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Accurate forecasting of vessel traffic flow is crucial for establishing a smart inland waterway transportation system, ensuring real-time safety supervision, and enabling prompt emergency response. While existing methods mainly focus on temporal correlation, there is a need for research on utilizing spatiotemporal features for area vessel traffic forecasting. The study proposes a novel spatiotemporal vessel traffic fluxes prediction model called STA-BiLSTM. The model combines the Graph Attention (GAT) network and Long Short-Term Memory (LSTM) network within the GAT-LSTM framework to capture the spatial and temporal aspects of vessel traffic patterns. The proposed model incorporates the attention mechanism and Bi-directional Long Short-Term Memory (Bi-LSTM) model to enhance the accuracy and robustness of ship traffic flow prediction by capturing the dependencies between time steps and waterway segments. Experimental results based on three actual Yangtze River vessel traffic flow datasets demonstrate the superiority of the proposed method over traditional approaches. Furthermore, ablation experiments validate the effective extraction of spatiotemporal dependency features between river segments by the STA-BiLSTM model. Finally, the paper showcases the significance of the proposed method through practical examples. • A new graph organizational form is used to inland waterway transport network. • GAT-LSTM framework performs the LSTM and GAT methods in inland waterway transport network. • STA-BiLSTM model is presented to deal with the vessel traffic flow forecasting problem. • The performance of the STA-BiLSTM model is higher than other competitors, which demonstrates the superiority of our model. [ABSTRACT FROM AUTHOR]

• A deep learning COVID-19 diagnostic system is developed using state-of-the-art deep learning architectures. • A detailed analysis of different methods used for detecting COVID-19 is presented in this paper. • Chest X-rays from different sources are collected to build a robust classification model. • The developed diagnostic model provided efficient results, even with a limited number of input images. • The results produced in this work are validated by an expert radiologist. In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients' X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. [ABSTRACT FROM AUTHOR]

• A Cross-Sensor Land-cOVEr framework (LoveCS) is proposed. • LoveCS advances cross-sensor domain adaptation. • LoveCS learns divergence between sensors from structure and optimization. • The effectiveness of LoveCS was evaluated in three cities of China. • High-resolution city-scale mapping can be achieved within 9 h on one GPU. Urban land-cover information is essential for resource allocation and sustainable urban development. Recently, deep learning algorithms have shown promising results in land-cover mapping with high spatial resolution (HSR) imagery. However, the limitation of the annotation and the divergence of the multi-sensor images always challenge the transferability of deep learning, thus hindering city-level or national-level mapping. In this paper, we propose a scheme to leverage small-scale airborne images with labels (source) for unlabeled large-scale spaceborne image (target) classification. Considering the sensor characteristics, a Cross-Sensor Land-cOVEr framework, called LoveCS, is introduced to address the difficulties of the spatial resolution inconsistency and spectral differences. As for the structural design, cross-sensor normalization is proposed to automatically learn sensor-specific normalization weights, thereby narrowing the spectral differences hierarchically. Furthermore, a dense multi-scale decoder is proposed to effectively fuse the multi-scale features from different sensors. As for the model optimization, self-training domain adaptation is adopted, and multi-scale pseudo-labeling is proposed to reduce the scale divergence brought by the spatial resolution inconsistency. The effectiveness of LoveCS was tested on data from the three cities of Nanjing, Changzhou, and Wuhan in China. The comprehensive results all show that LoveCS is superior to the existing domain adaptation methods in cross-sensor tasks, and has good generalizability. Compared with the existing land-cover products, the obtained results have the highest accuracy and spatial resolution (1.0 m). Overall, LoveCS provides a new perspective for large-scale land-cover mapping based on limited HSR images. [ABSTRACT FROM AUTHOR]

