Tanveer Ahmed

Work place: Department of Computer Science and Engineering Stamford University Bangladesh (SUB), Dhaka, Bangladesh

E-mail: tanveer.sub.094419@stamforduniversity.edu.bd


Research Interests: Human-Computer Interaction, Computer systems and computational processes, Computer Networks, Data Mining, Data Structures and Algorithms


Tanveer Ahmed received his Bachelor degree from the Department of Computer Science and Engineering (CSE), IUT in 2013. His research interest lies within Human Computer interaction, education data mining, computer networking and analysis intelligent systems.
He is currently working as a senior lecturer in Department of Computer Science and Engineering (CSE) of Stamford University Bangladesh (SUB), Dhaka, Bangladesh.

Author Articles
A Machine Learning based Approach for Mapping Personality Traits and Perceived Stress Scale of Undergraduate Students

By Ahmed A. Marouf Adnan F. Ashrafi Tanveer Ahmed Tarikuzzaman Emon

DOI: https://doi.org/10.5815/ijmecs.2019.08.05, Pub. Date: 8 Aug. 2019

This paper focuses on the personality traits of students and stress scale they had to face in undergraduate level. With the advancement of computer science and machine learning based applications, we have tried to inter-correlate the terms. In the area of computational psychology, it is important to understand participants’ psychological behavior using personality traits and predict how he/she is going to react on a certain level of the stressed situation. For the experiment, we have collected data of around 150 participants. The personality traits data are collected using the standard survey named The Big Five Personality Test created by IPIP organization and stress scale measurements are collected using scale devised by Sheldon Cohen named as Perceived Stress Scale hosted by Mind garden. The data are taken from Bangladeshi computer science undergraduate students and kept anonymous. In this paper, we have applied nine different machine learning based classification models are built for mapping the traits with stress scales. For performance evaluation, we have utilized precision, recall, f1-score, and accuracy. From the experimental findings, we found that Sequential Minimal Optimization (SMO) and k-NN classifier gives the highest prediction accuracy which is approximately 70%.

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