Unsupervised Structural Damage Detection Technique Based on a Deep Convolutional Autoencoder by Zahra Rastin, Gholamreza Ghodrati Amiri, Ehsan Darvishan

“…Most of the deep learning-based methods proposed for damage detection in civil structures are based on supervised algorithms that require data from the healthy state and different damaged states of the structure in the training phase. …”
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Advancements in Plant Pests Detection: Leveraging Convolutional Neural Networks for Smart Agriculture by Gopalakrishnan Nagaraj, Dakshinamurthy Sungeetha, Mohit Tiwari, Vandana Ahuja, Ajit Kumar Varma, Pankaj Agarwal

“…This article presents a summary of three perspectives, each of which is based on a different network design, in recent research on deep learning applied to the detection of plant diseases and pests. …”
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Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…The study collected hyperspectral images in 400~1000 nm of broccoli samples sprayed with different types of pesticides and clean water. Two data preprocessing methods, namely multivariate scattering correction (MSC) and Savitzky-Golay smoothing (SG smoothing), as well as principal component analysis (PCA), competitive adaptive reweighted sampling (CARS), and successive projection algorithm (SPA) were used to reduce the dimensionality. …”
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Time–Frequency Transformations for Enhanced Biomedical Signal Classification with Convolutional Neural Networks by Georgios Lekkas, Eleni Vrochidou, George A. Papakostas

“…In this study, we investigated the effectiveness of different 1D-to-2D transformation methods to classify electrocardiogram (ECG) and electroencephalogram (EEG) signals. …”
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Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network by Jianzhong Yuan, Wujie Zhou, Sijia Lv, Yuzhen Chen

“…The output from all different blocks is combined afterwards. Finally, transposed convolution layers were used for upsampling the feature maps to the same size with the original RGB image. …”
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Reconstruction of reservoir rock using attention-based convolutional recurrent neural network by Indrajeet Kumar, Anugrah Singh

“…Results show that ACRNN can reconstruct reservoir rock or porous media of different scales with approximately the same geometrical, statistical, and topological parameters of the reservoir rock images. …”
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Automatic melanoma detection using an optimized five-stream convolutional neural network by Vida Esmaeili, Mahmood Mohassel Feghhi, Hadi Seyedarabi

“…These challenges include the lack of a balanced dataset, high variability within melanoma lesions, differences in the locations of skin lesions in images, the similarity between different skin lesions, and the presence of various artifacts. …”
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Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks by Md. Rajib Hossain, Mohammed Moshiul Hoque, M. Ali Akber Dewan, Nazmul Siddique, Md. Nazmul Islam, Iqbal H. Sarker

“…Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. …”
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Hyperspectral Band Selection via Heterogeneous Graph Convolutional Self-Representation Network by Junde Chen, Wenzhao Li, Surendra Maharjan, Hesham El-Askary

“…In addition, they handle each HSI as an integrated unit to harness implicit spatial information, disregarding spatial distribution variations across different homogeneous regions. To fully leverage structural information, this study introduces a novel BS method, termed the dual heterogeneous graph convolutional network with enhanced self-representation (ESR-HGCN), for HSI BS. …”
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Hyperspectral Image Classification With Re-Attention Agent Transformer and Multiscale Partial Convolution by Junding Sun, Hongyuan Zhang, Jianlong Wang, Haifeng Sima, Shuanggen Jin

“…Convolutional neural networks (CNNs) focus solely on extracting local features, lacking the ability to capture global spectral-spatial information. …”
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Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear by WANG Shuyu, LI Hao, MA Gang, YUAN Yubo, BU Qiangsheng, YE Zhigang

“…In the future， the generalizability of this model under different meteorological conditions and geographical environments should be further explored to promote the development of solar power generation technology.…”
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Lightweight Sheep Face Recognition Model Combining Grouped Convolution and Parameter Fusion by Gaochao Liu, Lijun Kang, Yongqiang Dai

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Classification of maize seed hyperspectral images based on variable-depth convolutional kernels by Yating Hu, Hongchen Zhang, Hongchen Zhang, Changming Li, Qianfu Su, Wei Wang

“…However, conventional hyperspectral data processing approaches often fail to simultaneously capture both spectral and textural features effectively.MethodsTo overcome this limitation, we propose a novel convolutional neural network architecture with a variable-depth convolutional kernel structure (VD-CNN). …”
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Advanced Intelligent Data Hiding Using Video Stego and Convolutional Neural Networks by Inas Ali Abdulmunem, Eman S. Harba, Hind S. Harba

“…Steganography is a technique of concealing secret data within other quotidian files of the same or different types. Hiding data has been essential to digital information security. …”
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Application of the Algebraic Extension Method to the Construction of Orthogonal Bases for Partial Digital Convolutions by Aruzhan Kadyrzhan, Akhat Bakirov, Dina Shaltykova, Ibragim Suleimenov

“…This allows us to produce partial Fourier–Galois transforms corresponding to different Galois fields, for the same number of cycles. …”
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Fully automated MRI-based convolutional neural network for noninvasive diagnosis of cirrhosis by Tianying Zheng, Yajing Zhu, Yidi Chen, Shengshi Mai, Lixin Xu, Hanyu Jiang, Ting Duan, Yuanan Wu, Yali Qu, Yinan Chen, Bin Song

“…Abstract Objectives To develop and externally validate a fully automated diagnostic convolutional neural network (CNN) model for cirrhosis based on liver MRI and serum biomarkers. …”
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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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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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