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Mst Shapna Akter

Quantum Networking Quantum Machine Learning Natural Language Processing (NLP) AI Security Health-related AI Applications

About

Dr. Mst Shapna Akter is an Assistant Professor of Computer Science and Engineering at Oakland University in Rochester, Michigan, where she teaches and conducts research in areas like quantum networking, quantum machine learning, natural language processing (NLP), machine learning (ML), deep learning (DL), AI security, and health‑related AI applications. She earned her Ph.D. in Intelligent Systems and Robotics from the University of West Florida, and she has published extensively in top international conferences and journals (such as IEEE SERVICES, IEEE CNS, IEEE BigData, and Elsevier outlets), with over 40 scholarly publications and more than 200 citations. Her work also includes significant contributions as a program committee member and guest editor for special journal issues

Research Performance Summary

55
Total Papers
494
Total Citations
14
H-Index
2021-2026
Active Research Span

First Recorded Paper

Automatic segmentation of blood cells from microscopic slides: a comparative analysis

Year: 2021

Citations: 29

Venue: Tissue and Cell 73

Latest Recorded Paper

A Review of Routing and Resource Optimization in Quantum Networks

Year: 2026

Citations: 0

Venue: Electronics 15 (3)

Last 10 Years Publication Activity

This timeline shows the professor's yearly publication activity.

2026
1
2025
14
2024
19
2023
11
2022
8
2021
2

Publication Venues and Collaboration

Journal, Conference, and Book Publication Breakdown

Conference 34
Journal 0
Book 1

Top Coauthors

Akter Shahriar Cuzzocrea

Research Impact by Period

2025-2026

Period Stats
Papers 15
Citations 12
Avg. Citations / Paper 0.8
H-Index 3

2020-2024

Period Stats
Papers 40
Citations 482
Avg. Citations / Paper 12.1
H-Index 14

Contact and Professional Links

Contact Information

akter@oakland.edu
2483703542

Detected Research Keywords

Detection Source Code Quantum Machine Learning Software Supply Chain Vulnerability Detection Source Natural Language Processing Trustable Lstm Autoencoder Authentic Learning Approach Blood Cell Segmentation Null Pointer Dereference Interpretation Language Models