Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags
In recent years, radio frequency identification (RFID) technology has been utilized to monitor product movements within a supply chain in real time. By utilizing RFID technology, the products can be tracked automatically in real-time. However, the RFID cannot detect the movement and direction of the...
Main Authors: | Ganjar Alfian, Muhammad Syafrudin, Norma Latif Fitriyani, Sahirul Alam, Dinar Nugroho Pratomo, Lukman Subekti, Muhammad Qois Huzyan Octava, Ninis Dyah Yulianingsih, Fransiskus Tatas Dwi Atmaji, Filip Benes |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2023-03-01
|
Series: | Future Internet |
Subjects: | |
Online Access: | https://www.mdpi.com/1999-5903/15/3/103 |
Similar Items
-
Perancangan Federated Learning Berbasis Homomorphic Encryption untuk Perangkat Internet of Things
by: Yuris Mulya Saputra, et al.
Published: (2023-05-01) -
Anomaly Detection of Metallurgical Energy Data Based on iForest-AE
by: Zhangming Xiong, et al.
Published: (2022-10-01) -
Chronic Disease Prediction Model Using Integration of DBSCAN, SMOTE-ENN, and Random Forest
by: Fitriyani, Norma Latif, et al.
Published: (2022) -
iForest, a new journal by SISEF
by: Borghetti M
Published: (2008-03-01) -
Customer Shopping Behavior Analysis Using RFID and Machine Learning Models
by: Ganjar Alfian, et al.
Published: (2023-10-01)