Who is WithMe? EEG features for attention in a visual task, with auditory and rhythmic support by Renata Turkeš, Steven Mortier, Jorg De Winne, Jorg De Winne, Dick Botteldooren, Paul Devos, Steven Latré, Tim Verdonck

“…The performance of the different EEG representations is evaluated with the Support Vector Machine (SVM) accuracy on the WithMe data derived from a modified digit span experiment, and is benchmarked against baseline EEG-specific models, including a deep learning architecture known for effectively learning task-specific features.ResultsThe raw EEG time series outperform each of the considered data representations, but can fall short in comparison with the black-box deep learning approach that learns the best features.DiscussionThe findings are limited to the WithMe experimental paradigm, highlighting the need for further studies on diverse tasks to provide a more comprehensive understanding of their utility in the analysis of EEG data.…”
Get full text

A Review of Agricultural Film Mapping: Current Status, Challenges, and Future Directions by Mengmeng Zhang, Jinwei Dong, Quansheng Ge, Hasituya, Pengyu Hao

“…Deep learning has apparent advantages than traditional machine learning algorithms in extracting PGs details, rarely used for mapping PMF. …”
Get full text

A Radiograph Dataset for the Classification, Localization, and Segmentation of Primary Bone Tumors by Shunhan Yao, Yuanxiang Huang, Xiaoyu Wang, Yiwen Zhang, Ian Costa Paixao, Zhikang Wang, Charla Lu Chai, Hongtao Wang, Dinggui Lu, Geoffrey I Webb, Shanshan Li, Yuming Guo, Qingfeng Chen, Jiangning Song

“…With the recent advancement in deep learning algorithms, there is a surge of interest in computer-aided diagnosis of primary bone tumors. …”
Get full text

An Effective Dataset Preprocessing Method in Tilted Gear Defects Target Detection by Lifen Tu, Qi Peng, Aiqun Zhang, Xiao Yang, Jiaqi Wang

“…Gear defect detection is a crucial component in power automation systems. Methods based on deep learning have exhibited excellent performance in detecting gears. …”
Get full text

Bearing Defect Detection with Unsupervised Neural Networks by Jianqiao Xu, Zhaolu Zuo, Danchao Wu, Bing Li, Xiaoni Li, Deyi Kong

“…Therefore, the defect detection of the bearing has always been the focus of the bearing quality control. Deep learning has been successfully applied to the objection detection due to its excellent performance. …”
Get full text

Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images by Fariba Shaker, Zahra Baharlouei, Gerlind Plonka, Hossein Rabbani

“…In recent years, deep learning methods have excelled in Optical Coherence Tomography (OCT) image classification but demand high computational resources and extensive training data. …”
Get full text

FERA-Net: A Novel Algorithm for Mars Water-Ice Cloud Segmentation Integrating Feature Enhancement, Residual, and Attention Mechanisms by Xu Ma, Jialong Lai, Zhicheng Zhong, Feifei Cui

“…Experimental results demonstrate that, compared to existing deep learning methods, FERA-Net achieves significant improvements in the task of Martian water-ice cloud segmentation. …”
Get full text

Self-supervised denoising of grating-based phase-contrast computed tomography by Sami Wirtensohn, Clemens Schmid, Daniel Berthe, Dominik John, Lisa Heck, Kirsten Taphorn, Silja Flenner, Julia Herzen

“…To reduce the dose, we introduce the self-supervised deep learning network Noise2Inverse into the field of gbPC-CT. …”
Get full text

Classifying early apple scab infections in multispectral imagery using convolutional neural networks by Alexander J. Bleasdale, J. Duncan Whyatt

“…Multispectral imaging systems combined with deep learning classification models can be cost-effective tools for the early detection of apple scab (Venturia inaequalis) disease in commercial orchards. …”
Get full text

Development of metastasis and survival prediction model of luminal and non-luminal breast cancer with weakly supervised learning based on pathomics by Hui Liu, Linlin Ying, Xing Song, Xueping Xiang, Shumei Wei

“…In this study, our objective is to develop a deep learning model utilizing pathological images to predict the metastasis and survival outcomes for breast cancer patients. …”
Get full text

Using Information Extraction to Normalize the Training Data for Automatic Radiology Report Generation by Yuxiang Liao, Haishan Xiang, Hantao Liu, Irena Spasic

“…High lexico-syntactic variation across radiology reports even when they convey the same diagnostic information complicates evaluation and hence the training of deep learning models for Automatic Radiology Report Generation. …”
Get full text

Diagnostic accuracy of MRI-based radiomic features for EGFR mutation status in non-small cell lung cancer patients with brain metastases: a meta-analysis by Yuqin Long, Rong Zhao, Xianfeng Du

