Identifying Capsule Defect Based on an Improved Convolutional Neural Network by Junlin Zhou, Jiao He, Guoli Li, Yongbin Liu

“…Given the actual demand for capsule production, this study proposes a capsule defect detection and recognition method based on an improved convolutional neural network (CNN) algorithm. The algorithm is used for defect detection and classification in capsule production. …”
Get full text

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

“…Studying brain activity and deciphering the information in electroencephalogram (EEG) signals has become an emerging research field, and substantial advances have been made in the EEG-based classification of emotions. However, using different EEG features and complementarity to discriminate other emotions is still challenging. …”
Get full text

Brain tumor classification using deep convolutional neural networks by M. Nurtay, M. Kissina, A. Tau, A. Akhmetov, G. Alina, N. Mutovina

“…This study presents a comparative analysis of various convolutional neural network (CNN) models for brain tumor detection on MRI medical images. …”
Get full text

Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks by Chien-Liang Liu, Bin Xiao, Wen-Hoar Hsaio, Vincent S. Tseng

“…This work uses the two domains of EEGs, including frequency domain and time domain, to provide two different views for the same data source. Subsequently, this work proposes a multi-view convolutional neural network framework to predict the occurrence of epilepsy seizures with the goal of acquiring a shared representation of time-domain and frequency-domain features. …”
Get full text

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

“…As well, intrusion detection, the subject of this paper, relies heavily on it. Different intrusion detection models have been constructed using ANNs. …”
Get full text

Ocean wave power forecasting using convolutional neural networks by Pedro Bento, José Pombo, Maria do Rosário Calado, Sílvio Mariano

Get full text

Self-Denoising of BOTDA Using Deep Convolutional Neural Networks by Di Qi, Chun-Kit Chan, Xun Guan

“…We propose the self-denoising network (SDNet), a self-supervised network based on a convolutional neural network (CNN), for Brillouin trace denoising. …”
Get full text

Multimodal Brain Tumor Classification Using Convolutional Tumnet Architecture by M. Padma Usha, G. Kannan, M. Ramamoorthy

“…The proposed Tumnet was modeled with 5 convolutional layers, 3 pooling layers with ReLU activation function, and 3 fully connected layers. …”
Get full text

Contrastive Learning‑based Simplified Graph Convolutional Network Recommendation by YU Yuchen, WU Siqi, ZHAO Qinghua, WU Xuhong, WANG Lei

“…[Purposes] Considering the problems of the existing Graph Convolutional Network (GCN) recommendation models, such as low model convergence efficiency, over-smoothing, and deteriorative recommendations for long-tail items caused by the effect of high-degree nodes on presentation learning, a Contrastive Learning-based Simplified Graph Convolutional Network recommendation algorithm (SGCN-CL) is presented. …”
Get full text

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. …”
Get full text

Encrypted traffic classification method based on convolutional neural network by Rongna XIE, Zhuhong MA, Zongyu LI, Ye TIAN

“…Aiming at the problems of low accuracy, weak generality, and easy privacy violation of traditional encrypted network traffic classification methods, an encrypted traffic classification method based on convolutional neural network was proposed, which avoided relying on original traffic data and prevented overfitting of specific byte structure of the application.According to the data packet size and arrival time information of network traffic, a method to convert the original traffic into a two-dimensional picture was designed.Each cell in the histogram represented the number of packets with corresponding size that arrive at the corresponding time interval, avoiding reliance on packet payloads and privacy violations.The LeNet-5 convolutional neural network model was optimized to improve the classification accuracy.The inception module was embedded for multi-dimensional feature extraction and feature fusion.And the 1*1 convolution was used to control the feature dimension of the output.Besides, the average pooling layer and the convolutional layer were used to replace the fully connected layer to increase the calculation speed and avoid overfitting.The sliding window method was used in the object detection task, and each network unidirectional flow was divided into equal-sized blocks, ensuring that the blocks in the training set and the blocks in the test set in a single session do not overlap and expanding the dataset samples.The classification experiment results on the ISCX dataset show that for the application traffic classification task, the average accuracy rate reaches more than 95%.The comparative experimental results show that the traditional classification method has a significant decrease in accuracy or even fails when the types of training set and test set are different.However, the accuracy rate of the proposed method still reaches 89.2%, which proves that the method is universally suitable for encrypted traffic and non-encrypted traffic.All experiments are based on imbalanced datasets, and the experimental results may be further improved if balanced processing is performed.…”
Get full text

