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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Design of Low-Cost and Highly Energy-Efficient Convolutional Neural Networks Based on Deterministic Encoding by Tiance Tong, Qiang He, Xiaofei Nie, Yudi Zhao

“…To realize high-energy-efficiency and low-cost hardware neural networks at the near-sensor end, a hardware optimization design of convolutional neural networks based on the hybrid encoding of deterministic encoding and binary encoding is proposed. …”
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Neuroevolutionary Convolutional Neural Network Design for Low-Resolution Face Recognition by Jhon I. Pilataxi, Juan P. Perez, Claudio A. Perez, Kevin W. Bowyer

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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

“…Using the cropped lung images, we trained several pre-trained Deep Convolutional Neural Networks (DCNNs) on the images with hyperparameters optimized by a Bayesian algorithm. …”
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Dense skip-attention for convolutional networks by Wenjie Liu, Guoqing Wu, Han Wang, Fuji Ren

“…To overcome this limitation, we propose a dense skip-attention method for convolutional networks - a simple but effective approach to boost performance. …”
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Lightweight Convolutional Network for Bearing Fault Diagnosis by LIU Hui, LI Yang, HOU Yimin

“…In the field of bearing fault diagnosis, many convolutional models with excellent performance face challenges in industrial applications due to deployment cost constraints. …”
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Multithreaded Convolution Implementation Based on Block Methods by A. V. Sharamet

“…The analysis showed that one of the possible ways to reduce time costs is a multithreaded implementation of convolution based on block algorithms. …”
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Research on Arc Fault Detection Based on Conditional Batch Normalization Convolutional Neural Network with Cost-Sensitive Multi-Feature Extraction by Xin Ning, Tianli Ding, Hongwei Zhu

“…Although existing studies have made progress in improving the accuracy of their detection, most methods have not proposed effective solutions that address the cost-sensitive problem of feature selection. Thus, a multi-feature method is proposed by combining time-domain, frequency-domain, energy, and spatial features, which are integrated into a CBN (conditional batch normalization) convolutional neural network for detection. …”
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DEVELOPMENT OF A METHOD TO REDUCE COMPUTATIONAL COSTS IN COMMUNICATION SYSTEMS WITH UFMC THROUGH THE USE OF NUMBER-THEORETIC TRANSFORMATIONS by M. I. Kalmykov, D. V. Yurdanov, I. A. Kalmykov, I. D. Efremenkov, E. A. Voloshin

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Pilot study: a low-cost, point-of-care microfluidic chip for smartphone-integrated colorimetric detection using convolutional neural networks by Mithun Kanchan, Pedapudi Anantha Hari Arun, Siddhant Rakesh Chutke

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Retracted: Detecting Distance between Surfaces of Large Transparent Material Based on Low-Cost TOF Sensor and Deep Convolutional Neural Network by Advances in Materials Science and Engineering

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Detection of Damage on Inner and Outer Races of Ball Bearings Using a Low-Cost Monitoring System and Deep Convolution Neural Networks by Handeul You, Dongyeon Kim, Juchan Kim, Keunu Park, Sangjin Maeng

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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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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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Convolutional neural networks for accurate estimation of canopy cover by F. Puig, R. Gonzalez Perea, A. Daccache, M.A. Soriano, J.A. Rodríguez Díaz

“…In this study, we present a breakthrough in solving this challenge by developing a Convolutional Neural Network (CNN) designed to compute CC autonomously. …”
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An Ensemble of Convolutional Neural Networks for Sound Event Detection by Abdinabi Mukhamadiyev, Ilyos Khujayarov, Dilorom Nabieva, Jinsoo Cho

“…This research presents a comprehensive study of an ensemble convolutional recurrent neural network (CRNN) model designed for sound event detection (SED) in residential and public safety contexts. …”
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Privacy preserving federated learning with convolutional variational bottlenecks by Daniel Scheliga, Patrick Mäder, Marco Seeland

“…Therefore, as a third contribution, we propose a novel privacy module—the Convolutional Variational Bottleneck (CVB)—that can be placed early in a neural network without suffering from these drawbacks. …”
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Pansharpening Techniques: Optimizing the Loss Function for Convolutional Neural Networks by Rocco Restaino

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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

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Cooperative inference analysis based on DNN convolutional kernel partitioning by Jialin ZHI, Yinglei TENG, Xinyang ZHANG, Tao NIU, Mei SONG

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