“…Subgroup analysis indicated that deep learning models and studies conducted in Asian showed higher diagnostic accuracy compared to their respective counterparts.ConclusionsMRI-based radiomic features demonstrate a high potential for accurately detecting EGFR mutations in NSCLC patients with brain metastases, particularly when advanced deep learning techniques were employed. …”
Get full text

Evaluating GRU Algorithm and Double Moving Average for Predicting USDT Prices: A Case Study 2017-2024 by RAHMAT, Munirul ula, Zara Yunizar

“…While DMA is well-suited for stable trends and GRU excels in volatile conditions, LSTM outperforms both, reinforcing the effectiveness of deep learning for financial time-series forecasting.…”
Get full text

Research on the development of intelligent computing network for large models by GUO Liang, WANG Shaopeng, QUAN Wei, LI Jie

“…In recent years, the world has entered a period of vigorous development in intelligent computing. As deep learning models with huge parameters and complex structures, large model training requires fast synchronization of training parameters between multiple cards and servers, which imposes higher requirements on the bandwidth, latency, reliability, scalability and security of datacenter networks. …”
Get full text

Prediction of power grid fault repair time based on multi-model fusion by Jianyue PAN, Yizhen WU, Hanlin XU

“…There are many types of power grid faults,and the reasons are complicated.The prediction of fault repair time is difficult.Due to the rise of new technologies such as deep learning,it is feasible to accurately mine the faulty worksheet and accurately predict the fault repair time.Taking the historical grid fault repair worksheet as the research object,the multi-model fusion prediction method was proposed,and the prediction results of LightGBM,XGBoost and LSTM were weighted and fused.The experimental results show that the multi-model fusion prediction method can accurately estimate the fault repair time and provide better support for the automation and intelligence of grid fault repair.…”
Get full text

RSA-based image recognizable adversarial attack method by Yu ZHANG, Hailiang LI

“…Adversarial attack is an important part of deep learning security research.Relying on the RSA signature schemes and RSA encryption schemes in cryptography, an adversarial attack method that adversarial examples can be recognized by a specific classifier is proposed.Through the idea of one pixel attack, the normal image can have the ability to make other classifier misclassify while embedding additional information.It can be used in classifier authorization management, online image anti-counterfeiting, etc.The experiment show that the adversarial examples can be recognized under the specific classifier, and the disturbance noise is difficult to detect by the human eye.…”
Get full text

A novel model for retinal imaging in the diagnosis of Alzheimer’s disease by Kimia Heydari, Elizabeth J. Enichen, Serena Wang, Grace C. Nickel, Joseph C. Kvedar

“…This paper highlights Hao et al.’s development of a new deep learning tool, EyeAD, which studies Optical Coherence Tomography Angiography (OCT-A) of patients with Alzheimer’s. …”
Get full text

Dual-branch autoencoder network for attacking deep hashing image retrieval models by Sizheng FU, Chunjie CAO, Zhiyuan LIU, Fangjian TAO, Jingzhang SUN

“…Due to its powerful representation learning capabilities and efficient computing capabilities, deep learning-based hashing (deep hashing) methods are widely used in large-scale image retrieval.However, there are less studies on the security of deep hashing models.A dual-branch autoencoder network (DBAE) to study targeted attacks on such retrieval was proposed.The main goal of DBAE was to generate imperceptible adversarial samples as query images in order to make the images retrieved by the deep hashing model semantically irrelevant to the original image and relevant to the target image.Numerous experiments demonstrate that DBAE can successfully generate adversarial samples with small perturbations to mislead deep hashing models, and italso verifies the transferability of these perturbations under various settings.…”
Get full text

Application and research prospects of artificial intelligence in breast cancer pathological diagnosis by DA Qian, RUAN Miao, FEI Xiaochun, WANG Chaofu

“…With the advent of digital pathology slide scanners and the continuous evolution of deep learning algorithms, there has been a significant advancement in the application of artificial intelligence (AI) in the diagnosis and treatment of breast cancer. …”
Get full text

Large Pretrained Foundation Model for Key Performance Indicator Multivariate Time Series Anomaly Detection by Xu Wang, Qisheng Xu, Kele Xu, Ting Yu, Bo Ding, Dawei Feng, Yong Dou

“…In the realm of Key Performance Indicator (KPI) anomaly detection, deep learning has emerged as a pivotal technology. Yet, the development of effective deep learning models is hindered by several challenges: scarce and complex labeled data, noise interference from data handling, the necessity to capture temporal dependencies in time series KPI data, and the complexity of multivariate data analysis. …”
Get full text