Momina Moetesum

SEECS-NUST^ Islamabad%

Designation(s): Sr. Assistant Professor (CS)
Department(s): Computer Science, AI Enabling Technologies Research Center
Email: momina.buic@bahria.edu.pk
Contact: +92 (051) 111 111 028

Momina Moetesum completed her Ph.D. in Artificial Intelligence from Bahria University Islamabad, Pakistan, in 2021. During her research, she assessed various attributes of online and offline handwriting for its potential use as a biomarker for several neuropsychological and neurological disorders. Her research is motivated by the need for developing inexpensive, non-invasive, and effective screening tools for the early detection and diagnosis of neurodevelopmental and neurodegenerative diseases in the masses. Her Ph.D. research was declared the “Most innovative solution for a major societal issue” during a doctoral consortium held in Kyoto, Japan, in 2017. Currently, she is working as a Senior Assistant Professor in the Department of Computer Science at BUIC and is also a senior member of the CoEAI. Her expertise includes Machine Learning, Deep Learning, and Pattern Recognition. She is the manager of the Pakistan Pattern Recognition Society (PPRS), which is a recognized chapter of the International Association of Pattern Recognition (IAPR). She is also an active researcher in the field of Document Image Analysis. She has published several articles in highly reputed international journals and conferences. She regularly serves as a reviewer for international journals like Neural Computing and Applications, Expert Systems with Applications, and the International Journal of Document Analysis and Recognition. She is a member of the Program Committee member of a number of renowned International Conferences in various domains of Artificial Intelligence like Computer Vision, Pattern Recognition, Deep Learning, and Document Analysis.

Projects and Grants

  • Automated Insect Detection and Crop Health Monitoring, RAC-NESCOM, (2022 – In progress), Funding Amount: PKR 0.25 Million.
  • Early Detection of Neurological Disorders through Computerized Analysis of Handwriting – An Application to Parkinson’s Disease, Higher Education Commission, Pakistan, (2019 – 2021), Funding Amount: PKR 2.04 Million.
  • Automated Detection of Neuropsychological Impairments from Image-Based Visuo-constructive Screening Tests, Higher Education Commission, Pakistan and French Ministry of Foreign Affairs, (2015 – 2017), Funding Amount: PKR 1.40 Million.

Student Supervision

  • Muhammad Awais, Query-based Document Summarization (In progress)
  • Kanwal Fatima, Information Extraction from Chart Images (In progress)
  • Anam Bibi, Automated Insect Detection for Crop Health Monitoring (In progress)
  • Negarish Mushtaq, Criminal Tendency Detection from Facial Images (In progress)
  • Muhammad Bilal, Online Content Veracity Assessment Using Deep Representation Learning (Completed)
  • Ali Imran, Characterization of Negative Emotions from Handwriting and Drawing-Based Tasks (Completed)
  • Hussain Waheed Akhtar, Cardiovascular Disease Risk Prediction Using Machine Learning Techniques Deep Learning Based Kinship Verification (Completed)

