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

“…The MRI and CT brain tumor images of the same slices (308 slices of meningioma and sarcoma) are combined using three different types of pixel-level fusion methods. The presence/absence of a tumor is classified using the proposed Tumnet technique, and the tumor area is found accordingly. …”
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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. …”
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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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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.…”
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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. …”
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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. …”
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Speaker Identification and Verification Using Convolutional Neural Network CNN by Azhar S. Abdulaziz, Akram Dawood, Amar Daood

“…Meanwhile, 95% and 96% identification accuracy was achieved using two different datasets. Additionally, the suggested algorithm did not imply using any keyword or password because it is a context-independent approach. …”
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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. …”
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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. …”
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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

“…These findings underscore the methodology’s accuracy and effectiveness in identifying anomalies in OLTC operations and distinguishing between different transformer families. Consequently, it holds promise for preemptively identifying potential future anomalies.…”
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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>. …”
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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. …”
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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. …”
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Advection-Free Convolutional Neural Network for Convective Rainfall Nowcasting by Jenna Ritvanen, Bent Harnist, Miguel Aldana, Terhi Makinen, Seppo Pulkkinen

“…In the model, differences between consecutive rain rate fields in Lagrangian coordinates are fed into a U-Net-based CNN, known as RainNet, that was trained with the root-mean-squared-error loss function. …”
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Multimodal depression detection based on an attention graph convolution and transformer by Xiaowen Jia, Jingxia Chen, Kexin Liu, Qian Wang, Jialing He

“…Traditional depression detection methods typically rely on single-modal data, but these approaches are limited by individual differences, noise interference, and emotional fluctuations. …”
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Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks by P. V. V. Kishore, K. V. V. Kumar, E. Kiran Kumar, A. S. C. S. Sastry, M. Teja Kiran, D. Anil Kumar, M. V. D. Prasad

“…The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. …”
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Convolutional Neural Network-Based Low Light Image Enhancement Method by M.X. Li, C.J. Xu

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Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction by Xin Zan, Jasmine Siu Lee Lam

“…In this work, we design the multibranch adaptive fusion graph convolutional network (MBAF-GCN) that explicitly exploits the prior spatial-temporal characteristics at different temporal scales, and each branch is responsible for extracting spatial-temporal features at a specific scale. …”
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Inverse versus convolution treatment planning algorithms for gamma knife radiosurgery by Marwa Ghanim, Siham Abdullah, Moneer Faraj, Nabaa Alazawy

“…There was significant difference between the two algorithm plans for all dosimetric parameters, with the inverse plan providing higher coverage and selectivity than convolution plan, but taking longer time(p<0.05), while plan was inverse plan better than convolution plan in terms of gradient and conformity (p<0.05). …”
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Convolutional Edge Constraint-Based U-Net for Salient Object Detection by Le Han, Xuelong Li, Yongsheng Dong

“…The reason is that the hand-crafted features are the main basis for existing traditional methods to predict salient objects, which results in different pixels belonging to the same object often being predicted different saliency scores. …”
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