The challenge is to develop an AI application to improve the accessibility and discoverability of records in the NTRS. For example, You could use AI to read NTRS documents, generate text analytic data, and produce a list of topic keywords to help researchers find the documents they need. We need to think about what types of information future researchers will need to locate desired documents and what would be the best data to aid them in their search.
- A simple and user friendly web interface for users to seamlessly search the required research paper while leveraging the power of AI.
- Complete automated solution to promote collaborative work and peer reviews on research papers, allowing one to upload their own documents
- A robust filter system allowing the user to select from the plethora of documents, which ones they wish to take a look at.
- Frontend: HTML, CSS, Next.js, Bootstrap
- Backend: FastAPI, Node.js
- UI/UX: Figma, Adobe Photoshop
- ML/NLP: Python, NLTK, Spacy,
- DBMS and Cloud Services: MongoDB, AWS S3
fastapi==0.81.0, gTTS==2.2.4, gunicorn==20.1.0, nltk==3.7, numpy==1.22.2, pandas==1.4.0, Pillow==8.2.0, pydantic==1.9.2, PyMuPDF==1.20.2, PyPDF2==2.11.0, pytesseract==0.3.10, pytextrank==3.2.4, requests==2.27.1, sklearn==0.0, spacy==3.4.1, spacy-legacy==3.0.10, spacy-loggers==1.0.3, streamlit==1.13.0, summa==1.2.0, tokenizers==0.12.1, transformers==4.22.2, urllib3==1.26.8, uvicorn==0.18.3, yake==0.4.8, NTRS Open API, Google Chrome, cors, axios
- Srinaath Narasimhan
- Yashowardhan Samdhani
- Tuhin Dutta
- Vyshnavi Madhusudhan
- Mohammed Farhaan