Browsing by Author "Hamdi, Ali"
Now showing items 1-6 of 6
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A review on challenging issues in Arabic sentiment analysis
( Science Publications , 2016 , Article Review)Understanding what people think about an idea or how they evaluate a product, a service or a policy is important for individuals, companies and governments. Sentiment analysis is the process of automatically identifying ... -
Attention-Based Model for Accurate Stance Detection
( Springer Science and Business Media Deutschland GmbH , 2022 , Conference Paper)Effective representation learning is an essential building block for achieving many natural language processing tasks such as stance detection as performed implicitly by humans. Stance detection can assist in understanding ... -
C-SAR: Class-Specific and Adaptive Recognition for Arabic Handwritten Cheques
( Springer Science and Business Media Deutschland GmbH , 2022 , Book chapter)We propose C-SAR, a Class-specific and Adaptive Recognition algorithm for Arabic handwritten Cheques. Existing methods suffer from low accuracy due to the complex structure of Arabic script and high-dimensional datasets. ... -
Empathy and Persona of English vs. Arabic Chatbots: A Survey and Future Directions
( Springer Science and Business Media Deutschland GmbH , 2022 , Conference Paper)There is a high demand for chatbots across a wide range of sectors. Human-like chatbots engage meaningfully in dialogues while interpreting and expressing emotions and being consistent through understanding the user's ... -
Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections
( Elsevier , 2022 , Article)Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, ... -
MARL: Multimodal Attentional Representation Learning for Disease Prediction
( Springer Science and Business Media Deutschland GmbH , 2021 , Conference Paper)Existing learning models often utilise CT-scan images to predict lung diseases. These models are posed by high uncertainties that affect lung segmentation and visual feature learning. We introduce MARL, a novel Multimodal ...
