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Over the past few years, several studies have considered the problem of Electric Vehicle Path Planning with intermediate recharge (EVPP-R) that consists of finding the shortest path between two given points by traveling through one or many charging stations, without exceeding the vehicle’s range. Unfortunately, the exact solution to this problem has a high computational cost. Therefore, speedup techniques are generally necessary (e.g., contraction hierarchies). In this paper, we propose and evaluate a new fast and intuitive graph clustering technique, which is applied on a real map with charging station data. We show that by grouping nearby stations, we can reduce the number of stations considered by a factor of 13 and increase the speed of computation by a factor of 35, while having a very limited trade-off increase, of less than \(1\%\), on the average journey duration time.

One of the major types of cardiovascular diseases is Coronary Artery Disease (CAD). This study tackles the problem of CAD detection using a new accurate hybrid machine learning model. The proposed ensemble model combines several classical machine learning techniques. Our base algorithm is used with four different kernel functions (linear, polynomial, radial basis and sigmoid). The new model was applied to analyze the well-known Cleveland CAD dataset from the UCI repository. To improve the performance of the model, we first selected the most important features of this dataset using a genetic search algorithm. Second, we applied a multi-level filtering technique to balance the data using the ClassBalancer and Resample methods. Our model provided the average CAD prediction accuracy of 98.34% for the Cleveland data (the average was taken over the four kernel functions).

In the last few years, several studies have considered different variants of the Electric Vehicle Journey Planning (EVJP) problem that consists in finding the shortest path (according to time) between two given points, passing by several charging stations and respecting the range of the vehicle. The total time taken by the vehicle is the sum of the driving time, the charging time and the waiting time. Unfortunately, the consideration of the waiting time has been neglected by previous studies. This study aims to fill this gap by introducing: (1) a graph relabeling technique using a probabilistic model of charging station occupancy generated using real EV stations data; (2) an alternative paths generation technique which accounts for worse than expected waiting time at various charging stations. Our empirical results indicate that the a priori consideration of charging station occupancy by graph relabeling can reduce the waiting time by more than 75%, while having a negligible impact on the driving time, and that the generation of alternative paths helps reduce the waiting (and total) time even more. For our public station network dataset and the current station occupancy (for now quite low), the mean total journey time (computed over 1000 requests) decreased by 17.3 minutes when our new technique was used.