Quality enhancement of VVC intra-frame coding for multimedia services over the Internet by Seunghyun Cho, Dong-Wook Kim, Seung-Won Jung

“…In this article, versatile video coding, the next-generation video coding standard, is combined with a deep convolutional neural network to achieve state-of-the-art image compression efficiency. …”
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Vibration-Based Anomaly Detection in Industrial Machines: A Comparison of Autoencoders and Latent Spaces by Luca Radicioni, Francesco Morgan Bono, Simone Cinquemani

“…This study explores the application of unsupervised learning methods, particularly Convolutional Autoencoders (CAEs) and variational Autoencoders (VAEs), for anomaly detection (AD) in vibration signals. …”
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Analysis of the Urban Development Technologies of Ukek using Neural Networks (preliminary research results) by Singatulin Rustam A.

“…Complex patterns and associations between archaeological, historical, and geological data from different years were identified. The software tools included convolutional neural networks for image analysis, recurrent neural networks for time sequence analysis, and deep neural networks for complex classification, modeling, and verification tasks. …”
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Enhancing Clinical Decision Making by Predicting Readmission Risk in Patients With Heart Failure Using Machine Learning: Predictive Model Development Study by Xiangkui Jiang, Bingquan Wang

“…Subsequently, we constructed 6 predictive models using different algorithms: logistic regression, support vector machine, gradient boosting machine, Extreme Gradient Boosting, multilayer perception, and graph convolutional networks. …”
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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. …”
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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). …”
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DRGNet: Enhanced VVC Reconstructed Frames Using Dual-Path Residual Gating for High-Resolution Video by Zezhen Gai, Tanni Das, Kiho Choi

“…The proposed method is built upon a high-resolution dual-path residual gating system, which integrates deep features from different convolutional layers and introduces convolutional blocks equipped with gating mechanisms. …”
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Super-resolution reconstruction of mine image based on generative adversarial network by Fan ZHANG, Ying LIU, Hui SONG, Jiarong ZHANG, Haixing CHENG

“…Based on SRGAN, this method improves the network structure and loss function. First, two 5×5 convolutional layers are used in the low-level feature extraction layer and reconstruction layer of the generator, and non-linearity is added after each convolutional layer of the low-level feature extraction layer, and the high-level feature extraction layer adopts the residual structure, and the sub-pixel convolutional layer is cascaded to achieve super-resolution reconstruction of different multiples. …”
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Research on RF Intensity Temperature Sensing based on 1D-CNN by DING Meiqi, GUI Lin, WANG Ziyi, SHANG Disen, QIAN Min, LI Qiankun

“…【Objective】In order to improve the accuracy and efficiency of temperature sensing, the application of Microwave Photonic Filter (MPF) based on One-Dimensional Convolutional Neural Network (1D-CNN) in Radio Frequency (RF) intensity temperature sensing is studied.…”
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Multi-Neighborhood Sparse Feature Selection for Semantic Segmentation of LiDAR Point Clouds by Rui Zhang, Guanlong Huang, Fengpu Bao, Xin Guo

“…To address these problems, a sparse feature dynamic graph convolutional neural network, abbreviated as SFDGNet, is constructed in this paper for LiDAR point clouds of complex scenes. …”
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3D CNN Approach for Tennis Movement Recognition Using Spatiotemporal Features of Video by Volodymyr Shymanskyi, Ilona Klymenok

“…The implementation utilizes a 3D Convolutional Neural Network (3D CNN) to classify tennis player movements effectively. …”
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Bird Species Detection Net: Bird Species Detection Based on the Extraction of Local Details and Global Information Using a Dual-Feature Mixer by Chaoyang Li, Zhipeng He, Kai Lu, Chaoyang Fang

“…The prediction balance module balances the difference in feature space based on the pixel values of each category, thereby resolving category imbalances and improving the network’s detection accuracy. …”
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Motor imagery decoding network with multisubject dynamic transfer by Zhi Li, Mingai Li, Yufei Yang

“…Because of the inter-individual heterogeneity, the decoding model should demonstrate dynamic adaptation abilities.Domain adaptation (DA) is effective to enhance model generalization by reducing the inherent distribution difference among subjects. However, the existing DA methods usually mix the multiple source domains into a new domain, the resulting multi-source domain conflict may cause negative transfer. …”
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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.…”
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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.…”
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S²RCFormer: Spatial-Spectral Residual Cross-Attention Transformer for Multimodal Remote Sensing Data Classification by Yifei Xu, Lingming Cao, Jialu Li, Wenlong Li, Yaochen Li, Yingjie Zong, Aichen Wang, Yuan Rao, Shuiguang Deng

“…It mainly consists of a patchwise convolutional module (PTConv), pixelwise convolutional module (PXConv), residual cross-attention tokenization module (RCTM), and transformer feature fusion module (TFFM). …”
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Spectrogram Features-Based Automatic Speaker Identification For Smart Services by Rashid Jahangir, Mohammed Alreshoodi, Fawaz Khaled Alarfaj

“…This study investigates ASI based on features derived from spectrogram images through a convolution neural network (CNN) with rectangular-shaped kernels. …”
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Vision Transformer and Language Model Based Radiology Report Generation by Mashood Mohammad Mohsan, Muhammad Usman Akram, Ghulam Rasool, Norah Saleh Alghamdi, Muhammad Abdullah Aamer Baqai, Muhammad Abbas

“…In order to evaluate the proposed methodology, the Indiana University Chest X-Rays dataset is used where ablation study is also conducted with respect to different evaluations. The comparative analysis shows that the proposed methodology has represented remarkable performance when compared with existing techniques in terms of different performance parameters.…”
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Plot-scale peanut yield estimation using a phenotyping robot and transformer-based image analysis by Zhengkun Li, Rui Xu, Nino Brown, Barry L. Tillman, Changying Li

“…A workflow was developed to estimate yield accurately across different genotypes by counting the pods from stitched plot-scale images. …”
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A Novel Dual-Branch Global and Local Feature Extraction Network for SAR and Optical Image Registration by Xuanran Zhao, Yan Wu, Xin Hu, Zhikang Li, Ming Li

“…However, the inherent differences between the two modalities pose a challenge to the existing deep-learning algorithms that only depend on local features. …”
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