Sahan Dissanayaka
Final Year Computer Science Undergraduate at University of Colombo School of Computing
Sri Jayewardenepura Kotte, Sri Lanka
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Final Year CS Undergraduate at University of Colombo School of Computing | GSoC'21 | Gold Microsoft Learn Student Ambassador | Research Collaborator at the AI-Team
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Area of Expertise
Deploy end-to-end machine learning Pipeline with FastAPI Powered API & Postman
FastAPI is a high-performing web framework for building APIs with Python 3.7+ based on standard Python-type hints on par with node.js and Go which highly integrates with Machine Learning related web application development. This session leverages building a machine learning pipeline and then creating a FastAPI API to serve the model with the support of Postman for API Testing for a reliable Web ML API.
Hybrid Model Approach for Real-Time Acoustic Anomaly Detection using Time Series
Detecting anomalous behaviours help to find new knowledge of a given phenomenon since anomaly behaviours rarely occurred and give better insights to prevent those instances in the future. Usually, anomaly behaviours happen unexpectedly due to the unconscious nature of the objects. This project will directly or as a first insight helps to prevent happening severe anomaly behaviours generated in real-time via helping to diagnose them as early as possible.
Thus far, an increasing number of machine learning and deep learning algorithms have been developed to detect anomalies, but only a few applications focus on detecting acoustic anomalies
in real time. With respect to that, the core contribution of this research project is given by delivering an improved hybrid model architecture to capture more time series features for Real-Time Acoustic Anomaly Detection and enable it to use in the above
scenarios effectively.
Sahan Dissanayaka
Final Year Computer Science Undergraduate at University of Colombo School of Computing
Sri Jayewardenepura Kotte, Sri Lanka
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