Wyniki wyszukiwania dla: ISOLATION FOREST

• Using Isolation Forest and Alternative Data Products to Overcome Ground Truth Data Scarcity for Improved Deep Learning-based Agricultural Land Use Classification Models

  Publikacja

  • A. Pereira García
  • L. Porwol
  • A. Ojo

  - Rok 2023

  High-quality labelled datasets represent a cornerstone in the development of deep learning models for land use classification. The high cost of data collection, the inherent errors introduced during data mapping efforts, the lack of local knowledge, and the spatial variability of the data hinder the development of accurate and spatially-transferable deep learning models in the context of agriculture. In this paper, we investigate...

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• Outlier Detection with the Use of Isolation Forests

  Publikacja

  • K. Najman
  • K. Zieliński

  - Rok 2021

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• Anomaly Detection in Railway Sensor Data Environments: State-of-the-Art Methods and Empirical Performance Evaluation

  Publikacja

  • M. Bałdyga
  • K. Barański
  • J. Belter
  • M. Kalinowski
  • P. Weichbroth

  - SENSORS - Rok 2024

  To date, significant progress has been made in the field of railway anomaly detection using technologies such as real-time data analytics, the Internet of Things, and machine learning. As technology continues to evolve, the ability to detect and respond to anomalies in railway systems is once again in the spotlight. However, railway anomaly detection faces challenges related to the vast infrastructure, dynamic conditions, aging...

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