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Although there are a lot of methods for multi-label classification in the past research, there are still many problems. For example, in the real world, labels are not necessarily independent of each other, and there may be some connection between labels. Therefore, exploring and utilizing the interdependence between labels is a key issue in current research. For example, in the photo category, a picture that contains blue sky often also contains white clouds, and in the text category, a political story is less likely to be entertainment news. Therefore, the key to improve the accuracy of multi-label classification is to effectively learn the possible correlations between each label. Therefore, we propose a novel multi-label educational emotion prediction based on graph convolutional network and long and short term memory network fusion label correlation in online social networks. This model uses Word2Vec method to train word vectors and combines graph convolutional neural network (GCN) with long and short term memory network (LSTM). The GCN is used to dig deeper word features of text, the LSTM layer is used to learn the longterm dependence relationship between words, and the multi-granularity attention mechanism is used to assign higher weight to the affective word features. At the same time, label correlation matrix is used to complete the label feature vector and text features as the input of the classifier, and the correlation between labels is investigated. The experimental results on the open data set show that the proposed model has a good classification effect compared with other advanced methods. The research results promote the combination of deep learning and affective computing, and can promote the research of network user behavior analysis and prediction, which can be used in personalized recommendation, targeted advertising and other fields, and has wide academic significance and application prospects. [ABSTRACT FROM AUTHOR]

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

Summary: Real‐time estimation of crowd counting in underground metro systems, constrained by limited space, is crucial for managing heightened pedestrian volumes and responding promptly to emergencies. To address this challenge, we propose a passenger state transition‐based model, called STRmt, designed for the seamless and continuous monitoring of real‐time crowd movement within service areas of stations and trains, leveraging auto fare collection systems (AFC) as a comprehensive sensor network. Our innovation lies in modeling the dynamic movement of passengers within a metro system over time as a state transition process aligned with the train schedule. To achieve this, we introduce a spatio‐temporal deep learning framework, denoted as STnet, designed to dynamically predict these state transitions. The performance of our method is rigorously assessed through extensive experiments conducted spanning 2 years in Shenzhen, China, utilizing AFC data, train schedule data, and weather data. The results demonstrate that the proposed method surpasses baseline methods, achieving an estimation precision of 0.92. [ABSTRACT FROM AUTHOR]

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson's correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures. [ABSTRACT FROM AUTHOR]

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants. [ABSTRACT FROM AUTHOR]

The total electron content (TEC) of the ionosphere is one of the key parameters for both scientific study and engineering application. A hybrid deep learning network based on Grapy Convolution Network (GCN) and Long Short Term Memory (LSTM) is proposed to predict the regional ionospheric TEC 24 h in advance across China. The extensive 14-year dataset during 2005–2018 from the CODE TEC measurements, covering a wide variety of solar activity, are used to comprehensively evaluate the performance of the predictive model. The experimental results show that the model predicted TEC is in good agreement with the actual CODE TEC. Over 90% of differences between the actual TEC and the predicted TEC are within ±2 TECU, and the model errors obviously depend on seasons, latitudes and solar activity. Comparison with the previous work indicate that the proposed regional forecast model is competitive. [ABSTRACT FROM AUTHOR]

High-precision water quality prediction plays a vital role in preventing and controlling river pollution. However, river water's highly nonlinear and complex spatio-temporal dependencies pose significant challenges to water quality prediction tasks. In order to capture the spatial and temporal characteristics of water quality data simultaneously, this paper combines deep learning algorithms for river water quality prediction in the river network area of Jiangnan Plain, China. A water quality prediction method based on graph convolutional network (GCN) and long short-term memory neural network (LSTM), namely spatio-temporal graph convolutional network model (ST-GCN), is proposed. Specifically, the spatio-temporal graph is constructed based on the spatio-temporal correlation between river stations, the spatial features in the river network are extracted using GCN, and the temporal correlation of water quality data is obtained by integrating LSTM. The model was evaluated using R2, MAE, and RMSE, and the experimental results were 0.977, 0.238, and 0.291, respectively. Compared with traditional water quality prediction models, the ST-GCN model has significantly improved prediction accuracy, better stability, and generalization ability. HIGHLIGHTS The spatio-temporal graph is used to characterize the spatio-temporal correlation of different monitoring stations.; Based on the GCN and the LSTM, the ST-GCN model is suitable for extracting spatial and temporal features in graph data.; In terms of river water quality prediction, the model has significantly improved performance compared with the five baseline models.;

The water quality data of the river basin has dual dependencies in time and space. Based on the fact that most of the existing water quality prediction models are only based on the time dimension and cannot make effective use of the spatial correlation of water quality, a spatiotemporal prediction model based on graph convolution neural network GCN and long short-term memory network LSTM was proposed in this paper. Firstly, the whole watershed was constructed into the topology of station-to-station connection, and the distance along the river between stations was taken as the indicator of connectivity between stations. For each input time, an independent GCN was used to capture the spatial relationship between stations, and then the temporal variation characteristics were captured by LSTM. Finally, the multi-step prediction results were obtained by multi-layer perceptron (MLP). The south source of Qiantang River was selected as the research object, multi step prediction experiments were carried out on three water quality prediction tasks of pH, DO and CODMn at 15 monitoring stations in the basin. The results show that in the three water quality prediction tasks, the average percentage error MAPE of GCN-LSTM model compared with LSTM model is decreased by 15.29%, 11.77% and 9.8% respectively. Compared with some classical time series models, the model proposed in this paper can better capture the spatial relationship of watershed water quality, improve the accuracy of water quality prediction and provide a new method for watershed water environment quality prediction. The connections between monitoring points have differences, and using distance to represent the strength of the connections between monitoring points can make water quality prediction results more accurate. [ABSTRACT FROM AUTHOR]

Timely and accurate large-scale traffic prediction has gained increasing importance for traffic management. However, it is a challenging task due to the high nonlinearity of traffic flow and complex network topology. This study aims to develop a large-scale traffic flow prediction model exploring the interaction of multiple traffic parameters to improve the prediction performance. To achieve this goal, we proposed a spatiotemporal graph convolution network considering multiple traffic parameters (MP-STGCN). The proposed model consists of a spatial gated linear unit (SGLU) block, a graph convolutional network (GCN) block, and a long short-term memory (LSTM) block. SGLU is used to extract the spatiotemporal features of multi-parameter traffic data including traffic flow, average travel speed, and traffic occupancy. Based on topological linkages, the GCN is utilized to capture the spatial features of traffic data. Additionally, the LSTM is employed to extract the temporal features of the traffic data. The results carried out on PeMS dataset show that the proposed model consistently outperforms the competing models. Furthermore, our model can handle complex nonlinear traffic data and improve the accuracy of large-scale traffic prediction. [ABSTRACT FROM AUTHOR]

Aspect-based sentiment analysis (ABSA) has captured great attention from researchers and industrialists owing to their pulverized nature of sentiment analysis task and the goal to anticipate sentiment polarity of given aspect of provided text. Massive growth nudged the researchers to innovate methodologies and strategies for every distinct research analysis question which could muddle through the impending concerns and composite schema of ABSA. The exponential growth of deep learning has extensively labeled this task with several Deep Neural Network (DNN) models. This survey article furnishes a comparative review about the proposed cutting-edge deep learning methods to solve an ABSA problem infusing the common exemplar datasets, assessment metrics and available performance analysis of deep-learning methods. The critical analysis of the materialized current solutions has proposed future research pathways for researchers and hence is instrumental for tweaking sentiment classification at aspect-level. [ABSTRACT FROM AUTHOR]

As cloud system architectures evolve continuously, the interactions among distributed components in various roles become increasingly complex. This complexity makes it difficult to detect anomalies in cloud systems. The system status can no longer be determined through individual key performance indicators (KPIs) but through joint judgments based on synergistic relationships among distributed components. Furthermore, anomalies in modern cloud systems are usually not sudden crashes but rather gradual, chronic, localized failures or quality degradations in a weakly available state. Therefore, accurately modeling cloud systems and mining the hidden system state is crucial. To address this challenge, we propose an anomaly detection method with dynamic spatiotemporal learning (AD-DSTL). ADDSTL leverages the spatiotemporal dynamics of the system to train an endto- end deep learning model driven by data from system monitoring to detect underlying anomalous states in complex cloud systems. Unlike previous work that focuses on the KPIs of separate components, AD-DSTL builds a model for the entire system and characterizes its spatiotemporal dynamics based on graph convolutional networks (GCN) and long short-term memory (LSTM). We validated AD-DSTL using four datasets from different backgrounds, and it demonstrated superior robustness compared to other baseline algorithms. Moreover,when raising the target exception level, both the recall and precision of AD-DSTL reached approximately 0.9. Our experimental results demonstrate that AD-DSTL can meet the requirements of anomaly detection for complex cloud systems. [ABSTRACT FROM AUTHOR]

