A survey of outlier detection in high dimensional data streams
The rapid evolution of technology has led to the generation of high dimensional data
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
streams in a wide range of fields, such as genomics, signal processing, and finance. The …
[HTML][HTML] Securing Industrial Control Systems: Components, Cyber Threats, and Machine Learning-Driven Defense Strategies
Industrial Control Systems (ICS), which include Supervisory Control and Data Acquisition
(SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers …
(SCADA) systems, Distributed Control Systems (DCS), and Programmable Logic Controllers …
An improved PIO feature selection algorithm for IoT network intrusion detection system based on ensemble learning
OA Alghanam, W Almobaideen, M Saadeh… - Expert Systems with …, 2023 - Elsevier
With the rapid growth of the number of connected devices that exchange personal, sensitive,
and important data through the IoT based global network, attacks that are targeting security …
and important data through the IoT based global network, attacks that are targeting security …
Efficient density and cluster based incremental outlier detection in data streams
A Degirmenci, O Karal - Information Sciences, 2022 - Elsevier
In this paper, a novel, parameter-free, incremental local density and cluster-based outlier
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …
factor (iLDCBOF) method is presented that unifies incremental versions of local outlier factor …
[HTML][HTML] Optimized hybrid ensemble learning approaches applied to very short-term load forecasting
The significance of accurate short-term load forecasting (STLF) for modern power systems'
efficient and secure operation is paramount. This task is intricate due to cyclicity, non …
efficient and secure operation is paramount. This task is intricate due to cyclicity, non …
[HTML][HTML] Anomaly detection using a sliding window technique and data imputation with machine learning for hydrological time series
L Kulanuwat, C Chantrapornchai, M Maleewong… - Water, 2021 - mdpi.com
Water level data obtained from telemetry stations typically contains large number of outliers.
Anomaly detection and a data imputation are necessary steps in a data monitoring system …
Anomaly detection and a data imputation are necessary steps in a data monitoring system …
[HTML][HTML] Proposing an integrated approach to analyzing ESG data via machine learning and deep learning algorithms
In the COVID-19 era, people face situations that they have never experienced before, which
alerted the importance of the ESG. Investors also consider ESG indexes as an essential …
alerted the importance of the ESG. Investors also consider ESG indexes as an essential …
[HTML][HTML] An unsupervised anomaly detection framework for onboard monitoring of railway track geometrical defects using one-class support vector machine
Track geometry is one of the critical indicators of railway tracks' condition which requires
continuous monitoring and maintenance over time. In this paper, a novel artificial …
continuous monitoring and maintenance over time. In this paper, a novel artificial …
Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology
X Li, Y Wu, H Cheng, Q Xie, T Daim - Technological Forecasting and Social …, 2023 - Elsevier
With the high integration of science and technology development, how to early identify
technology opportunity is crucial for the governments' and enterprises' research and …
technology opportunity is crucial for the governments' and enterprises' research and …
Evaluating the performance of ensemble classifiers in stock returns prediction using effective features
MR Toochaei, F Moeini - Expert Systems with Applications, 2023 - Elsevier
Stock market prediction is considered as an important yet challenging aspect of financial
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …