Department of Computer Science and Engineering
Mr. Mayen Uddin Mojumdar  Assistant Professor
Name :
Mr. Mayen Uddin Mojumdar
Designation
Assistant Professor  
Department
Department of Computer Science and Engineering
Faculty
Faculty of Science and Information Technology
Personal Webpage
https://faculty.daffodilvarsity.edu.bd/profile/cse/mayen.html
E-mail
mayen.cse@diu.edu.bd
Phone
+8809617901233
Cell-Phone
+88 01402113506
  • Ph.D (Ongoing)  in Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh
  • M.Sc. in Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh
  • B.Sc. in Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

 

  • Teachers’ Training Program at Daffodil International University.
  • Faculty Development Program on OBE Activities and Accreditation by Daffodil International University.
  • Teacher Training By HRDI Diu : Learner Centric classroom
  • Teacher Training By HRDI Diu: Doers Bootcamp
  • Computer Vision
  • Digital Image Processing
  • Database Management Systems (DBMS)
  • Data Structures
  • Web Engineering
  • Operating Systems
  • Data Mining
  • Artificial Intelligence
  • Introduction to Biology and Chemistry for Computation
  • Research and Innovation Technical Writing

Journal Articles

  1. M. H. Meraj, M. Tajbid, M. U. Mojumdar, N. R. Chakraborty, M. J. M. Chowdhury, and K. Biswas, “CAE-BrainNet: A statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI,” Brain Informatics, vol. 13, no. 1, p. 40, Jul. 2026. doi: 10.1186/s40708-026-00325-x.
  2. M. J. Ahmed, R. Saha, A. K. Dutta, M. U. Mojumdar, and N. R. Chakraborty, “Banglaveg: A curated vegetable image dataset from bangladesh for precision agriculture,” Data in Brief, vol. 59, p. 111 441, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111441.
  3. M. Dutta, M. Uddin Mojumdar, M. Alamgir Kabir, N. Ranjan Chakraborty, S. Md Tanvir Siddiquee, and S. Abdullah, “Leu3: An attention augmented-based model for acute lymphoblastic leukemia classification,” IEEE Access, vol. 13, pp. 31 630–31 642, 2025. doi: 10.1109/ACCESS.2025.3542609.
  4. S. Howlader, M. S. Ahamed, M. U. Mojumdar, S. R. H. Noori, S. M. T. Siddiquee, and N. R. Chakraborty, “A comprehensive image dataset for the identification of eggplant leaf diseases and computer vision applications,” Data in Brief, vol. 59, p. 111 353, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111353.
  5. J. T. Jinia, M. S. Rahman, M. U. Mojumdar, and M. H. I. Bijoy, “Roseleafset: Real-world leaf image dataset for ai-based agricultural solutions,” Data in Brief, vol. 63, p. 112 108, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.112108.
  6. M. H. Mahi, A. R. Khan, M. H. Anik, S. R. H. Noori, A. Mahmud, and M. U. Mojumdar, “Meld: A multilingual ethnic dataset of chakma, garo, and marma in bengali script with english and standard bengali translation,” Data in Brief, vol. 61, p. 111 745, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111745.
  7. M. H. Mahi, A. R. Khan, Z. Hoque, and M. U. Mojumdar, “Feniverse: A parallel corpus of feni dialect, standard bengali, and english,” Data in Brief, p. 112 250, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.112250.
  8. M. H. Mahi, A. R. Khan, and M. U. Mojumdar, “Bangladial: A merged and imbalanced text dataset for bengali regional dialect analysis,” Data in Brief, vol. 63, p. 112 200, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.112200.
  9. M. U. Mojumdar, S. Islam, M. A. Mamun, R. Hasan, S. M. T. Siddiquee, and N. R. Chakraborty, “Afruitdb: A comprehensive dataset of six commonly used asian fruits for advanced grading and biodiversity insights,” Data in Brief, vol. 59, p. 111 380, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111380.
  10. M. U. Mojumdar, D. Sarker, M. Assaduzzaman, et al., “Anadetect: An extensive dataset for advancing anemia detection, diagnostic methods, and predictive analytics in healthcare,” Data in Brief, vol. 58, p. 111 195, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2024.111195.
  11. M. U. Mojumdar, D. Sarker, M. Assaduzzaman, et al., “Maternal health risk factors dataset: Clinical parameters and insights from rural bangladesh,” Data in Brief, vol. 59, p. 111 363, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111363.
  12. R. I. Rahat, M. S. Hossain, M. U. Mojumdar, and N. R. Chakraborty, “Dataset of seven tropical flower species from bangladesh: A resource for classification and ecological studies,” Data in Brief, vol. 59, p. 111 374, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111374.
  13. P. C. Sarkar, G. K. Pranta, M. U. Mojumdar, A. Mahmud, S. R. H. Noori, and N. R. Chakraborty, “Udcad-dfl-dl: A unique dataset for classifying and detecting agricultural diseases in dragon fruits and leaves,” Data in Brief, vol. 59, p. 111 411, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111411.
  14. A. R. Sayeem, J. F. Omi, M. Hasan, M. U. Mojumdar, and N. R. Chakraborty, “Iddmsld: An image dataset for detecting malabar spinach leaf diseases,” Data in Brief, vol. 58, p. 111 293, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111293.
  15. M. R. Shihab, N. I. Saim, M. U. Mojumdar, et al., “Image dataset for classification of diseases in guava fruits and leaves,” Data in Brief, vol. 59, p. 111 378, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111378.
  16. M. M. H. Shoib, S. Saeem, A. B. A. Tonima, and M. U. Mojumdar, “Idbgl: A unique image dataset of black gram (vigna mungo) leaves for disease detection and classification,” Data in Brief, vol. 59, p. 111 347, 2025, issn: 2352-3409. doi: https://doi.org/10.1016/j.dib.2025.111347.
  17. M. Dutta, M. R. I. Sujan, M. U. Mojumdar, et al., “Rice leaf disease classification—a comparative approach using convolutional neural network (cnn), cascading autoencoder with attention residual u-net (caar-u-net), and mobilenet-v2 architectures,” Technologies, vol. 12, no. 11, p. 214, 2024, issn: 2227-7080. doi: 10.3390/technologies12110214.

 

Conference Proceedings

  1. S. A. Akhy, H. Shouno, M. U. Mojumdar, et al., “Mpicam: A deep learning based real-time medicinal plant identification system for rural healthcare in bangladesh,” in Distributed Computing, HPC, Cloud Computing, Quantum Technologies, Embedded Systems, Modeling, and Simulation, H. R. Arabnia, L. Deligiannidis, M. Takata, et al., Eds., Cham: Springer Nature Switzerland, 2026, pp. 516–528, isbn: 978-3-032-22193-3.
  2. T. E. Alahi, S. D. Dip, M. U. Mojumdar, N. R. Chakraborty, and N. Haldar, “Image-based diagnosis of bean plant health: Detection and classification of diseases on beans leaves,” in 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–5. doi: 10.23919/INDIACom66777.2025.11115399.
  3. S. D. Dip, S. Mahapatra, M. J. Naeen, M. U. Mojumdar, R. S. R, and S. K. Banshal, “Automated disease identification in mango leaves using image analysis,” in 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–7. doi: 10.23919/INDIACom66777.2025.11115239.
  4. S. A. Jisan, S. K. Das, M. J. Naeen, N. Haldar, N. R. Chakraborty, and M. U. Mojumdar, “Advancing cauliflower leaf freshness and disease classification using deep learning models,” in 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–6. doi: 10.23919/INDIACom66777.2025.11115631.
  5. A. R. Khan, M. H. Mahi, M. U. Mojumdar, N. R. Chakraborty, M. A. Kabir, and A. N. Boruah, “Transformer-based model for identifying ethnic languages in bangladesh,” in 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–6. doi: 10.23919/INDIACom66777.2025.11115169.
  6. Z. M. Koli, M. J. Alam, S. A. Sharif, M. R. Mondol, M. U. Mojumdar, and M. M. Rahman, “Comparative analysis of deep learning models for automated mango pest classification with grad-cam explainability,” in 2025 28th International Conference on Computer and Information Technology (ICCIT), 2025, pp. 191–196. doi: 10.1109/ICCIT68739.2025.11490140.
  7. M. A. Mamun, S. M. T. Siddiquee, M. U. Mojumdar, R. K. R. Sarker, and S. K. Banshal, “A machine learning based approach to predict the effects of video games on youth behavior,” in 2025 12th International Conference on Computing for Sustainable Global Development (INDIACom), 2025, pp. 1–6. doi: 10.23919/INDIACom66777.2025.11115823.
  8. R. Ayon, A. S. Mubin, A.-A. G. Hridoy, M. U. Mojumdar, N. R. Chakraborty, and M. J. Islam, “Deep learning for tea leaf disease detection: Comparing vgg16, vision transformer, efficientnetb3, and xception,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–6. doi: 10.1109/ICCCNT61001.2024.10726203.
  9. S. K. Das, M. J. Naeen, M. J. Islam, M. A. H. Sajeeb, N. R. Chakraborty, and M. U. Mojumdar, “Improving bangla linguistics: Advanced lstm, bi-lstm, and seq2seq models for translating sylheti to modern bangla,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–7. doi: 10.1109/ICCCNT61001.2024.10723886.
  10. S. Dip, T. E. Alahi, M. Tajwar, M. A. Dewan, M. U. Mojumdar, and N. R. Chakraborty, “Empowering regional language: Nlp-driven conversion from rangpur dialect to standard bengali language,” in 2024 27th International Conference on Computer and Information Technology (ICCIT), 2024, pp. 2689–2694. doi: 10.1109/ICCIT64611.2024.11022045.
  11. M. Ferdous, M. U. Mojumdar, N. R. Chakraborty, and T. Bhuiyan, “Air pollution or gases behind toxicity for people awareness,” in Proceedings of the 2nd International Conference on Big Data, IoT and Machine Learning, M. S. Arefin, M. S. Kaiser, T. Bhuiyan, N. Dey, and M. Mahmud, Eds., Singapore: Springer Nature Singapore, 2024, pp. 951–960, isbn: 978-981-99-8937-9.
