Search Word(s) in Title, Keywords, Authors, and Abstract: LSTM

DeepAct: A Deep Neural Network Model for Activity Detection in Untrimmed Videos
Yeongtaek Song and Incheol Kim
Page: 150~161, Vol. 14, No.1, 2018
10.3745/JIPS.04.0059
Keywords: Activity Detection, Bi-directional LSTM, Deep Neural Networks, Untrimmed Video
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We propose a novel deep neural network model for detecting human activities in untrimmed videos. The process of human activity detection in a video involves two steps: a step to extract features that are effective in recognizing human activities in a long untrimmed video, followed by a step to detect human activities from those extracted features. To extract the rich features from video segments that could express unique patterns for each activity, we employ two different convolutional neural network models, C3D and I-ResNet. For detecting human activities from the sequence of extracted feature vectors, we use BLSTM, a bi-directional recurrent neural network model. By conducting experiments with ActivityNet 200, a large-scale benchmark dataset, we show the high performance of the proposed DeepAct model.


CNN-LSTM Coupled Model for Prediction of Waterworks Operation Data
Kerang Cao, Hangyung Kim, Chulhyun Hwang and Hoekyung Jung
Page: 1508~1520, Vol. 14, No.6, 2018
10.3745/JIPS.02.0104
Keywords: Big Data, CNN, Correlation Analysis, Deep-Learning, LSTM
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In this paper, we propose an improved model to provide users with a better long-term prediction of waterworks operation data. The existing prediction models have been studied in various types of models such as multiple linear regression model while considering time, days and seasonal characteristics. But the existing model shows the rate of prediction for demand fluctuation and long-term prediction is insufficient. Particularly in the deep running model, the long-short-term memory (LSTM) model has been applied to predict data of water purification plant because its time series prediction is highly reliable. However, it is necessary to reflect the correlation among various related factors, and a supplementary model is needed to improve the long-term predictability. In this paper, convolutional neural network (CNN) model is introduced to select various input variables that have a necessary correlation and to improve long term prediction rate, thus increasing the prediction rate through the LSTM predictive value and the combined structure. In addition, a multiple linear regression model is applied to compile the predicted data of CNN and LSTM, which then confirms the data as the final predicted outcome.