Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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Steganographer identification of JPEG image based on feature selection and graph convolutional representation by Qianqian ZHANG, Yi ZHANG, Hao LI, Yuanyuan MA, Xiangyang LUO

“…Aiming at the problem that the feature dimension of JPEG image steganalysis is too high, which leads to the complexity of distance calculation between users and a decrease in the identification performance of the steganographer, a method for steganographer recognition based on feature selection and graph convolutional representation was proposed.Firstly, the steganalysis features of the user’s images were extracted, and the feature subset with highseparability was selected.Then, the users were represented as a graph, and the features of users were obtained by training the graph convolutional neural network.Finally, because inter-class separability and intra-class aggregation were considered, the features of users that could capture the differences between users were learned.For steganographers who use JPEG steganography, such as nsF5, UED, J-UNIWARD, and so on, to embed secret information in images, the proposed method can reduce the feature dimensions and computing.The identification accuracy of various payloads can reach more than 80.4%, and it has an obvious advantage at the low payload.…”
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An anti‐jamming method in multistatic radar system based on convolutional neural network by Jieyi Liu, Maoguo Gong, Mingyang Zhang, Hao Li, Shanshan Zhao

“…In this study, a discrimination method in a multistatic radar system based on the convolutional neural network is proposed. This proposal combines the advantages of multiple‐radar systems cooperative detection technology with the convolutional neural network, and effectively applies to the field of anti‐deception jamming, which takes full advantage of unknown information of echo data to obtain multi‐dimensional, comprehensive, complete and deep feature differences besides correlation, so as to achieve a better jamming discrimination effect. …”
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Risk assessment of thyroid nodules with a multi-instance convolutional neural network by Da Yu, Tingting Song, Yancheng Yu, Hebin Zhang, Feng Gao, Zirong Wang, Jiacheng Wang

“…Statistical analysis showed that the performance differences were statistically significant (p <0.0001).ConclusionsThese results demonstrate the effectiveness and clinical utility of the proposed MIL-CNN framework in non-invasively stratifying thyroid nodule risk, supporting more informed clinical decisions and potentially reducing unnecessary biopsies and surgeries. …”
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3DVT: Hyperspectral Image Classification Using 3D Dilated Convolution and Mean Transformer by Xinling Su, Jingbo Shao

“…Hyperspectral imaging and laser technology both rely on different wavelengths of light to analyze the characteristics of materials, revealing their composition, state, or structure through precise spectral data. …”
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Fusion of Recurrence Plots and Gramian Angular Fields with Bayesian Optimization for Enhanced Time-Series Classification by Maria Mariani, Prince Appiah, Osei Tweneboah

“…We introduce a novel framework that transforms time series into image representations by fusing recurrence plots (RPs) with both Gramian Angular Summation Fields (GASFs) and Gramian Angular Difference Fields (GADFs). This fusion enriches the structural encoding of temporal dynamics. …”
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ACM-YOLOv10: Research on Classroom Learning Behavior Recognition Algorithm Based on Improved YOLOv10 by Beichen Qin, Haoyan Hu, Shaowen Du

“…The model is designed with an Asymmetric Depthwise Separable Convolution (ADSConv) module to replace the traditional convolutional layers. …”
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Scaling convolutional neural networks achieves expert level seizure detection in neonatal EEG by Robert Hogan, Sean R. Mathieson, Aurel Luca, Soraia Ventura, Sean Griffin, Geraldine B. Boylan, John M. O’Toole

“…This model also attained expert-level performance on both validation sets, a first in this field, with no significant difference in inter-rater agreement when the model replaces an expert (∣Δ κ∣ < 0.094, p > 0.05).…”
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Cross-device fault diagnosis method based on graph convolution and multi-sensor fusion by SUN Yuanshuai, KONG Fanqin, NIE Xiaoyin, XIE Gang

“…To address this problem, a cross-device fault diagnosis method based on graph convolution and multi-sensor fusion, convolutional domain graph convolution network (CDGCN) , was proposed. …”
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Brain-guided convolutional neural networks reveal task-specific representations in scene processing by Bruce C. Hansen, Michelle R. Greene, Henry A. S. Lewinsohn, Audrey E. Kris, Sophie Smyth, Binghui Tang

