Popular GitHub Repositories for Due Diligence

An agentic company research tool powered by LangGraph and Tavily that conducts deep diligence on companies using a multi-agent framework. It leverages Google Gemini 2.0 Flash and Chat GPT-4o-mini on the backend for inference.

A real-time drowsiness detection system for drivers, which alerts the driver if they fall asleep due to fatigue while still driving. The computer vision algorithm used for the implementation uses a trifold approach to detect drowsiness, including the measurement of forward head tilt angle, measurement of eye aspect ratio (to detect closure of eyes) and measurement of mouth aspect ratio (to detect yawning).

Machine Learning based Intrusion Detection Systems are difficult to evaluate due to a shortage of datasets representing accurately network traffic and their associated threats. In this project we attempt at solving this problem by presenting two taxonomies

GitHub Action to prevent GitHub from disabling scheduled workflows due to repository inactivity.

Gotcha GPT ensures the integrity of academic writing in universities and publishing houses due to advances in Artificial Intelligence (AI). It discriminates AI- and human-generated manuscripts (English and Portuguese).

Implementation of RL in the cloud for energy minimization due to migration and excess power consumption.

A project to train and evaluate different dnn models for plant disease detection problem, tackle the problem of scarce real-life representative data, experiment with different generative networks and generate more plant leaf image data and implement segmentation pipeline to avoid miss-classification due to unwanted input

This repo contains the code of a ai-trip-advisor, which generates trips with hotel recommendation, itineraries according to their budget and travel choice. Also i cannot add deployed link here because i used google maps places (NEW), photos, and geolocation api's due to which i have restriction of staying under expenditure of $200/monthly

This project involves automating the attendance system of RT Knits using Face Recognition. Due to Covid-19, people are obliged to wear masks hence, the system is successful in recognizing people despite wearing masks.

Machine learning techniques can be used to overcome these drawbacks which are cause due to the high dimensions of the data. So in this project I am using machine learning algorithms to predict the chances of getting cancer.

An MCP server that runs AI-driven venture capitalist agents (Fred Wilson, Peter Thiel, etc.), whose thinking is continuously enriched by Octagon Private Markets' real-time deals, valuations, and deep research intelligence. Use it to spin up programmable "VC brains" for pitch feedback, diligence simulations, term sheet negotiations, and more.

Many market analysts believe that predicting market’s stocks fluctuations is nearly impossible to achieve due to the number of variables involved, especially since many of these variables are based on irrational factors such as human sentiment. However, I developed a model that uses a combination of sentiment analysis, clustering and feed-forward neural network in order to predict the market trends.

Rainfall prediction is one of the challenging tasks in weather forecasting process. Accurate rainfall prediction is now more difficult than before due to the extreme climate variations. Machine learning techniques can predict rainfall by extracting hidden patterns from historical weather data.

Bike sharing systems are a new generation of traditional bike rentals where the whole process from membership, rental and return back has become automatic. Through these systems, users are able to easily rent a bike from a particular position and return back at another position. Currently, there are about over 500 bike-sharing programs around the world which is composed of over 500 thousand bicycles. Today, there exists great interest in these systems due to their important role in traffic, environmental and health issues. Apart from interesting real-world applications of bike sharing systems, the characteristics of data being generated by these systems make them attractive for the research. The goal here is to build an end-to-end regression task. Here the user will provide the data and the result will be given by the best performing hype

Predict the number of deaths due to covid19 in the next two weeks

Converting night into day is one of the most interesting applications in generative models, due to the great difficulty in recreating the scene during the day, especially in cases of extreme darkness, and thus the difficulty lies in imagining the scene during the day when the lighting is very weak.

This includes a novel method to measure the quality of the actions performed in Olympic weightlifting using human action recognition in videos. Human action recognition is a well-studied problem in computer vision and on the other hand action quality assessment is researched and experimented comparatively low. This is due to the lack of datasets that can be used to assess the quality of actions. In this research, we introduce a method to assess player techniques in weightlifting by using skeleton-based human action recognition. Furthermore, we introduce a new video dataset for action recognition in weightlifting which is annotated to frame level. We intended to develop a viable automated scoring system through action recognition that would be beneficial in the sports industry.

Open source code for generating interactive, data-driven simulations of vision loss due to eye disease

Very incomplete right now, pretrained ARGVAET system for generating, classifying, and predicting the properties of molecules. I couldn't upload the dataset or checkpoints due to size constraints.

It is a deep learning model used to restore and enhance faces in old photos and historical artworks. It leverages a pre-trained face GAN to provide realistic and faithful details that may have been lost due to aging or damage.