Video surveillance involves petabytes of data storage requiring expensive hardware, which might also be time-inefficient. The aim of this article is, therefore, to develop an intelligent system capable of analyzing long sequences of videos captured from CCTV, helping to mitigate catastrophe and mitigate the violent threats faced by citizens every day, economically and efficiently. Existing models have achieved high accuracy on available datasets, the primary focus is to improve speed (time-efficient) of violence detection and use very little storage (economical) such that the system can be used in real-time. The paper presents an end-to-end hybrid solution for detecting violence in real-time video frames incorporating both human and weapon detection algorithms applied in a synchronized way. The focus of this article is to propose a generic HWVd (Human Weapon Violence detection) model to detect all kinds of public violence. HWVd is a three-tier ensemble model to detect violence in videos. The first tier is human detection, which uses a LightTrack framework. In the second tier, a Fast Region-based Convolutional Neural Network (F-RCNN) to detect any weapon in videos is used. The third tier uses a pre-trained VGG 19 (a pre-trained model of CNN) for spatial feature extraction and Long Short Term Memory (LSTM) to detect violent activity. Lastly, the output of this framework is sent to the Support Vector Machine to classify the activity as (i) violence not involving weapon, (ii) violence involving weapon and (iii) non-violent. The accuracy obtained using the proposed model is 98%. [ABSTRACT FROM AUTHOR]

Forecasting air quality plays a crucial role in preventing and controlling air pollution. It is particularly significant for improving preparedness for heavily polluted weather conditions and ensuring the health and safety of the population. In this study, a novel deep learning model for predicting air quality spatio-temporal variations is introduced. The model, named graph long short-term memory with multi-head attention (GLSTMMA), is designed to capture the temporal patterns and spatial relationships within multivariate time series data related to air quality. The GLSTMMA model utilizes a hybrid neural network architecture to effectively learn the complex dependencies and correlations present in the data. The extraction of spatial features related to air quality involves the utilization of a graph convolutional network (GCN) to collect air quality data based on the geographical distribution of monitoring sites. The resulting graph structure is imported into a long short-term memory (LSTM) network to establish a Graph LSTM unit, facilitating the extraction of temporal dependencies in air quality. Leveraging a Graph LSTM unit, an encoder-multiple-attention decoder framework is formulated to enable a more profound and efficient exploration of spatio-temporal correlation features within air quality time series data. The research utilizes the 2019–2021 multi-source air quality dataset of Qinghai Province for experimental assessment. The results indicate that the model effectively leverages the impact of multi-source data, resulting in optimal accuracy in predicting six air pollutants.

During online social networks (OSNs), popularity prediction uncovers the final size of online content based on the observed cascade, which has been the critical technology for online recommendation, viral marketing, and rumor detection. Recently, representation learning could help to infer the mapping between the dynamic cascade and the final popularity efficiently, and has been a new research paradigm for popularity prediction. However, those methods are vulnerable to structure disturbance when lack of fine-grained supervision, as only the dynamic cascade is used. Therefore, we propose a novel trend and cascade based spatiotemporal evolution network (TCSE-Net), which preserves the distinguishable structure pattern while eliminating potential noise, via aligning and fusing the temporal popularity and cascade. To be specific, we first leveraged the Long-Short Term Memory (LSTM) and recurrent graph convolutional network (GCN) to learn the trend representation and the corresponding cascade representation respectively. Meanwhile, we represent node with it's layer, thereby the hierarchy is preserved in cascade representation through GCN. Then, both trend and cascade representations are aligned in time sequence and selectively assembled by a set of shared parameters for popularity prediction. The extensive experimental results show that our TCSE-Net outperforms state-of-the-art baselines on two real datasets. Related code will be publicly available on https://github.com/TAN-OpenLab/TCSE-Net. [ABSTRACT FROM AUTHOR]

