The Machine Learning for Natural Languages Processing and Computer Vision research group places a strong emphasis on the active involvement of undergraduate and graduate students in cutting-edge research projects. Students contribute as research assistants or through MSc theses that align closely with the group's research objectives, ensuring hands-on learning and meaningful contributions to the field.

Student Opportunities

Students in the group gain practical experience through opportunities to conduct experiments, analyze data, and contribute to the preparation of scientific papers. These activities are integral to their academic and professional development, equipping them with the skills needed for advanced research. The group also supports students in publishing their work in academic journals and presenting at international conferences.

MSc Theses Supervised by the Group

Several recent MSc theses supervised by the group demonstrate the alignment of student research with the group's focus. Notable examples include:

• Amani Abu Zeinah, A Unified Arabic and English Sign Language Recognition Model Based on Multi-Task Learning, Expected Aug 2025.
  
• Rabie Otoum, A Dual-Function Large Language Model for Correcting Arabic Spelling Mistakes and Adding Diacritics: Bridging Jordanian Dialect and Formal Arabic, Expected May 2025.
  
• Mohammad Alhadidi, Analysis of Daily Tasks Videos of Prostatic Arm Users Using Computer Vision Techniques, Expected Jan 2025.
  
• Albaraa Al-Kilani, Using Drone Cluster and Machine Learning for Search and Rescue in Natural Disasters, Expected May 2025.
• Moath Khalil, Translating Text from the Jordanian Dialect to Modern Standard Arabic Using Large Language Models, Aug 2024.
  
• Raed Al-Edwan, Evaluating the Use of Machine Learning in the Detection and Classification of Shoulder Pain Based on Facial Expressions, Aug 2024.
  
• Dima Ibrahim, Investigating and Adapting Pre-Trained Deep Learning Models in Digital Image Steganalysis, Aug 2024.
  
• Rozan Younis, Automatic Diacritization of Arabic Text and Poetry Using Pre-trained Byte-to-Byte Language Models and Multi-Phase Training, May 2024.
• Malak Smadi, Recognizing Texts of Jordanian Arabic Dialect Using Machine Learning Techniques, May 2024.
• Alaa Al-Saweir, Recognizing Texts of Jordanian Arabic Dialect Using Machine Learning Techniques, May 2024.
• Sarah Madi, Investigating Accurate Deep Learning Solutions for Recognizing Arabic Speech in the Jordanian Dialect, May 2024.Raghda Hasan, Correcting Wide-range of Arabic Spelling Mistakes Using Machine Learning and Transformers, Mar 2023.
• Mohammad Shawabkeh, Investigating Machine Arabic Poetry Generation Using Generative Recurrent Neural Networks, Aug 2022.
• Noor Khader, Evaluating the Use of Deep Learning in Classifying Encryption Algorithms for Digital Images, Sep 2022.
  
• Mohammad Alhmoud, Design and Implementation of a New Image Compression Algorithm Based on Machine Learning, Sep 2022.
  
• Mays Al-Qudah, A Hybrid Machine Learning Approach to Extract Sentiment from Arabic Tweets, May 2022.
• Batool Shdaifat, Applying Deep Learning and Transfer Learning for Classifying Images of Bacterial Colonies, Mar 2022.
• Boshra Al-Sadder, Using Machine Learning to Build Recognition Engine for Arabic Booking Chatbot, Jan 2022.
• Reem Shtaiwi, End-to-end machine learning solution for recognizing handwritten Arabic documents, Jan 2022.
• Shorouq Al-AlAwawdeh, Semantic Similarity of Arabic Questions using Machine Learning Solutions, Aug 2021.
• Mohammed Al-Qaraghuli, Correcting Arabic Soft Spelling Mistakes Using Deep Machine Learning and Transformers, Aug 2021.
• Abrar Samara, Performance Evaluation and Optimization for BiLSTM Neural Networks for Arabic Language Applications, Aug 2021.
• Ahmad Almajdoubah, Investigating Encoder-Decoder Recurrent Neural Network for Diacritizing Arabic Text and Correcting Spelling Mistakes, Aug 2021.
• Safaa Sawalhah, Towards End-to-End Machine Learning Solution for Recognizing Handwritten Arabic Documents, May 2020.
• Aya Al-Shamaileh, Machine learning Translation from Arab Vocal Improvisation to Instrumental Melodic Accompaniment, Dec 2019.

