Recently, aspect-level sentiment analysis methods using graph convolutional network (GCN)-based structures with fairly good performance have been introduced. However, previous GCN-based methods often experience one of the following limitations. First, GCNs usually use edges with binary weights. However, binary weights are not helpful in many tasks. Second, these GCNs only focus on extracting node features from some single words or phrases and ignore their context in the entire sentence or paragraph or only consider the information of independent phrases when determining the relation between two graph edges overlooking the semantic relation among these phrases. Finally, no studies simultaneously use the information on the context, the semantic relation, and the sentiment knowledge among words or phrases to build GCNs for aspect-level sentiment analysis. Therefore, to resolve these limitations, in this study, we propose a new method, the CANN-SSCG model, as follows. First, we built three separate heterogeneous graphs, namely, syntax-based, semantic-based, and context-based graphs. Second, we constructed a general heterogeneous graph (SSC graph) by combining the three constructed graphs. We then converted the nodes of the SSC graph into sentence vectors using a GCN with two layers (creating an SSC-GCN). Finally, we used a convolutional neural network algorithm with attention to position embeddings (CANN) on the output of the SSC-GCN model for aspect-level sentiment analysis. The experiments, which used three different datasets, including reviews and tweets, showed that the proposed method yields promising results based on the F1score.

Social networks are a very popular channel for people to communicate with, to find, to reference other users before making decisions, especially those concerning purchase. How can users’ opinions within social networks be used in making decisions cost-effective and reliable? In this paper, we propose an approach for supporting decision-making based on measuring the user satisfaction level by analyzing the sentiment of aspects and mining the fuzzy decision trees. Our proposal has been proved to overcome some of the disadvantages of previous methods. Specifically, we consider the fuzzy sentiments of users for aspects and the effects of user satisfaction, dissatisfaction, and hesitation for decision-making. The proposed method comprises four main stages. The first stage identifies a topic, which the user is interested. In the second stage, aspects of the topic and their sentiments within tweets are extracted. At the third stage, the user satisfaction level is calculated according to each kind of sentiment identified in the second step. Finally, a decision matrix is constructed, and the fuzzy decision tree is built to generate a set of rules for supporting users in decision-making. The experiments using tweets show that the proposed method achieves promising results regarding the accuracy and gained information.

Nowadays, resolving chaotic traffic situations, which usually link to traffic congestion, is an essential need. It poses many risks to commuters like traffic accidents, especially during bad weather situations. Besides, owing to the exponential growth of IoT technologies, it is easier than ever to collect a huge amount of urban sensing data. Therefore, building a system to anticipate congestion from the collected data could enhance public safety and give traffic police forces enough time to handle traffic flows in potentially dangerous areas. Moreover, if we can discover patterns in which predicted congestion usually happens, we can build reaction plans with various alert codes. They create dynamic risk maps that can provide useful knowledge to both authorities and travelers to make rescue and travel plans effectively. This paper proposes a novel framework to address these problems. The proposed framework employs the Enhanced-Fusion-3DCNN deep learning model to predict future long-term traffic congestion on a particular mesh-code at a particular time instance. The predicted traffic congestion data is later transformed into a temporal database and feed to the maximal periodic-frequent pattern algorithm to identify the sets of mesh-code in which regular congestion may happen in the predicted data. Experimental results on real-world traffic congestion data demonstrate that the proposed framework is efficient.

Having an effective and efficient model to predict traffic congestion using multi-sources data has challenged researchers for decades, especially when the number of data sources and data volume increase dramatically. In this research, we propose a new CNN-based approach that can absorb and wrap multi-sources data into a 2D/3D raster-image to predict traffic congestion. Thanks to the raster-image-based wrapping technique, the spatial-temporal correlation is conserved entirely. The proposed approach can (1) accurately predict traffic congestion over multi-scale areas at different time horizons, (2) additionally consider external factors that could affect traffic flows and might cause traffic congestion afterwards by using an immediate fusion strategy. Traffic congestion, precipitation data, posts on a social networking platform collected in Kobe city, Japan during the summer and fall of the two consecutive years are used to evaluate the proposed approach. The comparison to other methods working on the same topic is also conducted to confirm the advantage of the proposed approach. Last but not least, some insights regarding the consequences of spatial and temporal dimensions as well as external factors to different time windows are also discussed.

Predictive analytics are concerned as a type of complex event processing where a complex event can be predicted by utilizing insights extracted from a set of related events. This paper introduces a new complex event analysis for traffic risk prediction using 3D-CNN and a set of related events detected from multi-sources urban sensing data (e.g., congestion, traffic accident, precipitation). The contribution of this paper involves (1) the spatio-temporal information of multi-sources urban sensing data is reserved and wrapped into 3D raster images towards being able to leverage recent developments of 3D-CNN to conduct predictive analytics, (2) the imbalanced data problem which could severely affect the performance of deep learning models is tackled by straightening curved geographic chains, (3) traffic risks can be predicted well in both short-term and medium-term time horizons, and (4) The influence of related events detected from extra factors on a complex event can be explained explicitly. The proposed method is evaluated on the real dataset collected in Kobe, Japan during 2014 and 2015. The comparisons to baseline methods such as historical average and 2D-CNN show the advantage of the proposed method as well.

Policy-based security is an effective and convenient approach to manage information systems. By this approach, we can handle easily all behaviors of a system thought a set of rules. However, conflicts between rules are one of the most common problems we have to deal with in administrative process. In this paper, we propose a new axiomatic approach to solve this problem. Several postulates are presented and analyzed as well as some algorithms are proposed next. The algorithms also have been implemented and tested, and experimental results are presented.