“…Here, we developed a novel brain-guided convolutional neural network (CNN) where each convolutional layer was separately guided by neural responses taken at different time points while observers performed a pre-cued object detection task or a scene affordance task on the same set of images. …”
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Ground-Based Remote Sensing Cloud Image Segmentation Using Convolution-MLP Network by Shuang Liu, Jiafeng Zhang, Zhong Zhang, Shuzhen Hu, Baihua Xiao

“…To this end, we propose the attention-guided MLPs module to highlight salient features and suppress irrelevant features from the spatial and channel aspects. Meanwhile, different from existing MLPs methods where the long-range dependencies are learned from one single scale, we propose the dilated MLPs (DMLPs) to learn long-range dependencies at different scales by sampling different channels of tokens. …”
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Bearing Fault Detection and Classification Based on Temporal Convolutions and LSTM Network in Induction Machine by Mohammad Hoseintabar Marzebali, Saeed Hasani Borzadaran, Hoda Mashayekhi, Valiollah Mashayekhi

“…Therefore, a proper condition monitoring method that can classify the type and the severity of electrical machine faults in different load levels is crucial to avoid unwanted downtime and loss of operation. …”
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Comparative exploration of deep convolutional neural networks using real-time endoscopy images by Subhashree Mohapatra, Pukhraj Singh Jeji, Girish Kumar Pati, Manohar Mishra, Tripti Swarnkar

“…Until now various deep convolutional neural networks are designed and trained for the purpose of classifying different medical conditions related to the domain of gastroenterology. …”
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The diagnostic value of convolutional neural networks in thyroid cancer detection using ultrasound images by Pei Zhang, Qijian Xu, Feng Jiang

“…ObjectiveTo extract and analyze the image features of two-dimensional ultrasound images and elastic images of four thyroid nodules by radiomics, and then further convolution processing to construct a prediction model for thyroid cancer. …”
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Fast and intelligent detection of concrete cracks based on sound signals and convolutional neural network by Chunlei Ge, Yue Qin, Yue Qin, Kaizhong Xie, Zubiao Lu

“…Finally, comparative experiments with different frame lengths, different models and different signal-to-noise ratios (SNR) are conducted using the improved CNN.ResultsThe results show that the model validation process has the least loss and highest accuracy when the input frame length is 1024. …”
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A Convolutional Neural Network for Coastal Classification Based on ALOS and NOAA Satellite Data by Kinh Bac Dang, Van Bao Dang, Quang Thanh Bui, Van Vuong Nguyen, Thi Phuong Nga Pham, Van Liem Ngo

“…Nowadays, the integration of deep learning in remote sensing and GIS analysis can quickly classify and detect different characteristics on both land and sea. Therefore, the authors proposed the use of a convolutional neural network (ConvNet) for coastal classification based on these technologies and geomorphic profile graphs. …”
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SAR Images Change Detection Based on Attention Mechanism-Convolutional Wavelet Neural Network by Jiahui E, Lu Wang, Chunhui Zhao, P. Takis Mathiopoulos, Tomoaki Ohtsuki, Fumiyuki Adachi

“…To deal with these problems this article proposes a SAR images change detection scheme which is based upon an Attention Mechanism and Convolutional Wavelet Neural Network. First, employing Multiscale Superpixel Reconstructed Difference Image effectively enhances the edge information of the images. …”
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Fault Diagnosis for Rotating Machinery Based on Convolutional Neural Network and Empirical Mode Decomposition by Yuan Xie, Tao Zhang

“…We carried out experiments with vibration data of 52 different categories under different machine conditions to test the validity of the approach, and the results indicate it is more accurate and reliable than previous approaches.…”
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Chasing Dragons in the Dragon's Land: A Convoluted Struggle with Drugs and Deviance in Modern China by Aftab Haider, Ashraf Ali, Muhammad Zubair

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MCE-HGCN: Heterogeneous Graph Convolution Network for Analog IC Matching Constraints Extraction by Yong Zhang, Yong Yin, Ning Xu, Bowen Jia

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