Jesmin Akhter

Work place: Institute of Information Technology, Jahangirnagar University, Dhaka, Bangladesh



Research Interests: Data Structures and Algorithms, Computer Architecture and Organization, Software Organization and Properties, Software Engineering, Software, Computational Science and Engineering


Jesmin Akhter has received PhD degree in 2019 in the field of 4G wireless networks. from Department of Computer Science and Engineering of Jahangirnagar University, Savar, Dhaka, Bangladesh and obtained M.Sc Engineering degree in Computer Science and Engineering from Jahangirnagar University, Savar, Dhaka, Bangladesh in 2012. She also received her B.Sc. Engineering degree in Computer Science and Engineering from Jahangirnagar University, Savar, Dhaka, Bangladesh in 2004. Since 2008, she is a faculty member having current Designation "Professor" at the Institute of Information Technology in Jahangirnagar University, Savar, Dhaka, Bangladesh. Currently her research focuses are on IoT, network traffic, complexity and algorithms and software engineering. Being a dynamic and versatile person who is capable of merging innovative ideas, technology, knowledge, and experience for positive contribution towards the system development in the rapidly changing scenario of Information Technology and become a good teacher in the field of software and telecommunication systems.

Author Articles
Image Recognition Using Machine Learning with the Aid of MLR

By Meherunnesa Tania Diba Afroze Jesmin Akhter Abu Sayed Md. Mostafizur Rahaman Md. Imdadul Islam

DOI:, Pub. Date: 8 Dec. 2021

In this paper, we use three machine learning techniques: Linear Discriminant Analysis (LDA) along different Eigen vectors of an image, Fuzzy Inference System (FIS) and Fuzzy c-mean clustering (FCM) to recognize objects and human face. Again, Fuzzy c-mean clustering is combined with multiple linear regression (MLR) to reduce the four-dimensional variable into two dimensional variables to get the influence of all variables on the scatterplot. To keep the outlier within narrow range, the MLR is again applied in logistic regression. Individual method is found suitable for particular type of object recognition but does not reveal standard range of recognition for all types of objects. For example, LDA along Eigen vector provides high accuracy of detection for human face recognition but very poor performance is found against discrete objects like chair, butterfly etc. The FCM and FIS are found to provide moderate result in all kinds of object detection but combination of three methods of the paper provide expected result with low process time compared to deep leaning neural network.  

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