Mallat’s Scattering Transform Based Anomaly Sensing for Detection of Seizures in Scalp EEG
Sign Up to like & getrecommendations! Published in 2017 at "IEEE Access"
DOI: 10.1109/access.2017.2736014
Abstract: Epilepsy is one of the most common neurological disorders, which manifests as unprovoked seizures. The prevalence of epilepsy is higher in developing countries, where medical facilities are ill-equipped and under-staffed. Mobile EEG devices promise a… read more here.
Keywords: detection; anomaly sensing; mallat scattering; transform based ... See more keywords
Random Finite Set-Based Anomaly Detection for Safety Monitoring in Construction Sites
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Access"
DOI: 10.1109/access.2019.2932137
Abstract: Low visibility hazard detection in construction sites is a crucial task for prevention of fatal accidents. Manual monitoring of construction workers to ensure they follow the safety rules (e.g., wear high-visibility vests) is a cumbersome… read more here.
Keywords: based anomaly; monitoring; safety; construction ... See more keywords
PGGAN-Based Anomaly Classification on Chest X-Ray Using Weighted Multi-Scale Similarity
Sign Up to like & getrecommendations! Published in 2021 at "IEEE Access"
DOI: 10.1109/access.2021.3102954
Abstract: To use artificial intelligence to assist in diagnoses applications, a model to utilize quality data is required, which results in massive time and cost. In medical data, data imbalance occurs because the amount of data… read more here.
Keywords: based anomaly; multi scale; classification; similarity ... See more keywords
Federated-Learning-Based Anomaly Detection for IoT Security Attacks
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3077803
Abstract: The Internet of Things (IoT) is made up of billions of physical devices connected to the Internet via networks that perform tasks independently with less human intervention. Such brilliant automation of mundane tasks requires a… read more here.
Keywords: federated learning; learning based; based anomaly; iot security ... See more keywords
CapBad: Content-Agnostic, Payload-Based Anomaly Detector for Industrial Control Protocols
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2021.3138534
Abstract: Efficient anomaly detection methods are urgently needed to prevent attacks in the application layer of the Industrial Internet of Things (IIoT). The existing intrusion detection systems have certain limitations in detecting abnormal packets exploited by… read more here.
Keywords: anomaly detector; based anomaly; content agnostic; payload based ... See more keywords
Epileptic Seizure Detection by Cascading Isolation Forest-Based Anomaly Screening and EasyEnsemble
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Neural Systems and Rehabilitation Engineering"
DOI: 10.1109/tnsre.2022.3163503
Abstract: The electroencephalogram (EEG), for measuring the electrophysiological activity of the brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG-based seizure detection algorithms have already yielded high sensitivity, but training those algorithms… read more here.
Keywords: forest based; seizure; based anomaly; isolation forest ... See more keywords
Isolation‐based anomaly detection using nearest‐neighbor ensembles
Sign Up to like & getrecommendations! Published in 2018 at "Computational Intelligence"
DOI: 10.1111/coin.12156
Abstract: The first successful isolation‐based anomaly detector, ie, iForest, uses trees as a means to perform isolation. Although it has been shown to have advantages over existing anomaly detectors, we have identified 4 weaknesses, ie, its… read more here.
Keywords: isolation based; anomaly detection; using nearest; detection using ... See more keywords
Partial Discharge Detection Based on Anomaly Pattern Detection
Sign Up to like & getrecommendations! Published in 2020 at "Energies"
DOI: 10.3390/en13205444
Abstract: Recently, a lot of research has been carried out on partial discharge (PD) using machine learning techniques. However, most of these studies have focused on the identification of multiple PD sources, PD classification, or denoising… read more here.
Keywords: anomaly pattern; detection; pattern detection; partial discharge ... See more keywords