Accurate Arrhythmia Classification with Multi-Branch, Multi-Head Attention Temporal Convolutional Networks by Suzhao Bi, Rongjian Lu, Qiang Xu, Peiwen Zhang

“…To address these challenges, this paper proposes a method for arrhythmia classification based on a multi-branch, multi-head attention temporal convolutional network (MB-MHA-TCN). The model integrates three convolutional branch layers with different kernel sizes and dilation rates to capture features across varying temporal scales. …”
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Method for predicting cutter remaining life based on multi-scale cyclic convolutional network by Tao Li, Yinquan Yu, Chaoqiang Huang, Jinwen Yang, Yuanchang Zhong, Yong Hao

“…In the process of predicting the remaining cutter life, the deep-learning method such as convolutional neural network does not consider the time correlation of different degradation states, which directly affects the accuracy of the remaining cutter life prediction. …”
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Land-sea Clutter Classification Method Based on Multi-channel Graph Convolutional Networks by Can LI, Zengfu WANG, Xiaoxuan ZHANG, Quan PAN

“…Based on radar parameters, data characteristics, and sample proportions, we construct a land-sea clutter original dataset containing 12 different scenes and a land-sea clutter scarce dataset containing 36 different configurations. …”
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Selective Auditory Attention Detection Using Combined Transformer and Convolutional Graph Neural Networks by Masoud Geravanchizadeh, Amir Shaygan Asl, Sebelan Danishvar

“…Furthermore, examining the proposed model for different lengths of EEG segments shows that the model is faster than our previous graph-based detection method in terms of computational complexity. …”
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Assisting monofloral honey classification by automated pollen identification based on convolutional neural networks by José Miguel Valiente, Juan José Martín-Osuna, Ana María Peral, Isabel Escriche

“…This Ground Truth termed POLLEN24_SP, comprises 32,285 pollen/particle images (captured by an expert using optical microscopy), covering the 24 most prevalent types of pollen grains found in Spanish honeys. Twelve different pre-existing Convolutional Neural Networks (CNN) were evaluated, achieving an accuracy rate of up to 98.03 % with EfficientNetV2M. …”
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Automatic political discourse analysis with multi-scale convolutional neural networks and contextual data by Aritz Bilbao-Jayo, Aitor Almeida

“…To do so, the authors have built a discourse classifier using multi-scale convolutional neural networks in seven different languages: Spanish, Finnish, Danish, English, German, French, and Italian. …”
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The convolution-induced topology on L∞(G) and linearly dependent translates in L1(G) by G. Crombez, W. Govaerts

“…From this, we deduce that τc is different from the w∗-topology on L∞(G) whenever G is infinite. …”
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Target detection of helicopter electric power inspection based on the feature embedding convolution model. by Dakun Liu, Wei Zhou, Linzhen Zhou, Wen Guan

“…This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. …”
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Convolutional Interval-Valued Neutrosophic Network for Intelligent Evaluation of Smart Clothing Design Choices by Fengfang Wu

“…The Neutrosophic representation enables modeling uncertainty, inconsistency, and hesitancy in decision-making by assigning interval-ed membership degrees for different views of smart clothes design. Using the interval-valued representations, we enable robust learning and interpretation of user partialities while handling vague feedback. …”
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Integration of unpaired single cell omics data by deep transfer graph convolutional network. by Yulong Kan, Yunjing Qi, Zhongxiao Zhang, Xikeng Liang, Weihao Wang, Shuilin Jin

“…Thus, scTGCN shows high label transfer accuracy and effectively knowledge transfer across different modalities.…”
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SL-GCNN: A Graph Convolutional Neural Network for Granular Human Motion Recognition by Yang Li, Jingyu Zhang

“…By leveraging contrastive learning, the model refines hidden layer features and isolates output layer features, effectively addressing the challenges posed by subtle differences in granular motions. Experimental results on the two different datasets demonstrate that SL-GCNN outperforms existing state-of-the-art methods, achieving accuracies of 92.73% and 97.47% on the X-Sub and X-View benchmarks of NTU_RGB_plus_D, and 89.19% and 90.56% on the X-Sub and X-View benchmarks of NTU_RGB_plus_D_120, respectively. …”
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TEA-GCN: Transformer-Enhanced Adaptive Graph Convolutional Network for Traffic Flow Forecasting by Xiaxia He, Wenhui Zhang, Xiaoyu Li, Xiaodan Zhang

“…Specifically, we design an adaptive graph convolutional module to dynamically capture implicit road dependencies at different time levels and a local-global temporal attention module to simultaneously capture long-term and short-term temporal dependencies. …”
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Perbandingan Random Forest dan Convolutional Neural Network dalam Memprediksi Peralihan Pelanggan by Dewa Adji Kusuma, Atika Ratna Dewi, Andreas Rony Wijaya

“…The limitation of this research is how to handle the two algorithms being compared. Both use different approaches, namely Supervised Learning and Deep Learning. …”
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MagNet: Automated Magnetic Mineral Grain Morphometry Using Convolutional Neural Network by Zhaowen Pei, Liao Chang, Pengfei Xue, Richard J. Harrison

“…MagNet is open‐source and can easily be extended to process different types of mineral images. This tool has the potential, therefore, to extract key quantitative information of magnetic mineral populations within heterogeneous terrestrial and meteoritic samples for the interpretations of Earth and planetary processes.…”
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Bearing Life Prediction Method Based on Parallel Multichannel Recurrent Convolutional Neural Network by Jianmin Zhou, Sen Gao, Jiahui Li, Wenhao Xiong

“…The back of the model is the recurrent convolution layer to model the temporal dependence relationship under different degradation features. …”
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EFFICIENCY AND ACCURACY OF CONVOLUTIONAL AND FOURIER TRANSFORM LAYERS IN NEURAL NETWORKS FOR MEDICAL IMAGE CLASSIFICATION by Fauzi Nafi'udin, Hasih Pratiwi, Etik Zukhronah

“…However, the convolution layer has an advantage in terms of model size, although it is not significantly different from the Fourier transform layer. …”
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Recommending third-party APIs via using lightweight graph convolutional neural networks by Meijiao Zhang, Xianhao Pan, Jiajin Mai, Mingdong Tang, Tien-Hsiung Weng

“…It first learns the embedding of users and APIs from the user-API interaction graph, and then adopts a weighted summation operator to aggregate the embeddings learned from different propagation layers for API recommendation. …”
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Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks by Pengfei Xiao, Yecai Guo, Peixian Zhuang

“…To further improve the performance, we propose a local-global combination structure model, which combines the representations of different layers for recovering the rich details of infrared cloud images. …”
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A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks by Gizem Irmak, Ahmet Saygılı

“…In contrast, a novel convolutional neural network (CNN) framework, complete with unique parameters and layers, was utilized for deep learning. …”
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Facial Expression Recognition using Convolutional Neural Networks with Transfer Learning Resnet-50 by Annisa Ayu Istiqomah, Christy Atika Sari, Ajib Susanto, Eko Hari Rachmawanto

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