Fault Identification Model Using Convolutional Neural Networks with Transformer Architecture by Yongxin Fan, Yiming Dang, Yangming Guo

“…To address this, the present study proposes a novel hybrid deep learning framework that integrates Convolutional Neural Networks (CNN) for feature extraction with Transformer architecture for temporal modeling. …”
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Exudate Detection for Diabetic Retinopathy Using Pretrained Convolutional Neural Networks by Muhammad Mateen, Junhao Wen, Nasrullah Nasrullah, Song Sun, Shaukat Hayat

“…In this paper, pretrained convolutional neural network- (CNN-) based framework has been proposed for the detection of exudate. …”
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Joint Character-Level Convolutional and Generative Adversarial Networks for Text Classification by Tianshi Wang, Li Liu, Huaxiang Zhang, Long Zhang, Xiuxiu Chen

“…In the framework, we first quantify the texts by a character-level convolutional neural network and input the textual features into an adversarial network and a classifier, respectively. …”
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Large-Scale Video Retrieval via Deep Local Convolutional Features by Chen Zhang, Bin Hu, Yucong Suo, Zhiqiang Zou, Yimu Ji

“…In this paper, we study the challenge of image-to-video retrieval, which uses the query image to search relevant frames from a large collection of videos. A novel framework based on convolutional neural networks (CNNs) is proposed to perform large-scale video retrieval with low storage cost and high search efficiency. …”
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Dynamic Snake Convolution Neural Network for Enhanced Image Super-Resolution by Weiqiang Xin, Ziang Wu, Qi Zhu, Tingting Bi, Bing Li, Chunwei Tian

“…To optimize the network’s structure, DSCNN employs an enhanced residual network framework. This framework utilizes parallel convolutional layers and a global feature fusion mechanism to further strengthen feature extraction capability and gradient flow efficiency. …”
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Convolutional neural network-based low light image enhancement method by J. Guo

“…The purpose of the study is to offer a reference framework for low-light image enhancing techniques.…”
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Stochastic Hyperkernel Convolution Trains and <italic>h</italic>-Counting Processes by Abdourrahmane Mahamane Atto, Brani Vidakovic, Aluisio Pinheiro

“…The paper also highlights some statistical properties of the provided convolution train model, in addition to a framework based on wavelet packets for simulating or learning such a process from multiple observations of disturbed input trains.…”
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Enhancing Efficiency and Regularization in Convolutional Neural Networks: Strategies for Optimized Dropout by Mehdi Ghayoumi

“…<b>Background/Objectives:</b> Convolutional Neural Networks (CNNs), while effective in tasks such as image classification and language processing, often experience overfitting and inefficient training due to static, structure-agnostic regularization techniques like traditional dropout. …”
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Vegetation Coverage in Marsh Grass Photography Using Convolutional Neural Networks by Lucas Wayne Welch, Xudong Liu, Indika Kahanda, Sandeep Reddivari, Karthikeyan Umapathy

“…In this paper, aiming to automate this process, we propose a novel framework for such automation using deep neural networks. …”
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FOV Expansion of Bioinspired Multiband Polarimetric Imagers With Convolutional Neural Networks by Yongqiang Zhao, Miaomiao Wang, Guang Yang, Jonathan Cheung-Wai Chan

“…In order to overcome the limits, this paper presents a deep learning method for FOV expansion, incorporating the gradient prior of the image into a nine-dimensional convolutional neural network&#x0027;s framework to learn end-to-end mapping between the incomplete images and the FOV-expanded images. …”
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Atrous Convolution-Based Fusion Attention Mechanism for Brain Tumor Segmentation by Abhishek Jadhav, Akhtar Rasool, Manasi Gyanchandani

“…In this work, we introduce an Atrous Convolution-Based Fusion Attention Mechanism, a novel framework that combines local and global attention through an innovative fusion block. …”
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EXPLORING TRANSFER LEARNING AND CONVOLUTIONAL AUTOENCODER FOR EFFECTIVE KITCHEN UTENSILS CLASSIFICATION by Hashim Rosli, Rozniza Ali, Muhamad Suzuri Hitam, Ashanira Mat Deris, Noor Hafhizah Abd Rahim

“…We integrate pre-trained networks into an autoencoder framework to enhance feature extraction and image reconstruction. …”

Temporal Relational Graph Convolutional Network Approach to Financial Performance Prediction by Brindha Priyadarshini Jeyaraman, Bing Tian Dai, Yuan Fang

“…Our work contributes a systematic FKG construction method and a framework that utilizes both relational and textual embeddings for improved financial performance prediction.…”
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A review of lightweight convolutional neural networks for ultrasound signal classification by Bokun Zhang, Zhengping Li, Yuwen Hao, Lijun Wang, Xiaoxue Li, Yuan Yao

“…Among them, model compression deals with the overall framework to reduce network redundancy, and the latter aims at the lightweight design of the basic operational module “convolution” in the network. …”
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On-Chip Photonic Convolutional Processing Lights Up Fourier Neural Operator by Zilong Tao, Hao Ouyang, Qiuquan Yan, Shiyin Du, Hao Hao, Jun Zhang, Jie You

“…On the Radio ML 2016.10b dataset, our Fourier convolutional neural network achieves a peak identification accuracy of 95.50%, outperforming standard convolution-based networks. …”
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SMS spam detection using BERT and multi-graph convolutional networks by Linjie Shen, Yanbin Wang, Zhao Li, Wenrui Ma

“…To address these limitations, we propose the BERT with Triple-Graph Convolutional Networks (BERT-G3CN) model, the first framework to integrate BERT word embeddings with graph embeddings from Co-occurrence, Heterogeneous, and Integrated Syntactic Graphs. …”
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Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning by Manish Sharma, Jamison Heard, Eli Saber, Panagiotis Markopoulos

“…While Convolutional Neural Networks (CNNs) excel at learning complex latent-space representations, their over-parameterization can lead to overfitting and reduced performance, particularly with limited data. …”
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Detecting small seamounts in multibeam data using convolutional neural networks by Tobias Ziolkowski, Colin W. Devey, Agnes Koschmider

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Dense dynamic convolutional network for Bel canto vocal technique assessment by Zhenyi Hou, Xu Zhao, Shanggerile Jiang, Daijun Luo, Xinyu Sheng, Kaili Geng, Kejie Ye, Jiajing Xia, Yitao Zhang, Chenxi Ban, Jiaxing Chen, Yan Zou, Yuchao Feng, Xin Yuan, Guangyu Fan

“…To address the challenges posed by complex spectral features and meet the demands for objective vocal technique assessment, we introduce Omni-Dimensional Dynamic Convolution (ODConv). Additionally, we employ densely connected layers to optimize the framework, enabling efficient utilization of multi-scale features across multiple dynamic convolution layers. …”
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Detection of human activities using multi-layer convolutional neural network by Essam Abdellatef, Rasha M. Al-Makhlasawy, Wafaa A. Shalaby

“…The HARCNN model is designed with 10 convolutional blocks, referred to as “ConvBlk.” Each block integrates a convolutional layer, a ReLU activation function, and a batch normalization layer. …”
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