Results 41 to 50 of about 13,513 (170)
Video Anomaly Detection Based on Convolutional Recurrent AutoEncoder
Sensors, 2022 As an essential task in computer vision, video anomaly detection technology is used in video surveillance, scene understanding, road traffic analysis and other fields.
Bokun Wang, Caiqian Yang
doaj +1 more source
Signal recognition and background suppression by matched filters and neural networks for Tunka-Rex
, 2019 The Tunka Radio Extension (Tunka-Rex) is a digital antenna array, which measures the radio emission of the cosmic-ray air-showers in the frequency band of 30-80 MHz. Tunka-Rex is co-located with TAIGA experiment in Siberia and consists of 63 antennas, 57
Bezyazeekov, P. A. +28 more
core +1 more source
Multi-Person Brain Activity Recognition via Comprehensive EEG Signal Analysis
, 2017 An electroencephalography (EEG) based brain activity recognition is a fundamental field of study for a number of significant applications such as intention prediction, appliance control, and neurological disease diagnosis in smart home and smart ...
Gu, Tao +5 more
core +1 more source
Advanced Intelligent Systems, EarlyView.Identifying non‐small cell lung cancer (NSCLC) subtypes is essential for precision cancer treatment. Conventional methods are laborious, or time‐consuming. To address these concerns, RPSLearner is proposed, which combines random projection and stacking ensemble learning for accurate NSCLC subtyping. RPSLearner outperforms state‐of‐the‐art approaches in
Xinchao Wu, Jieqiong Wang, Shibiao Wan
wiley +1 more source
Advanced Intelligent Systems, EarlyView.FTGRN introduces an LLM‐enhanced framework for gene regulatory network inference through a two‐stage workflow. It combines a Transformer‐based model, pretrained on GPT‐4 derived gene embeddings and regulatory knowledge, with a fine‐tuning stage utilizing single‐cell RNA‐seq data.
Guangzheng Weng +7 more
wiley +1 more source
Applied Sciences, 2023 Deep learning-based automatic modulation recognition networks are susceptible to adversarial attacks, posing significant performance vulnerabilities. In response, we introduce a defense framework enriched by tailored autoencoder (AE) techniques.
Chao Han +5 more
doaj +1 more source
Advanced Intelligent Systems, EarlyView.Adaptive multi‐indicator contrastive predictive coding is introduced as a self‐supervised pretraining framework for multivariate EHR time series. An adaptive sliding‐window algorithm and 2D convolutional neural network encoder capture localized temporal patterns and global indicator dependencies, enabling label‐efficient disease prediction that ...
Hongxu Yuan +3 more
wiley +1 more source
Transformer-based autoencoder with ID constraint for unsupervised anomalous sound detection
EURASIP Journal on Audio, Speech, and Music Processing, 2023 Unsupervised anomalous sound detection (ASD) aims to detect unknown anomalous sounds of devices when only normal sound data is available. The autoencoder (AE) and self-supervised learning based methods are two mainstream methods.
Jian Guan +6 more
doaj +1 more source
Fibroblast Transcriptomics in Molecular Diagnostics of a Comprehensive Dystonia Cohort
Annals of Neurology, EarlyView.Objective Genomic sequencing leaves >50% of dystonia‐affected individuals without a diagnosis. Where DNA‐oriented approaches remain insufficient, integrating multiomics is essential to advance genome interpretation. Herein, we incorporated RNA sequencing (RNA‐seq) data from 167 patients with dystonia across a range of ages and presentations. Methods We
Alice Saparov +42 more
wiley +1 more source
Asia-Pacific Journal of Chemical Engineering, EarlyView.ABSTRACT Real‐time online detection of rare earth element component contents is a crucial link in ensuring the stable production of the rare earth extraction and separation industry and improving the quality of rare earth products. The traditional methods for predicting the content of rare earth element components based on just‐in‐time learning fail to
Zhaohui Huang +6 more
wiley +1 more source