  12. N. M. Pallab, M. U. Mojumdar, N. R. Chakraborty, L. Vetrivendan, and S. Akteri, “Machine learning based diagnosis of kidney abnormality recognition on ct scan images,” in 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024, pp. 1283–1289. doi: 10.23919/INDIACom61295.2024.10498420.
  13. M. M. Rahman, M. U. Mojumdar, M. M. Jamil, N. R. Chakraborty, R. Hasan, and V. Gupta, “Flower identification by deep learning approach and computer vision,” in 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024, pp. 1355–1360. doi: 10.23919/INDIACom61295.2024.10498434.
  14. M. M. Rahman, M. U. Mojumdar, H. A. Shifa, N. R. Chakraborty, N. P. Stenin, and M. A. Hasan, “Anemia disease prediction using machine learning techniques and performance analysis,” in 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024, pp. 1276–1282. doi: 10.23919/INDIACom61295.2024.10498962.
  15. T. A. Rimi, T. K. Readoy, M. T. Chowdhury, S. R. H. Noori, N. R. Chakraborty, and M. U. Mojumdar, “Efficient water pliability prediction for human consumption-exploratory data analysis (eda) and multi algos,” in 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2024, pp. 1–7. doi: 10.1109/ICCCNT61001.2024.10724388.
  16. H. A. Shifa, M. U. Mojumdar, M. M. Rahman, N. R. Chakraborty, and V. Gupta, “Machine learning models for maternal health risk prediction based on clinical data,” in 2024 11th International Conference on Computing for Sustainable Global Development (INDIACom), 2024, pp. 1312–1318. doi: 10.23919/INDIACom61295.2024.10498822.
  17. I. Haque, M. U. Mojumdar, N. R. Chakraborty, M. S. Rana, S. M. T. Siddiquee, and M. M. H. Ashik, “Malabar nightshade disease detection using deep learning technique,” in Sustainable Advanced Computing, S. Aurelia, S. S. Hiremath, K. Subramanian, and S. K. Biswas, Eds., Singapore: Springer Singapore, 2022, pp. 173–183, isbn: 978-981-16-9012-9.
  18. M. S. Keya, M. U. Emon, H. Akter, M. A. M. Imran, M. K. Hassan, and M. U. Mojumdar, “Predicting performance analysis of garments women working status in bangladesh using machine learning approaches,” in 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 602–608. doi: 10.1109/ICICT50816.2021.9358696.
  19. M. U. Mojumdar and N. R. Chakraborty, “Orange orange leaves diseases detection using computerized techniques,” in 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2021, pp. 1–4. doi: 10.1109/ICCCNT51525.2021.9579964.
  20. M. U. Mojumdar, S. R. Haider Noori, and F. Faisal, “Malabar nightshade disease detection and recognition using transfer learning,” in 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 2021, pp. 637–641. doi: 10.1109/ICCCA52192.2021.9666348.
  21. T. Y. Orin, M. U. Mojumdar, S. M. T. Siddiquee, and N. R. Chakraborty, “Cauliflower leaf disease detection using computerized techniques,” in 2021 IEEE 6th International Conference on Computing, Communication and Automation (ICCCA), 2021, pp. 730–733. doi: 10.1109/ICCCA52192.2021.9666437.
  22. T. Khan, F. Arafat, M. U. Mojumdar, A. Rajbongshi, S. M. T. Siddiquee, and N. R. Chakraborty, “A machine learning approach for predicting the sunspot of solar cycle,” in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT), 2020, pp. 1–4. doi: 10.1109/ICCCNT49239.2020.9225427.

