The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach by Rostislav Epifanov, Yana Fedotova, Savely Dyachuk, Alexandr Gostev, Andrei Karpenko, Rustam Mullyadzhanov

“…This study introduces a novel one-stage method for simultaneous vessel segmentation and centerline extraction using a multitask neural network. We designed a hybrid architecture that integrates convolutional and graph layers, along with a task-specific loss function, to effectively capture the topological relationships between segmentation and centerline extraction, leveraging their complementary features. …”
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Short-term Wind Power Forecasting Based on BWO‒VMD and TCN‒BiGRU by LU Jing, ZHANG Yanru, WANG Rui

“…Through the graph, error evaluation indicators, and time indicators, the experimental results show that although the time of the proposed model is not optimal, its error evaluation indicator value is the smallest, highlighting its advantages. …”
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Advancing breast cancer diagnosis: Integrating deep transfer learning and U-Net segmentation for precise classification and delineation of ultrasound images by Divine Senanu Ametefe, Dah John, Abdulmalik Adozuka Aliu, George Dzorgbenya Ametefe, Aisha Hamid, Tumani Darboe

“…A curated dataset of breast ultrasound images, categorized as normal, benign, or malignant, was used for model evaluation. Three pre-trained convolutional neural networks (CNNs), including VGG16, VGG19, and EfficientNet were implemented within a deep transfer learning framework due to their strong feature extraction capabilities. …”
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Recognition and classification techniques of marine mammal calls based on LSTM and expanded causal convolution by Wanlu Cheng, Wanlu Cheng, Hao Chen, Jiaming Jiang, Jiaming Jiang, Shuang Li, Shuang Li, Jingjing Wang, Jingjing Wang, Yanping Zhou

“…The model comprises three modules: (1) a frequency-domain feature extraction module employing dilated causal convolutions at multiple scales to capture multi-resolution spectral information from Mel spectrograms; (2) a time-domain feature extraction module that inputs Mel-frequency cepstral coefficients (MFCCs) into an LSTM enhanced with a time-attention mechanism to highlight key temporal features; and (3) a classification module leveraging transfer learning, where a pre-trained neural network is fine-tuned on real marine mammal call data to improve performance. …”
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A pregeneration–recognition method of detecting weak seafloor echoes for full-waveform airborne LiDAR bathymetry by Yadong Guo, Wenxue Xu, Yanxiong Liu, Fanlin Yang, Xue Ji, Yikai Feng, Qiuhua Tang

“…Then, an adaptive ellipsoidal neighborhood related to the point density is used to select neighborhood points, and eigenvalue-based spatial features are calculated. Finally, a back propagation neural network (BPNN) model is constructed using the points generated from surface–seafloor shots, and the seafloor points in seafloor-undefined shots are obtained by optimizing the BPNN results. …”
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Handwritten Amharic Character Recognition Through Transfer Learning: Integrating CNN Models and Machine Learning Classifiers by Natenaile Asmamaw Shiferaw, Zefree Lazarus Mayaluri, Prabodh Kumar Sahoo, Ganapati Panda, Prince Jain, Adyasha Rath, Md. Shabiul Islam, Mohammad Tariqul Islam

“…Transfer learning is applied using four CNN architectures: AlexNet, VGG16, VGG19, and ResNet50 as feature extractors. Initially, their performance is evaluated with softmax classifiers. …”
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Functional Connectivity Metrics in Temporal Lobe Epilepsy: A Machine Learning Perspective With MEG by M. V. Suhas, N. Mariyappa, A. Karunakar Kotegar, M. Ravindranadh Chowdary, K. Raghavendra, Ajay Asranna, L. G. Viswanathan, H. Anitha, Sanjib Sinha

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GNNMutation: a heterogeneous graph-based framework for cancer detection by Nuriye Özlem Özcan Şimşek, Arzucan Özgür, Fikret Gürgen

“…In this study, we introduce a novel approach based on graph neural networks that jointly considers genetic mutations and protein interactions for cancer prediction. …”
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Battery Health Diagnosis via Neural Surrogate Model: From Lab to Field by Hojin Cheon, Jihun Jeon, Byungil Jung, Hongseok Kim

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Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications by Fatima Hasan Saif, Mohamed Nasser Al-Andoli, Wan Mohd Yaakob Wan Bejuri

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CoPaD-Mark: A Coded Parallelizable Deep Learning-Based Scheme for Robust Image Watermarking by Andy M. Ramos, Cecilio Pimentel, Daniel P. B. Chaves

