Recognition of Industrial Spare Parts Using an Optimized Convolutional Neural Network Model by Chandralekha Mohan, Takfarinas Saber, Priyadharshini Jayadurga Nallathambi

“…The proposed model is assessed using industrial spare parts datasets, and its performance is compared against different transfer learning models using precision, accuracy, recall, and F1 score. …”
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SA-UMamba: Spatial attention convolutional neural networks for medical image segmentation. by Lei Liu, Zhao Huang, Shuai Wang, Jun Wang, Baosen Liu

“…Most recent medical image segmentation methods are based on a convolutional neural network (CNN) or Transformer model. …”
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Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance by Ruiao Zou, Nan Wang

“…This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on imbalanced data, aiming to optimize convolutional neural network (CNN) parameters to improve minority class sample recognition. …”
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Classification of Neuropsychiatric Disorders via Brain-Region-Selected Graph Convolutional Network by Zhenzhe Qin, Yongbo Li, Xiaoying Song, Li Chai

“…Additionally, we also designed a comprehensive loss function, including a group-level consistency loss function for preserving the same brain regions in subjects of the same category, and an anti-consistency function for maximizing brain region preservation differences between subjects of different categories. …”
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An Observation and Analysis the role of Convolutional Neural Network towards Lung Cancer Prediction by Suranjana Mitra, Annwesha Banerjee Majumder, Tanusree Saha

“…Machine learning and AI-based models can identify and classify types of lung cancer quite accurately, which helps in the early-stage detection of lung cancer that can increase the survival rate. In this paper, Convolutional Neural Network is used to classify Adenocarcinoma, squamous cell carcinoma and normal case CT scan images from the Chest CT Scan Images Dataset using different combinations of hidden layers and parameters in CNN models. …”
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CST-Net: community-guided structural-temporal convolutional networks for popularity prediction by Xuxu Zheng, Peng Bao, Lin Qi, Chen Tian, Huawei Shen

“…We validate the effectiveness of the proposed CST-Net by applying it on two different types of population-scale datasets, i.e., a microblogging dataset and an academic citation dataset. …”
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Deep convolutional neural network model for classifying common bean leaf diseases by Dagne Walle Girmaw, Tsehay Wasihun Muluneh

“…However, the quality and quantity of this crop are heavily affected by different leaf diseases and affect crop growth. Currently, common bean disease detection is performed through expert visual observation. …”
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Dynamic graph convolutional networks with Temporal representation learning for traffic flow prediction by Aihua Zhang

“…Specifically, a temporal graph convolution block is specifically devised, treating historical time slots as graph nodes and employing graph convolution to process dynamic time series. …”
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Advancing semantic segmentation: Enhanced UNet algorithm with attention mechanism and deformable convolution. by Effat Sahragard, Hassan Farsi, Sajad Mohamadzadeh

“…This finding highlights the importance of exploring different attention mechanisms and their impact on segmentation performance. …”
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CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network by Jinglin Zhang, Pu Liu, Feng Zhang, Qianqian Song

“…Abstract Clouds have an enormous influence on the Earth's energy balance, climate, and weather. Cloud types have different cloud radiative effects, which is an essential indicator of the cloud effect on radiation. …”
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Image super-resolution reconstruction network combining asymmetric convolution and feature distillation by ZHU Lei, FENG Da, ZHU Qiwei, ZHAO Han, WANG Qianqian

“…In order to further improve the image reconstruction effect of single image super-resolution (SISR) lightweight network, based on lightweight network RFDN, an image super-resolution reconstruction network combining asymmetric convolution and feature distillation was proposed. Firstly, asymmetric convolution was used to construct a feature extraction module, the asymmetric convolution of two different convolution kernels in parallel in the residual block enhances the feature extraction capability of the network. …”
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Diagnosis of abnormal sound in loudspeakers by integrated attention mechanism convolutional neural network by ZHOU Jinglei, WANG Xiaoming, LI Limin

“…Firstly, different types of abnormal sound signals were collected, and VMD was used to decompose the signals and extract the features of speaker abnormal sound, constructing labeled initial data. …”
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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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Analytical Comparison of Two Emotion Classification Models Based on Convolutional Neural Networks by Huiping Jiang, Demeng Wu, Rui Jiao, Zongnan Wang

“…Electroencephalography (EEG) is the measurement of neuronal activity in different areas of the brain through the use of electrodes. …”
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Hybrid Convolutional Neural Network for Localization of Epileptic Focus Based on iEEG by Linfeng Sui, Xuyang Zhao, Qibin Zhao, Toshihisa Tanaka, Jianting Cao

“…However, manual analysis and classification of the iEEG signal by clinicians are arduous and time-consuming and excessively depend on the experience. Due to individual differences of patients, the iEEG signal from different patients usually shows very diverse features even if the features belong to the same class. …”
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Novel Approach in Vegetation Detection Using Multi-Scale Convolutional Neural Network by Fatema A. Albalooshi

“…Moreover, the MSCNN architecture integrates multiple convolutional layers with varying kernel sizes (3 × 3, 5 × 5, and 7 × 7), enabling the model to extract features at different scales, which is vital for identifying diverse vegetation patterns across various landscapes. …”
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Intelligent Analysis of Hydraulic Concrete Vibration Time Based on Convolutional Neural Network by Hao Liu, Chengzhao Liu, Jiake Fu, Chenzhe Ma, Ye Zhang, Yumeng Lei

“…The system took the convolutional neural network as the basic framework, and divided the concrete vibration process into three different states: vibrating, not vibrating, and no vibration tube, realized the concrete vibration time through the analysis of concrete vibration video data. …”
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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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Gearbox fault diagnosis convolutional neural networks with multi-head attention mechanism by Xu Hang, Li Huawei, Yang Shufeng, Cui Jianghong, Li Youhua, He Yuanchun, Xie Guiping, Wu Yaoting

“…Then, the multi-head attention mechanism was incorporated to focus on different feature spaces and obtain diverse feature information. …”
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Convolutional neural network analysis of optical texture patterns in liquid-crystal skyrmions by J. Terroa, M. Tasinkevych, C. S. Dias

“…Machine learning can also be employed to identify phase transitions and classify different liquid crystalline phases and topological defects. …”
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