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

Subjects: “…Convolutional neural network…”
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Post-Disaster Building Damage Segmentation Using Convolutional Neural Networks by Revaldi Rahmatmulya, Agung Teguh Wibowo Almais, Mokhamad Amin Hariyadi

“…Effective and efficient actions are needed to assist in the recovery following natural disasters, one of which is aiding in the identification of building damage levels post-disaster. To address this issue, this research proposes a system capable of performing segmentation to determine the level of building damage post-natural disaster using convolutional neural network methods. …”
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ClipQ: Clipping Optimization for the Post-Training Quantization of Convolutional Neural Network by Yiming Chen, Hui Zhang, Chen Zhang, Yi Liu

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Lost-minimum post-training parameter quantization method for convolutional neural network by Fan ZHANG, Yun HUANG, Zizhuo FANG, Wei GUO

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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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Lifetime prediction of epoxy coating using convolutional neural networks and post processing image recognition methods by Fandi Meng, Yufan Chen, Jianning Chi, Huan Wang, Fuhui Wang, Li Liu

“…Initially, a targeted image recognition approach containing convolutional neural network (CNN) and post-processing was constructed for the crack area detection. …”
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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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Learning EEG Representations With Weighted Convolutional Siamese Network: A Large Multi-Session Post-Stroke Rehabilitation Study by Shuailei Zhang, Kai Keng Ang, Dezhi Zheng, Qianxin Hui, Xinlei Chen, Yang Li, Ning Tang, Effie Chew, Rosary Yuting Lim, Cuntai Guan

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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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Region‐based fully convolutional networks with deformable convolution and attention fusion for steel surface defect detection in industrial Internet of Things by Meixia Fu, Jiansheng Wu, Qu Wang, Lei Sun, Zhangchao Ma, Chaoyi Zhang, Wanqing Guan, Wei Li, Na Chen, Danshi Wang, Jianquan Wang

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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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Predicting Return-to-Sport Timeline: Classifying Anterior Cruciate Ligament Health Levels Post-Reconstruction Surgery Using Convolutional Neural Networks by Zeinab Jafari, Ali Sharifnezhad, Mohammad Razi, Mohammad Haghpanahi, Arash Maghsoudi

“…This study introduces a deep convolutional neural network (DCNN) designed to classify ACL health levels in injured athletes, aiding in RTS estimation. …”
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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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