Md Shajalal, PhD

Research:

Research Interests:

• • • EXplainable AI (XAI), Human-centered AI, Natural Language Processing (NLP), Information Retrieval (IR), Applied Machine/Deep Learning

Publication List (Google Scholar, Scopus, Web of Sceince, DBLP, Orcid ):

International Journal

• • 1. Md Shajalal, Alexander Boden and Gunnar Stevens, “ForecastExplainer: Explainable Household Energy Demand Forecasting by Approximating Shapley Values Using DeepLIFT”, Technological Forecasting and Social Changes, Elsevier (Impact Factor: 12.9, Q1) [PDF]

    2. Chiara Tellarini, Md Shajalal, Nico Castelli, Martin Stein, Alexander Boden, Toke Haunstrup Christensen, "A mixed-method approach to study the impacts of energy micro-generation combined with appliance-level feedback on everyday practices", Energy Efficiency, Springer [PDF]

    3. Md Shajalal, Milad Bohlouli, Hari Prasanna Das, Alexander Boden and Gunnar Stevens, "Improved Thermal Comfort Model Leveraging Conditional Tabular GAN Focusing on Feature Selection", IEEE Access, IEEE  [IF: 3.9, Q1] [PDF]

    4. Md. Rezaul Karim, Tanhim Islam, Md Shajalal, Oya Beyan, Christoph Lange, Michael Cochez, Dietrich Rebholz-Schuhmann, Stefan Decker,  ''Explainable AI for Bioinformatics: Methods, Tools, and Applications", Briefings in Bioinformatics, Oxford  University Press, Oxford Acedemic. [IF13.994, Q1] [PDF]

    5. Md Shajalal, Alexander Boden and Gunnar Stevens, "Explainable Product Backorder Prediction Exploiting CNN: Introducing Explainable Models in Businesses", Electronic Markets, Springer-Nature [Q1, IF: 8.5, PDF]

    6. 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, IF:2.39].

    7. 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  [PDF, IF: 4.20, Q1]

    8. 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 [PDF, IF: 3.74, Q1] 

    9. 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), Jun 2019 [PDF, IF: 9.018, Q1] 

    10. Machbah Uddin, Mohammad Aminul Islam, Md Shajalal, Afzal Hossain and Sayeed Iftekhar, "Paddy Seed Variety Identification Using T20-HOG and Haralick Textural Features", Complex and Intelligent Systems, volume 8, pages 657–671 (2022), Springer-Nature [PDF, IF: 4.927]. 

    11. Mohammad Aminul Islam, Machbah Uddin, Md Rakib Hasan and Md Shajalal, "Germinative Paddy Seed Identification Using Deep Convolutional Neural network", Multimedia Tools and Applications, Springer-Nature [IF: 3.6] [PDF].

International Conference [peer-reviewed]

• • 1. 1. Maksuda Bilkis Baby, Md Atabuzzman, Md Shajalal, "Attention-based Code Summarization for Multi-label Vulnerability Detection in Source Code", 24th International Conference on Cryptology and Network Security (CANS2025), Osaka, Japan, Lecture Notes in Computer Science. (Conditionally accepted). 

       2. Jacob Larock, Md Shajalal and Gunnar Stevens, "Interpretable Deepfake Voice Detection: A Hybrid Deep-Learning Model and Explanation Evaluation", 3rd World Conference in Explainable Artificial Intelligence (xAI2025) [Publication In process].

       3. Md Shajalal, Md Atabuzzaman, Alexander Boden, Gunnar Stevens and Delong DU, "Do the Explanations Make Sense? Explainable Fake Review Identification and Users’ Perspectives on Explanations", 3rd World Conference in Explainable Artificial Intelligence (xAI2025) [Publication In process].

       4. Shamima Rayhana, Md Shajalal, Md Atabuzzaman and Gunnar Stevens, "Interpretable Sexism Detection with Explainable Transformers", 3rd World Conference in Explainable Artificial Intelligence (xAI2025), [Publication In process].

