Study on Lightweight Bridge Crack Detection Algorithm Based on YOLO11 by Xuwei Dong, Jiashuo Yuan, Jinpeng Dai

“…Furthermore, a lightweight detection head (LDH) is introduced to process feature information from different channels using efficient grouped convolutions. …”
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Handwritten Words Image Character Extraction Adaptive Algorithm Based on the Multi-branch Structure by GUO Xiaojing, ZHAO Xiaoyuan, ZOU Songlin

“…Deep convolution layers are known for learning features at different abstraction levels, while lower layers capture more localized details. …”
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A Dual-Attentive Multimodal Fusion Method for Fault Diagnosis Under Varying Working Conditions by Yan Chu, Leqi Zhu, Mingfeng Lu

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Online Pre-Diagnosis of Multiple Faults in Proton Exchange Membrane Fuel Cells by Convolutional Neural Network Based Bi-Directional Long Short-Term Memory Parallel Model with Atten... by Junyi Chen, Huijun Ran, Ziyang Chen, Trevor Hocksun Kwan, Qinghe Yao

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Circular Slot Antenna with Pattern Diversity by Youngje Sung

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RACNet: risk assessment Net of cervical lesions in colposcopic images by Tianxiang Xu, Peizhong Liu, Ping Li, Xiaoxia Wang, Huifeng Xue, JingMing Guo, Binhua Dong, Pengming Sun

“…In colposcopy-assisted diagnosis, the difference between the different lesion grades of colposcopic images is small, and the visual similarity is high. …”
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Fully-Gated Denoising Auto-Encoder for Artifact Reduction in ECG Signals by Ahmed Shaheen, Liang Ye, Chrishni Karunaratne, Tapio Seppänen

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Modeling of Ultrasonic Flaw Detection Processes in the Task of Searching and Visualizing Internal Defects in Assemblies and Structures by B. V. Sobol, A. N. Soloviev, P. V. Vasiliev, A. A. Lyapin

“…The study uses the finite difference method in the time domain. It is applied to identify and visualize internal defects in materials using ultrasonic nondestructive testing and convolutional generative neural networks.Results. …”
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Probing Three-dimensional Magnetic Fields. IV. Synchrotron Polarization Derivative and Vision Transformer by Yue Hu, Alex Lazarian

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A Gesture Recognition Method Based on Deep Learning by DING Chi, LIN Jun, YOU Jun, YUAN Hao

“…In order to increase the accuracy and robustness of gesture recognition in video, and better utilize the advantages of different deep neural network architectures, it presented an approach to realize gesture recognition by combining multiple advanced convolutional neural network architectures. …”
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Research on Android malware detection method based on multimodal feature fusion by Ge Jike, He Mingkun, Chen Zuqin, Ling Jin, Zhang Yifan

“…Existing Android malware detection methods mainly use single-modal data to characterize program features, but fail to fully mine and fuse different feature information, resulting in unsatisfactory detection results. …”
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Visualization of internal defects using a deep generative neural network model and ultrasonic nondestructive testing by Р. V. Vasiliev, А. V. Senichev, I. Giorgio

“…The propagation of an ultrasonic wave is modeled by the finite difference method in the time domain. An ultrasonic signal received at the internal points of the control object is applied to the input of the convolutional neural network. …”
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Turkish Lira Banknote Classification using Transfer Learning and Deep Learning by Mirsat Yeşiltepe, Harun Elkıran, Jawad Rasheed

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Detecting Phishing URLs Based on a Deep Learning Approach to Prevent Cyber-Attacks by Qazi Emad ul Haq, Muhammad Hamza Faheem, Iftikhar Ahmad

“…Phishing is one of the most widely observed types of internet cyber-attack, through which hundreds of clients using different internet services are targeted every day through different replicated websites. …”
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CNN-based vane-type vortex generator modelling by Koldo Portal-Porras, Unai Fernandez-Gamiz, Ekaitz Zulueta, Roberto Garcia-Fernandez, Xabier Uralde-Guinea

“…In order to obtain data for training the network, 20 different CFD simulations were conducted, considering the same inflow conditions but different vane heights and angles of attack of the VGs. …”
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Method of multilayer object sectioning based on a light scattering model by S.D. Bazhitov, A.V. Larichev, A.V. Razgulin, T.E. Romanenko

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An automatic ICD coding method for clinical records based on deep neural network by Yichao DU, Tong XU, Jianhui MA, Enhong CHEN, Yi ZHENG, Tongzhu LIU, Guixian TONG

“…With the increase in the number of the international classification of diseases (ICD) codes,the difficulty and cost of manual coding based on clinical records have greatly increased,and automatic ICD coding technology has attracted widespread attention.A multi-scale residual graph convolution network automatic ICD coding technology was proposed.This technology uses a multi-scale residual network to capture text patterns of different lengths of clinical text and extracts the hierarchical relationship between labels based on the graph convolutional neural network to enhance the ability of automatic coding.The experimental results on the real medical data set MIMIC-III show that the P@k and Micro-F1 of this method are 72.2% and 53.9%,respectively,which significantly improves the prediction performance.…”
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Multimodal Chinese Sarcasm Detection Integrating Audio Attributes and Textual Features by Huixin Wu, Yang Zang, Limeng Zhao, Hongyang Zhou

“…To extract relevant features at different levels, we use a multi-scale convolutional architecture. …”
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Wavelet-CNN for temporal data: Enhancing long-term stock price prediction via multi-resolution wavelet decomposition and CNN-based feature extraction by Komei Hiruta, Junsuke Senoguchi

“…Specifically, we first acquire components with different temporal resolutions using wavelet transform, then convert the wavelet-transformed data into images, and finally perform CNN processing to automatically extract useful temporal features for prediction. …”
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From Pixels to Diagnosis: Implementing and Evaluating a CNN Model for Tomato Leaf Disease Detection by Zamir Osmenaj, Evgenia-Maria Tseliki, Sofia H. Kapellaki, George Tselikis, Nikolaos D. Tselikas

“…Our work involved the implementation of a custom convolutional neural network (CNN) trained on a diverse dataset of tomato leaf images. …”
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