Gallery — Portuguese

PLANO DE PESQUISA INDIVIDUAL Criado a partir da classe abntex2, desenvolvida pelo Centro de Pesquisa em Arquitetura da Informação (CPAI)

Cópia do modelo que se encontra no site do DCC.

In most corruption scandals, the use of front companies for money laundering is almost ubiquitous. This work proposes to apply image classification to detect such organizations, through the use of Convolutional Neural Networks (CNN), namely the AlexNet architecture. The images are obtained by address search in Google Street View API, and the resulting classification will be further used along with other features to detect front com- panies in order to help the auditors from the Ministry of Transparency and Office of the Comptroller General (CGU, in Portuguese). To this moment, we applied classification to almost 15 thousand suppliers scenes with active contracts with the Brazilian Government until September 2016, obtained through data matching between the Government Purchases database and the Brazilian Federal Revenue Office database (more recent scenes should be added as this work progresses). Preliminary results with a pre-trained AlexNet CNN show the need for developing new scene classes more suited to the Brazilian context. In order to do this, we propose to apply clustering algorithms in features extracted from the last fully-connected layer of this net. The classes obtained will be used to fine-tune the AlexNet CNN for future classification, through the use of training from scratch or fine tuning techniques.

Modelo de documento para Dissertacao/Tese de Mestrado/Doutoramento em Engenharia Electrotecnica e de Computadores. Raul Morais, 2007 - 2008 Manuel Cabral, 2008 Antonio Valente, 2004-2018

Este é um modelo de qualificação em Latex, baseado no modelo em Word disponibilizado na página do curso. Em 03/2018.