       5. Md Shajalal, Alexander Boden, Gunnar Stevens, Delong Du, Dean-Robin Kern, "Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments", The 2nd International Conference on eXplainable Artificial Intelligence (xAI2024), Malta, Springer LNCS  [PDF]

       6. Md. Atabuzzaman, Md Shajalal, Maksuda Bilkis Baby, and Alexander Boden, "Arabic Sentiment Analysis with Noisy Deep Explainable Model", 7th International Conference on Natural Language Processing and Information Retrieval (ACM NLPIR2023), ACM, Seul, South Korea. (Accepted) [Preprint] (Equal Contribution)

       7. Md. Rezaul Karim, Md. Shajalal, Alex Graß, Till Döhmen, Sisay Adugna Chala, Christian Beecks, Stefan Decker, "Interpreting Black-box Machine Learning Models for High Dimensional Datasets", 10th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA2023), Thessaloniki, Greece, [Core Rank A]Preprint on ArchXiv [PDF]

       8. Md. Rezaul Karim, Comet, Md. Shajalal, P. de Perthuis, D. Rebholz-Schuhmann, Stefan Decker, "From Large Language Models (LLMs) to Knowledge Graphs for Biomarker Discovery in Cancer", the 57th Hawaii International Conference on System Sciences (HICSS 2023), Hawai, USA. [Core Rank A]] 

       9. Md Atabuzzaman, Baby Maksuda Bilkis, Md Shajalal, "Transformer-based Bengali Textual Emotion Recognition", Proceedings of the 20th International Conference on Natural Language Processing (ICON), ACL Anthology, India 2023 [PDF]

       10. Md Shajalal, Sebastian Denef, Md. Rezaul Karim, Alexander Boden and Gunnar Stevens, ``Unveiling the Black Box: Explainable Deep Learning Models for Patent Classification'', The 1st World Conference on eXplainable Artificial Intelligence (xAI 2023), Lisbon, Portugal, Springer LNCS, [PDF]

       11. Md Shajalal, Milad Bohlouli, Hari Prasanna Das, Alexander Boden, and Gunnar Stevens, "Focus on What Matters: Improved Feature Selection Techniques for Personal Thermal Comfort Modelling" The 9th ACM International Conference on Systems for Energy-Efficient Built Environments (ACM BuildSys 2022), Boston, Massachusetts, USA. [PDF]

       12. Md Shajalal, Md. Atabuzzaman, Maksuda Bilkis Baby, Md Rezaul Karim and Alexander Boden,  "Textual Entailment Recognition with Semantic Features from Empirical Text Representation", International Conference on Speech and Language Technologies SPELLL 2022, Tamil nadu, India.

       13. Md Rezaul Karim, Sumon Kanti Dey, Tanhim Islam, Md Shajalal, Bharathi Raja Chakravarthi, "Multimodal hate speech detection from bengali memes and texts" , International Conference on Speech and Language Technologies SPELLL 2022, Tamil nadu, India.

       14. Maksuda Bilkis Baby, Bushra Ankhari, Md Shajalal, Md Atabuzzaman, Fazle Rabbi, Masud Ibn Afjal, "Identifying Duplicate Questions Leveraging Recurrent Neural Network",  4th International Conference on Trends in Computational and Cognitive Engineering (TCCE-2022) [Accepted]

       15. Md Shajalal, Alexander Boden, Gunnar Stevens "Towards User-centered Explainable Energy Demand Forecasting System", Thirteenth ACM International Conference on Future Energy Systems 2022 (ACM e-Energy'22), Massachusetts, USA [PDF]

       16. Kern Dean-Robin, Stevens Gunnar, Dethier Erik, Naveed Sidra, Alizadeh Fatemeh, Du Delong, "Md Shajalal, Peeking Inside the Schufa Blackbox: Explaining the German Housing Scoring System",  ACM CHI 2023 Workshop on Human-Centered Explainable AI (HCXAI), collocated with ACM CHI2023,  Hamburg, Germany. [PDF]

       17. Lu Jin, Alexander Boden, and Md Shajalal "Automated Decision Making Systems in Smart Homes: A Study on User Engagement and Design", ACM CHI Conference on Human Factors in Computing Systems 2022,  AutomationXP22: Engaging with Automation, New Orleans, LA USA [PDF]

       18. 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]

       19. 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]

       20. 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]

       21. 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] 

       22. 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]

       23. 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]

       24. Md Atabuzzaman, Md Shajalal and Masaki Aono, "Semantic Representation of Sentences Employing an Automated Threshold",  10th IEEE EEE International Conference on Informatics, Electronics and Vision 2021 (IEEE ICIEV 2021), Japan. [PDF]

       25. 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 (IEEE ICECE 2018), pp(113~116), BUET, Dhaka, Bangladesh, 20~22 December 2018. [PDF]

       26. 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]

       27. 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]

       28. 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 “Handling Class Imbalanced Data in Business Domain” Book: The Essentials of Machine Learning in Finance and Accounting, Routledge, Taylor Francis [PDF]

Thesis

• • • Towards Human-Centered Actionable Explainable AI-enabled Systems, PhD Dissertation, University of Siegen, Germany.

