Dhruvil Patel Email & Phone Number

Surat, Gj, In

Surat, Gujarat, India

Currently, I'm interning as a Research Assistant at P P Savani University, where I’m focused on developing innovative AI tools for mental health screening. My primary project involves depression detection using Natural Language Processing (NLP) and deep learning. I'm working closely with a team to apply methods like sentiment analysis, text classification, and predictive modeling to identify early signs of depression through social media data.A key part of my role involves conducting thorough literature reviews, analyzing research papers to refine our feature engineering techniques and improve model accuracy. I’m also collaborating with international researchers to design AI-powered diagnostic tools that could make a real impact in mental health technology. This internship has been an incredible learning experience, strengthening my skills in NLP, statistical analysis, and interdisciplinary research, while allowing me to contribute to meaningful advancements in mental health.

Surat

At SVNIT Surat, I worked as a Research Intern, where I focused on advancing EEG classification models using machine learning. During this internship, I engineered and optimized neural network algorithms that achieved a 90% accuracy rate—surpassing existing models in the field. Through techniques like parallel processing and batch processing, I improved training efficiency by 40%, ensuring robust and reliable model performance.This experience deepened my skills in data preprocessing, statistical analysis, and feature engineering. I also led a cross-functional team to implement standardized model evaluation protocols, fostering collaboration and quality control. Working at SVNIT gave me hands-on exposure to advanced machine learning techniques, efficient neural network training, and essential team coordination in a research setting, all of which have been invaluable in refining my approach to complex data-driven projects.

Surat, Gujarat, India

I worked with my team to build machine learning and deep learning models for detecting depression on social media. We used a mix of NLP techniques—like Linguistic Inquiry and Word Count (LIWC), sentiment analysis, and TF-IDF vectorization—to dig into the language patterns of users. With some creative feature engineering and computational linguistics, we boosted the accuracy of our model by 9%, making it even better at spotting early signs of depression.The project wasn’t just about building models; it was a deep dive into mental health informatics. We were able to pull out meaningful psychological insights from real social media data, which was both challenging and rewarding. And the best part? Our team’s work made it to ICICC 2024, where we’ll be presenting, and we’re getting published in Springer Lecture Notes in Networks and Systems (LNNS)!

India

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India

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