We describe a specialized methodology for segmenting 2D microscopy digital images of freshwater green microalgae. The goal is to obtain representative algae shapes to extract morphological features to be employed in a posterior step of taxonomical classification of the species. The proposed methodology relies on the seeded region growing principle and on a fine-tuned filtering preprocessing stage to smooth the input image. A contrast enhancement process then takes place to highlight algae regions on a binary pre-segmentation image. This binary image is also employed to determine where to place the seed points and to estimate the statistical probability distributions that characterize the target regions, i.e., the algae areas and the background, respectively. These preliminary stages produce the required information to set the homogeneity criterion for region growing. We evaluate the proposed methodology by comparing its resulting segmentations with a set of corresponding ground-truth segmentations (provided by an expert biologist) and also with segmentations obtained with existing strategies. The experimental results show that our solution achieves highly accurate segmentation rates with greater efficiency, as compared with the performance of standard segmentation approaches and with an alternative previous solution, based on level-sets, also specialized to handle this particular problem.
We propose a method based on Principal Component Analysis (PCA) for predicting students' performances and for identifying relevant patterns concerning their characteristics. The proposed method allowed us to study the predictive capability of students' performances and the effectiveness of PCA for interpreting patterns in educational data. The proposed method was validated using two public datasets describing students achievements, as well as their social and personal characteristics. Experiments were conducted by comparing the predictive performances between the datasets presenting high and reduced dimensions. The results reported that PCA retained relevant information of data and was useful for identifying implicit knowledge in students' data.
The field of Computer Science (CS) has been of little interest for girls straight out of high school, when considering undergraduate majors in Brazil. At the University of Brasília's CS Department, female students account for less than 10% of the student body. In an effort to understand the girls' lack of interest in computer related courses, we applied an anonymous questionnaire regarding their perceptions of the field. From 2011 to 2014 3707 female students completed this anonymous questionnaire. We compiled Association Rules in Data Mining to analyze these responses. The knowledge gained through this study could guide future research on the matter and provide guidelines for motivating girls to pursue careers in Computer Science.
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