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Hey πŸ‘‹, I am Rikshith Tirumanpuri (he/him). I am a Developer, Designer.

Welcome to my personal GitHub repository! This repository serves as a central hub for my personal projects, experiments, and learning endeavors.

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About Me

I am a dedicated software developer passionate about application programming, crafting APIs, and architecting cloud services. Beyond coding, I find joy in music and cooking, constantly exploring new tunes and experimenting with flavors in the kitchen. With a commitment to continuous learning and innovation, I strive to create impactful solutions that merge my technical expertise with my creative interests. Let's connect and collaborate on exciting projects! πŸš€πŸŽΆπŸ³

Skills

HTML5 CSS3 TailwindCSS React NodeJS Spring AWS

Contact

Feel free to reach out to me if you have any questions, suggestions, or just want to connect! You can contact me via email, LinkedIn, or Twitter.

Dev-Rikshith's Projects

alien_invastion icon alien_invastion

This repository contains the files of the game named alien invastion.

flashcardsapplication icon flashcardsapplication

This is the application to create a set of flash cards and save it in your system and then we can load them and learn using them.

traffic-congestion-detection-from-surveillance-videos-using-cnn icon traffic-congestion-detection-from-surveillance-videos-using-cnn

With the growing demand for Smart cities applications, traffic control and its management have huge demand and a highly interested research area. Surveillance images and videos can be monitored effectively to identify traffic congestions. There is existing research available on traffic signal controls through image processing and various machine learning methods. Traffic prediction through surveillance camera videos, images are very interesting as it can update with live data for users. The proposed work detects traffic prediction based on multiclass problems. There are four classes considered for this proposal are heavy traffic, less Traffic, accident prediction and fire accident prediction. As a result, the suggested approach outperforms existing systems that rely mostly on binary categorization. For image training and detection, the suggested work uses a single deep learning technique, Convolutional Neural Network (CNN). With low maintenance, the proposed system can be used for large-scale traffic surveillance systems. The proposed system attained the best accuracy of 80% for 20 epoch training with four detection classes, according to the results of the experiment

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