Enhancing Anomaly Detection in Agriculture by Considering the Impact of Concept Drift: An Exploratory Study Using Digital Replica-Reference-Cited by-同舟云学术

Enhancing Anomaly Detection in Agriculture by Considering the Impact of Concept Drift: An Exploratory Study Using Digital Replica

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 The 5th World Symposium on Software Engineering (WSSE)
language:
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Author:

Huo Dongyang¹^ORCID,Malik Asad Waqar²^ORCID,Ravana Sri Devi¹^ORCID,Rahman Anis Ur²^ORCID,Ahmedy Ismail³^ORCID

Affiliation:

1. Department of Information Systems, Faculty of Computer Science and Information Technology, Universiti Malaya, Universiti Malaya, Malaysia

2. Department of Computing, National University of Sciences and Technology (NUST), National University of Sciences and Technology (NUST), Pakistan

3. Department of Computer Systems and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Universiti Malaya, Malaysia

Funder

Fundamental Research Grant Scheme (FRGS), Ministry of Higher Education of Malaysia

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3631991.3632004

Reference20 articles.

1. Machine Learning Approaches for Anomaly Detection in IoT: An Overview and Future Research Directions

2. Anomaly detection in smart agriculture systems

3. Integrity Threat Identification for Distributed IoT in Precision Agriculture

4. Detecting abnormal sensors via machine learning: An IoT farming WSN-based architecture case study

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