Article Per Year (5 Year)
p-Index From 2021 - 2026
0.517
P-Index
This Author published in this journals
All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Kamel Maaloul
University of El-Oued
Computer Science & IT Electrical & Electronics Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Bluetooth beacons based indoor positioning in a shopping malls using machine learning Kamel Maaloul; Brahim Lejdel; Eliseo Clementini; Nedioui Med Abdelhamid
Bulletin of Electrical Engineering and Informatics Vol 12, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i2.4200

Abstract

The adoption of Bluetooth beacon technology demonstrates a broad interest in indoor positioning technology because of its low cost and ease of use. Bluetooth beacons usually have an accuracy of fewer than 4 meters. The use of machine learning (ML) leads to results with greater accuracy compared to using traditional filtering methods. In this paper, we provide indoor localization based on Bluetooth beacons using several different ML techniques. We used ML algorithms to locate customers' devices in shopping malls. The extra-trees classifier and k-neighbors classifier found the device with greater than 90% accuracy. Other algorithms were able to determine the location with less accuracy. The results also showed that Bluetooth technology is a valid solution to find the data used to analyze the spatial-temporal behavior of individuals.
Real-time human activity recognition from smart phone using linear support vector machines Kamel Maaloul; Lejdel Brahim; Nedioui Med Abdelhamid
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 21, No 3: June 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v21i3.24100

Abstract

The recognition of human activity (HAR) the use of cell devices embedded in its exten sively disbursed sensors affords guidance, instructions, and take care of citizens of smart cities. Consequently, it became essential to analyze human every day sports. To examine statistical models of human conduct, synthetic intelligence strategies such as machine studying can be used. Many studies have not studied type overall performance in real-time due to statistics series. To remedy this trouble, this paper proposes a structure primarily based on open supply technology and platforms consisting of Apache Kafka, for messages to flow over the internet, method them and provide shape for existing facts in real-time and formulates the trouble of identifying human pastime by using a smartphone tool as a type hassle using statistics collection by telephone sensors. The proposed version is skilled by some machine learning algorithms. The algorithm that has proven superior and quality results helps a linear vector machines.
Co-Authors Brahim Lejdel Eliseo Clementini Lejdel Brahim Nedioui Med Abdelhamid