Flu-CNN: identifying host specificity of Influenza A virus using convolutional networks by Mingda Hu, Nan Luo, Boqian Wang, Renjie Meng, Yunxiang Zhao, Zili Chai, Yuan Jin, Junjie Yue, Xin Wang, Wei Chen, Hongguang Ren

“…To address this, we proposed Flu-level Convolutional Neural Networks (Flu-CNN), a model designed to analyze genomic segments and identify IAV host specificity, with a particular focus on avian influenza viruses that could potentially infect humans. …”
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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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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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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

“…Our study also highlighted the effectiveness of using convolutional transformers for learning image features.…”
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Computing nasalance with MFCCs and Convolutional Neural Networks. by Andrés Lozano, Enrique Nava, María Dolores García Méndez, Ignacio Moreno-Torres

“…A new approach is proposed to compute nasalance using Convolutional Neural Networks (CNNs) trained with Mel-Frequency Cepstrum Coefficients (mfccNasalance). mfccNasalance is evaluated by examining its accuracy: 1) when the train and test data are from the same or from different dialects; 2) with test data that differs in dynamicity (e.g. rapidly produced diadochokinetic syllables versus short words); and 3) using multiple CNN configurations (i.e. kernel shape and use of 1 × 1 pointwise convolution). …”
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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

“…Methods We address this gap with informed data collection and the development of a new convolutional neural network architecture, PhytNet. Utilising a novel dataset of infrared cocoa tree images, we demonstrate PhytNet's development and compare its performance with existing architectures. …”
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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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Real-Time Super Resolution Utilizing Dilation and Depthwise Separable Convolution by Che-Cheng Chang, Wen-Pin Chen, Yi-Wei Lin, Yu-Jhan Lin, Po-Jui Pan

“…Therefore, we designed a new dilation depthwise super-resolution (DDSR) model that is composed of dilation convolution, depthwise separable convolution, and residual connection, to overcome the predicaments. …”
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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

“…In this study, we propose a new method that leverages deep learning, specifically convolutional neural networks (CNNs), to capture spatial features of environmental variables. …”
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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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