Saad Sadiq
Impact in
- Artificial Intelligence top 2%
- Anomaly Detection Techniques and Applications
- Topic Modeling
- Adversarial Robustness in Machine Learning
- AI in cancer detection
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- Advanced Neural Network Applications
Papers in
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- Anomaly Detection Techniques and Applications 4
- Topic Modeling 3
- Imbalanced Data Classification Techniques 2
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- Generative Adversarial Networks and Image Synthesis 2
- Digital Media Forensic Detection 2
- Co-authors
- Mei‐Ling Shyu (12 shared papers)Yilin Yan (5 shared papers)Yudong Tao (4 shared papers)Shu‐Ching Chen (7 shared papers)Samira Pouyanfar (2 shared papers)Haiman Tian (2 shared papers)Maria Presa Reyes (1 shared paper)S. S. Iyengar (1 shared paper)
- Journals
- ACM Computing Surveys (1 paper)Computers, materials & continua/Computers, materials & continua (Print) (1 paper)Proceedings of the National Academy of Sciences (1 paper)Journal of Computational and Graphical Statistics (1 paper)PubMed (1 paper)
- Partner nations
- United StatesNetherlandsMalaysia
In The Last Decade
Saad Sadiq
15 papers receiving 1.2k citations
Saad Sadiq's Hit Papers
Peers
Comparison fields: 5 of 161
- Artificial Intelligence 497
- Computer Vision and Pattern Recognition 295
- Health Informatics 16
- Signal Processing 87
- Health Information Management 30
Countries citing papers authored by Saad Sadiq
This map shows the geographic impact of Saad Sadiq'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 Saad Sadiq with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Saad Sadiq more than expected).
Fields of papers citing papers by Saad Sadiq
This network shows the impact of papers produced by Saad Sadiq. 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 Saad Sadiq. The network helps show where Saad Sadiq may publish in the future.
Co-authors
The 25 scholars most cited alongside Saad Sadiq, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A Survey on Deep Learning Hit paper breakdown → | 2018 | 934 |
| 2 | 2020 | 111 | |
| 3 | 2017 | 86 | |
| 4 | 2017 | 25 | |
| 5 | 2019 | 20 | |
| 6 | 2017 | 19 | |
| 7 | 2019 | 18 | |
| 8 | 2017 | 13 | |
| 9 | 2019 | 12 | |
| 10 | 2018 | 10 | |
| 11 | 2016 | 8 | |
| 12 | 2019 | 7 | |
| 13 | 2019 | 3 | |
| 14 | 2023 | 2 | |
| 15 | 2018 | 2 | |
| 16 | 2025 | 0 |
About Saad Sadiq
Saad Sadiq is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Molecular Biology, Sociology and Political Science and Information Systems, having authored 16 papers that have together received 1.3k indexed citations. Recurring topics across this work include Anomaly Detection Techniques and Applications (4 papers), Topic Modeling (3 papers), Generative Adversarial Networks and Image Synthesis (2 papers), Digital Media Forensic Detection (2 papers), Imbalanced Data Classification Techniques (2 papers), Misinformation and Its Impacts (2 papers), Advanced Causal Inference Techniques (1 paper) and Brain Tumor Detection and Classification (1 paper). The work is most often cited by research in Artificial Intelligence (497 citations), Computer Vision and Pattern Recognition (295 citations), Health Informatics (16 citations), Signal Processing (87 citations) and Health Information Management (30 citations). Saad Sadiq has collaborated with scholars based in United States, Netherlands and Malaysia. Frequent co-authors include Mei‐Ling Shyu, Yilin Yan, Yudong Tao, Shu‐Ching Chen, Samira Pouyanfar, Haiman Tian, Maria Presa Reyes, S. S. Iyengar, Daniel J. Feaster and Hemant Ishwaran. Their work appears in journals such as ACM Computing Surveys, Computers, materials & continua/Computers, materials & continua (Print), Proceedings of the National Academy of Sciences, Journal of Computational and Graphical Statistics and PubMed.
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.