Ingeniería en Sistemas, Electrónica e Industrial

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Start date: 2024Subject: search.filters.subject.FLASK

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    Sistema de recomendación para productos ecnológicos utilizando web scraping para la toma de decisiones en compra
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Software, 2025-02) Cayo Tipan Kevin Joel; Nogales Portero Rubén Eduardo
    The growing number of online stores and the abundance of information about technology products make it difficult for consumers to make purchasing decisions. Browsing multiple websites to compare options has become a complex and overwhelming process. This can lead to hasty decisions or the purchase of products that do not meet expectations, and even, in many cases, to the final abandonment of the purchase. The technology product recommendation system offers functionalities such as product visualization, generation of recommendations, visualization of product details and the option to add products to a list of favorites. For the development of the progressive web application (PWA) and its graphical interfaces, React.js was used. In addition, the Flask framework was used to implement the REST API services, while MongoDB was used as the database manager, ensuring efficient and structured information management. The results obtained from the evaluation of the recommendation system were positive. There was a notable decrease in the time required to search and make purchasing decisions. These results validate the effectiveness of the system in optimizing purchasing decisions.
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    Sistema informático aplicando python para la gestión de productos con series de tiempo en la panadería y pastelería “Flor de Cebada"
    (2025-02) Freire Valencia Jean Carlo; Aldas Flores Clay Fernando; Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información
    The present project focuses on the development of a software system aimed at improving product management processes in the bakery and pastry shop "Flor de Cebada." Leveraging Python, the Flask framework for the backend, and React for the frontend, alongside the application of machine learning, the system integrates time series forecasting to enhance trend detection and demand prediction. The goal is to optimize sales operations, minimize product waste, and support data-driven decision-making in a small business environment. The system includes an inventory management mechanism, enabling the registration, updating, and tracking of products, including essential details such as quantities, expiration dates, and prices. Additionally, sales processes are streamlined through barcode scanning, automatic calculations, and totals. A key feature is the generation of reports required by the business. The predictive analysis implemented in the system development provides accurate demand forecasts, helping align production with market needs. The system also generates electronic receipts for transactions, improving operational efficiency and user experience. The project was developed using the Extreme Programming methodology, emphasizing iterative design, stakeholder collaboration, and adaptability to changing requirements. The system delivers tangible benefits for business operations, enabling the bakery to achieve greater accuracy in inventory planning, enhance customer satisfaction, and establish a foundation for sustainable growth. This initiative highlights the potential of technology-driven solutions to address real-world challenges faced by small and medium-sized enterprises
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    Motor de sugerencias aplicando web scraping para la toma de decisión en la compra de calzado en la línea deportiva
    (Universidad Técnica de Ambato. Facultad de Ingeniería en Sistemas, Electrónica e Industrial. Carrera de Tecnologías de la Información, 2024-02) Ibarra Lucas, Jorge Luis; Nogales Portero, Rubén Eduardo
    Online footwear purchases are often challenged by the vast array of brands and designs, posing a difficulty for consumers in making decisions. The inability to physically try on shoes prior to purchasing leaves buyers reliant on information provided by manufacturers and reviews, which may be inaccurate or incomplete. This reliance on potentially unreliable sources makes informed decision-making even more challenging, leading consumers to invest time and effort in comparing models and brands, generating frustration and discouragement. To enhance the shopping experience, this project presents a comprehensive solution with Web Scraping techniques using Scrapy and Selenium, along with a content-based recommendation system, aiming to improve the decision-making process in the purchase of athletic footwear and save search time. The recommendation engine leverages information gathered through Web Scraping on pages of various brands, eliminating the need for exhaustive product searches. These data feed into a content-based recommendation system implemented in Flask, acting as a Web Server Gateway Interface (WSGI) server. Additionally, an interactive user interface is implemented using the ReactJs framework, providing users with the ability to intuitively view product recommendations. These recommendations are generated based on their individual preferences and previously selected products. The results obtained from this implementation reveal a significant improvement in the decision-making process for users, simplifying the search and providing personalized recommendations that align with their individual preferences.