For intelligent risk prediction and optimization in the shield tunnel excavation under uncertainty, this paper proposes a data-driven decision support framework based on the integration of deep neural network and gradient descent technique (DNN-GDO). More specifically, the DNN architecture for modeling non-linear relationships between influential factors and potential risk is superior in concurrently extracting and learning features from multi-variable inputs, while the GDO technique as a solution for the multi-objective optimization problem can be directly applied in the established DNN model to provide suggestions for reducing or preventing potential risks in the tunneling environment. To reveal the effectiveness of the developed approach, it is performed in a practical tunnel construction case in the Wuhan metro system, China. Sixteen decision variables belong to four categories of tunnel design, geology, the tunnel boring machine (TBM) operation, and surrounding building are taken into account, which are closely relevant to two significant risks named the accumulative ground settlement and building tilt rate. It has been found that the end-to-end learning pipeline based on DNN can yield high accuracy in the multi-output regression task, resulting in the mean-square error (MSE) of 0.737 and 0.002 for the two target risks, respectively. GDO is proven useful in not only finding the optimal solutions for effective risk control but also uncovering the key features for a better explanation of the optimization process. It has been proved that optimal solutions derived from GDO can flexibly guide to adjust input variables of interest to achieve the minimal risk degree. Besides, cutter rotating speed and ground volume of TBM along with the relative horizontal distance of buildings turn out to be the top 3 critical features contributing more to the tunnel-induced risk mitigation, which deserve more attention. In brief, the novel DNN-GDO decision tool supports to formulate meaningful strategies objectively and efficiently for tunneling safety assurance, which no longer largely relies on time-consuming and laborious manual assessment. • A data-driven approach based on deep neural network and gradient descent technique is proposed. • Gradient descent technique aims to provide suggestions for preventing potential risks in tunnels. • A practical tunnel construction case in China is applied for demonstration purposes. • Deep learning model can yield a high accuracy in the multi-output regression prediction task. • The derived optimal solutions can flexibly guide to adjust input variables to achieve minimal risk. [ABSTRACT FROM AUTHOR]

• Precipitation up to 3-hour is predicted based on radar echo images of Wuhan station. • ST-LSTM-SA is proposed by 3D convolution, spatiotemporal LSTM and self-attention. • ST-LSTM-SA has a better response to medium and heavy rain events. • ST-LSTM-SA outperforms ConvLSTM and machine learning methods at 1–3 h leads. Accurate and timely short-term forecasting services of precipitation variable are significant for people's lives and property security. The data-driven approaches demonstrate promising performance in the extrapolation of precipitation. In this paper, we proposed a spatiotemporal prediction model, namely the Spatial Temporal Long Short-Term Memory based on the self-attentive mechanism (ST-LSTM-SA). This model enables better aggregation of sequence features inspired by proposed improvements. The encapsulated 3D convolution is developed to fully exploit the short-term spatiotemporal information, and the channel correlation is modeled by self-attention mechanism to further improve representations in the long-term interaction, the effectiveness of which is validated in the ablation study. Comprehensive experiments have been conducted on the radar echo sequence, we successfully predicted future radar reflectivity images for next 3 h with data for previous three hours as inputs in Wuhan, China. Three machine learning methods: multiple linear regression (MLR), support vector regression (SVR) and artificial neural networks (ANN) and deep learning model—ConvLSTM have been chosen as comparative groups to corroborate the nowcasting availability of this model for radar echo extrapolation. Moreover, the supplementary experiment on minute dataset also illustrated the superiority of ST-LSTM-SA. The studies analyzed the forecasting performance in terms of image quality and rainfall error. The experimental results demonstrated the better versatility and performance of ST-LSTM-SA. These conclusions and attempts may provide efficient guidance for precipitation nowcasting in urban areas. [ABSTRACT FROM AUTHOR]

Predicting unsafe behaviour in advance can enable remedial measures to be put in place to mitigate likely accidents on construction sites. Prevailing safety studies in construction tend to be retrospective and focus on examining the conditions that contribute to unsafe behaviour from a psychological perspective. While such studies are warranted, they can also not visually comprehend the dynamic and complex conditions that influence unsafe behaviour. In this paper, we aim to contribute to filling this void and, in doing so, combine computer vision with Long-Short Term Memory (LSTM) to predict unsafe behaviours from videos automatically. Our proposed approach for predicting unsafe behaviour is based on: (1) tracking people using a SiamMask; (2) predicting the trajectory of people using an improved Social-LSTM; and (3) predicting unsafe behaviour using Franklin's point inclusion polygon (PNPoly) algorithm. We use the Wuhan metro project as a case to evaluate our approach's feasibility and effectiveness. Our adopted SiamMask method outperforms current techniques used for tracking people. Additionally, our improved Social-LSTM can achieve higher accuracy on trajectory prediction than other methods (e.g., Social-GAN). The research findings demonstrate that our developed computer vision approach can be used to accurately predict unsafe behaviour on construction sites. [ABSTRACT FROM AUTHOR]