 

 

Published Datasets

 

  1. Mayen Uddin Mojumdar; Tasnuva Muhtasim; Abdun Nafi Annafi (2025). Bangladeshi Fruit Grade classification. Mendeley Data. DOI: 10.17632/bp9fczshw5.1 https://doi.org/10.17632/bp9fczshw5.1
  2. Mayen Uddin Mojumdar; Supta Das Dip; Tabib E Alahi; Sheikh Tonmoy (2025). Bean Health and Disease Dataset: A Resource for Agricultural Research. Mendeley Data. DOI: 10.17632/3km9d246z2.3 https://doi.org/10.17632/3km9d246z2.3
  3. Pronob Sarkar; Mayen Uddin Mojumdar (2026). Multiclass Image Dataset of Tomato Pathologies: Integrating Leaf and Fruit Samples for Interpretable Deep Learning. Mendeley Data. DOI: 10.17632/xc94xbg239.1 https://doi.org/10.17632/xc94xbg239.1
  4. Mehraj Hossain Mahi; Anzir Rahman Khan; Mobashsher Hasan Anik; Mayen Uddin Mojumdar (2025). MELD: A Multilingual Ethnic Dataset of Chakma, Garo, and Marma in Bengali Script with English and Standard Bengali translation. Mendeley Data. DOI: 10.17632/dy5dyfygbp.4 https://doi.org/10.17632/dy5dyfygbp.4
  5. Md. Julkar Naeen; Sourav Kumar Das; Supta Das Dip; Tabib E Alahi; Md Faisal Tajwar; Abdullah Al Rahat; Samurtha Jahan Ayshe; Ummay Mahjabeen; Md Tanvir Tahmid; Mayen Uddin Mojumdar (2025). Bangla Dialect Dataset: Exploring Linguistic Diversity Across Regions. Mendeley Data. DOI: 10.17632/sm63ryv5dt.1 https://doi.org/10.17632/sm63ryv5dt.1
  6. Abdun Nafi Annafi; Tasnuva Muhtasim; Sheikh Kaif; Tahmeed Mohammad Farhan; Aynun Naher Mow; Mayen Uddin Mojumdar (2025). Automatic waste management dataset. Mendeley Data. DOI: 10.17632/d4vsy9c9wk.2 https://doi.org/10.17632/d4vsy9c9wk.2
  7. Tasnuva Muhtasim; Abdun Nafi; Mayen Uddin Mojumdar (2025). Dataset on Bangladeshi Healthy and Unhealthy Vegetables. Mendeley Data. DOI: 10.17632/3wby28tkcp.2 https://doi.org/10.17632/3wby28tkcp.2
  8. Md Jobayer Ahmed; Ratu Saha; Arpon Kishore Dutta; Mayen Uddin Mojumdar (2025). Vegetable Image Dataset for Classification Models: A Bangladeshi Perspective. Mendeley Data. DOI: 10.17632/b9rvg4f2st.4 https://doi.org/10.17632/b9rvg4f2st.4
  9. Jarin Tasmim Jinia; Md Sakibur Rahman; Mayen Uddin Mojumdar; Md Hasan Imam Bijoy (2025). RoseLeafSet: Real-World Leaf Image Dataset for AI-Based Agricultural Solutions. Mendeley Data. DOI: 10.17632/9g668bfhy5.3 https://doi.org/10.17632/9g668bfhy5.3
  10. Jarin Tasmim Jinia; Sakibur Rahman; Mayen Uddin Mojumdar (2024). A Comprehensive dataset on diseases affecting rose leaves: Identification, Symptoms, and Control Strategies. Mendeley Data. DOI: 10.17632/hmjtdzkfh3.2 https://doi.org/10.17632/hmjtdzkfh3.2
  11. Md Mehedi Hasan Shoib; Shahnewaz Saeem; Afia Benta Aziz Tonima; Mayen Uddin Mojumdar (2024). Image Dataset for Disease Detection in Black Gram (Vigna mungo) Leaves: A Resource for Machine Learning Research. Mendeley Data. DOI: 10.17632/z55yrbmn2d.3 https://doi.org/10.17632/z55yrbmn2d.3
