Automated Data Labeling and Anomaly Detection Using Airborne Sound Analysis
نویسندگان
چکیده
منابع مشابه
Automated Anomaly Detection
Preparing a dataset is a very important step in data mining. If the input to the process contains problems, noise, or errors, then the results will reflect this, as well. Not all possible combinations of the data should exist, as the data represent real-world observations. Correlation is expected among the variables. If all possible combinations were represented, then there would be no knowledg...
متن کاملCombining Exploratory Analysis and Automated Analysis for Anomaly Detection in Real-Time Data Streams
Security analysts can easily become overwhelmed with information, which can lead them to neglect critical alerts. This problem is exemplified in the 2013 Target data breach, which is one of the largest security breaches in history: it exposed 40 million credit card accounts and 70 million of the retailer’s customer profiles (Krebs, 2013). A forensic analysis of the attack (US Senate, 2014) foun...
متن کاملAnomaly Detection Analysis of Intrusion Data Using Supervised & Unsupervised Approach
Anomaly based network intrusion detection (ANID) is an important problem that has been researched within diverse research areas and various application domains. Several anomaly based network intrusion detection systems (ANIDS) can be found in the literature. Most ANIDSs employ supervised algorithms, whose performances highly depend on attack-free training data. However, this kind of training da...
متن کاملAutomated detection of coronavirus disease (COVID-19) by using data-mining techniques: a brief report
Background: The clinical field has vast sick data that has not been analyzed. Discovering a way to analyze this raw data and turn it into an information treasure can save many lives. Using data mining methods is an efficient way to analyze this large amount of raw data. It can predict the future with accurate knowledge of the past, providing new insights into disease diagnosis and prevention. S...
متن کاملDaytime Fire Detection Using Airborne Hyperspectral Data
The shortwave infrared region of the electromagnetic spectrum, covering wavelengths from 1400 to 2500 nm, can include significant emitted radiance from fire. There have been relatively few evaluations of the utility of shortwave infrared remote sensing data, and in particular hyperspectral remote sensing data, for fire detection. We used an Airborne Visible InfraRed Imaging Spectrometer (AVIRIS...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia CIRP
سال: 2020
ISSN: 2212-8271
DOI: 10.1016/j.procir.2020.04.121