Energy Load Prediction System
Published:
Developed a machine learning system to forecast energy consumption patterns, achieving 92% accuracy for 24-hour predictions using ARIMA, XGBoost, and LSTM models.
Published:
Developed a machine learning system to forecast energy consumption patterns, achieving 92% accuracy for 24-hour predictions using ARIMA, XGBoost, and LSTM models.
Published:
Engineered an end-to-end NLP pipeline for extracting quotes, identifying speakers, and classifying entities from unstructured text documents.
Published:
Leveraging protein language models and graph neural networks to predict protein properties and interactions for drug discovery applications.
Published:
An interactive mapping application for discovering and navigating to scenic locations around the Bay Area with personalized recommendations.
Published:
An open-source framework for building universal native apps with React that runs seamlessly on Android, iOS, and the web.
Published:
A modern Next.js application for audio processing and playback with a sleek UI and advanced features.
Published:
A machine learning recommendation system for H&M products, using collaborative filtering and content-based approaches.
Published:
A Graph Neural Network (GNN) based recommendation system for restaurants using the Yelp dataset.
Published:
A Retrieval-Augmented Generation (RAG) system that leverages Notion data for AI-powered question answering and knowledge retrieval.
Published in Journal 1, 2009
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1). http://apratim-mishra.github.io/files/paper1.pdf
Published:
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