Relevancite: your personal research validating AI assistant
Motivation Academic research depends on accurate citations, yet verifying whether a referenced paper truly supports a claim can be tedious and error-prone. In large literature reviews, this task becomes nearly impossible to perform manually for every claim. RelevanCite was created to address this challenge, aiming to reduce errors, save researchers time, and strengthen the integrity of scholarly work.
Read MoreNarrative Reconstruction, Stitching Together the Full Story Using LLMs
If you follow news online, especially from sources like Online Khabar, you know the drill. A big story, a political investigation, a natural disaster, a major development project, doesn’t fit in one article. Instead, it unfolds over days, weeks or months in separate reports: “New Witness in Case,” “Committee Grills Official,” “Court Hearing Postponed.” As a reader, it’s frustrating. You’re left with puzzle pieces scattered across time, trying to remember who said what, when it happened, and how it all connects.
Read MoreAutomated Fact Checking using LLMs and SERP
In today’s digital age, misinformation spreads rapidly, making it crucial to verify facts quickly and accurately. Leveraging Large Language Models (LLMs) and Search Engine Results Pages (SERP) APIs, we can automate the fact-checking process. Here’s a step-by-step guide on how to implement this.
Read MoreMLP based CNN for Classification
Perceptrons are the foundational elements of artificial neural networks, inspired by the biological neuron. Developed by Frank Rosenblatt in the late 1950s, they represent one of the earliest models of machine learning capable of learning from data.
Read MoreOMR
This project automates the evaluation of Optical Mark Recognition (OMR) answer sheets using Python. Leveraging computer vision techniques and OCR, it reads scanned OMR forms, detects marked responses, and computes total scores. The processed results are exported in a structured CSV format.
Read MoreCrop Yield Predictor for Cereal Crops of Nepal
Introduction This project implements a comprehensive pipeline for predicting cereal crop yields in Nepal using machine learning techniques. The workflow begins with raw data extraction via Optical Character Recognition (OCR) and culminates in model training and performance evaluation. The pipeline is built using Python and leverages powerful tools such as Tesseract OCR, Pandas, NumPy, scikit-learn, Matplotlib, and Seaborn.
Read MoreSales Statement Creator
A web app that I made using Python and Flask that generates PDFs of sales records for businesses. It can grab client details using their VAT/PAN number from the government’s website ird.gov.np. It then generates a report that shows the total transaction that occured between the parties in a PDF file.
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