| Early diagnosis and prediction of sepsis shock by combining static and dynamic information using convolutional-LSTM C Lin, Y Zhang, J Ivy, M Capan, R Arnold, JM Huddleston, M Chi 2018 IEEE international conference on healthcare informatics (ICHI), 219-228, 2018 | 96 | 2018 |
| Deep Learning vs. Bayesian Knowledge Tracing: Student Models for Interventions. Y Mao Journal of educational data mining 10 (2), 2018 | 68 | 2018 |
| Intervention-bkt: incorporating instructional interventions into bayesian knowledge tracing C Lin, M Chi Intelligent Tutoring Systems: 13th International Conference, ITS 2016 …, 2016 | 48 | 2016 |
| Lstm for septic shock: Adding unreliable labels to reliable predictions Y Zhang, C Lin, M Chi, J Ivy, M Capan, JM Huddleston 2017 IEEE International Conference on Big Data (Big Data), 1233-1242, 2017 | 37 | 2017 |
| Mono2micro: an ai-based toolchain for evolving monolithic enterprise applications to a microservice architecture AK Kalia, J Xiao, C Lin, S Sinha, J Rofrano, M Vukovic, D Banerjee Proceedings of the 28th ACM Joint Meeting on European Software Engineering …, 2020 | 30 | 2020 |
| A comparisons of bkt, rnn and lstm for learning gain prediction C Lin, M Chi Artificial Intelligence in Education: 18th International Conference, AIED …, 2017 | 27 | 2017 |
| Incorporating student response time and tutor instructional interventions into student modeling C Lin, S Shen, M Chi Proceedings of the 2016 Conference on user modeling adaptation and …, 2016 | 26 | 2016 |
| Going deeper: Automatic short-answer grading by combining student and question models Y Zhang, C Lin, M Chi User modeling and user-adapted interaction 30 (1), 51-80, 2020 | 22 | 2020 |
| NL2API: A framework for bootstrapping service recommendation using natural language queries C Lin, A Kalia, J Xiao, M Vukovic, N Anerousis 2018 IEEE international conference on web services (ICWS), 235-242, 2018 | 16 | 2018 |
| Generation of microservices from a monolithic application based on runtime traces J Xiao, A Kalia, C Lin, R Batta, S Sinha, J Rofrano, M Vukovic US Patent 11,176,027, 2021 | 8 | 2021 |
| Facilitation of domain and client-specific application program interface recommendations N Anerousis, A Kalia, C Lin, M Vukovic, J Xiao US Patent 10,803,108, 2020 | 8 | 2020 |
| Multi-layer facial representation learning for early prediction of septic shock C Lin, J Ivy, M Chi 2019 IEEE International Conference on Big Data (Big Data), 840-849, 2019 | 7 | 2019 |
| Comparisons of BKT, RNN and LSTM for predicting student learning gains C Lin, M Chi AIED, 2017 | 4 | 2017 |
| Artificial intelligence optimized cloud migration H Sun, J Rofrano, M Vukovic, C Lin US Patent App. 16/919,178, 2022 | 3 | 2022 |
| Facilitation of domain and client-specific application program interface recommendations N Anerousis, A Kalia, C Lin, M Vukovic, J Xiao US Patent 10,831,772, 2020 | 3 | 2020 |
| Generation of microservices from a monolithic application based on runtime traces J Xiao, A Kalia, C Lin, R Batta, S Sinha, J Rofrano, M Vukovic US Patent 11,663,115, 2023 | 2 | 2023 |
| Contrastive neural network training in an active learning environment C Lin, H Sun, J Rofrano, M Vukovic US Patent 11,501,165, 2022 | 2 | 2022 |
| Cloud readiness planning tool (CRPT): An AI-based framework to automate migration planning C Lin, H Sun, J Hwang, M Vukovic, J Rofrano 2019 IEEE 12th International Conference on Cloud Computing (CLOUD), 58-62, 2019 | 2 | 2019 |
| Automated validity evaluation for dynamic amendment C Lin, J Rofrano, A Kalia, M Vukovic, J Hwang, J Ma, L Mei, YB Dang US Patent 11,520,783, 2022 | 1 | 2022 |
| Advantages and challenges of using ai planning in cloud migration H Sun, M Vukovic, J Rofrano, C Lin Scheduling and Planning Applications Workshop, 2019 | 1 | 2019 |
Min ChiDepartment of Computer Science, North Carolina State UniversityDirección de correo verificada de ncsu.edu
