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As the density of railway operations increases annually, train delays typically result in a greater degree of economic loss. If we can accurately assess the current evolution of delayed trains, railway authorities will be able to make timely decisions and reduce travel anxiety among passengers. In this paper, a deep learning model integrating wavelet transform (WT), fully connected neural network (FCNN), and long short-term memory (LSTM) network is proposed to address the short-term delay prediction problem for trains. Its predictive performance is enhanced by introducing two front train selection schemes, two methods for converting train operating time features into time-series data, and five commonly used wavelet functions. For validation purposes, the model is tested using real-world operational data from two distinct high-speed railway lines in China, and common machine learning algorithms are compared, including random forest (RF), gradient boosting regression tree (GBRT), XGBoosting (XGB), and support vector regression (SVR). The final experimental results demonstrate that our proposed model outperforms the benchmark models and can be effectively applied to practical prediction problems. • We investigate the spatio-temporal effects of delay propagation, categorizing delay data attributes into front trains and target trains. Two feature selection schemes are proposed to assess the impact of front train features on prediction performance. • Wavelet Transform (WT) is employed for the first time in train delay prediction to eliminate random interference. The model's prediction performance is enhanced through denoising and expanding feature dimensionality. • Real data from two Chinese high-speed railways validate the model's accuracy, providing practical guidance for its application. [ABSTRACT FROM AUTHOR]

Fault prediction technology provides a way to reduce the loss caused by equipment failure. Currently, many efforts of failure prediction pay more attention to the fault trend and the remaining useful life. This paper devotes to predicting a specific failure mode of device in the future. We propose a two-step prediction method with two sub-models to predict the fault mode. The first model, named regression model, combines Attention-based-LSTM (Long Short-Term Memory) and Random Forest (RF) to predict time series trends, where wavelet packet analysis is adopted to extract elementary features of time series and form multivariable time series. Based on the status data forecasted by the first model, the second model, named classification model, combines Attention-based-LSTM and Extra-Tree (ET) to classify fault mode. The classified fault will occur in the device at some point in the future. In the regression model, Attention-based-LSTM performs deep level feature extraction of vibration sensor signals, and the result is fed to the last layer (i.e., an RF layer), which takes responsibility for predicting the future sequence. The future sequence is then provided to the classification model, where Attention-based-LSTM is used to extract implicit features, and ET classifier is used to accept these features and figure out the failure phenomenon. Our approach for predicting failure modes includes the severity and type of fault, which are rarely investigated synthetically. The experimental results of bearing data show that the sub-models can achieve high prediction accuracy and our method can predict the future failure mode with precision. [ABSTRACT FROM AUTHOR]

• A predicting model for the long-term epidemic trend of COVID-19 by using LSTM with rolling update mechanism is proposed. • The 150-days ahead epidemic trend of COVID-19 in Russia, Peru and Iran are estimated by our proposed model. • The results provide that the epidemic of Peru will end in early December. • The number of daily cases in Russia and Iran is expected to fall below 2000 and 1000 by mid-November and early December. • By introducing diffusion index, the effectiveness of preventive measures taken by the government are analyzed. The COVID-19 outbreak in late December 2019 is still spreading rapidly in many countries and regions around the world. It is thus urgent to predict the development and spread of the epidemic. In this paper, we have developed a forecasting model of COVID-19 by using a deep learning method with rolling update mechanism based on the epidemical data provided by Johns Hopkins University. First, as traditional epidemical models use the accumulative confirmed cases for training, it can only predict a rising trend of the epidemic and cannot predict when the epidemic will decline or end, an improved model is built based on long short-term memory (LSTM) with daily confirmed cases training set. Second, considering the existing forecasting model based on LSTM can only predict the epidemic trend within the next 30 days accurately, the rolling update mechanism is embedded with LSTM for long-term projections. Third, by introducing Diffusion Index (DI), the effectiveness of preventive measures like social isolation and lockdown on the spread of COVID-19 is analyzed in our novel research. The trends of the epidemic in 150 days ahead are modeled for Russia, Peru and Iran, three countries on different continents. Under our estimation, the current epidemic in Peru is predicted to continue until November 2020. The number of positive cases per day in Iran is expected to fall below 1000 by mid-November, with a gradual downward trend expected after several smaller peaks from July to September, while there will still be more than 2000 increase by early December in Russia. Moreover, our study highlights the importance of preventive measures which have been taken by the government, which shows that the strict controlment can significantly reduce the spread of COVID-19. [ABSTRACT FROM AUTHOR]

In this paper, Deep Learning-based models are used for predicting the number of novel coronavirus (COVID-19) positive reported cases for 32 states and union territories of India. Recurrent neural network (RNN) based long-short term memory (LSTM) variants such as Deep LSTM, Convolutional LSTM and Bi-directional LSTM are applied on Indian dataset to predict the number of positive cases. LSTM model with minimum error is chosen for predicting daily and weekly cases. It is observed that the proposed method yields high accuracy for short term prediction with error less than 3% for daily predictions and less than 8% for weekly predictions. Indian states are categorised into different zones based on the spread of positive cases and daily growth rate for easy identification of novel coronavirus hot-spots. Preventive measures to reduce the spread in respective zones are also suggested. A website is created where the state-wise predictions are updated using the proposed model for authorities,researchers and planners. This study can be applied by other countries for predicting COVID-19 cases at the state or national level. [ABSTRACT FROM AUTHOR]

Recently, large scale video content retrieval has gained more attention due to a large amount of user-generated images and video content available over the internet. Hashing is one of the effective techniques that encode the high dimensional features vectors into compact binary codes. The aim of hashing is to generate the short binary codes and map the similar hash code values to retrieve similar video from the database with minimum distance measure. Deep learning-based hashing networks are employed to learn the representative video feature to estimate the hash functions. However, the existing hashing approaches fail due to the frame-level feature representation and does not well exploits the effective temporal features in visual search. Furthermore, significant loss of features during the dimensionality reduction step causes low accuracy. Hence, it is essential to develop a deep learning-based hashing framework that should exploits both the strong spatial and temporal features for the scalable video search. The main objective is to learn the high dimensional features from the entire video and derive compact binary codes to retrieve the similar videos for the input query sequence. In this paper, we propose a joint network model of supervised stacked heterogeneous convolutional multi-kernel (Stacked HetConv-MK)-bidirectional Long Short Term Memory (BiDLSTM) network model that effectively encodes the rich structural as well as the discriminative features from the video sequence to estimate the compact binary codes. Initially, the video frames are passed to the stacked convolution networks with heterogeneous convolutional kernel size and residual learning to extract the spatial features at different views from the video sequences and to improve the learning efficiency. Then, the bidirectional network computes the sequence in both forward and backward directions and obtains the series of hidden state output. Finally, the fully connected structure with an activation unit performs hashing to learn the multiple codes for each video. Experimental analysis is performed on three datasets and the result shows a better accuracy measure than the other state-of-art approaches. [ABSTRACT FROM AUTHOR]