Research Background

My undergraduate thesis, A Faster Decoding Technique for Huffman Codes Using Adjacent Distance Array, focused on data compression and Huffman coding. The research introduced an adjacent distance array data structure to improve encoding-decoding time for English corpora.

The work was later extended through a transliteration-based approach in the paper Introduction to Adjacent Distance Array with Huffman Principle: A New Encoding and Decoding Technique for Transliteration Based Bengali Text Compression. This study explored how Bengali text could be transliterated into corresponding English alphabets or ASCII symbols and then compressed using the adjacent distance array with Huffman principle.

Further experimental evidence showed that the proposed method outperformed conventional Huffman-based approaches in terms of compression-decompression time for Bengali transliterated text. This outcome was presented in the article Method of Adjacent Distance Array Outperforms Conventional Huffman Codes to Decode Bengali Transliterated Text Swiftly, published in a Scopus-indexed journal.

More recently, the method was applied to short text message compression. The study evaluated SMS data using a benchmark corpus and demonstrated that the adjacent distance array approach could improve SMS compression performance. This work was published in 2024 as A Huffman-Based Short Message Service Compression Technique Using Adjacent Distance Array in the International Journal of Information and Communication Technology, a Scopus-indexed journal.

Current Research Transition

This research background has strengthened my skills in algorithm design, data structure development, experimental evaluation, and technical publication. Building on this foundation, my current research interest has shifted toward 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.