Link prediction is one of the most important methods to uncover evolving mechanisms of dynamic complex networks. Determining these links raises well-known technical challenges in terms of weak correlation, uncertainty and non-stationary. In this paper, we presented a novel gated graph convolutional network (GCN) based on spatio-temporal semi-variogram (STEM-GCN). It learns spacial and temporal features in order to achieve link prediction in the dynamic networks. In this STEM-GCN model, we first utilized the spatio-temporal semi-variogram to obtain the spacial and temporal correlations from the dynamic networks. Its spacial correlation helped us determine the hyper-parameters of STEM-GCN and speed up its training. The correlation smoothing strategy is also introduced to eliminate the noise through temporal correlation and to improve the accuracy of link prediction. Finally, the network dynamics are captured by propagating the spacial and temporal features between consecutive time steps with stacked memory cell structures. The extensive experiments on real data sets demonstrated the effectiveness of the proposed approach for link prediction in dynamic complex networks. [ABSTRACT FROM AUTHOR]

• Adual-layer correlation analysis framework (PCAF) based on five different models. • Use PCAF framework to conduct parameter analysis on thermohydraulic bench. • Achieve mapping of parameter relationships on bench to digital topological graph. • Design a method to rebuild topological graph to parameter correlation network. • Create a multi-dimensional parameter prediction framework based on LSTM and GCN. In nuclear power plants (NPPs) operations, the prediction of multi-dimensional parameters is found to help operators to grasp the condition of the system. However, majority of existing studies are focused on single-dimensional parameter prediction. In this study, a multi-dimensional parameter prediction framework of NPPs based on Long Short-Term Memory Network and Graph Convolution Network (LSTM-GCN) and a multi-model integrated parameter correlation analysis framework (PCAF) are proposed, in which PCAF is used to build a parameter correlation network for GCN, and LSTM-GCN is used to predict multi-dimensional parameter of NPPs. To verify the feasibility of the LSTM-GCN framework, multi-dimensional parameter prediction researches are conducted using data from a thermohydraulic experimental bench that simulates the operation of NPPs. Results indicate that compared to traditional prediction models, LSTM-GCN framework enhances the prediction accuracy of multi-dimensional parameter, which benefits from the ability of LSTM-GCN to utilize the temporal dependencies and spatial correlations of parameters. [ABSTRACT FROM AUTHOR]

Online ride-hailing order forecasting is a very important part of the intelligent traffic dispatch system. Accurate order forecasting can reduce the flow of invalid vehicles and improve the user experience of online ride-hailing. We propose a multi-view deep long short-term memory (LSTM) network architecture (MultiView deep LSTM framework), which uses convolutional neural network and graph convolutional network to extract the temporal and spatial characteristics of online ride-hailing orders, obtains the correlation information between regional orders through the order view, regional speed view, and weather factor view, and then uses LSTM unit and attention unit to predict the order volume in real time. We use Didi Haikou, China's online ride-hailing dataset for training, compare it with the prediction algorithms of other articles, and experiment with different choices of the contrast framework. The experimental results show that our deep learning framework can effectively capture comprehensive spatio-temporal correlation and obtain better results. The model maintained good performance at 15 min, 30 min, and 1 h. Experiments conducted on the actual demand data onto ride-hailing from Didi Haikou data prove that our method is better than the latest method. [ABSTRACT FROM AUTHOR]

Event Detection (ED) is a pivotal sub-task of Event Extraction(EE). It aims to locate triggers and categorize them into specific event types. Recent researches on ED have shown that graph convolutional neural networks with syntactic information can achieve advanced performance. However, these methods ignore the implicit importance score of tokens. This will weaken their ability of identifying trigger words. In addition, due to the long-tailed distribution in the corpus, previous methods perform poorly on sparsely labeled trigger words and are prone to overfitting on densely labeled ones. In this paper, we propose a Syntax-Enhanced GCN framework with a Decoupled Classification Rebalance mechanism (SEGCN-DCR) to address the above issues. Specifically, we exploit a tree-structured module based on dependency structure to reduce the noise by capturing global hierarchical syntactic information, and DCR mechanism to rescale the classifier weights, which makes classifier decision boundaries more reasonable. Experiments on benchmark ACE2005 show that the proposed method acquires state-of-the-art performance. [ABSTRACT FROM AUTHOR]

