Results 61 to 70 of about 352,249 (316)
Hybrid Discriminator With Correlative Autoencoder for Anomaly Detection
IEEE Access, 2021 Advances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection ...
Jungeon Lee +2 more
doaj +1 more source
SPG4 and Dementia: Expanding the Clinical Spectrum
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Hereditary spastic paraplegia (HSP) is a group of disorders characterized by progressive spasticity and lower limb weakness, with mutations in SPG4/SPAST being the most common cause. Detailed studies and clinical and molecular comparisons across different populations are missing.
Emanuele Panza +19 more
wiley +1 more source
Surface anomaly detection on island-based PV panels using edge neural networks
Zhejiang dianliSurface anomaly detection on photovoltaic （PV） panels is crucial for their operation and maintenance， especially in island environments where challenges such as small anomaly sizes and minimal color differences are prevalent. Due to the poor accuracy and
ZHANG Yinxian, ZHANG Zhanyao, ZHANG Xiya
doaj +1 more source
A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. [PDF]
PLoS ONE, 2016 Anomaly detection is the process of identifying unexpected items or events in datasets, which differ from the norm. In contrast to standard classification tasks, anomaly detection is often applied on unlabeled data, taking only the internal structure of ...
Markus Goldstein, Seiichi Uchida
doaj +1 more source
RNA Sequencing Resolves Cryptic Pathogenic Variants in Mitochondrial Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Mitochondrial diseases are the most common inherited metabolic disorders, characterized by pronounced clinical and genetic heterogeneity that complicates molecular diagnosis. Although DNA‐based sequencing approaches have become standard in genetic testing, up to half of patients remain without a definitive diagnosis.
Zhimei Liu +21 more
wiley +1 more source
UniFlow: Unified Normalizing Flow for Unsupervised Multi-Class Anomaly Detection
InformationMulti-class anomaly detection is more efficient and less resource-consuming in industrial anomaly detection scenes that involve multiple categories or exhibit large intra-class diversity.
Jianmei Zhong, Yanzhi Song
doaj +1 more source
International Journal of Distributed Sensor Networks, 2016 As WSNs gain popularity, they are becoming more and more necessary for traffic anomaly detection. Because worms, attacks, intrusions, and other kinds of malicious behaviors can be recognized by traffic analysis and anomaly detection, WSN traffic anomaly ...
Qin Yu +3 more
doaj +1 more source
Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review [PDF]
, 2023 Xue Yang, Xuejun Qi, Xiaobo Zhou
openalex +1 more source
Predictive Ability of Plasma p‐tau217 for β‐Amyloid Status: A Prospective Multicenter Study
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Plasma tau phosphorylated at threonine 217 (p‐tau217) measured with fully automated platforms has shown high accuracy for Alzheimer's disease (AD) diagnosis, but real‐world multicenter data remain limited. We aimed to validate the diagnostic performance of p‐tau217 for identifying AD pathology in a real‐world multicenter cohort ...
Miquel Massons +33 more
wiley +1 more source
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and Insights
IEEE AccessAnomaly detection is of paramount importance in many real-world domains characterized by evolving behavior, such as monitoring cyber-physical systems, human conditions and network traffic.
Kamil Faber +3 more
doaj +1 more source