  12. Fahim Faisal Talha; Md Soroar Jahan; Mayen Uddin Mojumdar (2024). Bone Fracture X-ray Dataset: Simple vs. Comminuted Fractures. Mendeley Data. DOI: 10.17632/vg95gvhj3y.3 https://doi.org/10.17632/vg95gvhj3y.3
  13. Md Zinnahtur Rahman Zitu; Shahariar Rahman Shifat; Mayen Uddin Mojumdar (2024). A Benchmark Dataset for Detecting Disease in Plant Leaves: An Essential Resource for Deep Learning Models. Mendeley Data. DOI: 10.17632/v46jkbbzv3.2 https://doi.org/10.17632/v46jkbbzv3.2
  14. Faria Afrose Limpa; Md. Anupam Abir Kolin; Mayen Uddin Mojumdar (2024). Urine Analysis Dataset: Insights into Health Indicators for Kidney, UTI, and Diabetes in Bangladesh. Mendeley Data. DOI: 10.17632/6xhr6sfsr9.1 https://doi.org/10.17632/6xhr6sfsr9.1
  15. Shahria Rahman; Md Forhadul Isalm; Mayen Uddin Mojumdar (2024). Dataset for Detecting Anthracnose in Hog Plum and Bottle Gourd Leaves. Mendeley Data. DOI: 10.17632/k74shn6zyb.1 https://doi.org/10.17632/k74shn6zyb.1
  16. Hasan Al Banna; Md. Fuad Hossain; Mayen Uddin Mojumdar (2024). Papaya Leaf Disease Image Dataset. Mendeley Data. DOI: 10.17632/3kwgxg4stb.1 https://doi.org/10.17632/3kwgxg4stb.1
  17. Istiak Jaman Anan; Tanber Ahmmed; Mayen Uddin Mojumdar (2024). Grape Leaf dataset for leaf Health Classification and Disease Detection. Mendeley Data. DOI: 10.17632/378c69nwk5.1 https://doi.org/10.17632/378c69nwk5.1
  18. Md Ali Emam Al; Md Rishad Chowdhury Fahad; Mayen Uddin Mojumdar (2024). Potato disease classification. Mendeley Data. DOI: 10.17632/ttgtfp3vjb.1 https://doi.org/10.17632/ttgtfp3vjb.1
  19. Rafi Labib; Sanjida Mim; Mayen Uddin Mojumdar (2024). Rice Leaf and Crop Disease Detection Dataset. Mendeley Data. DOI: 10.17632/g7tcwvshff.1 https://doi.org/10.17632/g7tcwvshff.1
  20. Md Shajedur Rahman; Rakibul Hasan; Mayen Uddin Mojumdar (2024). Mango Leaf Disease Dataset. Mendeley Data. DOI: 10.17632/7ghdbftp54.1 https://doi.org/10.17632/7ghdbftp54.1
  21. Md Asraful Sharker Nirob; Prayma Bishshash; A K M Fazlul Kobir Siam; Mayen Uddin Mojumdar (2025). CAIR-BGD-2025: Annotated Dataset for Bottle Gourd Disease & Growth Stages. Mendeley Data. DOI: 10.17632/x6rpc5nzzm.1 https://doi.org/10.17632/x6rpc5nzzm.1
  22. Mayen Uddin Mojumdar; Dhiman Sarker; Md Assaduzzaman; Md. Mohaimenur Rahman; Md. Anisul Haque Sajeeb; Md Shadikul Bari; Shah Md Tanvir Siddiquee; Narayan Ranjan Chakraborty (2024). Pediatric Anemia Dataset: Hematological Indicators and Diagnostic Classification. Mendeley Data. DOI: 10.17632/y7v7ff3wpj.1 https://doi.org/10.17632/y7v7ff3wpj.1
  23. Mayen Uddin Mojumdar; Md Al Mamun; Shahrin Islam; Rifat Hasan (2024). A Dataset of Common Asian Fruits for Quality Grading and Biodiversity Research. Mendeley Data. DOI: 10.17632/bz65dz2pbj.1 https://doi.org/10.17632/bz65dz2pbj.1
  24. Ajoy Saha; Hasnat Hridoy; Mayen Uddin Mojumdar (2024). Bangladeshi Fruit Dataset: Classification and Analysis of Seven Native Species for Agricultural Research. Mendeley Data. DOI: 10.17632/rtxr9djhhg.1 https://doi.org/10.17632/rtxr9djhhg.1
