DHCT-GAN: Improving EEG Signal Quality with a Dual-Branch Hybrid CNN–Transformer Network by Yinan Cai, Zhao Meng, Dian Huang

“…However, EEG signals are often influenced by various physiological artifacts, which can significantly affect data analysis and diagnosis. Recently, deep learning-based EEG denoising methods have exhibited unique advantages over traditional methods. …”
Get full text

A Single-sample Fault Diagnosis Method of a Wind Turbine Transmission Chain by Ruan Aiguo, Shen Zhongming, Liu Fabing, Zhao Hai, He Yangzhang, Qian Junbing, Zhang Wei

“…Compared with the improved fuzzy clustering method and the deep learning method based on the improved AlxeNet network, the method performs better. …”
Get full text

Edge and texture aware image denoising using median noise residue U-net with hand-crafted features by Soniya S., Sriharipriya K. C.

“…Although fully convolution neural networks (CNN) are capable of removing the noise using kernel filters and automatic extraction of features, it has failed to reconstruct the images for higher values of noise standard deviation. Additionally, deep learning models require a huge database to learn better from the inputs. …”
Get full text

An intelligent spam detection framework using fusion of spammer behavior and linguistic. by Amna Iqbal, Muhammad Younas, Muhammad Kashif Hanif, Muhammad Murad, Rabia Saleem, Muhammad Aater Javed

“…The unified representation of features is another challenging task in spam detection. Various deep learning approaches have been proposed for spam detection and classification but these methods are specialized in extracting the features but lack to capture feature dependencies effectively with other features but there is a lack of comprehensive models that integrate linguistic and behavioral features to improve the accuracy of spam detection. …”
Get full text

A Next-Generation Codebook Evolution Strategy for Massive Arrays Using Deep Neurals Networks by Minwoo Choi, Wonjin Sung

“…Precoder matrix indicator (PMI) and channel quality indicator (CQI) reports from the users have become the sources for the generation of a new set of codevectors, which are autonomously determined by the deep learning (DL) module at the base station (BS). The process is operated in an iterative fashion to produce updated versions of the codebook with the reduced return of the loss function at the deep neural network (DNN). …”
Get full text

Assessing bias and computational efficiency in vision transformers using early exits by Seth Nixon, Pietro Ruiu, Marinella Cadoni, Andrea Lagorio, Massimo Tistarelli

“…Abstract Face recognition with deep learning is generally approached as a problem of capacity. …”
Get full text

Fusion-Based Damage Segmentation for Multimodal Building Façade Images from an End-to-End Perspective by Pujin Wang, Jiehui Wang, Qiong Liu, Lin Fang, Jie Xiao

“…A novel infrared–visible image registration method using main orientation assignment for feature point extraction is developed, reaching a high RMSE of 14.35 to align the multimodal images. Then, a deep learning-based infrared–visible image fusion (IVIF) network is trained to preserve damage characteristics between the modalities. …”
Get full text

Bolt Loosening Detection Method Based on Improved YOLOv8 and Image Matching by Peihe Jiang, Yuhang Geng, Zhongqi Sang, Lifeng Lin

“…To address the challenges of detecting bolt loosening, this study reviews existing detection technologies, analyzes their advantages and limitations, and proposes a novel bolt-loosening detection algorithm based on image matching and deep learning. The algorithm comprises the following components: a bolt target detection model based on an improved YOLOv8 algorithm, image correction using perspective transformation, bolt contour detection and image processing, and feature matching to calculate the transformation matrix between images obtained before and after loosening, thereby determining the loosening angle of the bolt. …”
Get full text

Graph convolutional network as a fast statistical emulator for numerical ice sheet modeling by Younghyun Koo, Maryam Rahnemoonfar

“…However, considering ISSM is compatible only with central processing units (CPUs), it has limitations in economizing computational time to explore the linkage between climate forcings and ice dynamics. Although several deep learning emulators using graphic processing units (GPUs) have been proposed to accelerate ice sheet modeling, most of them rely on convolutional neural networks (CNNs) designed for regular grids. …”
Get full text

DFTD-YOLO: Lightweight Multi-Target Detection From Unmanned Aerial Vehicle Viewpoints by Yuteng Chen, Zhaoguang Liu

“…Due to the low detection accuracy of small and dense target objects in multi-target detection tasks from the unmanned aerial vehicle (UAV) perspective and the deployment of deep learning models for UAVs as embedded devices, these models must be lightweight. …”
Get full text

Spatial-Temporal Fusion Graph Neural Networks With Mixed Adjacency for Weather Forecasting by Ang Guo, Yanghe Liu, Shiyu Shao, Xiaowei Shi, Zhenni Feng