These projects reflect the group's commitment to advancing natural language processing and computer vision research, particularly in the Arabic language context.​

Student Publications

The group also prioritizes academic dissemination of student work, which has resulted in numerous publications in reputable conferences and journals. Recent publications led by student first authors include:

• M. Salameh, I. Jafar, "A Lightweight Efficient U-Net Model for Audio​ Super-Resolution," ​5th International Conference ​on Communications, ​Information, Electronics, ​and Energy Systems (CIEES), Vieliko Tarnovo, Bulgaria, 2024.  
  
• B. Saddar, R. Saddar, G. Abandah, I. Jafar, “Multi-Domain Machine Learning Approach of Named Entity Recognition for Arabic Booking Chatbot Engines Using Pre-Trained Bidirectional Transformers," The Jordanian Journal of Computers and Information Technology (JJCIT), Vol. 10, No. 1, Mar 2024, pp. 1-16.
• M. Smadi and G. Abandah, “Correcting Auditory Spelling Mistakes in Jordanian Dialect Using Machine Learning Techniques," 2024 15th International Conference on Information and Communication Systems (ICICS), Irbid, Jordan, 2024, pp. 1-6, doi: 10.1109/ICICS63486.2024.10638311.
• R. Hasan and G. Abandah, “Correcting Wide-range of Arabic Spelling Mistakes Using Machine Learning and Transformers," 2023 International Conference on Information Technology (ICIT), Aug 2023.
• B. Al-Rfooh, G. Abandah, and R. Al-Rfou, “'Fine-Tashkeel: Finetuning Byte-Level Models for Accurate Arabic Text Diacritization," in Proc. 2023 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, May 2023.
• M. Al-Fetyani, M. AlBarham, G. A. Abandah, A. Alsharkawi, and M. Dawas, “MASC: Massive Arabic Speech Corpus," in Proc. 2023 IEEE Spoken Language Technology Workshop (SLT), 2023.
• R. E. Shtaiwi, G. A. Abandah, and S. Sawalhah, "End-to-End Machine Learning Solution for Recognizing Handwritten Arabic Documents," in Proc. The 13th International Conference on Information and Communication Systems, 2022.
• M. Al-Qaraghuli, G. A. Abandah, and A. E. Suyyagh, "Correcting Arabic Soft Spelling Mistakes Using Transformers," in Proc. 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, 2021.
• M. M. Albaddawi and G. A. Abandah, "Pattern and Poet Recognition of Arabic Poems Using BiLSTM Networks," in Proc. 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, 2021.
• A. K. Samara and G. A. Abandah, "Investigating Fast BiLSTM Neural Networks for Arabic Language Applications," in Proc. 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, 2021.
• A. N. Almajdoubah, G. A. Abandah, and A. E. Suyyagh, "Investigating Recurrent Neural Networks for Diacritizing Arabic Text and Correcting Soft Spelling Mistakes," in Proc. 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, 2021.
• S. M. AlAwawdeh and G. A. Abandah, "Improving the Accuracy of Semantic Similarity Prediction of Arabic Questions Using Data Augmentation and Ensemble," in Proc. 2021 IEEE Jordan International Joint Conference on Electrical Engineering and Information Technology, 2021.

Fostering Future Research Competencies

The group's mentorship enables students to contribute to impactful research while gaining expertise in machine learning applications. By fostering collaboration between students and experienced researchers, the group ensures the development of future leaders in the field. Furthermore, the group welcomes and supports initiatives that align with its research focus, enhancing its impact on both academic and societal levels.