Building 3-D Human Data Based on Handed Measurement and CNN

3-dimension(3-D)printing technology is growing strongly with many applications,one of which is the garment industry.The application of human body models to the garment industry is necessary to respond to the increasing personalization demand and still guarantee aesthetics.This paper proposes amethod to construct 3-D human models by applying deep learning.We calculate the location of the main slices of the human body,including the neck,chest,belly,buttocks,and the rings of the extremities,using pre-existing information.Then,on the positioning frame,we find the key points(fixed and unaltered)of these key slices and update these points tomatch the current parameters.To add points to a star slice,we use a deep learning model tomimic the form of the human body at that slice position.We use interpolation to produce sub-slices of different body sections based on the main slices to create complete body parts morphologically.We combine all slices to construct a full 3-D representation of the human body.

Stock-Price Forecasting Based on XGBoost and LSTM

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.

An improved CRNN for Vietnamese Identity Card Information Recognition

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.