RESUMO
The Cumulative Unmanned Aerial Vehicle Routing Problem applies to area covering operations using UAVs. It is defined on a graph, the nodes of which, ensure the complete coverage of the underlying area of interest. The data generation process takes into account the characteristics of such operations, in particular, the viewing window of the UAVs' sensor, their maximum range, the size of the UAV fleet and the unknown locations of the targets within the area of interest. Instances are created simulating different scenarios, using different values for those UAV characteristics, as well as the different locations where the search targets might be positioned in the area of interest.
RESUMO
The term pap-smear refers to samples of human cells stained by the so-called Papanicolaou method. The purpose of the Papanicolaou method is to diagnose pre-cancerous cell changes before they progress to invasive carcinoma. In this paper a metaheuristic algorithm is proposed in order to classify the cells. Two databases are used, constructed in different times by expert MDs, consisting of 917 and 500 images of pap smear cells, respectively. Each cell is described by 20 numerical features, and the cells fall into 7 classes but a minimal requirement is to separate normal from abnormal cells, which is a 2 class problem. For finding the best possible performing feature subset selection problem, an effective genetic algorithm scheme is proposed. This algorithmic scheme is combined with a number of nearest neighbor based classifiers. Results show that classification accuracy generally outperforms other previously applied intelligent approaches.