“…The embedding layer employs a parallel structure with convolutional neural networks inspired by the Inception Net, while the extraction layer uses deformable convolutions. …”
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White Coat: Origins, Composition, Semantics, Pragmatics, and Word-Formation Potential of Russian Phraseological Unit by T. V. Leontyeva

“…Furthermore, it is shown that in network discourse, a transformation occurs into an evaluative nomination of a person as ‘the white coat’ (‘I was such a white coat’).…”
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End-to-End Mandarin Speech Reconstruction Based on Ultrasound Tongue Images Using Deep Learning by Fengji Li, Fei Shen, Ding Ma, Jie Zhou, Shaochuan Zhang, Li Wang, Fan Fan, Tao Liu, Xiaohong Chen, Tomoki Toda, Haijun Niu

“…Subsequently, a speech reconstruction model was built based on adversarial neural networks. The model includes a pretrained feature extractor to process ultrasound images, an upsampling block to generate speech, and discriminators to ensure the similarity and fidelity of the reconstructed speech. …”
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Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology by Petr Kuritcyn, Rosalie Kletzander, Sophia Eisenberg, Thomas Wittenberg, Volker Bruns, Katja Evert, Felix Keil, Paul K. Ziegler, Katrin Bankov, Peter Wild, Markus Eckstein, Arndt Hartmann, Carol I. Geppert, Michaela Benz

“…Various convolutional neural network (CNN) architectures can be used for feature extraction in the few-shot model. …”
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AttenFlow: Context-Aware Architecture with Consensus-Based Retrieval and Graph Attention for Automated Document Processing by Xianfeng Zhang, Bin Hu, Shukan Liu, Qiao Sun, Lin Chen

“…Second, we develop adversarial mutual-attention hybrid-dimensional graph attention network (AM-HDGAT) for text, which transforms document classification by modeling inter-document relationships through graph structures while integrating high-dimensional semantic features and low-dimensional statistical features through mutual-attention mechanisms. …”
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The study of promising secure information systems based on signal modeling by V. A. Sizov, D. M. Malinichev, Kh. K. Kuchmezov

“…Thus, when studying prospective protected information systems based on the use of 5G network technology, it is advisable to use a simulation of the signals of the channel-level interaction of subscribers, which allows you to evaluate the basic security parameters at the physical level.Materials and research methods. …”
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A Hybrid Convolutional–Transformer Approach for Accurate Electroencephalography (EEG)-Based Parkinson’s Disease Detection by Chayut Bunterngchit, Laith H. Baniata, Hayder Albayati, Mohammad H. Baniata, Khalid Alharbi, Fanar Hamad Alshammari, Sangwoo Kang

“…To overcome these challenges, this study proposes a convolutional transformer enhanced sequential model (CTESM), which integrates convolutional neural networks, transformer attention blocks, and long short-term memory layers to capture spatial, temporal, and sequential EEG features. …”
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REIV-TOXO Project: Results from a Spanish cohort of congenital toxoplasmosis (2015-2022). The beneficial effects of prenatal treatment on clinical outcomes of infected newborns. by Borja Guarch-Ibáñez, Clara Carreras-Abad, Marie Antoinette Frick, Daniel Blázquez-Gamero, Fernando Baquero-Artigao, Isabel Fuentes, Spanish Research Network of Congenital Toxoplasmosis (REIV-TOXO) group, Pere Soler-Palacin

“…The Spanish Research Network of Congenital Toxoplasmosis (REIV-TOXO) was created to describe the current status of CT in Spain. …”
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Multi-Scale Venation Pattern Analysis for Medicinal Plant Species Recognition by Arnav Sanjay Karnik, Nikhil Nair, Yashas Sagili, P. B. Pb

“…To validate the effectiveness of our approach, we developed and evaluated three distinct model architectures: 1) a modified ResNet-50 model utilizing transfer learning with an adapted input pipeline for venation-aware channels; 2) a custom-built convolutional neural network, VenationNet, explicitly designed for multi-scale venation analysis; and 3) a Dual-Stream CNN architecture that processes leaf texture and venation maps independently before merging via attention-based fusion. …”
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MSRD-CNN: Multi-Scale Residual Deep CNN for General-Purpose Image Manipulation Detection by Kapil Rana, Gurinder Singh, Puneet Goyal

“…Our network comprises of three stages: pre-processing, hierarchical high-level feature extraction, and classification. …”
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