Abdullah Abusorrah
Biographic Data
| ID | 9817966 |
|---|---|
| NAME | Abdullah Abusorrah |
| GIVEN NAMES | Abdullah |
| FAMILY NAME | Abusorrah |
| SIGNATURE | ABUSORRAH A |
| AFFILIATIONS | King Abdulaziz University |
| ORCID | 0000-0001-8025-0453 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2020 |
| H-INDEX | 0 |
Aspect-Based Sentiment Analysis
Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This artic…
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation
In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or…
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Aspect-Based Sentiment Analysis
Sentiment analysis is a process of analyzing, processing, concluding, and inferencing subjective texts with the sentiment. Companies use sentiment analysis for understanding public opinion, performing market research, analyzing brand reputation, recognizing customer experiences, and studying social media influence. According to the different needs for aspect granularity, it can be divided into document, sentence, and aspect-based ones. This artic…
Enhanced Subspace Distribution Matching for Fast Visual Domain Adaptation
In computer vision, when labeled images of the target domain are highly insufficient, it is challenging to build an accurate classifier. Domain adaptation stands for an effective solution to address it by utilizing available and related source domain which has sufficient labeled images, even when there is a substantial difference in properties and distributions of these two domains. Yet, most prior approaches merely reduce subspace conditional or…
Artificial Intelligence (2 works) · Computer Science (2 works) · Machine learning (2 works) · Advanced Text Analysis Techniques (1 work) · Algorithm (1 work) · Conditional probability distribution (1 work) · Data science (1 work) · Deep learning (1 work) · Domain adaptation (1 work) · Domain Adaptation and Few-Shot Learning (1 work)