With the development of big data, large-scale traffic flow forecasting which is a part of smart transportation has become an increasingly important research direction. Accurate and real-time traffic flow prediction is the key and difficult part of the traffic. The complex spatial topological structure and dynamic traffic flow information in urban roads constitute a changeable spatial correlation, and the daily traffic flow cycle and weekly traffic flow cycle constitute a complex time correlation. For the current mainstream model, there are two main limitations: 1. Most of the existing models only focus on time correlation and ignore spatial correlation. 2. Even if the spatial correlation is concerned, the topological relationship between spaces is not fully considered. This paper proposes a new traffic-flow prediction model, which named Principal Spatio-Temporal Graph Convolution Network (PST-GCN) model, which uses a combination of Principal Component Analysis (PCA), Graph Convolution Network (GCN), and Long Short-Term Memory model (LSTM). Specifically, PCA is used to reduce the dimension of data, GCN is used to learn the network topology of urban roads, LSTM is used to capture the time correlation of traffic flow. By comparing the results of different models, the proposed model is better than the current mainstream models.

The prediction of crowd flows is an important urban computing issue whose purpose is to predict the future number of incoming and outgoing people in regions. Measuring the complicated spatial–temporal dependencies with external factors, such as weather conditions and surrounding point-of-interest (POI) distribution is the most difficult aspect of predicting crowd flows movement. To overcome the above issue, this paper advises a unified dynamic deep spatio-temporal neural network model based on convolutional neural networks and long short-term memory, termed as (DHSTNet) to simultaneously predict crowd flows in every region of a city. The DHSTNet model is made up of four separate components: a recent, daily, weekly, and an external branch component. Our proposed approach simultaneously assigns various weights to different branches and integrates the four properties' outputs to generate final predictions. Moreover, to verify the generalization and scalability of the proposed model, we apply a Graph Convolutional Network (GCN) based on Long Short Term Memory (LSTM) with the previously published model, termed as GCN-DHSTNet; to capture the spatial patterns and short-term temporal features; and to illustrate its exceptional accomplishment in predicting the traffic crowd flows. The GCN-DHSTNet model not only depicts the spatio-temporal dependencies but also reveals the influence of different time granularity, which are recent, daily, weekly periodicity and external properties, respectively. Finally, a fully connected neural network is utilized to fuse the spatio-temporal features and external properties together. Using two different real-world traffic datasets, our evaluation suggests that the proposed GCN-DHSTNet method is approximately 7.9%–27.2% and 11.2%–11.9% better than the AAtt-DHSTNet method in terms of RMSE and MAPE metrics, respectively. Furthermore, AAtt-DHSTNet outperforms other state-of-the-art methods. • We design DHSTNet that considers spatio-temporal dependency and external factors of the crowd flows. • We classify temporal features into recent, daily, and weekly components. • We use GCN with DHSTNet, which at once handle dynamic spatio-temporal dependencies of traffic flows. • The spatio-temporal features of traffic flows data are dynamically learned by the proposed model. • Extensive evaluations on two real-world datasets indicate notable improvements over existing methods. [ABSTRACT FROM AUTHOR]

Address matching is a crucial task in various location-based businesses like take-out services and express delivery, which aims at identifying addresses referring to the same location in address databases. It is a challenging one due to various possible ways to express the address of a location, especially in Chinese. Traditional address matching approaches relying on string similarities and learning matching rules to identify addresses referring to the same location, could hardly solve the cases with redundant, incomplete or unusual expression of addresses. In this paper, to learn the geographical semantic representations for address strings, we novelly propose to get rich contexts for addresses from the Web through Web search engines, which could strongly enrich the semantic meaning of addresses that could be learned. Apart from that, we propose a two-stage geographical address representation learning model for address matching. In the first stage, we propose to use an encode-decoder architecture to learn the semantic vector representation for each address string where an up-sampling and sub-sampling strategy is applied to solve the problem of address redundancy and incompleteness. The attention mechanism is also applied to the model to highlight important features of addresses in their semantic representations. And in the second stage, we construct a single large graph from the corpus, which contains address elements and addresses as nodes, and the edges between nodes are built by word co-occurrence information to learn embedding representations for all the nodes on the graph. Our empirical study conducted on two real-world address datasets demonstrates that our approach greatly improves both precision (up to 8%) and recall (up to 12%) of the state-of-the-art existing methods. [ABSTRACT FROM AUTHOR]

