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@kannan_thanasekaran
Computer Science Engineer and UI/UX Designer.
As an enthusiastic college student with a dual focus on becoming a reputable UI/UX designer and mastering web development. My primary objective is to hone my skills in both areas and contribute to the growth of the organizations I’m involved with. With a passion for creating seamless user experiences and building intuitive websites, I’m committed to leveraging my academic journey to make meaningful contributions to the world of design and development.
During my academic journey, I have worked on several projects that have allowed me to apply my skills and knowledge. One of my notable projects is NeuroEmNet, a deep learning-based system for EEG-based emotion recognition. I developed this system by leveraging deep learning techniques for pattern recognition in EEG signals and conducted rigorous experimentation for validation. This project enhanced accuracy and efficiency in decoding emotional states from EEG data, facilitating advancements in affective computing and human-computer interaction.
Another project I worked on is the Cyclone Intensity Estimation, where I developed a deep learning-based model using CNN and RNN architectures. By implementing advanced neural network techniques on meteorological data, I significantly improved cyclone intensity prediction accuracy, potentially revolutionizing cyclone management strategies.
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Work history, roles, and key accomplishments
Bannari Amman Institute of Technology
Jan 2024 - Present (1 year 10 months)
• Developed a deep learning-based model for cyclone intensity estimation using CNN & RNN architectures.
• Implemented advanced neural network techniques on meteorological data to enhance cyclone prediction accuracy.
• Significantly improved cyclone intensity prediction accuracy, potentially
revolutionizing cyclone management strategies.
Bannari Amman Institute of Technology
Aug 2023 - Dec 2023 (4 months)
• Developed a deep learning-based system for EEG-based emotion recognition.
• Leveraged deep learning techniques for pattern recognition in EEG signals and conducted rigorous experimentation for validation.
• Enhanced accuracy and efficiency in decoding emotional states from EEG data, facilitating advancements in affective computing and human computer interaction.
Degrees, certifications, and relevant coursework
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