A Tank Experiment of the Autonomous Detection of Seabed-Contacting Segments for Submarine Pipelaying Operations by Bo Wang, Jie Wang, Chen Zheng, Ye Li, Jian Cao, Yueming Li

“…The results show that our modules can improve the performance of different neural network models for seabed-contacting segment detection. …”
Get full text

Development of Robust CNN Architecture for Grading and Classification of Renal Cell Carcinoma Histology Images by Amit Kumar Chanchal, Shyam Lal, Shilpa Suresh

“…Concatenating samples from three different parts of architecture allows for the encompassing of varied features, further improving grading and classification accuracy. …”
Get full text

Multi-Modal Deep Embedded Clustering (MM-DEC): A Novel Framework for Mineral Detection Using Hyperspectral Imagery by Priyanka Nair, Devesh Kumar Srivastava, Roheet Bhatnagar

“…Preprocessing pipeline includes denoising using Machine Learning(ML) and statistical techniques, followed by major land cover classification based on spectral indices including Normalized Difference Vegetation Index (NDVI); Normalized Difference Water Index (NDWI) and Normalized Difference Soil Index (NDSI). …”
Get full text

Enhancing Anti-Money Laundering Frameworks: An Application of Graph Neural Networks in Cryptocurrency Transaction Classification by Stefano Ferretti, Gabriele D'Angelo, Vittorio Ghini

“…Based on the dataset analysis, we experiment with different subsets of features. Our findings suggest that the use of Graph Neural Network convolutions, combined with a final linear layer and skip connections, allow for an improvement in the state-of-the-art results, especially when Chebyshev and GATv2 convolutions are used.…”
Get full text

Notice of Violation of IEEE Publication Principles: Ground-Based Cloud Image Recognition System Based on Multi-CNN and Feature Screening and Fusion by Ma Jingyi, Tiejun Zhang, Jing Guodong, Yan Wenjun, Yang Bin

“…With the popularity of convolutional neural networks in image processing, ground-based cloud image recognition algorithms based on convolutional neural network have become a research focus. …”
Get full text

Fault Detection in Induction Machines Using Learning Models and Fourier Spectrum Image Analysis by Kevin Barrera-Llanga, Jordi Burriel-Valencia, Angel Sapena-Bano, Javier Martinez-Roman

“…This analysis introduces a new approach by demonstrating how different convolutional blocks capture particular features: the first convolutional block captures signal shape, while the second identifies background features. …”
Get full text

Classification of pulmonary diseases from chest radiographs using deep transfer learning. by Muneeba Shamas, Huma Tauseef, Ashfaq Ahmad, Ali Raza, Yazeed Yasin Ghadi, Orken Mamyrbayev, Kymbat Momynzhanova, Tahani Jaser Alahmadi

“…This paper has explored the effectiveness of Convolutional Neural Networks and transfer learning to improve the predictive outcomes of fifteen different pulmonary diseases using chest radiographs. …”
Get full text

Sines, transient, noise neural modeling of piano notes by Riccardo Simionato, Stefano Fasciani

“…The noise sub-module uses a learnable time-varying filter, and the transients are generated using a deep convolutional network. From singular notes, we emulate the coupling between different keys in trichords with a convolutional-based network. …”
Get full text

Application research of 3D virtual interactive technology in interactive teaching of arts and crafts by Mingqi Yao

“…The results show that when the noise standard deviation is 50, model 1 can achieve the target accuracy with only 82 iterations, while model 2 requires as many as 56 iterations. For images of different types and noise intensities, the average PSNR values of model 1 are 29.3dB, 30.5dB and 28.9dB, respectively. …”
Get full text

ResnetCPS for Power Equipment and Defect Detection by Xingyu Yan, Lixin Jia, Xiao Liao, Wei Cui, Shuangsi Xue, Dapeng Yan, Hui Cao

“…The core idea is that the network output should remain consistent for the same object at different scales. The proposed framework facilitates weight sharing across different layers within the convolutional network, establishing connections between pertinent channels across layers and leveraging the scale invariance inherent in image datasets. …”
Get full text

The analysis of motion recognition model for badminton player movements using machine learning by Xuanmin Zhu, Lizhi Liu, Jingshuo Huang, Genyan Chen, Xi Ling, Yanshuo Chen

“…A badminton stroke recognition method based on Quantum Convolutional Neural Network (QCNN) is proposed. It is then compared with traditional Support Vector Machines (SVM) and Convolutional Neural Network (CNN). …”
Get full text

