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您的检索式：作者名="Trinh Tan Dat"
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| 1 | Stock-Price Forecasting Based on XGBoost and LSTM显示文摘Using time-series data analysis for stock-price forecasting(SPF)is complex and challenging because many factors can influence stock prices(e.g.,inflation,seasonality,economic policy,societal behaviors).Such factors can be analyzed over time for SPF.Machine learning and deep learning have been shown to obtain better forecasts of stock prices than traditional approaches.This study,therefore,proposed a method to enhance the performance of an SPF system based on advanced machine learning and deep learning approaches.First,we applied extreme gradient boosting as a feature-selection technique to extract important features from high-dimensional time-series data and remove redundant features.Then,we fed selected features into a deep long short-term memory(LSTM)network to forecast stock prices.The deep LSTM network was used to reflect the temporal nature of the input time series and fully exploit future con-textual information.The complex structure enables this network to capture more stochasticity within the stock price.The method does not change when applied to stock data or Forex data.Experimental results based on a Forex dataset covering 2008–2018 showed that our approach outperformed the baseline autoregressive integrated moving average approach with regard to mean absolute error,mean squared error,and root-mean-square error. | Pham Hoang Vuong Trinh Tan Dat Tieu Khoi Mai Pham Hoang Uyen Pham The Bao | 2022 | Computer Systems Science & Engineering2022，40，1： | 2 |
| 2 | An improved CRNN for Vietnamese Identity Card Information Recognition显示文摘This paper proposes an enhancement of an automatic text recognition system for extracting information from the front side of the Vietnamese citizen identity(CID)card.First,we apply Mask-RCNN to segment and align the CID card from the background.Next,we present two approaches to detect the CID card’s text lines using traditional image processing techniques compared to the EAST detector.Finally,we introduce a new end-to-end Convolutional Recurrent Neural Network(CRNN)model based on a combination of Connectionist Temporal Classification(CTC)and attention mechanism for Vietnamese text recognition by jointly train the CTC and attention objective functions together.The length of the CTC’s output label sequence is applied to the attention-based decoder prediction to make the final label sequence.This process helps to decrease irregular alignments and speed up the label sequence estimation during training and inference,instead of only relying on a data-driven attention-based encoder-decoder to estimate the label sequence in long sentences.We may directly learn the proposed model from a sequence of words without detailed annotations.We evaluate the proposed system using a real collected Vietnamese CID card dataset and find that our method provides a 4.28%in WER and outperforms the common techniques. | Trinh Tan Dat Le Tran Anh Dang Nguyen Nhat Truong Pham Cung Le Thien Vu Vu Ngoc Thanh Sang Pham Thi Vuong Pham The Bao | 2022 | Computer Systems Science & Engineering2022，40，2： | 0 |
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