when its metter
Sakthi
Saravanan
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04
Jegadit
Sakthi Saravanan
MS CS @ University of Illinois Chicago B.Tech CSE @ Amrita Vishwa Vidyapeetham, Coimbatore
Applied AI/ML/CV & Systems Development
AI-Stylish Fashion Agent
FotoFind: Galley Search Engine
Amrita Canteen App
Churn Prediction
AI Research & Model Development
Adv. LLM Reasoning with Speculative Decoding
AI Based Parameter Estimation of ML Models
Robotics and Hardware
Sea Dragon🐉 AUV
RoomRanger
Juggernaut – Battle Robot
Architectural Design & Planning
Visual Facility Design
Audio Facility Design
Construction
Art Direction
Brand Identities
Campaigns
ヾ(⌐■_■)ノ♪ Hello!!
My name is Jegadit S Saravanan, and I am a MS CS student at UIC. I did my Bachelors in Computer Science and Engineering at Amrita Vishwa Vidyapeetham, Coimbatore.
I used to be really into cybersecurity, but I’ve since shifted my focus to Computer Vision for healthcare & robotics. I am also intersted in AI/ML, & computation optimization as well.
I am always eager to explore new technologies & concepts. Throughout my academic journey & professional experiences, I’ve consistently sought opportunities to apply my skills and knowledge to real-world problems, pushing the boundaries & delivering impactful solutions.
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Accurate sensor data is crucial for the successful operation of Autonomous Underwater Vehicles (AUVs). However, dynamic underwater environments and inherent sensor limitations pose significant challenges. This paper investigates using the Kalman filter to enhance instantanoius sensor data accuracy in AUV navigation by integrating with Genetic Algorithms (GAs). The proposed Adaptive Genetic Algorithm-based Kalman Filter (AGAKF) adjusts filter parameters in real-time using GAs as compared to the existing methods that use batch processing. Extensive simulations and comparisons show that AGAKF achieves superior noise reduction and better signal preservation than the existing techniques, enhancing AUV navigation accuracy in diverse underwater environments.
In this study, parameter estimation of ML models, here specifically, random forest classifier was conducted on a heart disease dataset. A new approach to machine learning is suggested that hybridizes genetic algorithm with simulated annealing for estimating the hyperparameters of the random forest classifier. This application of hybrid optimization helped in increasing the accuracy of the machine learning model by 10% and is very promising in comparison to various different classification methods for this same problem.
Jegadit Sakthi Saravanan