Cloud-Based Outreach and Extension Decision Support System Using Machine Learning Models
Keywords:
Cloud-based, Decision Tree, DSS, Machine Learning, Predictive Model, RFAbstract
The Cloud-Based Outreach and Extension Decision Support System uses machine learning models to enhance outreach and extension initiatives by providing accurate predictions on the expected impacts of programs on stakeholders. The system operates on a cloud-based infrastructure, allowing for flexibility in data storage and convenient access to information from various locations. The integration of machine learning models allows for the analysis of historical data and the generation of accurate forecasts, empowering decision-makers to make informed choices regarding outreach and extension activities. Furthermore, the system design makes use of web technologies to offer a user-friendly and secure platform for outreach and extension decision support. Users, including the institution and the community, may safely access the system using a web browser using the Secure Sockets Layer. The system represents a significant step forward in optimizing outreach and extension efforts, enhancing stakeholder impact, and fostering a more responsive and adaptive approach to community engagement. Moreover, the Random Forest Decision Tree classification, the main learning algorithm used in the study, is combined with test results and training data to predict which outreach program and extension would be most appropriate for the chosen Laguna University community.
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