Building Footprint Extraction from High Resolution Aerial Images Using Generative Adversarial Network (GAN) Architecture by Arnick Abdollahi, Biswajeet Pradhan, Shilpa Gite, Abdullah Alamri

“…Thus, we introduce an end-to-end convolutional neural network called Generative Adversarial Network (GAN) in this study to tackle these issues. …”
Get full text

Health Monitoring of Carbon Fiber Reinforced Building Materials Based on Phase Unwrapping Algorithm by Min Yan, Jianjun Zhou, Shuai Guo, Yanlong Li

“…When testing the fracture of a single suspension rod, the difference in cable force detected was 115kN, with a variation amplitude of 12.15%. …”
Get full text

Network intrusion detection method based on VAE-CWGAN and fusion of statistical importance of feature by Taotao LIU, Yu FU, Kun WANG, Xueyuan DUAN

“…Considering the problems of traditional intrusion detection methods limited by the class imbalance of datasets and the poor representation of selected features, a detection method based on VAE-CWGAN and fusion of statistical importance of features was proposed.Firstly, data preprocessing was conducted to enhance data quality.Secondly, a VAE-CWGAN model was constructed to generate new samples, addressing the problem of imbalanced datasets, ensuring that the classification model no longer biased towards the majority class.Next, standard deviation, difference of median and mean were used to rank the features and fusion their statistical importance for feature selection, aiming to obtain more representative features, which made the model can better learn data information.Finally, the mixed data set after feature selection was classified through a one-dimensional convolutional neural network.Experimental results show that the proposed method demonstrates good performance advantages on three datasets, namely NSL-KDD, UNSW-NB15, and CIC-IDS-2017.The accuracy rates are 98.95%, 96.24%, and 99.92%, respectively, effectively improving the performance of intrusion detection.…”
Get full text

Pulmonary Disease Classification on Electrocardiograms Using Machine Learning by Aboubacar Abdoulaye Soumana, Prajwol Lamichhane, Mehlam Shabbir, Xudong Liu, Mona Nasseri, Scott Helgeson

“…In the task of classifying whether a patient has obstructive lung disease, our results show that deep neural network models outperformed the non-neural models, though the difference is within 3% on accuracy and F1-score metrics.…”
Get full text

Comparison of neural networks for suppression of multiplicative noise in images by V.A. Pavlov, A.A. Belov, V.T. Nguen, N. Jovanovski, A.S. Ovsyannikova

“…It is shown that different architectures require significantly different amount of training data to reach the same noise suppression quality. …”
Get full text

Quality prediction of air-cured cigar tobacco leaf using region-based neural networks combined with visible and near-infrared hyperspectral imaging by Jianxun Yin, Jun Wang, Jian Jiang, Jian Xu, Liang Zhao, Anfu Hu, Qian Xia, Zhihan Zhang, Ming Cai

“…Specifically, the quality of cigar tobacco leaves undergoes subtle changes due to environmental differences during the air-curing phase. This study aims to evaluate the feasibility of deep learning methods in overcoming data limitations to develop a VNIR-HSI prediction model for the quality of cigar tobacco leaves at different air-curing levels. …”
Get full text

3D CNN Approach for Tennis Movement Recognition Using Spatiotemporal Features of Video by Volodymyr Shymanskyi, Ilona Klymenok

“…Also, based on the results, it can be concluded that the use of 3D models can show good results and that it is worth continuing to experiment with their different types.…”
Get full text

Enhanced prediction of hemolytic activity in antimicrobial peptides using deep learning-based sequence analysis by Ibrahim Abdelbaky, Mohamed Elhakeem, Hilal Tayara, Elsayed Badr, Mustafa Abdul Salam

“…Peptide sequences are represented using one-hot encoding, and the CNN architecture consists of multiple convolutional and fully connected layers. The model was trained on six different datasets: HemoPI-1, HemoPI-2, HemoPI-3, RNN-Hem, Hlppredfuse, and AMP-Combined, achieving Matthew’s correlation coefficients of 0.9274, 0.5614, 0.6051, 0.6142, 0.8799, and 0.7484, respectively. …”
Get full text

Multibranch 3D-Dense Attention Network for Hyperspectral Image Classification by Junru Yin, Changsheng Qi, Wei Huang, Qiqiang Chen, Jiantao Qu

“…This network is able to reuse features to fully exploit the shallow spatial-spectral information of HSI. Meanwhile, the convolutional kernels of different sizes are used to extract multi-scale spatial-spectral features. …”
Get full text