Towards understanding of esports athletes' potentialities: The sensing system for data collection and analysis
A Korotin, N Khromov, A Stepanov… - … Advanced & Trusted …, 2019 - ieeexplore.ieee.org
eSports is a developing multidisciplinary research area. At present, there is a lack of relevant
data collected from real eSports athletes and lack of platforms which could be used for the …
data collected from real eSports athletes and lack of platforms which could be used for the …
Conformal anomaly detection on spatio-temporal observations with missing data
We develop a distribution-free, unsupervised anomaly detection method called ECAD,
which wraps around any regression algorithm and sequentially detects anomalies. Rooted …
which wraps around any regression algorithm and sequentially detects anomalies. Rooted …
Targeted change detection in remote sensing images
V Ignatiev, A Trekin, V Lobachev… - … on Machine Vision …, 2019 - spiedigitallibrary.org
Recent developments in the remote sensing systems and image processing made it
possible to propose a new method of the object classification and detection of the specific …
possible to propose a new method of the object classification and detection of the specific …
[PDF][PDF] Conformal kernel expected similarity for anomaly detection in time-series data
The problem of anomaly detection arises in many practical applications. Currently it is highly
important to be able to detect outliers in data streams, as recent years have seen a rapid …
important to be able to detect outliers in data streams, as recent years have seen a rapid …
Deep ensembles for imbalanced classification
N Kozlovskaia, A Zaytsev - 2017 16th IEEE International …, 2017 - ieeexplore.ieee.org
Most of the standard classification algorithms perform poorly when dealing with the case of
imbalanced classes ie when there is a class to which the overwhelming majority of samples …
imbalanced classes ie when there is a class to which the overwhelming majority of samples …
Anomaly pattern recognition with privileged information for sensor fault detection
D Smolyakov, N Sviridenko, E Burikov… - IAPR Workshop on …, 2018 - Springer
Detection of malfunction sensors is an important problem in the field of Internet of Things.
One of the classical approaches to recognize anomalous patterns in sensor data is to use …
One of the classical approaches to recognize anomalous patterns in sensor data is to use …
Learning ensembles of anomaly detectors on synthetic data
D Smolyakov, N Sviridenko, V Ishimtsev… - Advances in Neural …, 2019 - Springer
The main aim of this work is to develop and implement an automatic anomaly detection
algorithm for meteorological time-series. To achieve this goal we develop an approach to …
algorithm for meteorological time-series. To achieve this goal we develop an approach to …
Heterogeneous univariate outlier ensembles in multidimensional data
In outlier detection, recent major research has shifted from developing univariate methods to
multivariate methods due to the rapid growth of multidimensional data. However, one typical …
multivariate methods due to the rapid growth of multidimensional data. However, one typical …
Demand forecasting techniques for build-to-order lean manufacturing supply chains
R Rivera-Castro, I Nazarov, Y Xiang, A Pletneev… - Advances in Neural …, 2019 - Springer
Abstract Build-to-order (BTO) supply chains have become commonplace in industries such
as electronics, automotive and fashion. They enable building products based on individual …
as electronics, automotive and fashion. They enable building products based on individual …
A conformal anomaly detection based industrial fleet monitoring framework: A case study in district heating
The monitoring infrastructure of an industrial fleet can rely on the so-called unit-level and
subfleet-level models to observe the behavior of a target unit. However, such infrastructure …
subfleet-level models to observe the behavior of a target unit. However, such infrastructure …