ST-CFI: Swin Transformer with convolutional feature interactions for identifying plant diseases by Sheng Yu, Li Xie, Liang Dai

“…This paper introduces the Swin Transformer with Convolutional Feature Interactions (ST-CFI), a state-of-the-art deep learning framework designed for detecting plant diseases through the analysis of leaf images. …”
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Enhanced multiple sclerosis diagnosis by MRI image retrieval using convolutional autoencoders by R.M. Haggag, Eman M. Ali, M.E. Khalifa, Mohamed Taha

“…This study introduces a novel Content-Based Medical Image Retrieval (CBMIR) framework that leverages a newly designed Convolutional Autoencoder (CAE) model to improve the diagnostic evaluation of MS-related MRI scans. …”
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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

“…A four-layer CNN framework was constructed, and a total of 12 models were developed by varying the convolutional kernel depth to evaluate the impact on classification performance.ResultsExperimental results show that the proposed VD-CNN achieves optimal performance when the convolutional kernel depth is set to 15, attaining a training accuracy of 98.65% and a test accuracy of 96.97%. …”
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Voice activity detection in noisy conditions using tiny convolutional neural network by R. S. Vashkevich, E. S. Azarov

“…An extremely compact convolutional neural network is proposed. The model has only 385 trainable parameters. …”
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DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement by Raul Balmez, Alexandru Brateanu, Ciprian Orhei, Codruta O. Ancuti, Cosmin Ancuti

“…We present a novel transformer-based framework that enhances efficiency by utilizing depthwise separable convolutions instead of conventional approaches. …”
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TGNet: tensor-based graph convolutional networks for multimodal brain network analysis by Zhaoming Kong, Rong Zhou, Xinwei Luo, Songlin Zhao, Ann B. Ragin, Alex D. Leow, Lifang He

“…In this paper, we propose a novel tensor-based graph convolutional network (TGNet) framework that combines tensor decomposition with multi-layer GCNs to capture both the homogeneity and intricate graph structures of multimodal brain networks. …”
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Dance video action recognition algorithm based on improved hypergraph convolutional networks by Ni Zhen, Yiyi Jiang

“…In view of this, the study takes the hypergraph convolutional network under deep learning as the framework basis, optimizes the performance by introducing the self-attention module and the topology module, constructs the temporal refinement channel and the channel refinement channel, and adds the spatio-temporal hypergraph convolutional network for channel fusion, and finally proposes a new video action recognition model. …”
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High order Interaction and Wavelet Convolution Network for visible infrared person reidentification by Li Ma, Rui Kong, XinGuan Dai

“…Furthermore, our framework uses wavelet convolution to mine more diverse features and solve the problem of insufficient feature extraction. …”
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CST-Net: community-guided structural-temporal convolutional networks for popularity prediction by Xuxu Zheng, Peng Bao, Lin Qi, Chen Tian, Huawei Shen

“…In this article, we propose an end-to-end deep learning framework, called CST-Net, to combat the defects of existing methods. …”
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Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…To tackle this challenge, we introduce a novel framework termed Dynamic Graph Convolutional Networks with Temporal Representation Learning for Traffic Flow Prediction (DGCN-TRL). …”
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Enhanced Convolutional Neural Network for Accurate Crop Recommendation System on Climate Data by Adnan Myasar M., AI_Sadi Hafidh l., Abhilash Pideka Kundil

“…Our model, combining the CS-ICNN framework, offers enhanced recommendations by considering both soil-specific characteristics and environmental factors. …”
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Crossed Wavelet Convolution Network for Few-Shot Defect Detection of Industrial Chips by Zonghai Sun, Yiyu Lin, Yan Li, Zihan Lin

“…In this work, we propose a crossed wavelet convolution network (CWCN), including a dual-pipeline crossed wavelet convolution training framework (DPCWC) and a loss value calculation module named ProSL. …”
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Music genre classification with parallel convolutional neural networks and capuchin search algorithm by Yuxin Zhang, Teng Li

“…This study presents a unique method that blends convolutional neural network (CNN) models as an ensemble system to detect musical genres. …”
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Intelligent Analysis of Hydraulic Concrete Vibration Time Based on Convolutional Neural Network by Hao Liu, Chengzhao Liu, Jiake Fu, Chenzhe Ma, Ye Zhang, Yumeng Lei

“…The system took the convolutional neural network as the basic framework, and divided the concrete vibration process into three different states: vibrating, not vibrating, and no vibration tube, realized the concrete vibration time through the analysis of concrete vibration video data. …”
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Reservoir Stochastic Simulation Based on Octave Convolution and Multistage Generative Adversarial Network by Xuechao Wu, Wenyao Fan, Shijie Peng, Bing Qin, Qing Wang, Mingjie Li, Yang Li

“…Also, a higher computing consumption and overfitting issue easily occurred by stacking Convolutional Neural Networks (CNNs). Therefore, a hybrid framework combined with octave convolution and multi-stage GAN (OctSinGAN) is proposed to perform reservoir simulation. …”
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Hand-aware graph convolution network for skeleton-based sign language recognition by Juan Song, Huixuechun Wang, Jianan Li, Jian Zheng, Zhifu Zhao, Qingshan Li

“…To address this issue, we propose a novel hand-aware graph convolution network (HA-GCN) to focus on hand topological relationships of skeleton graph. …”
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CIT-EmotionNet: convolution interactive transformer network for EEG emotion recognition by Wei Lu, Lingnan Xia, Tien Ping Tan, Hua Ma

“…The model integrates convolutional neural network (CNN) and Transformer within a single framework in a parallel manner. …”
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Deep Learning Models for Image Classification Advances in Convolutional Neural Network Architectures by Kumar Pathak Prakash, M Srivani, M Diwakaran, R Purushothaman, Sheeba Adlin, R Ahila

“…Deep learning has improved image classification tasks dramatically, where Convolutional Neural Networks (CNNs) have prevailed as the most successful architecture. …”
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YOLO-SR: An optimized convolutional architecture for robust ship detection in SAR Imagery by Chi Kien Ha, Hoanh Nguyen, Vu Duc Van

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VG-CGARN: Video Generation Using Convolutional Generative Adversarial and Recurrent Networks by Fatemeh Sobhani Manesh, Amin Nazari, Muharram Mansoorizadeh, MirHossein Dezfoulian

“…This paper proposes a novel hybrid framework that combines convolutional neural networks (CNNs), recurrent neural networks (RNNs) with long short-term memory (LSTM) units, and generative adversarial networks (GANs) to synthesize temporally consistent and spatially realistic video sequences from still images. …”
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