An ensemble convolutional neural network model for brain stroke prediction using brain computed tomography images by Most. Jannatul Ferdous, Rifat Shahriyar

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Convolution Smooth: A Post-Training Quantization Method for Convolutional Neural Networks by Yongyuan Chen, Zhendao Wang

“…Convolutional neural network (CNN) quantization is an efficient model compression technique primarily used for accelerating inference and optimizing resources. …”
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A Cascade of Encoder–Decoder with Atrous Convolution and Ensemble Deep Convolutional Neural Networks for Tuberculosis Detection by Noppadol Maneerat, Athasart Narkthewan, Kazuhiko Hamamoto

“…Tuberculosis (TB) is the most serious worldwide infectious disease and the leading cause of death among people with HIV. …”
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Fine-art recognition using convolutional transformers by Yu Liu, Haozhe Bai, Jingchao Wang

“…As part of the most advanced architectures in the deep learning family, transformers are empowered by a multi-head attention mechanism, thus improving learning efficiency. …”
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Computing nasalance with MFCCs and Convolutional Neural Networks. by Andrés Lozano, Enrique Nava, María Dolores García Méndez, Ignacio Moreno-Torres

“…Future studies should explore how to optimize mfccNasalance by selecting the most adequate CNN model as a function of the dynamicity of the target speech data.…”
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A Hybrid Model for Soybean Yield Prediction Integrating Convolutional Neural Networks, Recurrent Neural Networks, and Graph Convolutional Networks by Vikram S. Ingole, Ujwala A. Kshirsagar, Vikash Singh, Manish Varun Yadav, Bipin Krishna, Roshan Kumar

“…Soybean yield prediction is one of the most critical activities for increasing agricultural productivity and ensuring food security. …”
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Convolution Representation of Traveling Pulses in Reaction-Diffusion Systems by Satoshi Kawaguchi

“…The stability of the spatially homogeneous state and most unstable wave number are examined. The practical utilities of the convolution representation of reaction-diffusion systems are discussed.…”
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CNNFET: Convolutional neural network feature Extraction Tools by Huseyin Atasoy, Yakup Kutlu

“…Therefore, feature extraction is one of the most important topics in machine learning. Deep convolutional neural networks are able to catch distinguishing features that can represent images or other digital signals. …”
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Exploring Attributions in Convolutional Neural Networks for Cow Identification by Dimitar Tanchev, Alexander Marazov, Gergana Balieva, Ivanka Lazarova, Ralitsa Rankova

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Tailoring convolutional neural networks for custom botanical data by Jamie R. Sykes, Katherine J. Denby, Daniel W. Franks

“…We also show that the most informative light spectra for detecting cocoa disease are outside the visible spectrum and that efforts to detect disease in cocoa should be focused on local symptoms, rather than the systemic effects of disease.…”
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Quantized convolutional neural networks: a hardware perspective by Li Zhang, Olga Krestinskaya, Mohammed E. Fouda, Ahmed M. Eltawil, Khaled Nabil Salama

“…In this work, we focus on Convolutional Neural Network (CNN) as an example of DNNs and conduct a comprehensive survey on various quantization and quantized training methods. …”
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Automated Cough Analysis with Convolutional Recurrent Neural Network by Yiping Wang, Mustafaa Wahab, Tianqi Hong, Kyle Molinari, Gail M. Gauvreau, Ruth P. Cusack, Zhen Gao, Imran Satia, Qiyin Fang

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Activation function cyclically switchable convolutional neural network model by İsmail Akgül

“…Therefore, selecting the most optimal AF for processing input data in neural networks is important. …”
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Predicting species distributions in the open ocean with convolutional neural networks by Morand, Gaétan, Joly, Alexis, Rouyer, Tristan, Lorieul, Titouan, Barde, Julien

“…The classifier accurately predicted the observed taxon as the most likely taxon in 69% of cases and included the observed taxon among the top three most likely predictions in 89% of cases. …”
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Epileptic Seizure Prediction With Multi-View Convolutional Neural Networks by Chien-Liang Liu, Bin Xiao, Wen-Hoar Hsaio, Vincent S. Tseng

“…To develop a model for seizure prediction, most studies relied on Electroencephalograms (EEGs) to capture physiological measurements of epilepsy. …”
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Detection of Southern Hemisphere Constellations Using Convolutional Neural Networks by Vladimir Riffo, Sebastian Flores, Eduardo Chuy-Kan, Victor Ariza

“…Constellations allow the identification of most stars and celestial objects visible in the night sky at a glance, without the use of a telescope. …”
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Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks by Zhanhao Mo, He Sui, Zhongwen Lv, Xiaoqian Huang, Guobin Li, Dinggang Shen, Lin Liu, Shu Liao

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Mining behavior pattern of mobile malware with convolutional neural network by Xin ZHANG, Weizhong QIANG, Yueming WU, Deqing ZOU, Hai JIN

“…The features extracted by existing malicious Android application detection methods are redundant and too abstract to reflect the behavior patterns of malicious applications in high-level semantics.In order to solve this problem,an interpretable detection method was proposed.Suspicious system call combinations clustering by social network analysis was converted to a single channel image.Convolution neural network was applied to classify Android application.The model trained was used to find the most suspicious system call combinations by convolution layer gradient weight classification activation mapping algorithm,thus mining and understanding malicious application behavior.The experimental results show that the method can correctly discover the behavior patterns of malicious applications on the basis of efficient detection.…”
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Real Time Eye Detector with Cascaded Convolutional Neural Networks by Bin Li, Hong Fu

“…First, a group of candidate regions with regional extreme points is quickly proposed; then, a set of convolution neural networks (CNNs) is adopted to determine the most likely eye region and classify the region as left or right eye; finally, the center of the eye is located with other CNNs. …”
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Multi-channel based edge-learning graph convolutional network by Shuai YANG, Ruiqin WANG, Hui MA

“…Usually the edges of the graph contain important information of the graph.However, most of deep learning models for graph learning, such as graph convolutional network (GCN) and graph attention network (GAT), do not fully utilize the characteristics of multi-dimensional edge features.Another problem is that there may be noise in the graph that affects the performance of graph learning.Multilayer perceptron (MLP) was used to denoise and optimize the graph data, and a multi-channel learning edge feature method was introduced on the basis of GCN.The multi-dimensional edge attributes of the graph were encoded, and the attributes contained in the original graph were modeled as multi-channel.Each channel corresponds to an edge feature attribute to constrain the training of graph nodes, which allows the algorithm to learn multi-dimensional edge features in the graph more reasonably.Experiments based on Cora, Tox21, Freesolv and other datasets had proved the effectiveness of denoising methods and multi-channel methods.…”
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