Sadid A. Hasan
About
In The Last Decade
Sadid A. Hasan
43 papers receiving 746 citations
Peers
Comparison fields: 5 of 103
- Artificial Intelligence 620
- Molecular Biology 171
- Computer Vision and Pattern Recognition 116
- Information Systems 84
- Radiology, Nuclear Medicine and Imaging 50
Countries citing papers authored by Sadid A. Hasan
This map shows the geographic impact of Sadid A. Hasan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sadid A. Hasan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sadid A. Hasan more than expected).
Fields of papers citing papers by Sadid A. Hasan
This network shows the impact of papers produced by Sadid A. Hasan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sadid A. Hasan. The network helps show where Sadid A. Hasan may publish in the future.
Co-authorship network of co-authors of Sadid A. Hasan
This figure shows the co-authorship network connecting the top 25 collaborators of Sadid A. Hasan. A scholar is included among the top collaborators of Sadid A. Hasan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sadid A. Hasan. Sadid A. Hasan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 74 | |
| 3 | Overview of the VQA-Med Task at ImageCLEF 2020: Visual Question Answering and Generation in the Medical Domain. | 26 |
| 4 | 4 | |
| 5 | Overview of ImageCLEF 2018 Medical Domain Visual Question Answering Task. | 26 |
| 6 | Towards Dataset Creation And Establishing Baselines for Sentence-level Neural Clinical Paraphrase Generation and Simplification. | 5 |
| 7 | Diagnostic Inferencing via Improving Clinical Concept Extraction with Deep Reinforcement Learning: A Preliminary Study | 18 |
| 8 | PRNA at ImageCLEF 2017 Caption Prediction and Concept Detection Tasks. | 9 |
| 9 | Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning | 10 |
| 10 | Open domain real-time question answering based on asynchronous multiperspective context-driven retrieval and neural paraphrasing. | 2 |
| 11 | A Hybrid Approach to Precision Medicine-related Biomedical Article Retrieval and Clinical Trial Matching. | 2 |
| 12 | Assorted Textual Features and Dynamic Push Strategies for Real-time Tweet Notification. | 0 |
| 13 | Neural Clinical Paraphrase Generation with Attention | 16 |
| 14 | Clinical Question Answering using Key-Value Memory Networks and Knowledge Graph. | 4 |
| 15 | Open Domain Real-Time Question Answering Based on Semantic and Syntactic Question Similarity. | 3 |
| 16 | Using Neural Embeddings for Diagnostic Inferencing in Clinical Question Answering. | 7 |
| 17 | On the Effectiveness of Using Syntactic and Shallow Semantic Tree Kernels for Automatic Assessment of Essays | 2 |
| 18 | Towards Automatic Topical Question Generation | 17 |
| 19 | On the Effectiveness of using Sentence Compression Models for Query-Focused Multi-Document Summarization | 17 |
| 20 | Using Syntactic and Shallow Semantic Kernels to Improve Multi-Modality Manifold-Ranking for Topic-Focused Multi-Document Summarization | 1 |
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.