Nora S. Alturayeif
University of Dammam
9 Papers
5 Citations
Nora S. Alturayeif is an academic researcher from University of Dammam. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 3, co-authored 4 publications. Previous affiliations of Nora S. Alturayeif include King Fahd University of Petroleum and Minerals.
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Papers
Preemptive Diagnosis of Chronic Kidney Disease Using Machine Learning Techniques
Reem A. Alassaf,Khawla A. Alsulaim,Noura Y. Alroomi,Nouf S. Alsharif,Mishael F. Aljubeir,Sunday O. Olatunji,Alaa Alahmadi,Mohammed Imran,Rahma A. Alzahrani,Nora S. Alturayeif +9 more
- 01 Nov 2018
TL;DR: This study aims to decrease the number of patients and the expenses needed for treatment by preemptively diagnosing chronic kidney disease accurately using data mining and machine learning techniques.
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DeepScratch: Scratch Programming Language Extension for Deep Learning Education
TL;DR: DeepScratch is presented, a new programming language extension to Scratch that provides powerful language elements to facilitate building and learning about deep learning models and the preliminary evaluation shows the usability and the effectiveness of this extension as a tool for kids to learn aboutdeep learning.
Preemptive Diagnosis of Diabetes Mellitus Using Machine Learning
Reem A. Alassaf,Khawla A. Alsulaim,Noura Y. Alroomi,Nouf S. Alsharif,Mishael F. Aljubeir,Sunday O. Olatunji,Alaa Alahmadi,Mohammed Imran,Rahma A. Alzahrani,Nora S. Alturayeif +9 more
- 25 Apr 2018
TL;DR: Experimental results show that ANN outperformed SVM, Naïve Bayes, and K-Nearest Neighbor with the testing accuracy of 77.5%, and the proposed system aims to preemptively diagnose DM in a region never explored before.
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Enhancing stance detection through sequential weighted multi-task learning
Nora S. Alturayeif,H.A. Luqman +1 more
TL;DR: This study introduces two MTL models, Parallel Multi-Task Learning (PMTL) and Sequential Multi- Task Learning (SMTL), which incorporate sentiment analysis and sarcasm detection tasks to enhance stance detection performance and addresses the complexities of MTL implementation with Transformer-based architectures and presents an accessible architecture.
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