Enhancing Convolutional Neural Network Robustness Against Image Noise via an Artificial Visual System by Bin Li, Yuki Todo, Sichen Tao, Cheng Tang, Yu Wang

“…The convolutional neural network (CNN) was initially inspired by the physiological visual system, and its structure has become increasingly complex after decades of development. …”
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Fault Diagnosis of Lithium Battery Modules via Symmetrized Dot Pattern and Convolutional Neural Networks by Meng-Hui Wang, Jing-Xuan Hong, Shiue-Der Lu

Subjects: “…convolutional neural network…”
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Classification of palm oil fruit ripeness based on AlexNet deep Convolutional Neural Network by Rudi Kurniawan, Samsuryadi Samsuryadi, Fatma Susilawati Mohamad, Harma Oktafia Lingga Wijaya, Budi Santoso

“…The experimental setup involved training AlexNet and comparing its performance with a conventional Convolutional Neural Network (CNN). The results demonstrated that AlexNet significantly outperforms the traditional CNN, achieving a validation loss of 0.0261 and an accuracy of 0.9962, compared to the CNN's validation loss of 0.0377 and accuracy of 0.9925. …”
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RESEARCH ON BEARING FAULT DIAGNOSIS BASED ON CEEMDAN FUZZY ENTROPY AND CONVOLUTIONAL NEURAL NETWORK (MT) by XIAO JunQing, JIN JiangTao, LI Chun, XU ZiFei, SUN Kang

Subjects: “…Convolutional neural network…”
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DDAC: a feature extraction method for model of image steganalysis based on convolutional neural network by Xiaodan WANG, Jingtai LI, Yafei SONG

“…To solve the problem that for image steganalysis based on convolution neural network, manual designed filter kernels were used to extract residual characteristics, but in practice, these kernels filter were not suitable for each steganography algorithm and have worse performance in application, a directional difference adaptive combination (DDAC) method was proposed.Firstly, the difference was calculated between center pixel and each directional pixel around, and 1 × 1 convolution was adopted to achieve linear combinations of directional difference.Since the combination parameters self-adaptively update according to loss function, filter kernels could be more effective in extracting diverse residual characteristics of embedding information.Secondly, truncated linear unit (TLU) was applied to raise the ratio of embedding information residual to image information residual.The model’s coveragence was accelerated and the ability of feature extraction was promoted.Experimental results indicate that substituting the proposed method could improve the accuracy of Ye-net and Yedroudj-net by 1.30%~8.21% in WOW and S-UNIWARD datasets.Compared with fix and adjustable SRM filter kernels methods, the accuracy of test model using DDAC increases 0.60%~20.72% in various datasets, and the training progress was more stable.DDAC-net was proved to be more effective in comparsion with other steganalysis model.…”
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Klasifikasi Sinyal Phonocardiogram Menggunakan Short Time Fourier Transform dan Convolutional Neural Network by Muhammad Alwi Adnan Amal, Dodi Zulherman, Rahmat Widadi

“…Penelitian ini bertujuan merancang suatu sistem klasifikasi sinyal PCG berdasarkan metode ekstraksi fitur menggunakan Short Time Fourier Transform (STFT) dan metode klasifikasi menggunakan Convolutional Neural Network (CNN). Pengujian rancangan sistem menggunakan dataset sekunder dengan 2.575 rekaman PCG normal dan 665 rekaman PCG abnormal dalam format wav. …”
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Improving person re-identification based on two-stage training of convolutional neural networks and augmentation by S. A. Ihnatsyeva, R. P. Bohush

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Normal and Abnormal Masses Detection in Mammography Images Using Deep Convolutional Neural Network (DCNN) by Farnaz Hoseini, Hamed Sepehrzadeh

Subjects: “…separated by semicolons convolutional neural network (cnn)…”
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A Lightweight Convolutional Neural Network for Real-time Detection of Aircraft Engine Blade Damage by Wenzhe Wang, Hua Su, Xinliang Liu, Jawad Munir, Jingqiu Wang

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Resource allocation strategy for ultra-dense Internet of things based on graph convolutional neural network by HUANG Jie, LI Xingxing, YANG Fan, DING Ruijie, CAI Jieliang, YAO Fenghang, ZHANG Xin

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Image Semantic Recognition Algorithm of Colorimetric Sensor Array Based on Deep Convolutional Neural Network by Xihua Chen, Xing Yang

“…And it is realized by image semantic processing of colorimetric sensor array and deep convolutional neural network processing of imaging. And through the experimental experiments based on convolutional neural network image segmentation processing, the results show that the efficiency of extracting features corresponding to different layers in the convolutional neural network is that the extraction efficiency of feature 1 and feature 2 is higher in the processing of 4 layers. …”
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LoCS-Net: Localizing convolutional spiking neural network for fast visual place recognition by Ugur Akcal, Ugur Akcal, Ugur Akcal, Ivan Georgiev Raikov, Ekaterina Dmitrievna Gribkova, Ekaterina Dmitrievna Gribkova, Anwesa Choudhuri, Anwesa Choudhuri, Seung Hyun Kim, Mattia Gazzola, Rhanor Gillette, Rhanor Gillette, Ivan Soltesz, Girish Chowdhary, Girish Chowdhary, Girish Chowdhary

Subjects: “…spiking neural networks…”
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Image-Based Concrete Crack Detection Using Convolutional Neural Network and Exhaustive Search Technique by Shengyuan Li, Xuefeng Zhao

“…To overcome these challenges, this paper proposes an image-based crack detection method using a deep convolutional neural network (CNN). A CNN is designed through modifying AlexNet and then trained and validated using a built database with 60000 images. …”
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Development of Deep Convolutional Neural Network with Adaptive Batch Normalization Algorithm for Bearing Fault Diagnosis by Chao Fu, Qing Lv, Hsiung-Cheng Lin

“…Many previous works using a deep convolutional neural network (CNN) have achieved excellent performance in finding fault information from feature extraction of detected signals. …”
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Incorporating edge convolution and correlative self-attention into graph neural network for material properties prediction by Zexi Yang, Qi Yu, Yapeng Zhan, Jiying Liu

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Automatic Classification System of Drainage Hole Blockage Based on Convolution Neural Network Transfer Learning by Jianbing Lv, Weijun Wu, Xiaoyu Kang, Juan Huang, Gongfa Chen, Shuai Teng, Hejie Gao

“…This paper studies an algorithm for the automatic classification of drainage hole blockage degree based on convolutional neural network transfer learning to explore the intelligent detection method of drainage hole blockage. …”
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Convolutional neural network prediction of the particle size distribution of soil from close-range images by Enrico Soranzo, Carlotta Guardiani, Wei Wu

“…In this study, we propose a convolutional neural network approach for predicting the particle size distribution using soil image analysis. …”
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Enhancing Physical Spatial Resolution of Synthetic Aperture Sonar Images Based on Convolutional Neural Network by Pan Xu, Dongbao Gao, Shui Yu, Guangming Li, Yun Zhao, Guojun Xu

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Deep Learning Based Dual Channel Banana Grading System Using Convolution Neural Network by Raghavendra S, Souvik Ganguli, P. Thirumarai Selvan, Mitali Madhusmita Nayak, Sushovan Chaudhury, Randell U. Espina, Isaac Ofori

“…Deep learning has recently been hailed as the most advanced computer vision technology for image classification. The invention of convolutional neural network (CNN) simplified the effort of feature engineering. …”
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Vibe++ background segmentation method combining MeanShift clustering analysis and convolutional neural network by Zihao LIU, Xiaojun JIA, Sulan ZHANG, Zhiling XU, Jun ZHANG

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