Peer-reviewed Journal Publications (†: Corresponding Author)

  1. Chatterjee, Sujoy, and Sunghoon Lim†. "A Multi-objective Differential Evolutionary Method for Constrained Crowd Judgment Analysis.IEEE Access 8 (2020): 87647-87664.
  2. Lim, Sunghoon, and Conrad S. Tucker†. "Mining Twitter data for causal links between tweets and real-world outcomes." Expert Systems with Applications: X 3 (2019): 100007.
  3. Lim, Sunghoon, Conrad S. Tucker†, Kathryn Jablokow, and Bart Pursel. "A semantic network model for measuring engagement and performance in online learning platforms." Computer Applications in Engineering Education 26, no. 5 (2018): 1481-1492.
  4. Tuarob, Suppawong, Sunghoon Lim, and Conrad S. Tucker†. "Automated Discovery of Product Feature Inferences within Large Scale Implicit Social Media Data."  Journal of Computing and Information Science in Engineering 18, no. 2 (2018): 021017.
  5. Lim, Sunghoon, and Conrad S. Tucker†. "Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers." Journal of Mechanical Design 139, no. 11 (2017): 111409.
  6. Lim, Sunghoon, Conrad S. Tucker†, and Soundar Kumara. "An unsupervised machine learning model for discovering latent infectious diseases using social media data." Journal of Biomedical Informatics 66 (2017): 82-94.  (PubMed)
  7. Lim, Sunghoon, and Conrad S. Tucker†. "A Bayesian Sampling Method for Product Feature Extraction From Large-Scale Textual Data." Journal of Mechanical Design 138, no. 6 (2016): 061403.


Peer-reviewed Journal Publications: Under Review (†: Corresponding Author)

  1. Lim, Sunghoon†Sun Jun Kim, YoungJae Park, and Nahyun Kwon. "A deep learning-based time series model with missing value handling techniques to predict various types of liquid cargo traffic." Expert Systems with Applications (Under Review)
  2. Choi, Jae Gyeong, Chan Woo Kong, Gyeongho Kim, and Sunghoon Lim†. "Car crash detection using ensemble deep learning and multimodal data from dashboard cameras." Expert Systems with Applications (Under Review)
  3. Tama, Bayu Adhi, and Sunghoon Lim†. "Ensemble Learning for Intrusion Detection Systems: A Systematic Mapping Study and Cross-benchmark Evaluation." Computer Science Review (Under Review)
  4. Tama, Bayu Adhi, and Sunghoon Lim†. "Review of classification algorithms for medical decision support system: A comparative performance evaluation." Electronics (Under Review)
  5. Nkenyereye, Lewis, Bayu Adhi Tama, and Sunghoon Lim†. "A Stacking-based Deep Neural Network Approach for Effective Network Anomaly Detection." Computers, Materials & Continua (Under Review)
  6. Hwang, Seong Wook, and Sunghoon Lim†. "The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM." European Journal of Operational Research (Under Review)


Peer-reviewed Conference Proceedings (†: Corresponding Author)

  1. Lim, Sunghoon, Conrad S. Tucker, Kathryn Jablokow, and Bart Pursel. "Quantifying the Mismatch between Course Content and Students’ Dialogue in Online Learning Environments." In ASME 2017 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference, American Society of Mechanical Engineers, 2017. (DEC Technical Committee Best Paper Award consisting of a prize of $1,000)


International Conference Presentations (*: Presenter, †: Corresponding Author)

  1. Lim, Sunghoon*†, Chan Woo Kong, and Jae Gyeong Choi. "CAR CRASH DETECTION USING DEEP LEARNING AND MULTIMODAL DATA." IISE Annual Conference & Expo, New Orleans, Louisiana, 2020.
  2. Hwang, Seong Wook*, and Sunghoon Lim†. "The Charging Infrastructure Design Problem with Electric Taxi Demand Prediction Using Convolutional LSTM." INFORMS Annual Meeting, Seattle, Washington, 2019.


Domestic Conference Presentations (*: Presenter, †: Corresponding Author)

  1. Choi, Jae Gyeong*, Chan Woo Kong, and Sunghoon Lim†. "Developing machine-learning-based car crash detection systems
using video and audio data.” KIIE Fall Conference, Seoul, Republic of Korea, 2019.
  2. Baek, DaeSeon, and Sunghoon Lim*†. "Smart farming: Developing growth programs and reforming environmental conditions for hog raising using machine vision and deep learning." KIIE/KORMS/KSS Joint Spring Conference, Gwangju, Republic of Korea, 2019.