Publications

Journal Publications
[j07]
Moetesum, M., Hadi, F., Imran, M., Minhas, A. A., & Vasilakos, A. V. (2016). An adaptive and efficient buffer management scheme for resource-constrained delay tolerant networks. Wireless networks, 22(7), 2189-2201.
[j06]
Moetesum, M., Younus, S. W., Warsi, M. A., & Siddiqi, I. (2018). Segmentation and recognition of electronic components in hand-drawn circuit diagrams. EAI Endorsed Transactions on Scalable Information Systems, 5(16).
[j05]
Moetesum, M., Siddiqi, I., Vincent, N., & Cloppet, F. (2019). Assessing visual attributes of handwriting for prediction of neurological disorders—A case study on Parkinson’s disease. Pattern Recognition Letters, 121, 19-27.
[j04]
Moetesum, M., Siddiqi, I., Ehsan, S., & Vincent, N. (2020). Deformation modeling and classification using deep convolutional neural networks for computerized analysis of neuropsychological drawings. Neural Computing and Applications, 32(16), 12909-12933.
[j03]
Diaz, M., Moetesum, M., Siddiqi, I., & Vessio, G. (2021). Sequence-based dynamic handwriting analysis for Parkinson’s disease detection with one-dimensional convolutions and BiGRUs. Expert Systems with Applications, 168, 114405.
[j02]
Moetesum, M., Diaz, M., Masroor, U., Siddiqi, I., & Vessio, G. (2022). A survey of visual and procedural handwriting analysis for neuropsychological assessment. Neural Computing and Applications, 1-18.
[j01]
Bibi, M., Hamid, A., Moetesum, M., & Siddiqi, I. (2022). Document forgery detection using source printer identification: A comparative study of text‐dependent versus text‐independent analysis. Expert Systems, e13020.
Conference Proceedings
[c16]
Khawar, S., Kaleem, A., Moetesum, M., & Siddiqi, I. (2022). Feature Relevance Analysis for Handwriting Based Identification of Parkinson’s Disease. In Mediterranean Conference on Pattern Recognition and Artificial Intelligence (pp.158-171). Springer, Cham.
[c15]
Zeeshan, M. O., Siddiqi, I., & Moetesum, M. (2021, September). Two-Step Fine-Tuned Convolutional Neural Networks for Multi-label Classification of Children’s Drawings. In International Conference on Document Analysis and Recognition (pp.321-334). Springer, Cham.
[c14]
Nasir, S., Siddiqi, I., & Moetesum, M. (2021, September). Writer Characterization from Handwriting on Papyri Using Multi-step Feature Learning. In International Conference on Document Analysis and Recognition (pp. 451-465). Springer, Cham.
[c13]
Moetesum, M., Siddiqi, I., Javed, F., & Masroor, U. (2020, September). Dynamic Handwriting Analysis for Parkinson's Disease Identification using C-BiGRU Model. In 2020 17th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 115-120). IEEE.
[c12]
Hamid, A., Bibi, M., Moetesum, M., & Siddiqi, I. (2019, September). Deep learning based approach for historical manuscript dating. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 967-972). IEEE.
[c11]
Moetesum, M., Siddiqi, I., & Vincent, N. (2019, September). Deformation classification of drawings for assessment of visual-motor perceptual maturity. In 2019 International Conference on Document Analysis and Recognition (ICDAR) (pp. 941-946). IEEE.
[c10]
Hamid, A., Bibi, M., Siddiqi, I., & Moetesum, M. (2018, December). Historical manuscript dating using textural measures. In 2018 International Conference on Frontiers of Information Technology (FIT) (pp. 235-240). IEEE.
[c09]
Moetesum, M., Zeeshan, O., & Siddiqi, I. (2019, May). Multi-object sketch segmentation using convolutional object detectors. In Tenth International Conference on Graphics and Image Processing (ICGIP 2018) (Vol. 11069, pp. 652-657). SPIE.
[c08]
Ismail, B., & Moetesum, M. (2018, November). Automated Detection and Quantification of Erythrocytes and Leukocytes from Giesma Stains of Blood Smear. In 2018 14th International Conference on Emerging Technologies (ICET) (pp. 1-6). IEEE.
[c07]
Moetesum, M., Siddiqi, I., Djeddi, C., Hannad, Y., & Al-Maadeed, S. (2018, August). Data driven feature extraction for gender classification using multi-script handwritten texts. In 2018 16th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 564-569). IEEE.
[c06]
Begum, H., Shaheen, S., Moetesum, M., & Siddiqi, I. (2017, December). Digital beethoven—an android based virtual piano. In 2017 13th International Conference on Emerging Technologies (ICET) (pp. 1-5). IEEE.
[c05]
Moetesum, M., Aslam, T., Saeed, H., Siddiqi, I., & Masroor, U. (2017, December). Sketch-based facial expression recognition for human figure drawing psychological test. In 2017 International Conference on Frontiers of Information Technology (FIT) (pp. 258-263). IEEE.
[c04]
Nazar, H. B., Moetesum, M., Ehsan, S., Siddiqi, I., Khurshid, K., Vincent, N., & McDonald-Maier, K. D. (2017, November). Classification of graphomotor impressions using convolutional neural networks: an application to automated neuro-psychological screening tests. In 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR) (Vol. 1, pp. 432-437). IEEE.
[c03]
Mirza, A., Moetesum, M., Siddiqi, I., & Djeddi, C. (2016, October). Gender classification from offline handwriting images using textural features. In 2016 15th International Conference on Frontiers in Handwriting Recognition (ICFHR) (pp. 395-398). IEEE.
[c02]
Moetesum, M., Siddiqi, I., Masroor, U., Vincent, N., & Cloppet, F. (2016, August). Segmentation and classification of offline hand drawn images for the bgt neuropsychological screening test. In Eighth International Conference on Digital Image Processing (ICDIP 2016) (Vol. 10033, pp. 928-932). SPIE.
[c01]
Moetesum, M., Siddiqi, I., Masroor, U., & Djeddi, C. (2015, August). Automated scoring of bender gestalt test using image analysis techniques. In 2015 13th International Conference on Document Analysis and Recognition (ICDAR) (pp. 666-670). IEEE.
Book Chapters
[b01]
Moetesum, M., & Siddiqi, I. (2018). Socially believable robots. Human-Robot Interaction: Theory and Application, 1.