MarshCover: A Web-based Tool for Estimating Vegetation Coverage in Marsh Images Using Convolutional Neural Networks by Lucas Wayne Welch, Xudong Liu

“…To this end, we address two classification problems for this paper: a binary classification problem classifying points as vegetated and unvegetated, and a multiclass classification problem that classifies points into either an unvegetated class or one of five different species classes. Our experiments identify the VGG16 model as the best classifier to embed in MarshCover for both the binary classification problem and the full classification problem with a two model classifier (called two-shot). …”
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Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga by Ricky Mardianto, Stefanie Quinevera, Siti Rochimah

“…The classification process can be performed by examining external features such as fruit color, shape, and size. Classifying different types of mango fruits accurately can assist researchers in developing superior varieties and also aid farmers for cultivation purposes, sales, distribution, and selecting the right varieties for local growth and weather conditions. …”
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Multi-frequency EEG and multi-functional connectivity graph convolutional network based detection method of patients with Alzheimer’s disease by Yujian Liu, Libing An, Haiqiang Yang, Shuzhi Sam Ge

“…This network comprehensively captures abnormalities in brain network structures induced by AD, across different frequency bands and connectivity modes. By leveraging a multi-dimensional feature extraction and fusion strategy, the model effectively identifies EEG pattern changes associated with AD, enhancing detection accuracy. …”
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A novel end-to-end learning framework for inferring lncRNA-disease associations based on convolution neural network by Shunxian Zhou, Sisi Chen, Jinhai Le, Yangtai Xu, Lei Wang

“…And then, by combining these hidden features of diseases and lncRNAs with known lncRNA-disease associations, we designed five different loss functions. Next, based on errors obtained by these loss functions, we would perform back propagation to fit parameters in CNMCLDA, and complete those missing values in lncRNA-disease relational matrix according to these fitted parameters. …”
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Modeling Face Detection Application Using Convolutional Neural Network and Face-API for Effective and Efficient Online Attendance Tracking by Carles Juliandy, Ng Poi Wong, Darwin

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Multimodal data fusion for Alzheimer's disease based on dynamic heterogeneous graph convolutional neural network and generative adversarial network by Xiaoyu Chen, Shuaiqun Wang, Wei Kong

“…The complex and diverse causes of AD make it challenging to fully exploit the complementary information among different data types. To address these challenges, we propose a multi-modal data fusion method based on a Dynamic Heterogeneous Attention Network (DHAN) and Generative Adversarial Networks (GAN). …”
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Graph convolutional neural networks improved target-specific scoring functions for cGAS and kRAS in virtual screening by Bo Wang, Muhammad Junaid, Wenjin Li

“…The comprehensive performance evaluation of different target-specific scoring functions shows that they hold significant potential for applications in structure-based virtual screening. …”
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MugenNet: A Novel Combined Convolution Neural Network and Transformer Network with Application in Colonic Polyp Image Segmentation by Chen Peng, Zhiqin Qian, Kunyu Wang, Lanzhu Zhang, Qi Luo, Zhuming Bi, Wenjun Zhang

“…Transformer is another method that can be adapted to the automatic segmentation method by employing a self-attention mechanism, which essentially assigns different importance weights to each piece of information, thus achieving high computational efficiency during segmentation. …”
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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

“…Experimental results show that the proposed model improved resilience to intrusions and malicious activities for binary as well as multi-class classification, expanding its applicability across different intrusion detection scenarios. Furthermore, our DCNN model outperforms similar intrusion detection systems in terms of positive predicted value, true positive rate, F1 measure, and accuracy. …”
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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

“…We apply all methods to three different experimental turbulent Rayleigh–Bénard convection datasets with varying complexity. …”
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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

“…The discrete wavelet transform (DWT) has been used to extract features from the transient current signal for different faults in 500 kV transmission line under various parameters such as fault location, fault inception angle, ground resistance and fault resistance and time series data has been obtained for fault classification. …”
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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

“…An advanced traffic pattern fusion strategy is introduced, utilizing the Kullback–Leibler Divergence (KLD) metric to measure traffic divergence across different scenarios. This approach improves the efficiency of the Continual Learning (CL) process by enabling the model to adapt to new traffic patterns more effectively over time. …”
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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

“…The developed model and image concatenation method offer a novel methodology for epilepsy diagnosis that can be extended to different datasets, potentially providing a valuable tool to support neurologists globally.…”
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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

“…Additionally, a pyramid-type CNN architecture was designed to simultaneously evaluate both fine details and broader structures by combining low- and high-resolution information through feature maps extracted from different CNN layers. This approach enabled the model to learn complex features more effectively. …”
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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

“…Furthermore, its superiority across different data scales, especially in small-sample learning and stability, highlights its strong generalization capability.…”
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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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