltaic arrays: A robust and efficient approach using feature engineering and 1D-CNN by Yousif Mahmoud Ali, Lei Ding

“…To overcome these challenges and limitations, this study proposes a robust and efficient method based on feature engineering and one-dimensional convolutional neural networks (1D-CNN). …”
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Multi-Scale Feature Fusion GANomaly with Dilated Neighborhood Attention for Oil and Gas Pipeline Sound Anomaly Detection by Yizhuo Zhang, Zhengfeng Sun, Shen Shi, Huiling Yu

“…The primary challenge in reconstruction-based pipeline audio anomaly detection is to prevent the loss of critical information and ensure the high-quality reconstruction of feature maps. …”
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Comprehensive Performance Comparison of Signal Processing Features in Machine Learning Classification of Alcohol Intoxication on Small Gait Datasets by Muxi Qi, Samuel Chibuoyim Uche, Emmanuel Agu

“…These statistically significant features were utilized to train supervised classifiers and assess their impact on alcohol intoxication detection accuracy. …”
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The relative importance of model type and input features for water supply forecasting in snow-dominated basins of the southwestern US by Madeline R. Pernat, Joseph Kasprzyk, Edith Zagona, Sydney D. Walker, Ben Livneh

“…A new wrapper-based feature selection method identifies the Best Feature Set—selected from a broad pool of station-based, meteorological, and climatological features—for each basin–model type combination. …”
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Method of automated construction of pattern recognition algorithms on phase paths by Dmitry Kovalenko

“…Besides, the training set could be incompletely defined. The algorithm described here is based on the idea of applying an algebraic approach to the labeling of trajectories. …”
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Classifying Serrated Tussock Cover from Aerial Imagery Using RGB Bands, RGB Indices, and Texture Features by Daniel Pham, Deepak Gautam, Kathryn Sheffield

“…Three random forest classifiers were trained by utilising spectral features (RGB bands and indices), texture features derived from the Grey-Level Co-occurrence Matrix, and a combination of all the features. …”
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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

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Leveraging LLMs for optimised feature selection and embedding in structured data: A case study on graduate employment classification by Radiah Haque, Hui-Ngo Goh, Choo-Yee Ting, Albert Quek, M.D. Rakibul Hasan

“…The models are trained in four stages: 1) original dataset without feature selection or word embedding, 2) dataset with selected optimal features, 3) transformed data with word embedding, and 4) transformed data with feature selection applied both before and after word embedding. …”
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May Patients with Chronic Stroke Benefit from Robotic Gait Training with an End-Effector? A Case-Control Study by Mirjam Bonanno, Paolo De Pasquale, Antonino Lombardo Facciale, Biagio Dauccio, Rosaria De Luca, Angelo Quartarone, Rocco Salvatore Calabrò

“…The data of these subjects were compared with those coming from a sample of twelve individuals (control group, CG) matched for clinical and demographic features who underwent the same amount of conventional gait training (CGT), in addition to standard rehabilitation therapy. …”
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Multi-stage framework using transformer models, feature fusion and ensemble learning for enhancing eye disease classification by Abdulaziz AlMohimeed

“…Hybrid models are developed based on Transformer models: Vision Transformer (ViT), Data-efficient Image Transformer (DeiT), and Swin Transformer are used to extract deep features from images, Principal Component Analysis (PCA) is used to reduce the complexity of extracted features, and Machine Learning (ML) models are used as classifiers to enhance performance. …”
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A Method of Word Sense Disambiguation with Recurrent Netural Networks by ZHANG Chunxiang, ZHOU Xuesong, GAO Xueyao

“…Word, part.of.speech and semantic categories are extracted as disambiguation features. Based on disambiguation features, recurrent neural network is used to construct word sense disambiguation classifier. …”
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Enhanced staging of renal cell carcinoma using tumor morphology features: model development and multi-source validation by Enyu Yuan, Yuntian Chen, Lei Ye, Ben He, ChunLei He, Junchao Ma, Ting Yang, Hao Zeng, Ling Yang, Jin Yao, Bin Song

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BeliN: A novel corpus for Bengali religious news headline generation using contextual feature fusion by Md Osama, Ashim Dey, Kawsar Ahmed, Muhammad Ashad Kabir

“…This study addresses this limitation by introducing a novel corpus, BeliN (Bengali Religious News) – comprising religious news articles from prominent Bangladeshi online newspapers, and MultiGen – a contextual multi-input feature fusion headline generation approach. Leveraging transformer-based pre-trained language models such as BanglaT5, mBART, mT5, and mT0, MultiGen integrates additional contextual features – including category, aspect, and sentiment – with the news content. …”
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Landslide mapping with deep learning: the role of pre-/post-event SAR features and multi-sensor data fusion by Aiym Orynbaikyzy, Frauke Albrecht, Wei Yao, Mahdi Motagh, Wandi Wang, Sandro Martinis, Simon Plank

“…Increasing the number of pre-/post-event SAR features improves the SAR-based accuracies. To promote further advancements in automated landslide mapping using deep learning, the landslide reference dataset generated in this study is freely available at (https://doi.org/10.5281/zenodo.15284357).…”
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