Siddardha Kaja

As a Data/ML Engineer at Air Canada, I'm passionate about both practical applications and fundamental research. My work involves developing and implementing innovative solutions to challenging problems within the aviation industry. Concurrently, I'm pursuing research interests in machine semantics, seeking to develop a deeper theoretical understanding of the algorithms and systems I work with. I am actively seeking opportunities to pursue a PhD in a related field.

My research contributions have been recognized through publications in the Journal of Ambient Intelligence and Humanized Computing, a leading journal in the field, and presentations at prominent international conferences including the International Conference on Future Networks and Communications (FNC), the International Conference on Ambient Systems, Networks and Technologies (ANT), and the International Conference on Mobile Systems and Pervasive Computing (MOBISPC)

Every data packet has to pass through few intermediate nodes to reach its destination. Among other reasons, tremendous growth in internet devices encourages those intermediate nodes to drop the data packets. Optimizing the data packet route is an effective solution to deal with packet loss. Advanced machine learning approaches have been identified as a powerful support tool for routing optimization in node networks. Furthermore, as hardware infrastructure develops, the capabilities of cloud computing have expanded enormously. Improved connection, processing power, and memory units enable real-time machine learning. This thesis suggests and evaluates a unique technique for optimising the packet path by one hop for intermediate nodes as a backup. It offers information on the transmission trend and the tendencies of certain intermediate nodes or groups of neighbouring nodes in a network. We carried out a series of experiments and validated our idea using real-world node data.

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