Kyungtae Kim
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Researcher
Also affiliated: Pohang University of Science and Technology (2019); Seoul National University (2020); Sogang University (2016–2020); Kangwon National University (2020); Yonsei University (2009); University of Ulsan (2012); Institute of Agrifood Research and Technology (2019); National Institute of Environmental Research (2012–2013)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Kyungtae Kim's research explores the intersection of computer science and various applied domains. His recent work includes developing advanced methods for malware detection in Android systems, utilizing fine-grained opcode analysis and data augmentation to evaluate zero-day threats. Kim also investigates machine learning applications, such as enhancing deep hashing with graph filters and autoencoder-based embeddings. His research extends to understanding user behavior and influencing factors, including the impact of game design features on purchase intention and the relationship between menu quality and customer satisfaction in the restaurant industry. Additionally, his work has addressed environmental monitoring through AI-enhanced prediction of dissolved oxygen concentration in bodies of water using time-series data. Kim has an h-index of 6, with 18 total publications and 234 citations, and collaborates with Donghoon Kim at Arkansas State University on shared publications.
Metrics
- h-index: 6
- Publications: 18
- Citations: 236
Selected Publications
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Enhancing deep hashing with graph filters and autoencoder-based embeddings (2026)
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Enhanced Android Malware Detection: Fine-Grained Opcode analysis, Data Augmentation and Zero-Day Evaluation (2025)
Collaboration Network
Top Collaborators
- Enhanced Android Malware Detection: Fine-Grained Opcode analysis, Data Augmentation and Zero-Day Evaluation
- Enhanced Android Malware Detection: Fine-Grained Opcode analysis, Data Augmentation and Zero-Day Evaluation
- Enhanced Android Malware Detection: Fine-Grained Opcode analysis, Data Augmentation and Zero-Day Evaluation
- Enhancing deep hashing with graph filters and autoencoder-based embeddings
- Enhancing deep hashing with graph filters and autoencoder-based embeddings
- Enhancing deep hashing with graph filters and autoencoder-based embeddings
- Enhancing deep hashing with graph filters and autoencoder-based embeddings
- Enhancing deep hashing with graph filters and autoencoder-based embeddings
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