Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

“…Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.…”
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Unsupervised intrusion detection model based on temporal convolutional network by LIAO Jinju, DING Jiawei, FENG Guanghui

“…Most existing intrusion detection models rely on long short-term memory (LSTM) networks to consider time-dependencies among data. …”
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Hybrid Clayton-Frank Convolution-Based Bivariate Archimedean Copula by Maxwell Akwasi Boateng, Akoto Yaw Omari-Sasu, Richard Kodzo Avuglah, Nana Kena Frempong

“…This study exploits the closure property of the converse convolution operator to come up with a hybrid Clayton-Frank Archimedean copula for two random variables. …”
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Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks by Rocco Restaino

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Rice Plant Disease Detection using Convolutional Neural Networks by A. Bala Ayyappan, T. Gobinath, M. Kumar, A. Sivaramakrishnan

“…In this paper, we use Convolutional Neural Networks (CNNs) and deep learning approaches to identify various rice plant diseases like blast, brown spot and bacterial blight. …”
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Convolutional Neural Networks in the SSI Analysis for Mine-Induced Vibrations by Maciej Cyprian Zajac, Krystyna Kuzniar

“… Deep neural networks (DNNs) have recently become one of the most often used soft computational tools for numerical analysis. …”
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A New Support Vector Machine Based on Convolution Product by Wei-Chang Yeh, Yunzhi Jiang, Shi-Yi Tan, Chih-Yen Yeh

“…The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. …”
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Identifying Capsule Defect Based on an Improved Convolutional Neural Network by Junlin Zhou, Jiao He, Guoli Li, Yongbin Liu

“…Capsules are commonly used as containers for most pharmaceuticals, and capsule quality is closely related to human health. …”
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Emotion recognition based on convolutional gated recurrent units with attention by Zhu Ye, Yuan Jing, Qinghua Wang, Pengrui Li, Zhihong Liu, Mingjing Yan, Yongqing Zhang, Dongrui Gao

“…To address this issue, we propose an Attention-Based Depthwise Parameterized Convolutional Gated Recurrent Unit (AB-DPCGRU) model and validate it with the mixed experiment on the SEED and SEED-IV datasets. …”
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An intrusion detection model based on Convolutional Kolmogorov-Arnold Networks by Zhen Wang, Anazida Zainal, Maheyzah Md Siraj, Fuad A. Ghaleb, Xue Hao, Shaoyong Han

“…Our paper presents Convolutional Kolmogorov-Arnold Networks (CKANs), which are designed to overcome these difficulties and provide an interpretable and accurate intrusion detection model. …”
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Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture by M. Padma Usha, G. Kannan, M. Ramamoorthy

“…The most common and aggressive tumor is brain malignancy, which has a short life span in the fourth grade of the disease. …”
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Hand Tremor Characterization from a Spatiotemporal Convolutional Representation by Jessica Pedraza Cadena, John Edinson Archila Valderrama, Franklin Sierra-Jerez, Alejandra Moreno Tarazona, Fabio Martínez Carrillo

“…The strategy includes a convolutional architecture that extracts spatiotemporal patterns correlated with tremor, propagated through different layers until discrimination between PD and control subjects is achieved. …”
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High-efficiency sparse convolution operator for event-based cameras by Sen Zhang, Fusheng Zha, Fusheng Zha, Xiangji Wang, Mantian Li, Wei Guo, Pengfei Wang, Xiaolin Li, Lining Sun

“…To address this gap, we propose a sparse convolution operator tailored for event-based cameras. …”
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Karatsuba Algorithm Revisited for 2D Convolution Computation Optimization by Qi Wang, Jianghan Zhu, Can He, Shihang Wang, Xingbo Wang, Yuan Ren, Terry Tao Ye

“…Convolutional computations consist of many dot-product operations (multiplication–accumulation, or MAC), for which the Winograd algorithm is currently the most widely used method to reduce the number of MACs. …”
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Battery Life Evaluation Method Based on Temporal Convolution Network by SUN Yushu, AN Juan, HUANG Cunqiang, ZHANG Shunzhen, DANG Yanyang, PEI Wei, TANG Xisheng

“…ObjectiveTo improve the technical economy of battery system applications, a temporal convolutional network (TCN) is employed to evaluate battery life from two perspectives: State of health (SOH) and remaining useful life (RUL). …”
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Triple Graph Convolutional Network for Hyperspectral Image Feature Fusion and Classification by Maryam Imani, Daniele Cerra

“…Most graph-based networks utilize superpixel generation methods as a preprocessing step, considering superpixels as graph nodes. …”
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Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition by Jhon I. Pilataxi, Juan P. Perez, Claudio A. Perez, Kevin W. Bowyer

“…Face recognition (FR) is one of the most widely used biometric methods for identity authentication. …”
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Fingerprint recognition using convolution neural network with inversion and augmented techniques by Reena Garg, Gunjan Singh, Aditya Singh, Manu Pratap Singh

“…Fingerprints are considered as one of the most important and prominent feature for an individual identification. …”
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Improving the Accuracy of Batik Classification using Deep Convolutional Auto Encoder by Muhammad Faqih Dzulqarnain, Abdul Fadlil, Imam Riadi

“…The autoencoder was employed to enrich the image data prior to convolutional processing. By forcing the autoencoder to learn a lower-dimensional latent representation that captures the most salient features of the batik patterns. …”
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AFD: Defending Convolutional Neural Networks Without Using Adversarial Samples by Nupur Thakur, Yuzhen Ding, Baoxin Li

“…In this paper, we present a novel training strategy named Adversarial-Free Defense (AFD), which introduces a minimal change to a network architecture (by modifying the first convolution layer) while employing a learning algorithm that leads to special properties of the first-layer kernels. …”
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