Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks by Dong Wang, Junxing Zheng, Jichen Zhong, Lin Gao, Shuling Huang, Jiajia Zheng

“…A multi-branch parallel dilated convolutional feature fusion module is designed to effectively integrate multi-scale features from the different encoders. …”
Get full text

A convolutional neural network driven suspension control strategy to enhance sustainability of high-speed trains by Duo Zhang, Hong-Wei Li, Fang-Ru Zhou, Yin-Ying Tang, Qi-Yuan Peng

“…For addressing this challenge, a convolutional neural network driven control strategy is proposed for the suspension system of high-speed vehicles to simultaneously reduce energy consumption and carbody vibration on curved tracks. …”
Get full text

Early Diagnose Alzheimer's Disease by Convolution Neural Network-based Histogram Features Extracting and Canny Edge by Karrar A. Kadhim, Farhan Mohamed, Fallah H Najjar, Ghalib Ahmed Salman

“… Alzheimer's disease (AD) increasingly affects the elderly and is a major killer of those 65 and over. Different deep-learning methods are used for automatic diagnosis, yet they have some limitations. …”
Get full text

Advancements and outlooks in utilizing Convolutional Neural Networks for plant disease severity assessment: A comprehensive review by Douglas Leite, Alisson Brito, Gregorio Faccioli

“…Once limited to disease detection, Convolutional Neural Networks (CNNs) applications now exhibit automatic severity calculation and classification solutions. …”
Get full text

A novel intelligent fault diagnosis method for gearbox based on multi-dimensional attention denoising convolution by Wei Liu, Zeqiao Zhang, Zhiwei Ye, Qiyi He

“…Abstract In the field of intelligent fault diagnosis, particularly concerning rotating machinery, convolutional neural networks (CNNs) face significant challenges when applied to real industrial vibration data. …”
Get full text

Improving multi-talker binaural DOA estimation by combining periodicity and spatial features in convolutional neural networks by Reza Varzandeh, Simon Doclo, Volker Hohmann

“…The two-stage CNN incorporates a PD feature reduction stage prior to the joint processing of PD and CPS phase features. We investigate different design choices for the CNN architecture, including varying temporal reduction strategies and spectro-temporal filtering approaches. …”
Get full text

An efficient surface electromyography-based gesture recognition algorithm based on multiscale fusion convolution and channel attention by Bin Jiang, Hao Wu, Qingling Xia, Hanguang Xiao, Bo Peng, Li Wang, Yun Zhao

“…It uses fast dimensionality reduction, asymmetric convolution decomposition, and pooling to suppress the accumulation of parameters, then reducing the algorithmic complexity; The ECA is adopted to reweight the output features of Inception in different channels, enabling the RIE model to focus on information that is more relevant to gestures. 52-, 49-, and 52-class gesture recognition experiments are conducted on NinaPro DB1, DB3, and DB4 datasets, which contain 14,040, 3234, and 3120 gesture samples, respectively. …”
Get full text

3D Convolutional Neural Networks for Brain Tumor Analysis in Multimodal MRI: A Systematic Review by Hamza S. Alsmadi, Hazlina Hamdan, Norwati Mustapha, Noridayu Manshor

“…Convolutional neural networks (CNNs) have emerged as a preferred approach for medical image analysis. …”
Get full text

A Dual-Branches Multiscale Dynamic Partial Convolutional Attention Network for Remote Sensing Change Detection by Wenbin Tang, Shuli Cheng, Anyu Du

“…We introduce dynamic multiscale convolution attention module, which consists of multiscale context aggregation (MCA) module and dynamic partial convolution attention (DPCATT) module. …”
Get full text

An immunohistochemistry-based classification of colorectal cancer resembling the consensus molecular subtypes using convolutional neural networks by Tuomas Kaprio, Jaana Hagström, Jussi Kasurinen, Ioannis Gkekas, Sofia Edin, Ines Beilmann-Lehtonen, Karin Strigård, Richard Palmqvist, Ulf Gunnarson, Camilla Böckelman, Caj Haglund

