Lightweight Attention-Based CNN Architecture for CSI Feedback of RIS-Assisted MISO Systems by Anming Dong, Yupeng Xue, Sufang Li, Wendong Xu, Jiguo Yu

“…Furthermore, by incorporating an efficient channel attention (ECA) mechanism, the model dynamically allocates weights to different feature channels, thereby enhancing the capture of critical features. …”
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Deep Attention Networks With Multi-Temporal Information Fusion for Sleep Apnea Detection by Meng Jiao, Changyue Song, Xiaochen Xian, Shihao Yang, Feng Liu

“…Recognizing that features derived from different temporal scales vary in their contribution to classification, we integrate a multi-head attention module with a self-attention mechanism to learn the weights for each feature vector. …”
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Detection of <i>Helicobacter pylori</i> Infection in Histopathological Gastric Biopsies Using Deep Learning Models by Rafael Parra-Medina, Carlos Zambrano-Betancourt, Sergio Peña-Rojas, Lina Quintero-Ortiz, Maria Victoria Caro, Ivan Romero, Javier Hernan Gil-Gómez, John Jaime Sprockel, Sandra Cancino, Andres Mosquera-Zamudio

“…The aim of the present article is to detect the presence of <i>HP</i> infection from our own institutional dataset of histopathological gastric biopsy samples using different pretrained and recognized DCNN and AutoML approaches. …”
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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…In addition, the multi-source feature fusion demonstrates strong adaptability across different growth environments. In Xinxiang, the R2 ranges from 0.76 to 0.88, the RMSE ranges from 0.35 to 0.50, and the rRMSE ranges from 8.73 % to 12.40 %. …”
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Spectropolarimetric Inversion in Four Dimensions with Deep Learning (SPIn4D). I. Overview, Magnetohydrodynamic Modeling, and Stokes Profile Synthesis by Kai E. Yang, Lucas A. Tarr, Matthias Rempel, S. Curt Dodds, Sarah A. Jaeggli, Peter Sadowski, Thomas A. Schad, Ian Cunnyngham, Jiayi Liu, Yannik Glaser, Xudong Sun

“…Specifically, our radiative MHD model simulates the small-scale dynamo actions that are prevalent in quiet-Sun and plage regions. Six cases with different mean magnetic fields have been explored; each case covers six solar-hours, totaling 109 TB in data volume. …”
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Comparative Analysis of Vision Transformers and CNN Models for Driver Fatigue Classification by Fadhlan Hafizhelmi Kamaru Zaman, Kok Mun Ng, Syahrul Afzal Che Abdullah

“… This study provides a comprehensive evaluation of Convolutional Neural Network (CNN) and Vision Transformer (ViT) models for driver fatigue classification, a critical issue in road safety. …”
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Explainable clinical diagnosis through unexploited yet optimized fine-tuned ConvNeXt Models for accurate monkeypox disease classification by Muhammad Waqar, Zeshan Aslam Khan, Shanzey Tariq Khawaja, Naveed Ishtiaq Chaudhary, Saadia Khan, Khalid Mehmood Cheema, Muhammad Farhan Khan, Syed Sohail Ahmed, Muhammad Asif Zahoor Raja

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A bird song detector for improving bird identification through deep learning: A case study from Doñana by Alba Márquez-Rodríguez, Miguel Ángel Mohedano-Munoz, Manuel J. Marín-Jiménez, Eduardo Santamaría-García, Giulia Bastianelli, Pedro Jordano, Irene Mendoza

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Optimizing Fingerprint Identification: CNNs With Raw Images Versus Handcrafted Features for Real-Time Systems by Shaik Salma, Tauheed Ahmed, Garimella Ramamurthy

“…Despite progress, existing systems still face challenges with noise, database differences, and real-time speed. This study investigates the balance between accuracy and computational efficiency(thereby speed) by comparing two approaches: training a Convolutional Neural Network (CNN) with raw fingerprint images and training a CNN using handcrafted fingerprint features. …”
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Text analysis of DNS queries for data exfiltration protection of computer networks by Ya. V. Bubnov, N. N. Ivanov

