Md Shajalal - Research

My Research Interests:

- - Web Search Technology, Information Retrieval, Natural Language Processing, Sentiment Analysis and Machine Learning

Publication List (Google Scholar, Scopus, Publon, DBLP, Orcid ):

International Journal

- 1. Md Shajalal and Masaki Aono, “Coverage-based Query subtopic Diversification Leveraging Semantic Relevance” Knowledge and Information Systems 62, pp 2873–2891 (2020) Springer-Verlag London Ltd., part of Springer Nature 2020 , April 2020, [PDF] (Q1 Journal, IF:2.39).
  2. Md Shajalal and Masaki Aono, "Semantic Textual Similarity between Sentences Using Bilingual Word Semantics" Progress in Artificial Intelligence, Volume-8, Issue-2, pp(263-272) Springer-Verlag GmbH Germany, part of Springer Nature, Jun 2019 [Q2 Journal] [PDF]
  3. Md Atabuzzaman, Md Shajalal, M Elius Ahmed, Masud Ibn Afzal and Masaki Aono, "Leveraging grammatical roles for measuring semantic similarity between texts" IEEE Access, IEEE, 2021 [IF: 3.74, Q1] [In Press]
  4. Md Shajalal, Petr Hajek and Mohamad Zoynul Abedin, "Product Backorder Prediction with Deep Neural Networks on Imbalanced Data" International Journal of production research, Taylor and Francis Volume-8, Issue-2, pp(263-272) Springer-Verlag GmbH Germany, part of Springer Nature, Jun 2019 [IF: 4.57, Q1] [PDF]

International Conference [peer-reviewed]

- 1. Md Zia Ullah, Md Shajalal, Abu Nowshed Chy, and Masaki Aono, "Query Subtopic Mining Exploiting Word Embedding for Search Result Diversification", Asia Information Retrieval Symposium, pp(308-314), Lecture Notes in Computer Science, Springer-Verlag GmbH Germany, part of Springer Nature.[PDF] [Best Presentation Award]
  2. Md Shajalal, Md Zia Ullah, Abu Nowshed Chy, and Masaki Aono, "Query Subtopic Diversification based on Cluster Ranking and Semantic Features", IEEE International Conference on Advanced Informatics: Concepts, Theory and Application (IEEE ICAICTA 2016), pp(1-6), August 13-16, Penang, Malaysia, 2016. [PDF]
  3. Md Shajalal, Masaki Aono, and Muhammad Anwarul Azim, "Aspect-based Query Expansion for Search Results Diversification", The 7th IEEE International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition (ICIEVE-icIVPR 2018), pp(147~152) Kitakyushu, Fukuaka, Japan [PDF]
  4. Md Shajalal and Masaki Aono, "Semantic Sentence Modeling for Learning Textual Similarity Exploiting LSTM" Proceedings of 2nd International conference on cyber security and computer science 2020 (ICONCS2020), Springer Nature Lecture Notes LNICST, Springer-Verleg GmbH Germany. [PDF]
  5. Md Atabuzzaman and Md Shajalal, "Measuring semantic similarity of Bengali texts with Parts-of-Speech tags and word-level semantics" 23rd IEEE International Conference on Computer and Information Technology, IEEE ICCIT2020, AUST, Dhaka 2020 [PDF]
  6. Md Elius Ahmed, Md Shajalal, Md. Atabuzzaman and Masaki Aono, "Semantic similarity based on word recurrence ratio focusing on WordNet" 23rd IEEE International Conference on Computer and Information Technology, IEEE ICCIT2020, AUST, Dhaka 2020 [PDF]
  7. Md Atabuzzaman, Md Shajalal and Masaki Aono, "Semantic Representation of Sentences Employing an Automated Threshold", 10th IEEE ICIEV 2021, Japan. [Accepted]
  8. Md Shajalal and Masaki Aono, "Sentence-Level Semantic Textual Similarity Using Word-Level Semantics" The 10th IEEE International Conference on Computer and Electrical Engineering 2018 (ICECE 2018), pp(113~116), BUET, Dhaka, Bangladesh, 20~22 December 2018. [PDF]
  9. Md Shajalal and Masaki Aono "Semantic Textual Similarity in Bengali Text", IEEE International Conference on Bangla Speech and Language Processing (IEEE ICBSLP 2018), SUST, Sylhet, Bangladesh, Sep. 2018. [PDF]
  10. Md Zia Ullah, Md Shajalal, and Masaki Aono, "KDEIM at NTCIR-12 IMine-2 Search Intent Mining Task: Query Understanding Through Diversified Ranking of Subtopics", Proceedings of NTCIR-12, pp(60-63) , NII, 2016. [PDF]
  11. Abu Nowshed Chy, Md Zia Ullah, Md Shajalal, and Masaki Aono, KDETM at NTCIR-12 Temporalia Task: Combining a Rule-based Classifier with Weakly Supervised Learning for Temporal Intent Disambiguation, Proceedings of NTCIR-12, pp(281-284), NII, 2016. [PDF]

Book Chapter

- 1. Md Shajalal and Masaki Aono, Health Information retrieval, Book Chapter on Signal Processing Techniques for Computational Health Informatics, 192(2020) Springer Nature Switzerland AG [PDF]
  2. Md Shajalal and MZ Abedin “Book Chapter: Handling Class Imbalanced Data in Business Domain” Book: The Essentials of Machine Learning in Finance and Accounting, Routledge, Taylor Francis [PDF]

Thesis

- - Mining and Diversifying Query Subtopics from Web Documents Focusing on Sematic Features, Submitted as a requirement for the degree of Master of Engineering in the Department of Computer Science and Engineering, Toyohashy University of Technology, Toyohashi, Aichi, Japan Supervisor: Professor Masaki Aono, Ph.D. (USA)
  - Mining Query Subtopic for Search Result Diversification, Submitted as a research progress report to the Department of Computer Science and Engineering, Toyohashy University of Technology, Toyohashi, Aichi, Japan Supervisor: Professor Masaki Aono, Ph.D. (USA)
  - Ensuring Security of the Centralized Persistent Computing System, Submitted as a requirement for the degree of B.Sc. (Engg.) in the Department of Computer Science and Engineering, University of Chittagong, Bangladesh. Supervisor: Professor Muhammad Anwarul Azim

Current Projects:

- - Search Result Diversification: With the gigantic size of the Web, ignoring the information needs underlying a query can misguide the search engine. To tackle this problem, an effective approach is to diversify the search results for the query. It aims to select diverse documents considering the information needs or subtopics for the query in order to cover as many search intents as possible. The objective of this project is to diversify the search results exploiting coverage-based subtopic modeling.
  - Query Subtopic Mining: Generally, users are laconic in describing their search intention when submitting query into the search engine. Therefore, a large number of search queries are usually short, ambiguous, and have multiple interpretations. The task of identifying possible subtopics with diverse intents underlying such queries is known as subtopic mining, where a subtopic string is a query that specializes and/or disambiguates the search intent of the original query. This project is aimed at mining and diversifying query subtopics underlying a query.
  - Semantic Textual Similarity: Estimating semantic textual similarity between sentences is indispensable for many information retrieval tasks. Traditional lexical similarity measures cannot compute the similarity beyond a trivial level. In this project, we aim to capture the inner meaning of the sentences and estimate the semantic similarity between the texts. In this regard, we are doing experiments using different deep learning based techniques.
  - NTCIR-13 WWW subtask: Ad hoc Web Search Task (English)