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Publications

Journal articles:
  1. Alturayeif, N. and Ahmad, I. (2025). Ease: An enhanced active learning framework for aspect-based sentiment analysis based on sample diversity and data augmentation. Expert Systems with Applications, 261:125525.
  2. Al-Shaibani, M. S., & Ahmad, I. (2024). Dotless Arabic text for Natural Language Processing. Computational Linguistics, 1-42.
  3. Alomari, D., & Ahmad, I. (2024). Exploring Character Trigrams for Robust Arabic Text Classification: A Comparative Analysis in the Face of Vocabulary Expansion and Misspelled Words. IEEE Access.
  4. Elmajali, S., & Ahmad, I. (2024). Towards early detection of depression: detecting depression symptoms in Arabic tweets using pretrained transformers. IEEE Access.
  5. Ahmad, I. (2023). A Hybrid Rule-Based and Machine Learning System for Arabic Check Courtesy Amount Recognition. Sensors, 23(9), 4260.
  6. Alnaseef, F., Niazi, M., Mahmood, S., Alshayeb, M., & Ahmad, I. (2023). Towards a Successful Secure Software Acquisition. Information and Software Technology, 107315.
  7. Alyafeai, Z., Al-shaibani, M. S., Ghaleb, M., & Ahmad, I. (2023). Evaluating various tokenizers for Arabic text classification. Neural Processing Letters, 55(3), 2911-2933.
  8. Alhathloul, Z.; Ahmad, I. (2022). Automatic dottization of Arabic text (Rasms) using deep recurrent neural networks, Pattern Recognition Letters, 162, 47–55.
  9. AlDhafer, O., Ahmad, I., & Mahmood, S. (2022). An end-to-end deep learning system for requirements classification using recurrent neural networks. Information and Software Technology 147 (2022), 106877.
  10. Shawahna, A., Sait, S. M., El-Maleh, A., Ahmad, I. (2022) FxP-QNet: A Post-Training Quantizer for the Design of Mixed Low-Precision DNNs with Dynamic Fixed-Point Representation, IEEE Access, 10, 30202-30231.
  11. Eltay M, Zidouri A, Ahmad I, Elarian Y. (2022). Generative adversarial network based adaptive data augmentation for handwritten Arabic text recognition. PeerJ Computer Science 8:e861
  12. Kashifi, MT.; Ahmad, I. (2022) An Efficient Histogram-based Gradient Boosting Approach for Accident Severity Prediction with Multisource Data. Transportation Research Record, 2676(6), 236–258.
  13. Alkhulaifi, A.; Jamal, A.; Ahmad, I (2021). Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks. Appl. Sci. 2021, 11, 11595.
  14. Alkhulaifi, A.; Alsahli, F.; Ahmad, I. (2021). Knowledge Distillation in Deep Learning and its Applications. PeerJ Computer Science 7:e474. [pdf]
  15. Nemer, I.; Sheltami, T.; Ahmad, I.; Yasar, A.U.-H.; Abdeen, M.A.R. (2021). RF-Based UAV Detection and Identification Using Hierarchical Learning Approach. Sensors 2021, 21, 1947.
  16. Syed, M. N., Hassan, M. R., Ahmad, I., Hassan, M. M., & de Albuquerque, V. H. C. (2021). A Novel Linear Classifier for Class Imbalance Data Arising in Failure-prone Air Pressure Systems. IEEE Access.
  17. Al-shaibani, M. S., Alyafeai, Z., & Ahmad, I. (2020). MetRec: A Dataset for Meter Classification of Arabic Poetry. Data in Brief, 106497.
  18. Ahmad, I., Awaida, S., & Mahmoud, S. A. (2020). Arabic literal amount sub-word recognition using multiple features and classifiers. International Journal of Applied Pattern Recognition, 6(2), 103–123.
  19. Al-shaibani, M. S., Alyafeai, Z., & Ahmad, I. (2020). Meter Classification of Arabic Poems Using Deep Bidirectional Recurrent Neural Networks. Pattern Recognition Letters, 136, 1–7.
  20. Eltay, M., Zidouri, A., & Ahmad, I. (2020). Exploring Deep Learning Approaches to Recognize Handwritten Arabic Texts. IEEE Access, 8, 89882-89898. [pdf]
  21. Ahmad, I., & Fink, G. A. (2019). Handwritten Arabic text recognition using multi-stage sub-core-shape HMMs. International Journal on Document Analysis and Recognition (IJDAR), 22(3), 329–349.