  25. Pronob Sarkar; Gourab Kumar Pranta; Mayen Uddin Mojumdar (2024). Dragon fruit & leaf Dataset from Bangladesh for Classification and Ecological Research. Mendeley Data. DOI: 10.17632/cfchfdpfw5.1 https://doi.org/10.17632/cfchfdpfw5.1
  26. Md Montsir Rahman Shihab; Nafiu Islam Saim; Mayen Uddin Mojumdar (2024). Image Dataset for Detection and classification of Diseases of Guava Fruits and Leaves. Mendeley Data. DOI: 10.17632/fspx44mwfp.1 https://doi.org/10.17632/fspx44mwfp.1
  27. Mayen Uddin Mojumdar; Md Assaduzzaman; Dhiman Sarker; Hasin Arman Shifa; Md. Anisul Haque Sajeeb; Shadikul Bari; Narayan Ranjan Chakraborty; Mohammad Jahangir Alam (2024). Maternal Health Risk Assessment Dataset. Mendeley Data. DOI: 10.17632/p5w98dvbbk.1 https://doi.org/10.17632/p5w98dvbbk.1
  28. Md. Lisan Islam; Mayen Uddin Mojumdar (2024). Carambola Leaf Disease Dataset. Mendeley Data. DOI: 10.17632/jn857kmcnf.1 https://doi.org/10.17632/jn857kmcnf.1
  29. S M Tanvir Anjum Fahim; Momotaz Begum Moon; Mayen Uddin Mojumdar (2024). Tropical Fruit Leaf Disease Detection Dataset: Jujube, Star Fruit, and Guava. Mendeley Data. DOI: 10.17632/6wsm8n8bsh.1 https://doi.org/10.17632/6wsm8n8bsh.1
  30. Ibrahim Kholilullah; Junayed Saad; Nure Alam Siddiki Rachin; Mayen Uddin Mojumdar (2025). Vehicle License Plate Detection Dataset. Mendeley Data. DOI: 10.17632/nntnnzffw4.3 https://doi.org/10.17632/nntnnzffw4.3
  31. Md Nafijul Islam; Md Nayeem Hasan Habib; Sojib Hasan; Mayen Uddin Mojumdar (2026). RiceLeafBD: A Field-Collected Rice Leaf Disease Dataset from Bangladesh. Mendeley Data. DOI: 10.17632/sjkptpgv79.1 https://doi.org/10.17632/sjkptpgv79.1
  • Artificial Intelligence (AI)
  • Machine Learning and Deep Learning
  • Data Science and Data Analytics
  • Natural Language Processing (NLP)
  • Computer Vision
  • Internet of Things (IoT)
  • Health Informatics
  • AI in Healthcare and Medical Image Analysis
  • Merit Scholarship for Outstanding Academic Performance in Postgraduate Studies, awarded by Daffodil International University, Bangladesh.
  • Merit Scholarship for Outstanding Academic Performance in Undergraduate Studies, awarded by Daffodil International University, Bangladesh.
  • Merit Scholarship for Outstanding Academic Performance in the Secondary School Certificate (SSC) Examination, awarded by the Board of Intermediate and Secondary Education, Cumilla, Bangladesh
  • Senior Lecturer, Department of Computer Science and Engineering Daffodil International University, Dhaka, Bangladesh
    Duration: 1 July 2023 – 31 July 2025
  • Lecturer, Department of Computer Science and Engineering
    Daffodil International University, Dhaka, Bangladesh
    Duration: 1 January 2022 – 30 June 2023
  • Lecturer (Contractual), Department of Computer Science and Engineering Daffodil International University, Dhaka, Bangladesh
    Duration: 10 September 2019 – 30 December 2021