Machine Learning in Biomedical Data

Abstract

In the digital age, the amount of worldwide data that is being generated on a daily basis is rapidly growing, reaching 175 zettabytes by 2025. These massive volumes of data have led to growing interest in using Machine Learning (ML) algorithms to extract valuable insights from databases. ML techniques can be considered as the foundation of a broad spectrum of next-generation technologies, including medical applications.

In this presentation, the role of ML in medical applications will be discussed. A newly developed data-driven classification algorithm will be explained, and its performance for the classification of biological datasets will be investigated and compared with the well-known classification models.

Bio

Behnaz Fakhar Firouzeh is in the final semester of her Ph.D. in Electrical and Computer Engineering at Carleton University. She has been working on signal processing and Compressive Sensing (CS) for over 8 years. She also has 5 years of experience in developing constraint optimization algorithms. Her developed algorithms successfully have been applied in different areas such as signal processing, Machine Learning, and artificial intelligence. Behnaz has (co)authored several articles in different journals and conference proceedings.

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