Final Year B.Tech Computer Science Engineering Student
Interested in Artificial Intelligence, Machine Learning, Computer Vision, Natural Language Processing, Large Language Models, and Software Development.
I am a final-year Computer Science Engineering student with a strong interest in Artificial Intelligence and Machine Learning. I enjoy building end-to-end AI applications and software systems that solve real-world problems using deep learning, computer vision, natural language processing, data analytics, and modern backend technologies.
I am currently expanding my knowledge in Retrieval-Augmented Generation (RAG), Visual Question Answering (VQA), Multi-Agent AI systems, and Software Engineering while continuously improving my problem-solving and development skills.
- Python
- Java
- C
- SQL
- PyTorch
- TensorFlow
- Scikit-learn
- OpenCV
- Hugging Face Transformers
- LangChain
- Flask
- FastAPI
- Streamlit
- REST APIs
- SQLite
- MySQL
- Git
- GitHub
- Google Colab
- Visual Studio Code
Developed a desktop analytics application that automatically tracks active application usage, categorizes digital activities, stores usage history in SQLite, and visualizes productivity insights through an interactive Flask dashboard with REST APIs.
Repository:
https://github.com/hima-12376/productivity-analytics-dashboard
Developed a deep learning-based medicinal herb classification system using a custom dataset of Dasapushpam herbs. Compared MobileNetV2, EfficientNet-B0, and YOLOv8n-cls using transfer learning and achieved up to 95% validation accuracy.
Repository:
https://github.com/hima-12376/dasapushpam-plant-classification
Built an AI-powered misinformation monitoring system for rumor credibility analysis, emotion detection, AI-generated media detection, misinformation spread simulation, and interactive visualization.
Repository:
https://github.com/hima-12376/multimodal-misinformation-monitoring-dashboard
Live Demo:
https://rumor-spread-simulator-stnxghueychmfaw8vnwyqc.streamlit.app/
Developed an Aspect-Based Sentiment Analysis (ABSA) system using transformer-based models to analyze course reviews, extract aspects, classify sentiment, and visualize insights through an interactive web application.
Repository:
https://github.com/hima-12376/course-review-absa
- Multi-Agent RAG Smart Document Question Answering
- Visual Question Answering (VQA) using Deep Learning
- Data Structures and Algorithms
Email
himapaul16@gmail.com
Thank you for visiting my GitHub profile.