    • 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, Toyohashi 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)

Current Projects:

• XAI in Smart Domestic Technology: This project aimed at applying eXplainable Artificial Intelligence (XAI) for smart, domestic technology focusing user preference. Currently, we are working on developing a energy-aware human-centric energy recommender system.

• Thermal Comfort preference Prediction: Occupants' personal thermal comfort (PTC) is indispensable for their well-being, physical and mental health, and work efficiency. The heating system controlled by Artificial intelligence (AI) can calibrate the indoor thermal condition automatically by analyzing different physiological and environmental variables. Predicting occupants' personal thermal comfort preferences in a smart home can be a prerequisite to adjusting the indoor temperature that might provide a comfortable environment. In this project, we focus closely on thermal preference prediction.

• Community Question Answering and Retrieval:

Previous 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-12 IMINE-2 Subtask: Mining Subtopics for user's query [Website]

    • NTCIR-13 WWW subtask: Ad hoc Web Search Task (English)

Teaching

Taught Courses

Major Course in Computer Science

• Software Engineering, Bachelor in Computer Science, HSTU

• Web Engineering, Bachelor in Computer Science, HSTU

• Web Engineering Project, Bachelor in Computer Science, BAUST

• Computer Programming Language II, Bachelor in Physics, HSTU

• Computer Programming II, Bachelor in Food Processing, HSTU

• Introduction to Computer Science,  Bachelor in Agriculture, HSTU

• Object Oriented Programming, Bachelor in Computer Science, BAUST

• Technical Writing and Presentation, Bachelor in Computer Science, BAUST

• Computer Networks, Bachelor in Computer Science, BAUST

• Neural Networks, Masters in Computer Science, BAU

• Object Oriented Programming, Bachelor in Computer Science, BAUST

• Computer Applications, Bachelor in Agriculture, BAU

Supervised Students

• • • 1. Amey, Masters Thesis

      2. Jacob Llarock, Bachelor thesis

      3. Shamima Rayhana, Undergraduate Thesis

      4. Maksuda Bilkis, Undergraduate Thesis

      5. Bushra Ankhari, Undergraduate Thesis

      6. Md Atabuzzaman Babu, Undergraduate Thesis

      7. Md Elius Ahmed Miraz, Undergraduate Thesis

      8. Amrita Dash Gupta, Undergraduate Thesis

      9. Taukir Ahmed, Undergraduate Thesis

Resources

Bengali STS Data Set

Dataset: Semantic Textual Similarity in Bengali Text

The textual similarity between texts is an important and mandatory task in many applications in information retrieval. The performance of many natural language processing (NLP)  applications such as text summarization, machine translation, plagiarism detection, sentiment analysis etc. is also dependent on the textual and semantic similarity. There are also some other applications that used the similarity such as relevance feedback, text classification, word sense disambiguation, subtopic mining, web search and so on.  

Task Definition: Given two Bengali sentences,  proposed methods need to return a continuous valued similarity score on a scale from 0 to 5, with 0 indicating that the semantics of the sentences are completely independent and 5 signifying semantic equivalence. Performance is assessed by computing the Pearson correlation between machine assigned semantic similarity scores and human judgments. For details please refer to our paper title "Semantic Textual Similarity in Bengali Text"  [Paper Link]

If you use this dataset, please don't forget to cite our following paper: [Cite This Paper]

Information Retrieval Resources

• • • Information retrieval resources-Stanford University 

    • Resources - Special Interest Group of Information Retrieval (SIGIR)

    • Information Retrieval Resources - University of Maryland

    • Information Retrieval Resources - Niraj

    • Numerous Dataset for information Retrieval Task

    • Text Retrieval Conference TREC

    • National Institute of Informatics (NII) Test Collections for IR Systems

Web Search [Ad-hoc and Diversifed]

• • • Text Retrieval Conferences (TREC) Web Track Guidelines (2009~2013)

    • Dataset- TREC Web Track [2009, 2010, 2011, 2012, 2013]

    • Clueweb Document Collection [Clueweb09,  Clueweb12]

    • NTCIR 13 WWW

Diversified Subtopic Mining

• • • NTCIR 10 INTENT

    •  NTCIR11 IMINE1

    •  NTCIR 12 IMINE2

Natural Language Processing

• • • Natural Language Processing with Deep Learning

    • Natural Language Processing Tasks and Selected References

    • Deep Learning Application for Natural Language Processing

    • CNN for Natural Language Processing

    • NLTK Copora

Sentiment Analysis

• • • Dataset- Sentiment Classification, TreeBank, IMDB

    • Book -  A Practical Guide to Sentiment Analysis

Advice on Research and Writing [From CMU, USA]