Sania Partovian
Educational background
Multidisciplinary-oriented by education and experience, she has been exposed to a number of diverse working environments and academia in the field of Artificial Intelligence and Mechateronics.
Work Experience
It is obvious studying Mechatronics and study basic of mechanic engineering and electrical engineering broaden her horizon which strongest them in real world as she has worked in industry that gave her the chance to get in touch with real world problems. Also, she has engaged herself in academic role as lecturer for more than 4 years in the field of Artificial Intelligence.
Research Topic
Smart-troubleshooting in Industry 4.0
Research Abstract
Primary objective of smart-troubleshooting is to reduce the time and effort required to resolve issues, minimize downtime, and improve system performance and reliability. Industry 4.0 represents a new era of manufacturing that involves the integration of digital technologies with industrial processes. In this era, smart devices and machines are widely used, generating a vast amount of data in the form of log files.
Based on the research background, the research started by identifying research trends, methodologies, and challenges involved in log file analysis for smart-troubleshooting.
This research is aiming to propose Machine Learning methods for smart-troubleshooting in IoT devices with the aid of log files and product information.
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