  22. Elarian, Y., Ahmad, I., Zidouri, A., & Al-Khatib, W. G. (2019), LUCIDAH: Ligative and Unligative Characters in a Dataset for Arabic Handwriting, International Journal of Advanced Computer Science and Applications (IJACSA), Vol. 10, No.8, 406–415. [pdf]
  23. Ahmad, I., Mahmoud, S. A., & Fink, G. A. (2016). Open-vocabulary recognition of machine-printed Arabic text using hidden Markov models. Pattern Recognition, 51, 97–111. [pdf]
  24. Elarian, Y. S., Ahmad, I., Awaida, S. M., Al-Khatib, W. G., & Zidouri, A. (2015). An Arabic handwriting synthesis system. Pattern Recognition, 48(3), 849–861.
  25. Elish, M. O., Aljamaan, H., & Ahmad, I. (2015). Three empirical studies on predicting software maintainability using ensemble methods. Soft Computing, 19(9), 2511–2524.
  26. Mahmoud, S. A., Ahmad, I., Al-Khatib, W. G., Alshayeb, M., Tanvir Parvez, M., Märgner, V., & Fink, G. A. (2014). KHATT: An open Arabic offline handwritten text database. Pattern Recognition, 47(3), 1096–1112.
  27. Elarian, Y. S., Abdel-Aal, R., Ahmad, I., Parvez, M. T., & Zidouri, A. (2014). Handwriting synthesis: classifications and techniques. International Journal on Document Analysis and Recognition (IJDAR), 17(4), 455–469.
  28. Ahmad, I., & Mahmoud, S. A. (2013). Arabic Bank Check Processing: State of the Art. Journal of Computer Science and Technology, 28(2), 285–299.
  29. Ahmed, M. A., Ahmad, I., & AlGhamdi, J. S. (2013). Probabilistic size proxy for software effort prediction: A framework. Information and Software Technology, 55(2), 241–251.
  30. Eleiche, A. M., Ahmad, I., & Elish, M. O. (2012). Design Requirements in Software and Engineering Systems. Industrial Engineering & Management Systems, 11(1), 70–81.
Conference articles:
  1. Alhubaiti, O., & Ahmad, I. (2023, August). Typefaces and Ligatures in Printed Arabic Text: A Deep Learning-Based OCR Perspective. In International Conference on Document Analysis and Recognition, ICDAR (pp. 5-18). Cham: Springer Nature Switzerland.
  2. Eltay, M., Zidouri, A., & Ahmad, I., Elarian, Y. (2021). Improving Handwritten Arabic Text Recognition Using an Adaptive Data-Augmentation Algorithm. In ICDAR 2021 Workshop on Arabic and Derived Script Analysis and Recognition (ASAR), pp. 322-335. [pdf]
  3. Alyafeai, Z., & Ahmad, I. (2021). Arabic Compact Language Modelling for Resource Limited Devices. In the Sixth Arabic Natural Language Processing Workshop (WANLP 2021), in conjunction with European Chapter of the Association for Computational Linguistics (EACL), pp. 53-59. [pdf]
  4. Alsobaie, H., & Ahmad, I. (2020). Compression Techniques for Handwritten Digit Recognition. In 2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT) (pp. 1-6). IEEE.
  5. Almohammedi, A. M., & Ahmad, I. (2020, November). Feature Compression Based on Discrete Cosine Transform for Handwritten Digit Recognition. In 2020 First International Conference of Smart Systems and Emerging Technologies (SMARTTECH) (pp. 13-20). IEEE.
  6. Ahmad, I. (2019). Performance of Classifiers on Noisy-Labeled Training Data: An Empirical Study on Handwritten Digit Classification Task. In International Work-Conference on Artificial Neural Networks (pp. 414-425). Springer.
