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frameworks for full-stack web development (Node.js, .NET Core, Angular) and data pipelines (Apache Kafka, AWS Glue), comparing performance, scalability and security (RBAC, OAuth 2.0). Study cases of digital
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of LIS ́s researchers, in the framework of Project WATERSCAN - Intelligent Optoelectronic System for Monitoring Water Distribution Networks and Detecting Leaks (CENTRO2030-FEDER-01468500), co-financed by
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of classes, using Machine Learning (ML) techniques such as Decision Trees, K-Nearest Neighbors (KNN), XGBoost, Support Vector Machines (SVM), or Neural Networks. Explore and implement clustering algorithms
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