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Research on Machine Learning Algorithm-Based Approach for Detecting Abnormal Data from Environmental Sensors in Different Dimensions

Published: 13 February 2024 Publication History

Abstract

With the development of environmental sensor technology, more and more environmental sensors are widely used in various fields, such as environmental monitoring, smart home, smart city and so on. However, due to the complexity and diversity of environmental sensor data, there are a high number of anomalous data in sensor data, which can lead to misjudgment and false positives, compromising data reliability and accuracy. Therefore, the detection method of abnormal data in different dimensions of environmental sensors based on machine learning algorithms were studied in this paper. Firstly, the environmental sensor data of IBRL (Intel Berkeley Research Lab) data set was preprocessed, including data cleaning, data normalization and feature extraction. Then, the simulation experiment was completed using the data set, and the data of different dimensions was detected using machine learning algorithms such as OneClass Support Vector Machine algorithm, Local Outlier Factor algorithm, and Isolation Forest algorithm. Finally, through experimental comparison and analysis of the application effects of the above three algorithms in anomaly detection of environmental data in different dimensions, the Isolation Forest algorithm with the best comprehensive detection effect was finally selected for practical engineering projects. In practical engineering projects, maintenance costs can be effectively reduced, project safety and reliability can be improved, and the purpose of improving project efficiency can ultimately be achieved by the proposed method.

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      cover image ACM Other conferences
      JCRAI '23: Proceedings of the 2023 International Joint Conference on Robotics and Artificial Intelligence
      July 2023
      216 pages
      ISBN:9798400707704
      DOI:10.1145/3632971
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 13 February 2024

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      Author Tags

      1. Abnormal data detection
      2. Environmental sensor
      3. Machine learning algorithm

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      • Natural Science Foundation of Fujian Province of Grant

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