HTC-HAD: A Hybrid Transformer-CNN Approach for Hyperspectral Anomaly Detection by Minghua Zhao, Wen Zheng, Jing Hu

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Deep machine learning identified fish flesh using multispectral imaging by Zhuoran Xun, Xuemeng Wang, Hao Xue, Qingzheng Zhang, Wanqi Yang, Hua Zhang, Mingzhu Li, Shangang Jia, Jiangyong Qu, Xumin Wang

“…We found that nCDA images transformed from MSI data showed significant differences in flesh splices of the 20 fish species. …”
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Deep Learning for Glioblastoma Multiforme Detection from MRI: A Statistical Analysis for Demographic Bias by Kebin Contreras, Julio Gutierrez-Rengifo, Oscar Casanova-Carvajal, Angel Luis Alvarez, Patricia E. Vélez-Varela, Ana Lorena Urbano-Bojorge

“…This morphological discrepancy demonstrates the generalisation capacity of the model across anatomical and acquisition differences, achieving an F1-score of 0.88. Furthermore, statistical tests, such as Shapiro–Wilk, Mann–Whitney U, and Chi-square, confirmed the absence of demographic bias in model predictions, based on <i>p</i>-values, confidence intervals, and statistical power analyses supporting its demographic fairness. …”
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RDAU-Net: A U-Shaped Semantic Segmentation Network for Buildings near Rivers and Lakes Based on a Fusion Approach by Yipeng Wang, Dongmei Wang, Teng Xu, Yifan Shi, Wenguang Liang, Yihong Wang, George P. Petropoulos, Yansong Bao

“…To address the above issues, the present study proposes the design of a U-shaped segmentation network of buildings called RDAU-Net that works through extraction and fuses a convolutional neural network and a transformer to segment buildings. …”
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DreamOn: a data augmentation strategy to narrow the robustness gap between expert radiologists and deep learning classifiers by Luc Lerch, Luc Lerch, Lukas S. Huber, Lukas S. Huber, Amith Kamath, Alexander Pöllinger, Aurélie Pahud de Mortanges, Verena C. Obmann, Florian Dammann, Walter Senn, Walter Senn, Mauricio Reyes, Mauricio Reyes, Mauricio Reyes

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Subtypes detection of papillary thyroid cancer from methylation assay via Deep Neural Network by Andrea Colacino, Andrea Soricelli, Michele Ceccarelli, Ornella Affinito, Monica Franzese

“…Methods: To address this issue, we first performed a pan-cancer analysis to train a convolutional 1-D Neural Network (CNN) using supervised learning. …”
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Impacted lower third molar classification and difficulty index assessment: comparisons among dental students, general practitioners and deep learning model assistance by Paniti Achararit, Chawan Manaspon, Chavin Jongwannasiri, Promphakkon Kulthanaamondhita, Chumpot Itthichaisri, Soranun Chantarangsu, Thanaphum Osathanon, Ekarat Phattarataratip, Kraisorn Sappayatosok

“…Among the groups, performance tests without CNN assistance revealed no significant differences in any category. However, compared with DSs, GPs took significantly less time for the class and total time, a trend that persisted when CNN assistance was used. …”
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Micro-expression recognition method based on progressive attention by ZHAN Ziwei, SUN Zhaocai, LI Xiang, WU Zhendong

“…First, the multi-scale convolutional module is used to learn fine-grained features from different receptive fields, extracting rich details. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…Background Phishing attacks are now regarded as one of the most prevalent cyberattacks that often compromise the security of different communication and internet networks. Phishing websites are created with the goal of generating cyber threats in order to ascertain the user’s financial information. …”
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Dual conditional GAN based on external attention for semantic image synthesis by Gang Liu, Qijun Zhou, Xiaoxiao Xie, Qingchen Yu

“…The graph attention (GAT) is added to the generator to strengthen the relationship between different categories in the generated image. A graph convolutional segmentation network (GSeg) is designed to learn information for each category. …”
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A steel surface defect detection method based on improved RetinaNet by Zhanglin Yang, Yu Liu

“…Abstract To address the issue of low detection accuracy caused by the variety of steel surface defect types, large shape differences, and the similarity between defects and the background, this paper proposes an improved method for detecting steel surface defects based on RetinaNet. …”
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A Rotation Target Detection Network Based on Multi-Kernel Interaction and Hierarchical Expansion by Qi Wang, Guanghu Xu, Donglin Jing

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METRIC: Multiple preferences learning with refined item attributes for multimodal recommendation by Yunfei Zhao, Jie Guo, Longyu Wen, Letian Wang

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YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning by Chenxing Wu, Changlong Cai, Feng Xiao, Jiahao Wang, Yulin Guo, Longhui Ma

“…Second, a Multiscale Lightweight Convolution (MLConv) is designed, and a lightweight feature extraction module, MLCSP, is constructed to enhance the extraction of detailed information. …”
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Jordanian banknote data recognition: A CNN-based approach with attention mechanism by Ahmad Nasayreh, Ameera S. Jaradat, Hasan Gharaibeh, Waed Dawaghreh, Rabia Mehamad Al Mamlook, Yaqeen Alqudah, Qais Al-Na'amneh, Mohammad Sh. Daoud, Hazem Migdady, Laith Abualigah

“…The study made use of a data set from Kaggle that includes a collection of Jordanian banknotes in five different denominations. Image processing techniques were employed to produce artificial images by boosting the brightness of real ones. …”
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Protective Effects of Withania Somnifera Against Cisplatin-Induced Acute Kidney Injury in Rats: A Histomorphometric Analysis by Aaqiba Rasheed, Nadia Younus, Nausheen Jamshed, Lubna Faisal, Naureen Waseem, Rana Muhammad Zeeshan, Omar Shamim

“…However, no statistically significant differences in kidney weight were observed among the other groups (P>0.05). …”
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Partial-Net: A Method for Data Gaps Reconstruction on Mars Images by Depei Gu, Dingruibo Miao, Jianguo Yan, Zhigang Tu, Jean-Pierre Barriot

“…This often results in artifacts, such as color differences and blurriness. In addition, existing mask sets commonly used in computer vision cannot simulate and learn the particular irregular shapes of data gaps in Mars images well. …”
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Improving Solar Radiation Forecasting in Cloudy Conditions by Integrating Satellite Observations by Qiangsheng Bu, Shuyi Zhuang, Fei Luo, Zhigang Ye, Yubo Yuan, Tianrui Ma, Tao Da

“…Forecast errors are related to cloud regimes, of which the cloud amount leads to a maximum relative RMSE difference of about 50% with an additional 5% from cloud variability. …”
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Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN by Seokchan Kim, Yeong-Jae Kim, Dongwook Lee, Hanmin Lee

“…The robustness to different tires and driving conditions makes the proposed algorithm practical for estimating road surface conditions in real vehicles.…”
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Segmentation of dermatoscopic images of skin lesions. Comparison of methods by A. F. Smalyuk, M. S. Dzeshka, I. D. Kupchykava

“…The proposed algorithm makes it possible to detect differences in images even if there is a significant difference in the brightness and color levels of the compared images, and also ignores small insignificant details, such as noise, dermatoscope optics marks, hair, etc. …”
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