Alabrak, M., M. Megahed, A. A. Alkhouly, A. Mohammed, H. Elfandy, N. Tahoun, and Hoda Ismail, "Artificial intelligence role in subclassifying cytology of thyroid follicular neoplasm", The Asian Pacific Journal of Cancer Prevention , vol. 23, issue 4, pp. in press, 2023.
Abdelhay, M., and A. Mohammed, MAQA: Medical Arabic Q&A Dataset}, : Harvard Dataverse, pp. 10.7910/DVN/Y2JBEZ, 08/01, 2022.
Ouf, M., Y. A. Hamid, and A. Mohammed, "An Enhanced Deep Learning Approach for Breast Cancer Detection in Histopathology Images", The 3rd International Conference on Artificial Intelligence and Computer Vision (AICV2023), Springer, pp. 27-36, 2023.
Abdelhay1, M., and A. Mohammed, "Deep Learning approach for Arabic Healthcare: MedicalBot", Social Network Analysis and Mining volume, vol. 13, issue 1, pp. 71, 2023.
Kora, R., and A. Mohammed, "An enhanced approach for sentiment analysis based on meta-ensemble deep learning.", Social Network Analysis and Mining , vol. 13, issue 1, pp. 38, 2023.
Rahma, A., S. Azab, and A. Mohammed, "A Comprehensive Survey on Arabic Sarcasm Detection: Approaches, Challenges and Future Trends", IEEE Access, vol. 11, pp. 18261 - 18280, 2023.
Mohammed, A., and R. Kora, "A comprehensive review on ensemble deep learning: Opportunities and challenges", ournal of King Saud University Computer and Information Sciences, vol. 35, issue 2022, pp. 757-774, 2023.
Mansour, A. E., A. Mohammed, H. A. E. A. Elsayed, and S. Elramly, "Spatial-Net for Human-Object Interaction Detection", IEEE Access, vol. 10, pp. 88920-88931, 2022. Abstract
Amin, M., H. Hefny, and M. Ammar, "Sign language gloss translation using deep learning models", International Journal of Advanced Computer Science and Applications, vol. 12, no. 11: Science and Information (SAI) Organization Limited, 2021. Abstract
Eldin, S. S., A. Mohammed, H. Hefny, and A. S. E. Ahmed, "An Enhanced Opinion Retrieval Approach on Arabic Text for Customer Requirements Expansion", Journal of King Saud University - Computer and Information Sciences, vol. 33, no. 3, pp. 351-363, 2021. AbstractWebsite

Recently, most companies market their products on the web to recognize their customers’ requirements and to improve their services’ quality according to the customers’ feedback and opinions. A huge amount of reviews and opinions are posted daily on products. Obtaining and quickly analyzing these opinions become a difficult task. These opinions might lead to a tendency or disinclination to a specific point of view. To represent the products’ opinions from customers’ perspectives, opinion retrieval becomes a demanding and essential task for automatically extracting, analyzing, and summarizing customers’ reviews. Usually, online products are offered by several suppliers in e-commerce. Therefore, to keep up the competitiveness among suppliers, the need for innovative requirements is required. This paper proposed an enhanced opinion retrieval approach depending on the explicit feature based opinion mining. The proposed approach expands the initial products’ requirements using extended heuristics and linguistic patterns of the Arabic opinions. Besides the relevant score, several factors, like features’ weight, the opinion importance, and the sentiment polarity are used to rank the retrieved results. The experimental results show the capability of the proposed approach to automatically extract more innovative features compared to the conditional random field (CRF) results.