“…The consensus molecular subtype (CMS) is a transcriptome-based system classifying CRC tumors into four subtypes with different characteristics: CMS1 (immune), CMS2 (canonical), CMS3 (metabolic), and CMS4 (mesenchymal). …”
Get full text

Cross Attention-Based Multi-Scale Convolutional Fusion Network for Hyperspectral and LiDAR Joint Classification by Haimiao Ge, Liguo Wang, Haizhu Pan, Yanzhong Liu, Cheng Li, Dan Lv, Huiyu Ma

“…However, the traditional convolutional neural network fusion techniques always provide poor extraction of discriminative spatial–spectral features from diversified land covers and overlook the correlation and complementarity between different data sources. …”
Get full text

BlendNet: a blending-based convolutional neural network for effective deep learning of electrocardiogram signals by S. Premanand, Sathiya Narayanan

“…IntroductionIn recent years, Deep Learning (DL) architectures such as Convolutional Neural Network (CNN) and its variants have been shown to be effective in the diagnosis of cardiovascular disease from ElectroCardioGram (ECG) signals. …”
Get full text

Prediction of the water solubility by a graph convolutional-based neural network on a highly curated dataset by Nadin Ulrich, Karsten Voigt, Anton Kudria, Alexander Böhme, Ralf-Uwe Ebert

“…To this end, we developed a graph convolutional neural network model (GNN) to predict the water solubility in the form of log S w based on a highly curated dataset of 9800 chemicals. …”
Get full text

Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…In this work, an Enhanced Convolutional Neural Network model (E-CNN) is proposed for optimal prediction of beam indices with the aid of real-world GPS position data. …”
Get full text

A Multiscale Convolution SAR Image Target Recognition Method Based on Complex-Valued Neural Networks by Guangyu Hou, Zhihui Xin, Guisheng Liao, Penghui Huang, Yuhao Huang, Rui Zou

“…Second, in order to further enhance the representation ability of the model, multiscale information of the target is extracted through different convolution kernel sizes. Finally, the network constructs many complex-valued operation blocks to enhance the robustness of the designed network, including the complex-valued residual block, complex-valued Mish activation function, and complex-valued CBAM. …”
Get full text

Peatland pixel-level classification via multispectral, multiresolution and multisensor data using convolutional neural network by Luca Zelioli, Fahimeh Farahnakian, Maarit Middleton, Timo P. Pitkänen, Sakari Tuominen, Paavo Nevalainen, Jonne Pohjankukka, Jukka Heikkonen

“…To address these challenges, we propose a novel multi-modal convolutional neural network (CNN) architecture designed specifically for pixel-level peatland classification. …”
Get full text

Redefining dental image processing: De-convolutional component with residual prolonged bypass for enhanced teeth segmentation by Prasun Kumar, Verma Anil, Mishra Rajiv

“…Early detection and diagnosis can control this rapid growth in dental diseases. Therefore, different traditional techniques are employed for the detection of dental problems. …”
Get full text

A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging by Aram Lee, J. J. Kavelaars, Hossen Teimoorinia, Wesley Fraser, Edward Ashton

“…We present a deep learning method that utilizes convolutional neural networks (CNNs) to discover trans-Neptunian objects (TNOs) in wide-field survey imaging data. …”
Get full text

A Convolutional Neural Network Tool for Early Diagnosis and Precision Surgery in Endometriosis-Associated Ovarian Cancer by Christian Macis, Miriam Santoro, Vladislav Zybin, Stella Di Costanzo, Camelia Alexandra Coada, Giulia Dondi, Pierandrea De Iaco, Anna Myriam Perrone, Lidia Strigari

“…A Python pipeline was developed using the transfer learning approach, adopting four different convolution neural networks (CNNs). Each architecture (i.e., VGG19, Xception, ResNet50, and DenseNet121) was trained on each of the axial slices of CE-CT images and refined using the validation dataset. …”
Get full text

UPN-GCN: Update Positive-Negative Graph Convolution Neural Network in Non-Euclidean Structured Data by Lijun Fan, Shichao Yi, Wenrui Guan, Pingxin Wang

“…Graph Convolutional neural Networks (GCNs) demonstrate exceptional effectiveness when working with data that have non-Euclidean structures. …”
Get full text