An object detection model AAPW-YOLO for UAV remote sensing images based on adaptive convolution and reconstructed feature fusion by Yiming Wu, Xiaofang Mu, Hong Shi, Mingxing Hou

“…To overcome these challenges, this paper presents a model for detecting small objects, AAPW-YOLO, based on adaptive convolution and reconstructed feature fusion. In the AAPW-YOLO model, we improve the standard convolution and the CSP Bottleneck with 2 Convolutions (C2f) structure in the You Only Look Once v8 (YOLOv8) backbone network by using Alterable Kernel Convolution (AKConv), which improves the network’s proficiency in capturing features across various scales while considerably lowering the model’s parameter count. …”
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DCNN: a novel binary and multi-class network intrusion detection model via deep convolutional neural network by Ahmed Shebl, E. I. Elsedimy, A. Ismail, A. A. Salama, Mostafa Herajy

“…In this paper, we propose a novel intelligent detection system based on convolutional neural network, namely DCNN. The proposed model can be utilized to efficiently analyze and detect attacks and intrusions in intelligent network systems (e.g., suspicious network traffic activities and policy violations). …”
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Slim multi-scale convolutional autoencoder-based reduced-order models for interpretable features of a complex dynamical system by Philipp Teutsch, Philipp Pfeffer, Mohammad Sharifi Ghazijahani, Christian Cierpka, Jörg Schumacher, Patrick Mäder

“…Within the context of reduced-order models, convolutional autoencoders (CAEs) pose a universally applicable alternative to conventional approaches. …”
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A Convolutional Mixer-Based Deep Learning Network for Alzheimer’s Disease Classification from Structural Magnetic Resonance Imaging by M. Krithika Alias Anbu Devi, K. Suganthi

“…<b>Methods:</b> This work proposes a novel AD classification architecture that integrates depthwise separable convolutional layers with traditional convolutional layers to efficiently extract features from structural magnetic resonance imaging (sMRI) scans. …”
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Leveraging commonality across multiple tissue slices for enhanced whole slide image classification using graph convolutional networks by Sakonporn Noree, Willmer Rafell Quinones Robles, Young Sin Ko, Mun Yong Yi

“…While deep learning approaches have shown promise in WSI analysis, they mostly overlook potential common patterns across different slices of the original tissue. Methods We propose a novel technique that leverages inter-slice commonality to enhance classification performance. …”
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Fault detection and classification in overhead transmission lines through comprehensive feature extraction using temporal convolution neural network by Nadeem Ahmed Tunio, Ashfaque Ahmed Hashmani, Suhail Khokhar, Mohsin Ali Tunio, Muhammad Faheem

“…Moreover, the temporal convolutional neural network (TCN) is used for fault classification in 500 kV transmission network due to its robust framework. …”
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A framework for continual learning in real-time traffic forecasting utilizing spatial–temporal graph convolutional recurrent networks by Mariam Labib Francies, Abeer Twakol Khalil, Hanan M. Amer, Mohamed Maher Ata

“…To address these challenges, this research presents an innovative framework known as the Continual Learning-based Spatial–Temporal Graph Convolutional Recurrent Neural Network (STGNN-CL) for persistent and accurate long-term traffic flow prediction. …”
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Epilepsy Diagnosis from EEG Signals Using Continuous Wavelet Transform-Based Depthwise Convolutional Neural Network Model by Fırat Dişli, Mehmet Gedikpınar, Hüseyin Fırat, Abdulkadir Şengür, Hanifi Güldemir, Deepika Koundal

“…<b>Methods:</b> This study proposes a continuous wavelet transform-based depthwise convolutional neural network (DCNN) for epilepsy diagnosis. …”
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A Classifier Model Using Fine-Tuned Convolutional Neural Network and Transfer Learning Approaches for Prostate Cancer Detection by Murat Sarıateş, Erdal Özbay

