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

Digital Library


Search: "[ keyword: LSTM ]" (9)
  1. 1. A Study on the Energy Usage Prediction and Energy Demand Shift Model to Increase Energy Efficiency
    JaeHwan Kim, SeMo Yang, KangYoon Lee, Vol. 24, No. 2, pp. 57-66, Apr. 2023
    10.7472/jksii.2023.24.2.57
    Keywords: Energy, Energy usage fee, LSTM, clustering, Time Series K-means, shift of demand
  2. 2. A Study on Emotion Recognition of Chunk-Based Time Series Speech
    Hyun-Sam Shin, Jun-Ki Hong, Sung-Chan Hong, Vol. 24, No. 2, pp. 11-18, Apr. 2023
    10.7472/jksii.2023.24.2.11
    Keywords:
  3. 3. Personal Driving Style based ADAS Customization using Machine Learning for Public Driving Safety
    Giyoung Hwang, Dongjun Jung, Yunyeong Goh, Jong-Moon Chung, Vol. 24, No. 1, pp. 39-47, Feb. 2023
    10.7472/jksii.2023.24.1.39
    Keywords: autonomous vehicles, ADAS, Machine Learning, SVM, LSTM, GRU, Public Driving Safety
  4. 4. Bidirectional LSTM based light-weighted malware detection modelusing Windows PE format binary data
    Kwang-Yun PARK, Soo-Jin LEE, Vol. 23, No. 1, pp. 87-93, Feb. 2022
    10.7472/jksii.2022.23.1.87
    Keywords: Bidirectional LSTM, Windows PE malware, Detection, EMBER2018
  5. 5. Deep Learning based Abnormal Vibration Prediction of Drone
    Jun-Ki Hong, Yang-Kyoo Lee, Vol. 22, No. 3, pp. 67-73, Jun. 2021
    10.7472/jksii.2021.22.3.67
    Keywords: Deep Learning, RNN, LSTM, Drone, Motor, Vibration
  6. 6. Prediction of infectious diseases using multiple web data and LSTM
    Yeongha Kim, Inhwan Kim, Beakcheol Jang, Vol. 21, No. 5, pp. 139-148, Oct. 2020
    10.7472/jksii.2020.21.5.139
    Keywords: Machine Learning, Predict infectious diseases, Web data, LSTM
  7. 7. An LSTM Neural Network Model for Forecasting Daily Peak Electric Load of EV Charging Stations
    Haesung Lee, Byungsung Lee, Hyun Ahn, Vol. 21, No. 5, pp. 119-127, Oct. 2020
    10.7472/jksii.2020.21.5.119
    Keywords: EV, Peak electric load, Load forecasting, Deep Learning, LSTM
  8. 8. 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
  9. 9. A Comparative Study on the Optimal Model for abnormal Detection event of Heart Rate Time Series Data Based on the Correlation between PPG and ECG
    Jin-soo Kim, Kang-yoon Lee, Vol. 20, No. 6, pp. 137-142, Dec. 2019
    10.7472/jksii.2019.20.6.137
    Keywords: Photoplethysmography(PPG), Electrocardiogram(ECG), Abnormal event detection, SVM, LSTM