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Name: André Machado

Type: User

Bio: Msc. Computer Science

Location: Teresina, Pi - Brazil

Blog: https://www.linkedin.com/in/andré-de-lima-machado/

André Machado's Projects

activity-status-check

Testing Activities in an Android app (show in the logcat which activity was invoked)

backend-grades-escolares-api

API para controle de grades escolares, utilizando conceito de Rotas e a biblioteca winston para arquivos de log

counter

An aplication to test the stateful widget of Flutter

datasets-list

Datasets which I dont actively work on but still want to remember/work on later.

filter-and-reduce-training

Desafio de bootcamp: usar funções filter e reduce sobre dados de arquivos json para responder uma série de perguntas

financial-control-mongodb-import

Importação à coleção do MongoDB das informações do arquivo official-db/transactionsArray.json

Usando funcionalidades do Git (para versionamento), banco de dados Mongo (para persistência), e Heroku (para implantação). Passos: 1 - Clonagem de respositório; 2 - criação de modelo com Mongoose; 3 - integração com coleção no Mongodb; 4 - repositório copiado (grades-app2) e publicado na Heroku, com o frontend

grades-api2

Cópia de grades-api1, publicando no Heroku na forma de backend

grozi_tf

Using TensorFlow to recognize groceries at Mattin's.

manual-roteiro-integracao-login-unico

Roteiro de Integração do Login Único para os órgãos

manual-script-chatbot-login-unico

Manual de Chatbot do Login Único

my-bank-api

API integrada ao MongoDB Atlas, com endpoints para manipular os dados, usando Mongoose para controlar o modelo (schema).

net-salary-android

An Android App to calculate the net salary

net-salary-react

A simple React project to calculate the net salary

react-juros-compostos

Implementação de React Hooks para o Cálculo de Juros Compostos

school-grades

I developed a FrontEnd for the BackEnd built previously, an API to control school grades

scripts.js

Testes com scripts js

series-app

App React Native com Navigator

shopping-assistance-for-visually-impaired-people

Recognition of grocery products in store shelves poses peculiar challenges. Firstly, the task mandates the recognition of an extremely high number of different items, in the order of several thousand for medium-small shops, with many of them featuring small inter and intra class variability. Then, available product databases usually include just one or a few studio-quality images per product (referred to herein as reference images), whilst at test time recognition is performed on pictures displaying a portion of a shelf containing several products and taken in the store by cheap cameras (referred to as query images). Moreover, as the items on sale in a store as well as their appearance change frequently over time, a practical recognition system should handle seamlessly new products/packages. Inspired by recent advances in object detection and image retrieval, we propose to leverage on state-of-the-art image classification based on deep learning. In the study conducted our objective is to use this technology to help blind people around the world, which will help them to shop easily to identity the grocery product on the shelves. For image classification various CNN based image classification algorithms have been applied in this study. Our system is computationally expensive at training time but can perform recognition rapidly and accurately at test time.

web

react

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