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@InProceedings{RochaVPBAMSRSMN:2021:CoStMe,
               author = "Rocha, Carlos Vinicios Martins and Vieira, Pedro Henrique Carvalho 
                         and Pinto, Antonio Moreira and Bernhard, Pedro Vinnicius and 
                         Anchieta Junior, Ricardo Jos{\'e} Fernandes and Marques, Ricardo 
                         Costa da Silva and Silva, Italo Francyles Santos da and Rocha, 
                         Simara Vieira da and Silva, Arist{\'o}fanes Corr{\^e}a and 
                         Monteiro, Eliana M{\'a}rcia Garros and Nogueira, Hugo Daniel 
                         Castro Silva",
          affiliation = "{Federal University of Maranh{\~a}o} and {Federal University of 
                         Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and 
                         {Federal University of Maranh{\~a}o} and {Federal University of 
                         Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and 
                         {Federal University of Maranh{\~a}o} and {Federal University of 
                         Maranh{\~a}o} and {Federal University of Maranh{\~a}o} and 
                         {Equatorial Energy Group} and {Equatorial Energy Group}",
                title = "A Comparative Study of Methods based on Deep Neural Networks for 
                         Self-reading of Energy Consumption in a Chatbot Application 
                         Context",
            booktitle = "Proceedings...",
                 year = "2021",
               editor = "Paiva, Afonso and Menotti, David and Baranoski, Gladimir V. G. and 
                         Proen{\c{c}}a, Hugo Pedro and Junior, Antonio Lopes Apolinario 
                         and Papa, Jo{\~a}o Paulo and Pagliosa, Paulo and dos Santos, 
                         Thiago Oliveira and e S{\'a}, Asla Medeiros and da Silveira, 
                         Thiago Lopes Trugillo and Brazil, Emilio Vital and Ponti, Moacir 
                         A. and Fernandes, Leandro A. F. and Avila, Sandra",
         organization = "Conference on Graphics, Patterns and Images, 34. (SIBGRAPI)",
            publisher = "IEEE Computer Society",
              address = "Los Alamitos",
             keywords = "self-reading, energy consumption, chatbot application, image 
                         processing, deep learning.",
             abstract = "Self-reading is a process in which the consumer is responsible for 
                         measuring his own energy consumption, which can be done through 
                         digital platforms, such as websites or mobile applications. The 
                         Equatorial Energy group's electric utilities have been working on 
                         developing a chatbot application through which consumers can send 
                         an image of their energy meter to a server that runs a method 
                         based on image processing and deep learning for the automatic 
                         recognition of consumption reading. However, the incorporation of 
                         these methods in a solution available to the public should 
                         consider factors such as response time and accuracy, so that it 
                         presents a satisfactory response time when it needs to handle a 
                         large number of simultaneous requests. Therefore, this paper 
                         presents a comparative study between approaches developed for the 
                         automatic recognition of consumption readings in images of 
                         electric meters sent to the server. Response time performances are 
                         analyzed through stress tests that simulate the real application 
                         scenario. The mean average precision (mAP) and the accuracy 
                         metrics of the methods are also analyzed in order to evaluate the 
                         generalization of the used convolutional neural networks.",
  conference-location = "Gramado (Virtual), Brazil",
      conference-year = "October 18th to October 22nd, 2021",
             language = "en",
           targetfile = "Autoclara___Sibgrapi_2021___English__sem_subpastas_.pdf",
        urlaccessdate = "2022, Jan. 24"
}


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