• We proposed a new self-attention based recurrent convolutional neural network for prediction of cerebral infarction disease. Experiment results exhibit that the proposed model based on clinical intelligence approach perform appropriate diagnosis for prediction of cerebral infarction disease. •.Clinical notes are extracted from real-life hospital dataset to use in experiment. After that, put forward a model which automatically learns high-level semantic features from unstructured clinical text using sliding window convolution approach with the intra-layer recurrent connection. • Self-attention mechanism is used to get the focus of the model on essential words which contribute to meaning in the clinical text by using softmax probability distribution at the interior of the model. Background and Objective: Nowadays computer-aided disease diagnosis from medical data through deep learning methods has become a wide area of research. Existing works of analyzing clinical text data in the medical domain, which substantiate useful information related to patients with disease in large quantity, benefits early-stage disease diagnosis. However, benefits of analysis not achieved well when the traditional rule-based and classical machine learning methods used; which are unable to handle the unstructured clinical text and only a single method is not able to handle all challenges related to the analysis of the unstructured text, Moreover, the contribution of all words in clinical text is not the same in the prediction of disease. Therefore, there is a need to develop a neural model which solve the above clinical application problems, is an interesting topic which needs to be explored. Methods: Thus considering the above problems, first, this paper present self-attention based recurrent convolutional neural network (RCNN) model using real-life clinical text data collected from a hospital in Wuhan, China. This model automatically learns high-level semantic features from clinical text by using bi-direction recurrent connection within convolution. Second, to deal with other clinical text challenges, we combine the ability of RCNN with the self-attention mechanism. Thus, self-attention gets the focus of the model on essential convolve features which have effective meaning in the clinical text by calculating the probability of each convolve feature through softmax. Results: The proposed model is evaluated on real-life hospital dataset and used measurement metrics as Accuracy and recall. Experiment results exhibit that the proposed model reaches up to accuracy 95.71%, which is better than many existing methods for cerebral infarction disease. Conclusions: This article presented the self-attention based RCNN model by combining the RCNN with self-attention mechanism for prediction of cerebral infarction disease. The obtained results show that the presented model better predict the cerebral infarction disease risk compared to many existing methods. The same model can also be used for the prediction of other disease risks. [ABSTRACT FROM AUTHOR]

Quality management in shield tunneling projects is a challenging problem. To improve the quality of segment erection, the shield driver must constantly adjust the shield's attitude and position to reduce a snakelike motion for fitting the design tunnel axis based on the driver's experience, which is untimely and less reliable during excavation. Considering the disadvantage of this control method, this paper presents a predictive framework for the attitude and position in shield tunneling by applying a hybrid deep learning model. This framework contains a wavelet transform noise filter, convolutional neural network feature extractor, and long short-term memory predictor for determining the attitude and position of the shield machine in the future. The prediction framework is tested with the collected data of Mixshield operated in the river-crossing tunnel project of Yangtze Sanyang Road, Wuhan, China. Six variables characterizing the shield attitude and position are selected to validate the feasibility and performance of our method. Results reveal that the proposed model outperforms the other three similar models in predictive accuracy and provides decision support for adjusting the attitude and position in shield tunneling. • A new method for predicting the attitude and position of shield machine was proposed. • A hybrid deep learning model consisting of wavelet transform, CNN, and LSTM was constructed for the prediction task. • The on-site data collected from the largest river-crossing tunnel in China has been tested. • The prediction results comparing with other three models and the potential practice value were discussed. [ABSTRACT FROM AUTHOR]