Processo de implantação de um sistema integrado de gestão em uma organização pública à luz da teoria da hospitalidade :um estudo de caso

In the present time, public organizations are employing more and more solutions that uses information technology in order to ofer more transparency and better services for all citizens. Integrated Systems are IT which carry in their kernel features of integration and the use of a unique database. These systems bring several benefits and face some obstacles that make their adoption difficult. The conversion to a integrated system may take years and, thus, the study of the adoption of this IT in public sector organizations become very stimulant due to some peculiarities of this sector and the features of this technology. First of all, information about the particular integrated system in study and about its process of conversion are offered. Then, the researcher designs the configuration of the conversion process aim of this study the agents envolved and the moments and the tools used to support the process in order to elaborate the methodology of the conversion process understood as the set of procedures and tools used during all the conversion process. After this, the researcher points out, together with all the members of the conversion team, the negative and positive factors during the project. Finally, these factors were analysed through the Hospitality Theory lens which, in the researcher opinion, was very useful to understand the elements, events and moments that interfered in the project. The results consolidated empirically the Hospitality Theory presumptions, showing yet a limitation of this theory in the case in study

Classificação de imagens de ambientes coralinos: uma abordagem empregando uma combinação de classificadores e máquina de vetor de suporte

The use of the maps obtained from remote sensing orbital images submitted to digital processing became fundamental to optimize conservation and monitoring actions of the coral reefs. However, the accuracy reached in the mapping of submerged areas is limited by variation of the water column that degrades the signal received by the orbital sensor and introduces errors in the final result of the classification. The limited capacity of the traditional methods based on conventional statistical techniques to solve the problems related to the inter-classes took the search of alternative strategies in the area of the Computational Intelligence. In this work an ensemble classifiers was built based on the combination of Support Vector Machines and Minimum Distance Classifier with the objective of classifying remotely sensed images of coral reefs ecosystem. The system is composed by three stages, through which the progressive refinement of the classification process happens. The patterns that received an ambiguous classification in a certain stage of the process were revalued in the subsequent stage. The prediction non ambiguous for all the data happened through the reduction or elimination of the false positive. The images were classified into five bottom-types: deep water; under-water corals; inter-tidal corals; algal and sandy bottom. The highest overall accuracy (89%) was obtained from SVM with polynomial kernel. The accuracy of the classified image was compared through the use of error matrix to the results obtained by the application of other classification methods based on a single classifier (neural network and the k-means algorithm). In the final, the comparison of results achieved demonstrated the potential of the ensemble classifiers as a tool of classification of images from submerged areas subject to the noise caused by atmospheric effects and the water column