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Traffic congestion affects quality of life by inducing frustration and wasting time. The congestion is also critical to vehicles with high emergencies such as ambulances or police cars. This leads to additional CO2 emissions. Traffic management requires the accurate modeling of congestion levels. Two main observable parameters identify the congestion state of a city: vehicle speed and density. Congestion has an intuitive definition rather than a quantitative one, and is associated with the disorder and randomness occurring in traffic parameters. Therefore, statistical analysis offers an efficient and natural framework for modeling such disorders. In this study, a differential-entropy-based approach was proposed for labelling purposes. Subsequently, supervised congestion prediction from traffic meta-parameters based on a convolutional neural network was proposed. Traffic parameters includes node localization, date, day of the week, time of day, special road conditions, and holidays. The proposed model is validated on the CityPulse dataset, which is a set of vehicle traffic records, collected in Aarhus city in Denmark over a period of six months, for 449 observation nodes. Simulation results on the CityPulse dataset illustrate that the proposed approach yields accurate prediction rates for different nodes considered. The proposed system can prevent traffic congestion by reorienting the drivers to follow other itineraries.

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The present paper proposes a fully automated three-dimensional (3-D) system for breast and lesion segmentation of Dynamic Contrast Enhanced MRI (DCE-MRI). Such a system as the Computer-Aided Diagnostic system (CAD) can be used to support radiologists by marking suspicious areas. The proposed 3-D-CAD system includes three modules. The first one concerns breast area segmentation based on image content analysis, the Moore-Neighbor tracing algorithm, and the Dijkstra algorithm. The second one concerns the automation of locating and selecting lesions that starts by preprocessing the already segmented breast regions; then, a K-means algorithm allows extraction of regions including suspicious tissues. The third one is the superimposition of all detected lesions from selected slices to create a 3-D view of the lesion. The 3-D reconstruction is based on the Marching Cube algorithm. The validation of breast area segmentation reveals the robustness of the proposed process versus different breast densities, complex forms, and challenging cases. The segmentation of the breast part from 120 slices with the proposed method is achieved in 20.57 ± 5.2 s, which is faster than existing methods. In addition to calculated metrics as Dice similarity coefficient (DSC) and rates of true positives, the module of lesion extraction is validated by experimented radiologists. The proposed workflow of this module shows competitive results compared to the existing methods of automated lesion segmentation and a total ability for eliminating the extra regions to lesions.

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