“…The paper proposes a method of detecting such DNS requests based on text classification of domain names by convolutional neural network. The efficiency of the method is based on assumption that domain names exploited for data exfiltration differ from domain names formed from words of natural language. …”
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Development of low-cost portable spectrometer equipped with 18-band spectral sensors using deep learning model for evaluating moisture content of rubber sheets by Amorndej Puttipipatkajorn, Amornrit Puttipipatkajorn

“…During testing of the instrument, the results indicated that its predictive performance did not differ significantly from that of the primary calibration model. …”
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Metric learning unveiling disparities: A novel approach to recognize false trigger images in wildlife monitoring by Rui Zhu, Enting Zhao, Chunhe Hu, Jiangjian Xie, Junguo Zhang, Huijian Hu

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Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review by Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian

“…However, progress in COVID-19 detection is hindered by ongoing issues stemming from restricted and non-uniform datasets, as well as domain differences in image standards and complications with both diagnostic overfitting and poor generalization capabilities. …”
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Enhanced Osteoporosis Detection Using Artificial Intelligence: A Deep Learning Approach to Panoramic Radiographs with an Emphasis on the Mental Foramen by Robert Gaudin, Wolfram Otto, Iman Ghanad, Stephan Kewenig, Carsten Rendenbach, Vasilios Alevizakos, Pascal Grün, Florian Kofler, Max Heiland, Constantin von See

“…A total of 250 PRs from three groups (A: osteoporosis group, B: non-osteoporosis group matching A in age and gender, C: non-osteoporosis group differing from A in age and gender) were cropped to the mental foramen region. …”
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Automatic Detection and Calculation of Mining Subsidence in Large-Scale Interferograms With Transformer-CNN Model by Hongdong Fan, Jialin Xin, Tao Lin, Jun Wang

“…Subsequently, the Residual Convolution Block and Swin Transformer Block were adopted as the fundamental building blocks of the model architecture to develop RAUNet, a synergistic network designed for detecting and calculating wide-area mining subsidence zones. …”
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FilletCam AI: A handheld tool for precise fillet color profiling of Atlantic salmon and rainbow trout by Rakesh Ranjan, Harsh Shroff, Kata Sharrer, Scott Tsukuda, Christopher Good

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A Parallel Image Denoising Network Based on Nonparametric Attention and Multiscale Feature Fusion by Jing Mao, Lianming Sun, Jie Chen, Shunyuan Yu

“…The lower branch network used multiple dilation convolution residual blocks with different dilation rates to increase the receptive field and extend more contextual information to obtain the global features of the noise in the image. …”
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MT-SCnet: multi-scale token divided and spatial-channel fusion transformer network for microscopic hyperspectral image segmentation by Xueying Cao, Hongmin Gao, Haoyan Zhang, Shuyu Fei, Peipei Xu, Peipei Xu, Zhijian Wang

“…It divides token at different scale based on mirror padding and promotes information interaction and fusion between different tokens to obtain more representative features for subsequent global feature extraction. …”
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A Low-Complexity Transformer-CNN Hybrid Model Combining Dynamic Attention for Remote Sensing Image Compression by L. L. Zhang，X. J. Wang，J. H. Liu, Q. Z. Fang

“…However, conventional CNN is complex to adaptively capture important information from different image regions. In addition, previous transformer-based compression methods have introduced high computational complexity to the models. …”
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Interpreting CNN models for musical instrument recognition using multi-spectrogram heatmap analysis: a preliminary study by Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

“…This task poses significant challenges due to the complexity and variability of musical signals.MethodsIn this study, we employed convolutional neural networks (CNNs) to analyze the contributions of various spectrogram representations—STFT, Log-Mel, MFCC, Chroma, Spectral Contrast, and Tonnetz—to the classification of ten different musical instruments. …”
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