Golf swing classification with multiple deep convolutional neural networks by Libin Jiao, Rongfang Bie, Hao Wu, Yu Wei, Jixin Ma, Anton Umek, Anton Kos

“…In this article, we investigate golf swing data classification methods based on varieties of representative convolutional neural networks (deep convolutional neural networks) which are fed with swing data from embedded multi-sensors, to group the multi-channel golf swing data labeled by hybrid categories from different golf players and swing shapes. …”
Get full text

Detection of Southern Hemisphere Constellations Using Convolutional Neural Networks by Vladimir Riffo, Sebastian Flores, Eduardo Chuy-Kan, Victor Ariza

“…For this, a convolutional neural network model called You Only Look Once was used. …”
Get full text

Speaker Identification and Verification Using Convolutional Neural Network CNN by Azhar S. Abdulaziz, Akram Dawood, Amar Daood

“…On the other hand, the conducted experiments showed that the one-dimensional convolutional neural network (1D-CNN) proved its superiority over other models for speaker identification. …”
Get full text

Lung Segmentation with Lightweight Convolutional Attention Residual U-Net by Meftahul Jannat, Shaikh Afnan Birahim, Mohammad Asif Hasan, Tonmoy Roy, Lubna Sultana, Hasan Sarker, Samia Fairuz, Hanaa A. Abdallah

“…Lung segmentation is key to overcoming this challenge through different deep learning (DL) techniques. Many researchers are working to improve the performance and efficiency of lung segmentation models. …”
Get full text

Process tomography of structured optical gates with convolutional neural networks by Tareq Jaouni, Francesco Di Colandrea, Lorenzo Amato, Filippo Cardano, Ebrahim Karimi

“…This technique combines the outcomes of different projective measurements to reconstruct the underlying process matrix, typically extracted from maximum-likelihood estimation. …”
Get full text

Convolutional Variational Autoencoder for Anomaly Detection in On-Load Tap Changers by Fataneh Dabaghi-Zarandi, Hassan Ezzaidi, Michel Gauvin, Patrick Picher, Issouf Fofana, Vahid Behjat

“…To detect anomalies in OLTCs and analyze the generated vibration signals, a convolutional variational autoencoder (CVAE) is utilized, trained individually for each transformer family. …”
Get full text

Atmospheric Turbulence Strength Estimation Using Convolution Neural Network by Siyu Gao, Xiaoyun Liu, Yonghao Chen, Jinyang Jiang, Ying Liu, Yueqiu Jiang

“…Moreover, the mix training different levels of turbulence strength improves the estimation accuracy of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula> compared to that with the same order of <inline-formula><tex-math notation="LaTeX">$C^{2}_{n}$</tex-math></inline-formula>. …”
Get full text

English Text Recognition Based on Convolutional Neural Network (CNN) by Razia Maroof, Irfan Ahmed Usmani, Atruba Feroze

“… Text recognition from images has many challenges due to differences in the appearance of text such as font, color, size, and background. …”
Get full text

Graph Convolutional Recommendation System Based on Bilateral Attention Mechanism by Hui Yang, Changchun Yang

“…Through an analysis of previous knowledge graph convolutional network recommendation systems, the following problems have been identified: (1) Some graph convolutional networks only consider the neighborhood aggregation of items while neglecting the neighborhood aggregation of users. (2) User rating differences are not taken into account. …”
Get full text