“…Methods: In this study, a basic convolutional neural network (CNN) model was developed and subsequently optimized using techniques such as L2 regularization, Tanh activation, dropout, and early stopping to enhance its performance. …”
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On Traffic Prediction With Knowledge-Driven Spatial&#x2013;Temporal Graph Convolutional Network Aided by Selected Attention Mechanism by Yuwen Qian, Tianyang Qiu, Chuan Ma, Yiyang Ni, Long Yuan, Xiangwei Zhou, Jun Li

“…In this paper, we propose the knowledge-driven graph convolutional network (KGCN) aided by the gated recurrent unit with a selected attention mechanism (GSAM) to predict traffic flow. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…The model incorporates an adaptive fusion method based on a multi-branch convolutional structure, enabling unified processing of both acoustic and vibration signals. …”
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Local-Global Feature Extraction Network With Dynamic 3-D Convolution and Residual Attention Transformer for Hyperspectral Image Classification by Qiqiang Chen, Zhengyang Li, Junru Yin, Wei Huang, Tianming Zhan

“…Then, the dynamic local feature extraction module utilizes dynamic 3-D convolution, which can adapt to different samples. This allows the network to focus on valuable pixels in 3-D samples. …”
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GCN-Transformer: Graph Convolutional Network and Transformer for Multi-Person Pose Forecasting Using Sensor-Based Motion Data by Romeo Šajina, Goran Oreški, Marina Ivašić-Kos

“…This paper introduces GCN-Transformer, a novel model for multi-person pose forecasting that leverages the integration of Graph Convolutional Network and Transformer architectures. …”
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Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach by Debasmita Das, Chayna Sarkar, Biswadeep Das

“…The VGG network that we have constructed with very small convolutional filters consists of 13 convolutional layers and 3 fully connected layers. …”
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Automated Detection of High Frequency Oscillations in Intracranial EEG Using the Combination of Short-Time Energy and Convolutional Neural Networks by Dakun Lai, Xinyue Zhang, Kefei Ma, Zichu Chen, Wenjing Chen, Heng Zhang, Han Yuan, Lei Ding

“…A new methodology is presented in this paper for the automated detection of HFOs based on their 2D time&#x2013;frequency map employing the short-time energy (STE) estimation and the convolutional neural network (CNN) classification algorithm. …”
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Development and verification of a convolutional neural network-based model for automatic mandibular canal localization on multicenter CBCT images by Xiao Pan, Chengtao Wang, Xuhui Luo, Qi Dong, Haiyang Sun, Wentao Zhang, Hongyan Qu, Runzhi Deng, Zitong Lin

“…Abstract Objectives Development and verification of a convolutional neural network (CNN)-based deep learning (DL) model for mandibular canal (MC) localization on multicenter cone beam computed tomography (CBCT) images. …”
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RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path by Zerui Zhang, Jianyun Gao, Shu Li, Hao Wang

“…In the shallow encoding part of RMCNet, we incorporated a 3D multiscale convolution (3D-Multiscale Convolution) module to more effectively extract tumors of varying sizes. …”
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Multi-convolutional neural network brain image denoising study based on feature distillation learning and dense residual attention by Huimin Qu, Haiyan Xie, Qianying Wang

“…Due to the complexity of the brain's structure and minor density differences, noise can increase diagnosis difficulty, so high-quality images are essential for disease detection, prognosis assessment, and treatment plan development. …”
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VNet: An End-to-End Fully Convolutional Neural Network for Road Extraction From High-Resolution Remote Sensing Data by Arnick Abdollahi, Biswajeet Pradhan, Abdullah Alamri

“…In the present study, we introduce a new deep learning-based convolutional network called VNet model to produce a high-resolution road segmentation map. …”
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One-Dimensional Convolutional Neural Network for Automated Kimchi Cabbage Downy Mildew Detection Using Aerial Hyperspectral Images by Yang Lyu, Lukas Wiku Kuswidiyanto, Pingan Wang, Hyun-Ho Noh, Hee-Young Jung, Xiongzhe Han

“…Spectral analysis of the late and early stages of downy mildew infection revealed notable differences in the red-edge band, with infected plants exhibiting increased red-edge reflectance. …”
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