“…Many existing approaches based on deep learning models, e.g., recurrent neutral networks and graph neural networks, have been proposed for weather forecasting. …”
Get full text

RMGANets: reinforcement learning-enhanced multi-relational attention graph-aware network for anti-money laundering detection by Qianyu Wang, Wei-Tek Tsai, Bowen Du

“…Abstract Given the anonymity and complexity of illegal transactions, traditional deep-learning methods struggle to establish correlations between transaction addresses, cash flows, and physical users. …”
Get full text

Temporal-Spatial Traffic Flow Prediction Model Based on Prompt Learning by Siteng Cai, Gang Liu, Jing He, Yulun Du, Zhichao Si, Yunhao Jiang

“…Existing studies utilizing deep learning for traffic flow prediction often suffer from distribution shift issues, leading to poor generalization capabilities when dealing with data that has different spatiotemporal distributions. …”
Get full text

DCWM-LSTM: A Novel Attack Detection Framework for Robotic Arms by Esra Nergis Yolacan, Hande Cavsi Zaim

“…In response to these challenges, this study proposes a new approach that leverages deep learning techniques for attack detection. We present a new framework, LSTM-based Dynamic Compound Weight Mechanism (DCWM), designed to identify cyberattacks targeting robotic arms effectively. …”
Get full text

Adversarial subdomain adaptation network for mismatched steganalysis by Lei ZHANG, Hongxia WANG

“…Once data in the training and test sets come from different cover sources, that is, under the condition of cover source mismatch, it usually makes the detection accuracy rate of an outstanding steganalysis model to be reduced.In practical applications, the analyzers need to process images collected from the Internet.However, compared with the training set data, these suspicious images are likely to have completely different capture and processing histories, which may lead to the degradation of steganalysis model.It is also why steganalysis tools are difficult to deploy successfully in the real-world applications.To improve the practical application value of steganalysis methods based on deep learning, test sample information is utilized and domain adaptation method is used to solve the problem of cover source mismatch.Regarding the training set data as the source domain and test set data as the target domain, the detection performance of steganalysis models in the target domain is enhanced by minimizing the discrepancy between the feature distribution of source domain and target domain.ASAN (adversarial subdomain adaptation network) was proposed from the perspective of feature generation on the one hand.The source domain features and target domain features generated by the steganalysis model were required to be as similar as possible, so that the discriminator cannot distinguish which domain the features came from.On the other hand, to reduce the difference of feature distribution between domains, the subdomain adaptation method was adopted to reduce the unexpected change of the distribution of related subdomains.The distance between the cover and stego samples was enlarged effectively to improve the classification accuracy.After testing three steganography algorithms on multiple datasets, it is confirmed that the proposed method can effectively improve the detection accuracy rate of the model in the case of dataset mismatch and algorithm mismatch and it can also reduce the negative impact of the mismatch problem of the model.…”
Get full text

Proactive Detection of Malicious Webpages Using Hybrid Natural Language Processing and Ensemble Learning Techniques by Althaf Ali A, Rama Devi K, Syed Siraj Ahmed N, Ramchandran P, Parvathi S

“…Future directions include the integration of deep learning architectures and adaptive filtering techniques to further refine detection capabilities.…”
Get full text

A Classifier Based on K-Nearest Neighbors Using Weighted Summation of Reconstruction Errors by Rassoul Hajizadeh, Mohammad Ali Hosseinzadeh

Get full text

Smartphone image dataset for radish plant leaf disease classification from BangladeshMendeley Data by Mahamudul Hasan, Raiyan Gani, Mohammad Rifat Ahmmad Rashid, Maherun Nessa Isty, Raka Kamara, Taslima Khan Tarin

“…Utilizing this robust dataset, deep learning models can be trained to identify the leaf diseases which helps to detect the diseases in order to reduce the harm of the cultivation of radish. …”
Get full text

Chemical Process Fault Diagnosis Based on Improved ResNet Fusing CBAM and SPP by Xiaochen Yan, Yang Zhang, Qibing Jin

“…The Tennessee-Eastman (TE) process is used as the experimental object to compare the improved ResNet with several other deep learning models. The experimental results show that the improved ResNet model achieves the best fault diagnosis results. …”
Get full text

Research on YOLOv5 Oracle Recognition Algorithm Based on Multi-Module Fusion by Xinhang Zhang, Zhenhua Ma, Yaru Zhang, Huiying Ru

“…However, traditional methods and some deep learning models have limited ability to capture the complex forms and fine details of oracle bone script, which makes it difficult to fully detect subtle differences between characters. …”
Get full text