This paper investigates the prediction of geological conditions ahead of tunnel boring machines (TBM) using a hybrid deep learning approach. By integrating graph convolutional network (GCN) and long short-term memory (LSTM) networks, the spatial and temporal features from TBM parameters and geological information are extracted for accurate prediction. The results from the case study indicate that (1) The proposed approach provides estimation with a high accuracy of 0.9986; (2) The past geological information has a significant contribution to the model; (3) The proposed approach outperforms several state-of-the-art methods including support vector machine (SVM), extreme gradient boosting (XGBoost) and LSTM method. The proposed hybrid deep learning approach can be a useful tool that provides reliable estimation of the advanced geological conditions in real-time. • A novel deep learning approach is proposed for advance geological perception. • The dependencies among the TBM parameters and geological information are considered. • A deep-learning framework that incorporates GCN and LSTM is established. • A realistic tunnel project in Singapore is used to demonstrate as a case study. • A high prediction accuracy can be achieved with average accuracy of 0.9986. [ABSTRACT FROM AUTHOR]

Ship speed information plays an important role in the maritime intelligent transportation system, and ship speed prediction has attracted extensive attention in maritime community. Previous studies suggest that ship speed is mainly affected by her previous movement status, regardless the interferences from neighboring locations. To address the issue, a novel deep learning framework is proposed to predict ship speed via the support of k-hop graph convolutional network (k-GCN) and long–short term memory (LSTM). Firstly, a ship network model is established with a graph network. The nodes in the proposed networks demonstrate ship trajectory segments, whilst the segments are connected to obtain edges. Secondly, the GCN is used to capture the spatial correlation between the nodes, and the LSTM is used to exploit spatial–temporal correlation among the nodes. Finally, the proposed k-GCN-LSTM model is used to predict ship speed, which are further verified on different real-world ship trajectory data against with other models. Results suggest that the proposed model obtains satisfied prediction performance in terms of typical error measurement indicators. The research findings help maritime traffic participants better avoid potential collisions and enhance maritime traffic safety. • GCN and LSTM are introduced to consider the spatiotemporal correlation of ships. • High-hop neighborhood of nodes is considered to extract more spatial features. • A cell state gate is added in the LSTM cell to consider neighboring cell states. • Three experiments are conducted to verify the performance of the proposed model. [ABSTRACT FROM AUTHOR]

Human activity recognition is one of the most studied topics in the field of computer vision. In recent years, with the availability of RGB-D sensors and powerful deep learning techniques, research on human activity recognition has gained momentum. From simple human atomic actions, the research has advanced towards recognizing more complex human activities using RGB-D data. This paper presents a comprehensive survey of the advanced deep learning based recognition methods and categorizes them in human atomic action, human–human interaction, human–object interaction. The reviewed methods are further classified based on the individual modality used for recognition i.e. RGB based, depth based, skeleton based, and hybrid. We also review and categorize recent challenging RGB-D datasets for the same. In addition, the paper also briefly reviews RGB-D datasets and methods for online activity recognition. The paper concludes with a discussion on limitations, challenges, and recent trends for promising future directions. • A comprehensive categorical survey of RGB-D human action/interaction recognition. • Categorized RGB-D datasets, methods into human actions, human–object interactions. • In-depth analysis of well-known deep learning methods for actions and interactions. • Examined distinct characteristics of RGB-D modalities for actions/interactions. • Discussed limitations, challenges, and promising future directions in the domain. [ABSTRACT FROM AUTHOR]

With increasingly cyber-attacks and intrusion techniques, the threat of network security has become more and more serious. However, existing solutions are no longer sufficient in terms of accuracy as attacks continue to grow in quantity and complexity. Prior methods mainly focused on the application of deep learning techniques to analyze data changes in traffic flow. The cunning Cyber-attacks cannot be detected because some advanced attack techniques can conceal attacks and make them might seem innocuous in statistics. At the same time, traditional models only concentrate on the statistics of traffic sent by individual hosts, so the potential relationships of communication patterns in network traffic might be ignored. It makes these solutions are not competent for dealing with the various uncertainty in network traffic. In this paper, we propose an efficient anomaly detection approach, called AnoGLA, which considering the complex communication patterns between network structure and node properties. To mine the hidden relationship between network traffic, we built graph structured data in network traffic and exploits graph convolution network (GCN) for modeling. And we also combine long short-term memory network (LSTM) with Attention mechanism to extract the change information of the graph at different times. The effectiveness and robustness of proposed method are evaluated on two real-world datasets. The experiment results indicate that our scheme can effectively detect anomaly flow and outperforms the previous ones in network anomaly detection tasks. [ABSTRACT FROM AUTHOR]