• Journal of Internet Computing and Services
    ISSN 2287 - 1136 (Online) / ISSN 1598 - 0170 (Print)
    https://jics.or.kr/

Digital Library


Search: "[ keyword: model ]" (140)
  1. 21. Analysis of the Influence of Domestic Open Banking Quality Factors on Intention to Use
    Bo-chun Jung, Suk-ki Hong, Vol. 22, No. 5, pp. 69-77, Oct. 2021
    10.7472/jksii.2021.22.5.69
    Keywords: open banking, data sharing, Technology Acceptance Model(TAM), FinTech
  2. 22. A Study on Analysis of national R&D research trends for Artificial Intelligence using LDA topic modeling
  3. 23. Active Senior Contents Trend Analysis using LDA Topic Modeling
    Dongwoo Lee, Yoosin Kim, Eunjung Shin, Vol. 22, No. 5, pp. 35-45, Oct. 2021
    10.7472/jksii.2021.22.5.35
    Keywords: Active Senior, Big data, Text Mining, LDA Topic Modeling, Trend Analysis
  4. 24. Formal Model of Extended Reinforcement Learning (E-RL) System
    Do Yeong Jeon, Myeong Ho Song, Soo Dong Kim, Vol. 22, No. 4, pp. 13-28, Aug. 2021
    10.7472/jksii.2021.22.4.13
    Keywords: Reinforcement Learning (RL), Advanced RL, Formal Model, Design Methods, and Advanced Navigator System
  5. 25. Visualization models for tracking software requirements and managing their changes
    YooRi Song, Hyeon Soo Kim, Vol. 22, No. 3, pp. 59-66, Jun. 2021
    10.7472/jksii.2021.22.3.59
    Keywords: Software Requirement, Traceability, Software Change Management, Visualization Model
  6. 26. Actantial Model-based Character Role Recognition using Emotional Flow Graph among Characters in Text Stories
  7. 27. A Study on Defense and Attack Model for Cyber Command Control System based Cyber Kill Chain
  8. 28. Regeneration of a defective Railroad Surface for defect detection with Deep Convolution Neural Networks
  9. 29. Deep Learning-based Abnormal Behavior Detection System for Dementia Patients
    Kookjin Kim, Seungjin Lee, Sungjoong Kim, Jaegeun Kim, Dongil Shin, Dong-kyoo shin, Vol. 21, No. 3, pp. 133-144, Jun. 2020
    10.7472/jksii.2020.21.3.133
    Keywords: Abnomaly detection, deep-learning, Autoencoder, Long Short-Term Memory models
  10. 30. LSTM-based Business Process Remaining Time Prediction Model Featured in Activity-centric Normalization Techniques
    Seong-Hun Ham, Hyun Ahn, Kwanghoon Pio Kim, Vol. 21, No. 3, pp. 83-92, Jun. 2020
    10.7472/jksii.2020.21.3.83
    Keywords: predictive process monitoring, remaining time prediction, LSTM model, Deep Learning, Process Mining