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. …”
Get full text

Recommending third-party APIs via using lightweight graph convolutional neural networks by Meijiao Zhang, Xianhao Pan, Jiajin Mai, Mingdong Tang, Tien-Hsiung Weng

“…Based on the model of lightweight graph convolutional neural network, this paper proposes an effective API recommendation method by exploiting both low-order and high-order interactions between users and APIs. …”
Get full text

Removing Stripe Noise From Infrared Cloud Images via Deep Convolutional Networks by Pengfei Xiao, Yecai Guo, Peixian Zhuang

“…Inspired by the wide inference networks, we use wider CNNs with more convolutions in the first part of the proposed network, which is helpful for learning the similar pixel-distribution features from noisy images. …”
Get full text

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. …”
Get full text

Facial Expression Recognition using Convolutional Neural Networks with Transfer Learning Resnet-50 by Annisa Ayu Istiqomah, Christy Atika Sari, Ajib Susanto, Eko Hari Rachmawanto

Get full text

Detection of Autism Spectrum Disorder Using A 1-Dimensional Convolutional Neural Network by Aythem Khairi Kareem, Mohammed M. AL-Ani, Ahmed Adil Nafea

Get full text

Local kernel renormalization as a mechanism for feature learning in overparametrized convolutional neural networks by R. Aiudi, R. Pacelli, P. Baglioni, A. Vezzani, R. Burioni, P. Rotondo

“…In this work, we present a theoretical framework that provides a rationale for these differences in one-hidden-layer networks; we derive an effective action in the so-called proportional limit for an architecture with one convolutional hidden layer and compare it with the result available for fully-connected networks. …”
Get full text

Training Sample Formation for Convolution Neural Networks to Person Re-Identification from Video by S. A. Ihnatsyeva, R. P. Bohush

“…To improve the person re-identification system accuracy, an integrated approach is proposed in the formation of a training sample for convolutional neural networks, which involves the use of a new image dataset, an increase in the training examples number using existing datasets, and the use of a number of transformations to increase their diversity. …”
Get full text

Diagnosis of Alzheimer Disease Using 2D MRI Slices by Convolutional Neural Network by Fanar E. K. Al-Khuzaie, Oguz Bayat, Adil D. Duru

“…There are many kinds of brain abnormalities that cause changes in different parts of the brain. Alzheimer’s disease is a chronic condition that degenerates the cells of the brain leading to memory asthenia. …”
Get full text

CLASS IMBALANCE PROBLEM IN ANTI-FRAUD PROBLEM: METRICS, SAMPLING AND CONVOLUTIONAL NEURAL NETWORKS by Ruslan Ch. Bobonazarov

“…Researchers, using publicly available datasets, apply different approaches to model evaluation, some of which are not effective in conditions of severe class imbalance. …”
Get full text

ECT Image Reconstruction Algorithm Based on Multiscale Dual-Channel Convolutional Neural Network by Lili Wang, Xiao Liu, Deyun Chen, Hailu Yang, Chengdong Wang

“…The middle layer of the network consists of two fully convolutional structures. Convolutional layers and jump connections are designed separately for different channels, which greatly improves the network’s ability to extract feature information and reduces the number of feature maps required for each layer. …”
Get full text

ExShall-CNN: An Explainable Shallow Convolutional Neural Network for Medical Image Segmentation by Vahid Khalkhali, Sayed Mehedi Azim, Iman Dehzangi

“…We introduce the explainable shallow convolutional neural network (ExShall-CNN), which combines the interpretability of hand-crafted features with the performance of advanced deep convolutional networks like U-Net for medical image segmentation. …”
Get full text

Evaluation of convolutional neural networks for the classification of falls from heterogeneous thermal vision sensors by Miguel Ángel López-Medina, Macarena Espinilla, Chris Nugent, Javier Medina Quero

“…In this work, we analyze the capabilities of non-invasive thermal vision sensors to detect falls using several architectures of convolutional neural networks. First, we integrate two thermal vision sensors with different capabilities: (1) low resolution with a wide viewing angle and (2) high resolution with a central viewing angle. …”
Get full text

Cross-Scale Spatial Refinement Graph Convolutional Network for Skeleton-Based Action Recognition by Chengyuan Ke, Sheng Liu, Zhenghao Ke, Yuan Feng, Shengyong Chen

“…To address this issue, we propose a Cross-scale Spatial Refinement Graph Convolutional Network (CSR-GCN), which aims to improve action recognition accuracy by effectively capturing fine-grained features of skeleton sequences. …”
Get full text

Recognition of common shortwave protocols and their subcarrier modulations based on multi-scale convolutional GRU. by Jiuxiao Cao, Rui Zhu, Zhen Wang, Jun Wang, Guohao Shi, Peng Chu

“…The model transforms temporal signals into two-dimensional representations, applies parallel convolutional branches with different receptive fields, and captures temporal dependencies through a bidirectional GRU. …”
Get full text

DDoS-MSCT: A DDoS Attack Detection Method Based on Multiscale Convolution and Transformer by Bangli Wang, Yuxuan Jiang, You Liao, Zhen Li

“…The LFEM employs convolutional kernels of different sizes, accompanied by dilated convolutions, with the aim of enhancing the receptive field and capturing multiscale features simultaneously. …”
Get full text

Edge Convolution Graph Neural Network Assisted Power Allocation for Wireless IoT Networks by Jihyung Kim, Yeji Cho, Junghyun Kim

“…We propose a novel power control technique called PC-ECGNN, which uses edge convolution to optimize power allocation in wireless IoT networks. …”
Get full text

Supervised Convolutional Encoder-Decoder With Gated Linear Units for Detecting Fetal R-Peaks by Yao Chen, Jian Wang, Jing Zhang, Junkun Zhang, Zhentao Qin, Xinran Liu

“…The GLU convolutional layer can effectively extract and aggregate the features to improve the generalization ability. …”
Get full text

Lane Boundary Detection for Intelligent Vehicles Using Deep Convolutional Neural Network Architecture by Xuewen Chen, Chenxi Xia, Xiaohai Chen

“…To address the limitation of 2D lane detection methods with monocular vision, which fail to capture the three-dimensional position of lane boundaries, this study proposes a convolutional neural network architecture for 3D lane detection. …”
Get full text

Research on rolling bearing compound fault diagnosis based on AMOMCKD and convolutional neural network by Runfang Hao, Yunpeng Bai, Kun Yang, Yongqiang Cheng, Shengjun Chang

“…To address this problem, this paper proposes a rolling bearing compound fault diagnosis method AMOMCKD-CNN based on adaptive multi-objective maximum correlation kurtosis deconvolution (AMOMCKD) and convolutional neural network (CNN) with parameter optimization. …”
Get full text