Subramaniam Kannan

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About

Subramaniam Kannan is a researcher at the forefront of conversational artificial intelligence, with a specialized focus on enhancing human-computer interaction through natural language processing. His primary research areas include typographic error detection and correction in chatbot systems, leveraging computational linguistics to improve the robustness of AI-driven communication tools. Kannan’s most notable contribution is his pioneering work on the "Typographic Error Identification and Correction in Chatbot Using N-gram Overlapping Approach" (2022), which addresses a critical bottleneck in chatbot performance: the accurate interpretation of user input marred by spelling mistakes. By applying n-gram overlapping techniques, his method significantly boosts the error resilience of virtual agents, directly impacting the reliability of business-sector chatbots. This work, already garnering 2 citations, lays essential groundwork for more seamless AI interactions. Kannan’s research is particularly relevant to the growing demand for intelligent virtual assistants, where even minor typographical errors can derail user experience. His achievements underscore a commitment to bridging the gap between raw AI capability and practical, user-friendly deployment, making him a valuable contributor to the evolution of conversational agents.