Cluster-Based Improved Isolation Forest [PDF]
Entropy, 2022 Outlier detection is an important research direction in the field of data mining. Aiming at the problem of unstable detection results and low efficiency caused by randomly dividing features of the data set in the Isolation Forest algorithm in outlier ...
Chen Shao +3 more
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Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data
Sensors, 2021 In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not ...
Donghyun Kim +4 more
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A New Extreme Detection Method for Remote Compound Extremes in Southeast China
Frontiers in Earth Science, 2021 The compound heat wave and extreme precipitation events are responsible for severe damages to the environment and human societies. Although major advances have been made in understanding the compound extremes (e.g., drought and heat wave), little is ...
Luqing Wang +6 more
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Effects of forest fragmentation on amazonian understory bird communities [PDF]
Acta Amazonica, 1989 SUMMARYData form an intensive mist-netting mark-recapture program in the central Amazon demostrate significant changes in the undesrtory avian community in isolate patches of 1 and 10 ha of terra firme forest.
Richard O. Bierregaard Jr +1 more
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Efficient Driver Drunk Detection by Sensors: A Manifold Learning-Based Anomaly Detector
IEEE Access, 2022 This study presents an effective data-driven anomaly detection scheme for drunk driving detection. Specifically, the proposed anomaly detection approach amalgamates the desirable features of the t-distributed stochastic neighbor embedding (t-SNE) as a ...
Abdelkader Dairi, Fouzi Harrou, Ying Sun
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Integration of Machine Learning Solutions in the Building Automation System
Energies, 2023 This publication presents a system for integrating machine learning and artificial intelligence solutions with building automation systems. The platform is based on cloud solutions and can integrate with one of the most popular virtual building ...
Bartlomiej Kawa, Piotr Borkowski
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Hyperspectral Anomaly Detection via Optimal Kernel and High-Order Moment Correlation Representation
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral anomaly detection has been a hot topic in the field of remote sensing due to its potential application prospects. However, anomaly detection still has two typical problems to be solved.
Zhuang Li, Ye Zhang, Junping Zhang
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Intrusion Detection Based on Autoencoder and Isolation Forest in Fog Computing
IEEE Access, 2020 Fog Computing has emerged as an extension to cloud computing by providing an efficient infrastructure to support IoT. Fog computing acting as a mediator provides local processing of the end-users' requests and reduced delays in communication between the ...
Kishwar Sadaf, Jabeen Sultana
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An Anomaly Detection Method for Wireless Sensor Networks Based on the Improved Isolation Forest
Applied Sciences, 2023 With the continuous development of technologies such as the Internet of Things (IoT) and cloud computing, sensors collect and store large amounts of sensory data, realizing real-time recording and perception of the environment.
Junxiang Chen +4 more
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Anomaly Prediction in Electricity Consumption Using a Combination of Machine Learning Techniques
International Journal of Technology, 2022 Electricity demand is increasing proportionally to the increase in power usage. Without a doubt, energy efficiency has gained significant importance and attention, with one of the primary concerns being the detection and forecasting of abnormal ...
Rawan ELhadad +2 more
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