  7. Helali, M., Alneghaimish, A., and Ahmad, I. (2017). Handwritten Digit Recognition under Constrained Training Conditions. In Proceedings of the 8th International Conference of Pattern Recognition Systems, Madrid, Spain 2017. [pdf]
  8. Ahmad, I., and Fink, G. A. (2016). Class-Based Contextual Modeling for Handwritten Arabic Text Recognition. In Proceedings of the 15th International Conference on Frontiers in Handwriting Recognition (ICFHR 2016), Shenzhen, China, 2016. [pdf]
  9. Ahmad, I., & Fink, G. A. (2015). Multi-stage HMM based Arabic text recognition with rescoring. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR 2015) (pp. 751–755). IEEE. [pdf]
  10. Ahmad, I., & Fink, G. A. (2015). Training an Arabic handwriting recognizer without a handwritten training data set. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR 2015) (pp. 476–480). IEEE. [pdf]
  11. Elarian, Y. S., Ahmad, I., Awaida, S. M., Al-Khatib, W. G., & Zidouri, A. (2015). Arabic Ligatures: Analysis and Application in Text Recognition. In Proceedings of the 13th International Conference on Document Analysis and Recognition (ICDAR 2015) (pp. 896–900). IEEE.
  12. Ahmad, I., Fink, G. A., & Mahmoud, S. A. (2014). Improvements in Sub-character HMM Model Based Arabic Text Recognition. In Proceedings of the 14th International Conference on Frontiers in Handwriting Recognition (ICFHR 2014) (pp. 537–542). Crete: IEEE. [pdf]
  13. Ahmad, I., Rothacker, L., Fink, G. A., & Mahmoud, S. A. (2013). Novel Sub-character HMM Models for Arabic Text Recognition. In Proceedings of the 12th International Conference on Document Analysis and Recognition (ICDAR 2013) (pp. 658–662). IEEE. [pdf]
  14. Ahmad, I. (2013). A Technique for Skew Detection of Printed Arabic Documents. In Proc. of 10th International Conference Computer Graphics, Imaging and Visualization (CGIV) (pp. 62–67). IEEE Computer Society.
  15. Aljamaan, H., Elish, M. O., & Ahmad, I. (2013). An Ensemble of Computational Intelligence Models for Software Maintenance Effort Prediction. In I. Rojas, G. Joya, & J. Gabestany (Eds.), Advances in Computational Intelligence SE - 60 (Vol. 7902, pp. 592–603). Springer Berlin Heidelberg. 
  16. Mahmoud, S. A., Ahmad, I., Alshayeb, M., Al-Khatib, W. G., Parvez, M. T., Fink, G. A., … EL Abed, H. (2012). KHATT: Arabic Offline Handwritten Text Database. In Proceedings of the 13th International Conference on Frontiers in Handwriting Recognition (ICFHR 2012) (pp. 447–452). IEEE.
  17. Ahmad, I., & Mahmoud, S. A. (2012). Arabic Bank Check Analysis and Zone Extraction. In A. Campilho & M. Kamel (Eds.), Image Analysis and Recognition (Vol. 7324, pp. 141–148). Springer Berlin Heidelberg.
  18. Mahmoud, S. A., Ahmad, I., Alshayeb, M., & Al-Khatib, W. G. (2011). A database for offline Arabic handwritten text recognition. In Lecture Notes in Computer Science, Vol. 6754 LNCS, pp. 397–406. Springer, Berlin, Heidelberg.
  19. El-Attar, M., & Ahmad, I. (2011). Improving Quality in Misuse Case Models: A Risk-Based Approach. In Computer and Information Science (ICIS), 2011 IEEE/ACIS 10th International Conference on (pp. 337–342).
  20. Helmy, T., Ahmad, I., & Alvi, A. K. (2008). A Framework for Fair and Reliable Resource Sharing in Distributed Systems. In P. Kacsuk, R. Lovas, & Z. Németh (Eds.), Distributed and Parallel Systems (pp. 115–128). Boston, MA: Springer US.
Book chapter:
  1. Ahmad, I., Mahmoud, S. A., & Parvez, M. T. (2012). Printed Arabic Text Recognition. In V. Märgner & H. El Abed (Eds.), Guide to OCR for Arabic Scripts (pp. 147–168). Springer London.
Patents:
  1. Irfan Ahmad, A Technique for Skew Detection in Printed Arabic Documents, US Patent US9288362 B2, 2016.
  2. Irfan Ahmad and Sabri A Mahmoud, Adaptive Sliding Windows for Text Recognition, US Patent US 9,501,708, 2016.
  3. Irfan Ahmad and Sabri A. Mahmoud, Arabic Bank Check Analysis and Zone Extraction Method, US Patent US8,699,780 B1, 2014.