Browsing by Author "Ali, Mohammad Hanif"

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A Machine Learning Approach for Driver Identification
(Institute of Electrical and Electronics Engineers Inc., 2023-04-15) Ali Khan, Md. Abbas; Ali, Mohammad Hanif; Haque, Fazlul; Habib, Md. Tarek
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.
A Novel Compound Feature Based Driver Identification
(Daffodil International University, 2022-01-20) Khan, Md. Abbas Ali; Ali, Mohammad Hanif; Haque, AKM Fazlul; Islam, Md. Iktidar; Islam, Mohammad Monirul
Abstract: In today's world, it is time to identify the driver through technology. At present, it is possible to find out the driving style of the drivers from every car through controller area network (CAN-BUS) sensor data which was not possible through the conventional car. Many researchers did their work and their main purpose was to find out the driver driving style from end-to-end analysis of CAN-BUS sensor data. So, it is potential to identify each driver individually based on the driver's driving style. We propose a novel compound feature-based driver identification to reduce the number of input attributes based on some mathematical operation. Now, the role of machine learning in the field of any type of data analysis is incomparable and significant. The state-of-the-art algorithms have been applied in different fields. Occasionally these are tested in a similar domain. As a result, we have used some prominent algorithms of machine learning, which show different results in the field of aspiration of the model. The other goal of this study is to compare the conspicuous classification algorithms in the index of performance metrics in driver behavior identification. Hence, we compare the performance of SVM, Naïve Bayes, Logistic Regression, k-NN, Random Forest, Decision tree, Gradient boosting.
An App-Based IoT-NFC Controlled Remote Access Security Through Cryptographic Algorithm
(Scopus, 2021) Khan, Md. Abbas Ali; Ali, Mohammad Hanif; Haque, A. K. M. Fazlul; Debnath, Chandan; Jabiullah, Md. Ismail; Rahman, Md. Riazur
In the twenty-first century, a human being is passing through the world with generosity of technology and most of it’s the systems are being operated by automated or remote access control. However, sensor technology is already playing a vital role to control the smart home, smart office, etc. However, it is about to beyond a smart city. Remote access control is a part of the leading technology. An app-based innovative remote access control framework is adding an extra security to make this technology more convenient, secured and illustrate the usability of a person along with an authenticated system of the executive. NFC is used as a communication technology, and a microcontroller camera is also used for detection. An authentication process drives through a smartphone application over the IoT framework. A definitive objective of this paper is to ensure the security of remote access control, notification to the comer and admin, accessibility, usability and permissibility to enter the premises. In order to maintain the integrity and the confidentiality of data cryptographic, techniques like computational 512 bits hash functions are considered and encrypt the hashed data once AES-192 is used. The additional part of this paper is to measure the performance of an employee.
An Efficient and Optimized Tracking Framework through Optimizing Algorithm in a Deep Forest using NFC
(Indonesian Journal of Electrical Engineering and Computer Science, 2020) Khan, Md. Abbas Ali; Ali, Mohammad Hanif; Haque, A.K.M Fazlul; Debnath, Chandan; Bhowmik, Shohag Kumar
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.