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All Journal Indonesian Journal of Electrical Engineering and Computer Science
Mohammad Hanif Ali
Jahangirnagar University

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An efficient and optimized tracking framework through optimizing algorithm in a deep forest using NFC Md. Abbas Ali Khan; Mohammad Hanif Ali; A.K.M Fazlul Haque; Chandan Debnath; Shohag Kumar Bhowmik
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 2: August 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i2.pp884-889

Abstract

NFC is applying in various field of contemporary technology. Especially of convenience tag usability in any place. One of the facilities which can be added in the tracking system is the implementation of Near Field Communication in order to guide each tourist in the deep forest or any other location. In the deep forest, tracking or location detection activities need to be done efficiently, like desired path finding in a deep forest. At present, the tracking procedure in deep forest is working with the help of guides or local citizens. Currently, in any restricted area such as the “Sundarban” forest, no outside general people are allowed to travel in the jungle without any authorized guide which is not an efficient way to travel smoothly. The use of Near Field Communication can solve the problem related to lost the way, safety, and easily help the travelers to track the desired destination without the help of human resources or any guide. The NFC tags that hold mapping information of the area, in the point of tag setup all tags will be set up on several trees along with sequence.
A machine learning approach for driver identification Md. Abbas Ali Khan; Mohammad Hanif Ali; Fazlul Haque; Md. Tarek Habib
Indonesian Journal of Electrical Engineering and Computer Science Vol 30, No 1: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v30.i1.pp276-288

Abstract

Driver identification is a momentous field of modern decorated vehicles in the perspective of the controller area network (CAN-Bus). Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-Bus but there are some difficulties because of the variation of a protocol of different models of vehicle. We aim to identify the driver through supervised learning algorithms based on driving behavior analysis. To identify the driver, a driver verification technique is proposed that evaluate driving pattern using the measurement of CAN sensor data. In this paper on-board diagnostic (OBD-II) is used to capture the data from CAN-Bus sensor and the sensors are listed under SAE J1979 statement. According to the service of OBD-II drive identification is possible. However, we have gained two types of accuracy on a full data set with 10 drivers and a partial data set with two drivers. The accuracy is good with less number of drivers compared to a higher number of drivers. We have achieved statistically significant results in terms of accuracy in contrast to the baseline algorithm.
Co-Authors A.K.M Fazlul Haque Chandan Debnath Fazlul Haque Md. Abbas Ali Khan Md. Abbas Ali Khan Md. Tarek Habib Shohag Kumar Bhowmik