Multimodal radiomics integrating deep learning and clinical features for diagnosing multidrug-resistant tuberculosis in HIV/AIDS patients by Chang Song, Aichun Huang, Chunyan Zhao, Lemin Wen, Shulin Song, Yanrong Lin, Chaoyan Xu, Hangbiao Qiang, Qingdong Zhu

“…Background: This study aimed to develop and validate a predictive model based on multimodal data, including clinical features, radiomics features, and deep learning features, to distinguish multidrug-resistant tuberculosis (MDR-TB) in HIV/AIDS patients, thereby improving diagnostic accuracy. …”
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ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort by Farida Mohsen, Ali Safa, Zubair Shah

“…Using data from the Qatar Biobank (QBB), we compared ECG-DiaNet against unimodal models based solely on ECG or CRFs. A development cohort (n = 2043) was utilized for model training and internal validation, while a separate longitudinal cohort (n = 395) with a median five-year follow-up served as the test set. …”
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EEG-Driven Arm Movement Decoding: Combining Connectivity and Amplitude Features for Enhanced Brain–Computer Interface Performance by Hamidreza Darvishi, Ahmadreza Mohammadi, Mohammad Hossein Maghami, Meysam Sadeghi, Mohamad Sawan

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Multi-scale CNN-CrossViT network for offline handwritten signature recognition and verification by Wanying Li, Mahpirat Muhammat, Xuebin Xu, Alimjan Aysa, Kurban Ubul

“…To address this challenge, we introduced the cross-attention vision transformer (CrossViT) and constructed a hybrid architecture that combines convolutional neural networks (CNN) to extract stronger multi-scale features from signature images. In the CrossViT branch, depth features of different sizes image blocks are extracted, and information exchange with another branch is achieved through a token based cross-attention mechanism. …”
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Integrating Viewing Direction and Image Features for Robust Multi-View Multi-Object 3D Pedestrian Tracking by R. Ali, M. Mehltretter, C. Heipke

“…For each image, this directional information is combined with the 2D features extracted from that image, before 3D features are computed, using the 2D features from all images. …”
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SENTIMENT ANALYSIS OF REVIEWS ON X APPS ON GOOGLE PLAY STORE USING SUPPORT VECTOR MACHINE AND N-GRAM FEATURE SELECTION by Fahri Aimar Kusumo, Dewi Retno Sari Saputro, Purnami Widyaningsih

“…N-gram feature selection is a statistics-based approach to classifying text. …”
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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features by Ning Yan, Yasen Qin, Haotian Wang, Qi Wang, Fangyu Hu, Yuwei Wu, Xuedong Zhang, Xu Li

“…The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values of 0.931 and 0.877 for the training and validation sets, respectively. …”
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Breast Lesion Detection Using Weakly Dependent Customized Features and Machine Learning Models with Explainable Artificial Intelligence by Simona Moldovanu, Dan Munteanu, Keka C. Biswas, Luminita Moraru

“…This research proposes a novel strategy for accurate breast lesion classification that combines explainable artificial intelligence (XAI), machine learning (ML) classifiers, and customized weakly dependent features from ultrasound (BU) images. Two new weakly dependent feature classes are proposed to improve the diagnostic accuracy and diversify the training data. …”
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Features of using artificial intelligence technologies in the self-education activities of academic and pedagogical staff in higher education institutions by Samko Alla

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Heart Sound Classification Using Harmonic and Percussive Spectral Features from Phonocardiograms with a Deep ANN Approach by Anupinder Singh, Vinay Arora, Mandeep Singh

“…These results underscore the effectiveness of harmonic-based features and the robustness of the ANN in heart sound classification. …”
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FDRL: a data-driven algorithm for forecasting subsidence velocities in Himalayas using conventional and traditional soil features by Sahil Sankhyan, Ajoy Kumar, Praveen Kumar, Aaditya Sharma, K. V. Uday, Varun Dutt

“…The FDRL model outperformed baseline regression models with a training Root Mean Squared Error (RMSE) of 1.11 mm/year and a test RMSE of 1.32 mm/year. …”
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Radiomic features at contrast-enhanced CT predict proliferative hepatocellular carcinoma and its prognosis after transarterial chemoembolization by Haifeng He, Zhichao Feng, Junhong Duan, Wenzhi Deng, Zuowei Wu, Yizi He, Qi Liang, Yongzhi Xie

“…The radiomics model comprising 9 radiomic features and exhibited good performance for predicting proliferative HCCs. …”
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Radiomics models to predict axillary lymph node metastasis in breast cancer and analysis of the biological significance of radiomic features by Xinhua Li, Minping Hong, Zhendong Lu, Zilin Liu, Lifu Lin, Hongfa Xu

“…ObjectivesTo explore the effectiveness of radiomics in predicting axillary lymph node metastasis (ALNM) and the relationship between radiomics features and genes.MethodThe 379 patients with breast cancer (186 ALNM-positive and 193 ALNM-negative) recruited from three hospitals were divided into the training (n=224), testing (n=96), and validation (n=59) cohorts. …”
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A review of deep learning in blink detection by Jianbin Xiong, Weikun Dai, Qi Wang, Xiangjun Dong, Baoyu Ye, Jianxiang Yang

“…Compared with traditional methods, the blink detection method based on deep learning offers superior feature learning ability and higher detection accuracy. …”
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Diagnostic value of CT radiomics and clinical features in differentiating focal organizing pneumonia from peripheral lung cancer by Weihua Tang, Huadong Chen, Peijun Liu, Yunxuan Zhang, Yunxuan Zhang

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Instance segmentation of oyster mushroom datasets: A novel data sampling methodology for training and evaluation of deep learning models by Christos Charisis, Meiqing Wang, Dimitrios Argyropoulos

“…Also, the study aims to examine the ability of five feature extraction backbone configurations of Mask R-CNN: i) CNN-based (ResNet50, ResNeXt101 and ConvNeXt) and ii) Transformer-based (Swin small and tiny) to accurately detect and segment single mushroom instances within the cluster in the images. …”
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Identification of lethality-related m7G methylation modification patterns and the regulatory features of immune microenvironment in sepsis by Dan Wang, Rujie Huo, Lu Ye

“…This study aimed to explore the patterns of lethality-related m7G regulatory factor-mediated RNA methylation modification and immune microenvironment regulatory features in sepsis. Methods: Three sepsis-related datasets (E-MTAB-4421 and E-MTAB-4451 as training sets and GSE185263 as a validation set) were collected, and differentially expressed m7G-related genes were analyzed between survivors and non-survivors. …”
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SceEmoNet: A Sentiment Analysis Model with Scene Construction Capability by Yi Liang, Dongfang Han, Zhenzhen He, Bo Kong, Shuanglin Wen

“…We then use the Contrastive Language-Image Pre-training (CLIP) model, a multimodal feature extraction model, to extract aligned features from different modalities, preventing significant feature differences caused by data heterogeneity. …”
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Spatial&#x2013;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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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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