Pranta Sarker

I am a Lecturer in the Department of Science and Engineering at Department of Science and Engineering at North East University Bangladesh, currently on study leave and completing a Master of Information Technology and Systems majoring in Business Analytics at Victorian Institute of Technology, Sydney, Australia.

My research background includes data compression, Huffman coding, adjacent distance array, algorithm design, and text processing. I have 4 research publications, including 3 Scopus-indexed publications and 1 Springer book chapter/proceedings publication.

However, my current research interest is blockchain-based trust and reputation management for e-commerce platforms, with a focus on transparent trust scoring, fake review detection, privacy-aware digital transactions, and AI-assisted review integrity.

I am seeking HDR/MRes/MPhil supervision and RTP scholarship opportunities for 2027 in IT/Computer Science, particularly in blockchain, trust management, cybersecurity, e-commerce reputation systems, applied AI, and information systems.

Proposed Research Direction

A Blockchain-Based Trust Management Framework with AI-Assisted Fake Review Detection for E-Commerce Platforms

This proposed research aims to explore how blockchain can support transparent, tamper-resistant, and privacy-aware trust management in e-commerce reputation systems. The project may also investigate AI-assisted methods for detecting fake or unreliable reviews and improving the reliability of trust scores in online marketplaces.

Research Interests

Blockchain-based trust management
E-commerce reputation systems
AI-assisted fake review detection
Privacy and security in digital platforms
Data compression and algorithm design

Selected Publications

AIS
A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array

Mir Lutfur Rahman, Pranta Sarker, and Ahsan Habib

In Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore., May 2020

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The evaluation of Huffman-based algorithm can be measured in two ways: one in terms of space and another is decoding speed. As memory requirement is going to be reasonable, the time effectiveness of Huffman decoding is more significant. So, this research concentrated on making the decoding speed faster for Huffman codes as well as maintaining the compression ratio for text data. For this, a new technique based on an adjacent distance array is introduced here. Adjacent distance array can save the distances for each adjacent symbol by calculating their ASCII values. Also, we have used a threshold value so that we can skip large codeword for all the adjacent symbols that would be produced by traversing whole Huffman tree. During the decoding process, the adjacent distance array is used to decode adjacent symbols by computing the distances instead of traversing the whole Huffman tree. We have described the encoding and decoding processes for the proposed technique. Besides, we have tested the proposed method on a few renowned corpuses and compared the performance with existing Huffman-based methods. Therefore, we have found that the proposed method performs more effectively in terms of compression–decompression time.
@incollection{10.1007/978-981-15-3607-6_25, title = {{A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array}}, year = {2020}, booktitle = {Proceedings of International Joint Conference on Computational Intelligence. Algorithms for Intelligent Systems. Springer, Singapore.}, author = {Rahman, Mir Lutfur and Sarker, Pranta and Habib, Ahsan}, editor = {Uddin, Mohammad Shorif and Bansal, Jagdish Chand}, pages = {309--316}, publisher = {Springer Nature,}, month = may, doi = {https://doi.org/10.1007/978-981-15-3607-6_25}, address = {Singapore}, isbn = {978-981-15-3607-6}, dimensions = {true}, }
IJCDS
Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly

Pranta Sarker, and Mir Lutfur Rahman

International Journal of Computing and Digital Systems, Jan 2022

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This research works on high symbolic Bengali text and transforms it into corresponding less symbolic English complying with the transliteration method. The Huffman-based approaches serve to compress retaining the original quality of the data. On the other hand, faster encoding and decoding is the most sophisticated sphere in data compression. We propose an adjacent distance array, a novel data structure based on the Huffman principle for encoding and decoding the character of transliterated text. The encoding and decoding algorithms have been explained for the introduced modus operandi and juxtaposed with conventional Huffman-based algorithms. Our research is outdoing than any regular Huffman-based algorithms, concentrating on the speed of the encoding and decoding manner discovered after estimating all decisions.
@article{Sarker2022MethodSwiftly, year = {2022}, month = jan, journal = {International Journal of Computing and Digital Systems}, author = {Sarker, Pranta and Rahman, Mir Lutfur}, number = {1}, pages = {595--608}, volume = {11}, publisher = {International Journal of Computing and Digital Systems,}, doi = {https://dx.doi.org/10.12785/ijcds/110148}, dimensions = {true}, }