Ismail Keshta
Biographic Data
| ID | 9623934 |
|---|---|
| NAME | Ismail Keshta |
| GIVEN NAMES | Ismail |
| FAMILY NAME | Keshta |
| SIGNATURE | KESHTA I |
| AFFILIATIONS | Alfaisal University |
| ORCID | 0000-0001-9803-5882 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2023 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media
The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning of aspect terms and their relationship with context. The model combines a modified dynamic weighting layer with bidirectional long short-term memory (BiLSTM) to accurately acquire context. …
Few-shot learning-based human behavior recognition model
IoT-Based Federated Learning Model for Hypertensive Retinopathy Lesions Classification
Traditional classification algorithms struggle to categorize hypertensive retinopathy (HR) lesions correctly because they lack obvious characteristics. A regional IoT-enabled federated learning-based HR categorization approach (IoT-FHR) incorporating global and local attributes is suggested as a solution to this issue. The local feature arterial and venous nicking (AVN) classification model is fused with the overall IoT-FHR classification model t…
No prominent works on this page.
IoT-Based Federated Learning Model for Hypertensive Retinopathy Lesions Classification
Traditional classification algorithms struggle to categorize hypertensive retinopathy (HR) lesions correctly because they lack obvious characteristics. A regional IoT-enabled federated learning-based HR categorization approach (IoT-FHR) incorporating global and local attributes is suggested as a solution to this issue. The local feature arterial and venous nicking (AVN) classification model is fused with the overall IoT-FHR classification model t…
Few-shot learning-based human behavior recognition model
Deep Learning Model for Interpretability and Explainability of Aspect-Level Sentiment Analysis Based on Social Media
The interactive attention graph convolution network (IAGCN), a novel model proposed in this article, will revolutionize aspect-level sentiment analysis (SA). IAGCN effectively addresses these key features, in contrast to prior research that ignored the meaning of aspect terms and their relationship with context. The model combines a modified dynamic weighting layer with bidirectional long short-term memory (BiLSTM) to accurately acquire context. …
Artificial Intelligence (3 works) · Computer Science (3 works) · Machine learning (2 works) · Advanced Text Analysis Techniques (1 work) · Artificial neural network (1 work) · Assertion (1 work) · Categorization (1 work) · Context-Aware Activity Recognition Systems (1 work) · Contextual image classification (1 work) · Data science (1 work)