Merisandra Cortes de Mattos
@unesc.net
Postgraduate Program in Production Systems
Universidade do Extremo Sul Catarinense
RESEARCH, TEACHING, or OTHER INTERESTS
Artificial Intelligence, Multidisciplinary
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
P. W. Simões, S. Cesconetto, Larissa Letieli Toniazzo de Abreu, Merisandra Côrtes de Mattos, José Márcio Cassettari Junior, E. Comunello, L. Ceretta and S. Manenti
This paper presents the profile and experience of sexuality generated from a data mining classification task. We used a database about sexuality and gender violence performed on a university population in southern Brazil. The data mining task identified two relationships between the variables, which enabled the distinction of subgroups that better detail the profile and experience of sexuality. The identification of the relationships between the variables define behavioral models and factors of risk that will help define the algorithms being implemented in the data mining classification task.
P. W. Simões, P. J. Martins, R. A. Casagrande, Kristian Madeira, Merisandra Côrtes de Mattos, S. Manenti, Maria Inês da Rosa, F. Dal-Pizzol, Ramon Venson, Leandro Natal Coral,et al.
Using the framework for developing parallel applications Java Parallel Programming Framework were conducted performance analysis of an application for the clustering data by the method of fuzzy logic combined with Gustafson-Kessel algorithm. In addition to running in a distributed environment, for comparative purposes, were also conducted collections of processing time in environments with a single Personal Computer approach. With the results obtained by collecting time of application, there was a statistical analysis to validate the application and the algorithm as well as the use of computational clustering as a way to increase performance applications.
Carlos Cassiano Denipotti Veronezi, Priscyla Waleska Targino de Azevedo Simões, Robson Luiz dos Santos, Edroaldo Lummertz da Rocha, Suelen Melão, Merisandra Côrtes de Mattos, and Cristian Cechinel
Georg Thieme Verlag KG
OBJECTIVE: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar column radiographs in order to aid in the process of diagnosing primary osteoarthritis. METHODS: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographs of the lumbar column, which were provided by a radiology clinic located in the municipality of Criciuma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar column and those with patterns that were difficult to characterize were discarded, thus resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographs for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. RESULTS: After 90 cycles, the validation was carried out on the best results, thereby reaching accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. CONCLUSIONS: Even though the effectiveness shown was moderate, this study is of innovative nature. Hence, the values show that the technique used has a promising future, thus pointing towards further studies covering the image and cycle processing methodology with a larger quantity of radiographs.
Cristian Cechinel, Salvador Sánchez-Alonso, Miguel-Ángel Sicilia, and Merisandra Côrtes de Mattos
Springer Berlin Heidelberg