Development of an Invoice Software with Artificial Intelligence Using AWS

This work presents the development of a software that starts from an investigation about the use of this type of software for Ecuadorian companies, which by law must make all invoices electronically, except for popular businesses in which we focus. As a result, software developed that allows invoicing on a small scale, which is a benefit for this type of business, allowing them to keep a better record of transactions for later use either in administrative management or to verify tax data. It is considered that as technology has advanced and increasingly digital businesses have developed, physical invoicing can become complex to maintain, so technology helps us to simplify processes that would normally be an extra effort to maintain. As well as helping us in the storage, management, access and sending of such documents, thus prioritizing the service to customers. It is also important to emphasize that thanks to the new popularity of artificial intelligence we can resort to this to help us in processes such as the classification of commercial transactions, this is because we can train models to extract keywords that help us to classify, this is an impact and improvement when having a law or ordinance that changes the way to classify these transactions. As an architecture based on microservices hosted in the cloud, we used a globally recognized provider such as Amazon Web Services. That allows us to streamline the development process without worrying about the infrastructure, also its costs are affordable and allows us to host artificial intelligence models in an effortless way. Publication Link : https://dspace.ups.edu.ec/handle/123456789/27270?locale=en

Data WareHouse Proyect

The project involves creating a modular web application that allows access to information from a multidimensional database. Architecturally, the application is divided into three parts: Front-end, Back-end, and Data Persistence. For the front-end, a user interface should be developed using HTML, CSS, and JavaScript. It is recommended to use libraries such as Bootstrap, JQuery, Datatables, and Highcharts. In the second part, the back-end will be developed using Node.js. Communication between the front-end and the back-end will be handled through RESTful services. Finally, data persistence will be managed by a multidimensional database with PostgreSQL. From a functional perspective, the application should include two modules: a) Reports and b) Maintenance. The Maintenance module will allow for the management (adding, modifying, deleting) of existing information in the database. The Reports module will display the stored information through a dashboard, featuring at least two (2) different statistical charts and three (3) tables. Data can be analyzed using at least two (2) different filters. The table data should be exportable to spreadsheets and PDF files. Below is a component diagram of the application.

Computer Science Contest

This project aims to perform an analysis using heatmaps and a dataset provided by the MSP (Ministry of Public Health). This dataset contains a total of 1,048,575 outpatient consultation records collected during 2021. The dataset includes information such as: Province, Zone, Date, Clinical Case Code, Description, etc. From these parameters, we will focus on the province, clinical case code, and description, as we aim to highlight the recurrence of these cases in each province by representing the data count in a heatmap. The goal is to identify which provinces are more prone and vulnerable to specific clinical cases and, based on this, to take the necessary measures.

Dashboard for Sensor Analysis and Testing on Raspberry Pi 4 Using AWS

This project involves creating an interactive dashboard that allows for the analysis and testing of the performance of various sensors connected to a Raspberry Pi 4. The following AWS services and technologies will be used: Elastic Beanstalk To deploy and manage the backend infrastructure in a scalable and automated manner. Node.js As the programming language to develop the server logic that will collect and process sensor data Cognito For user authentication and management, ensuring secure and controlled access to the system Approach This approach will integrate the IoT connectivity of the Raspberry Pi 4 with a cloud-based solution, enabling real-time monitoring of sensor performance and facilitating data-driven decision-making.