Results 21 to 30 of about 4,573,641 (310)
Review of Applicable Outlier Detection Methods to Treat Geomechanical Data
Geotechnics, 2023 The reliability of geomechanical models and engineering designs depend heavily on high-quality data. In geomechanical projects, collecting and analyzing laboratory data is crucial in characterizing the mechanical properties of soils and rocks.
Behzad Dastjerdy +2 more
semanticscholar +1 more source
COPOD: Copula-Based Outlier Detection [PDF]
Industrial Conference on Data Mining, 2020 Outlier detection refers to the identification of rare items that are deviant from the general data distribution. Existing approaches suffer from high computational complexity, low predictive capability, and limited interpretability.
Zheng Li +4 more
semanticscholar +1 more source
Journal of Big Data, 2023 The proposed framework consists of three modules as an outlier detection method for indoor air quality data. We first use a long short-term memory autoencoder (LSTM-AE) based reconstruction error detector, which designs the LSTM layer in the shape of an ...
Junhyeok Park, Youngsuk Seo, Jaehyuk Cho
semanticscholar +1 more source
Applied Sciences, 2023 A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc.
Yuehua Huang +4 more
doaj +1 more source
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior Method [PDF]
IEEE International Conference on Data Engineering, 2022 A large number of studies on Graph Outlier Detection (GOD) have emerged in recent years due to its wide applications, in which Unsupervised Node Outlier Detection (UNOD) on attributed networks is an important area.
Yihong Huang +3 more
semanticscholar +1 more source
Applied Sciences, 2023 Collecting time-series receive signal strength (RSS) observations and averaging them is a common method for dealing with RSS fluctuation. However, outliers in the time-series observations affect the averaging process, making this method less efficient ...
A. Yaro, Filip Malý, Pavel Prazák
semanticscholar +1 more source
Sensitivity analysis with iterative outlier detection for systematic reviews and meta-analyses. [PDF]
Stat MedMeta‐analysis is a widely used tool for synthesizing results from multiple studies. The collected studies are deemed heterogeneous when they do not share a common underlying effect size; thus, the factors attributable to the heterogeneity need to be ...
Meng Z, Wang J, Lin L, Wu C.
europepmc +2 more sources
A Survey of Outlier Detection Techniques in IoT: Review and Classification
J. Sens. Actuator Networks, 2022 The Internet of Things (IoT) is a fact today where a high number of nodes are used for various applications. From small home networks to large-scale networks, the aim is the same: transmitting data from the sensors to the base station.
Mustafa Al Samara +3 more
semanticscholar +1 more source
A Novel Outlier Detection Method for Multivariate Data
IEEE Transactions on Knowledge and Data Engineering, 2022 Detecting anomalous objects from given data has a broad range of real-world applications. Although there is a rich number of outlier detection algorithms, most of them involve hidden assumptions and restrictions.
Yahya Almardeny +2 more
semanticscholar +1 more source
Attribute Grouping-based Categorical Outlier Detection Using Isolation Forest Ensemble Strategy [PDF]
Jisuanji kexueAttribute grouping is one of the effective steps in high-dimensional outlier detection,but the current ensemble strategies in attribute grouping-based outlier detection only take into account the local outlier information within each attribute group,and ...
SONG Yijing, ZHANG Jifu
doaj +1 more source