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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Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease by Uday Pratap Singh, Siddharth Singh Chouhan, Sukirty Jain, Sanjeev Jain

“…Therefore, for this paper, a multilayer convolutional neural network (MCNN) is proposed for the classification of the Mango leaves infected by the Anthracnose fungal disease. …”
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Convolutional neural network analysis of optical texture patterns in liquid-crystal skyrmions by J. Terroa, M. Tasinkevych, C. S. Dias

“…Our method focuses specifically on the skyrmion-localised regions, reducing significantly the computational cost. By training convolutional neural networks on simulated polarised optical microscopy images of liquid crystal skyrmions, we showcase the ability of trained networks to accurately predict several selected parameters such as the free energy, cholesteric pitch, and strength of applied electric fields. …”
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PERFORMANCE REFINEMENT OF CONVOLUTIONAL NEURAL NETWORK ARCHITECTURES FOR SOLVING BIG DATA PROBLEMS by Saud Aljaloud

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Improvement of a Subpixel Convolutional Neural Network for a Super-Resolution Image by Muhammed Fatih Ağalday, Ahmet Çinar

“…Since the super-resolution process is performed in the high-resolution area, it adds a memory cost and computational complexity. In our proposed model, a low-resolution image is given as input to a convolutional neural network to reduce computational complexity. …”
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Pesticide Residue Detection in Broccoli Based on Hyperspectral Technology and Convolutional Neural Network by Dan WANG, Yuqing LUAN, Zuojun TAN, Wei WEI

“…The detection of pesticide residues in agricultural products is an important step in ensuring the food safety of agricultural products, while traditional detection methods are cumbersome and costly. Using broccoli as a sample, this article used hyperspectral technology combined with machine learning algorithms and deep learning algorithms to provide a simple, fast, low-cost, and non-destructive method for detecting pesticide residues in broccoli. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…Algorithms applied in convolutional neural network (CNN) could lead to an enhanced speed of identifying and distinguishing a disease, which in turn leads to early detection and treatment. …”
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Traffic Scene Depth Analysis Based on Depthwise Separable Convolutional Neural Network by Jianzhong Yuan, Wujie Zhou, Sijia Lv, Yuzhen Chen

“…Subsequently, features containing advanced depth information were extracted using a block based on an ensemble of convolution layers and a block based on depth separable convolution layers. …”
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A composite improved attention convolutional network for motor imagery EEG classification by Wenzhe Liao, Zipeng Miao, Shuaibo Liang, Linyan Zhang, Chen Li

“…CIACNet utilizes a dual-branch convolutional neural network (CNN) to extract rich temporal features, an improved convolutional block attention module (CBAM) to enhance feature extraction, temporal convolutional network (TCN) to capture advanced temporal features, and multi-level feature concatenation for more comprehensive feature representation.ResultsThe CIACNet model performs well on both the BCI IV-2a and BCI IV-2b datasets, achieving accuracies of 85.15 and 90.05%, respectively, with a kappa score of 0.80 on both datasets. …”
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Real-Time Road Crack Mapping Using an Optimized Convolutional Neural Network by M-Mahdi Naddaf-Sh, SeyedSaeid Hosseini, Jing Zhang, Nicholas A. Brake, Hassan Zargarzadeh

“…A descriptive approach is considered for identifying cracks from collected images using a convolutional neural network (CNN) that classifies several types of cracks. …”
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A Convolutional Neural Network-Based Stress Prediction Method for Airfoil Structures by Wendi Jia, Quanlong Chen

“…The prediction results are compared with those obtained from traditional convolutional neural networks (CNNs) and the Unet model. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…Disease detection through observation is costly, time-consuming, and inaccurate. As a result, in this paper, a novel deep convolutional neural network model is proposed for the automatic identification of common bean leaf diseases. …”
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Leveraging Deep Learning and Convolutional Neural Network for Digital Waste Image Classification by Fauzan Naufal Anis, Sukmasetya Pristi, Nuryanto Nuryanto

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Automatic plant disease detection using computationally efficient convolutional neural network by Muhammad Rizwan, Samina Bibi, Sana Ul Haq, Muhammad Asif, Tariqullah Jan, Mohammad Haseeb Zafar

“…The manual approach, where plant pathologists inspect fields, is costly, error‐prone, and time‐consuming. Alternatively, automatic approaches utilize 2D plant images processed through machine learning. …”
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Convolutional spatio-temporal sequential inference model for human interaction behavior recognition by Lizhong Jin, Rulong Fan, Xiaoling Han, Xueying Cui

“…Existing methods, including skeleton sequence-based and RGB video-based models, have achieved impressive accuracy but often suffer from high computational costs and limited effectiveness in modeling human interaction behaviors.MethodsTo address these limitations, we propose a lightweight Convolutional Spatiotemporal Sequence Inference Model (CSSIModel) for recognizing human interaction behaviors. …”
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Multistep Prediction Model for Photovoltaic Power Generation Based on Time Convolution and DLinear by WANG Shuyu, LI Hao, MA Gang, YUAN Yubo, BU Qiangsheng, YE Zhigang

“…[Methods] This paper presents a multistep prediction model for photovoltaic power generation based on a temporal convolutional network （TCN） and DLinear combined model. …”
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A Lightweight Deep Learning Model for Profiled SCA Based on Random Convolution Kernels by Yu Ou, Yongzhuang Wei, René Rodríguez-Aldama, Fengrong Zhang

“…In this article, a DL-SCA model is proposed by introducing a non-trained DL technique called random convolutional kernels, which allows us to extract the features of leakage like using a transformer model. …”
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An Efficient License Plate Detection Approach Using Lightweight Deep Convolutional Neural Networks by Hoanh Nguyen

“…Benefited from deep convolutional neural networks, various license plate detection methods based on deep networks have been proposed and achieved significant improvements compared with traditional methods. …”
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Detecting ear lesions in slaughtered pigs through open-source convolutional neural networks by Matteo D’Angelo, Domenico Sciota, Anastasia Romano, Alfonso Rosamilia, Chiara Guarnieri, Chiara Cecchini, Alberto Olivastri, Giuseppe Marruchella

“…This study aims to train open-source convolutional neural networks for detecting ear biting lesions in slaughtered pigs, as a pre-requisite for a systematic and cost-effective welfare monitoring. …”
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Sign language recognition based on dual-channel star-attention convolutional neural network by Jing Qin, Mengjiao Wang

“…Addressing these challenges, this study proposes an economical and stable dual-channel star-attention convolutional neural network (SACNN) deep learning network model based on computer vision technology. …”
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