Popular GitHub Repositories for Claim Denial

Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).

Machine Learning for Network Intrusion Detection & Misc Cyber Security Utilities

NotPixel automatic claim and paint bot? easy to use without extras.

MemStream: Memory-Based Streaming Anomaly Detection

Auto chat, leave, join discord server, delete, reaction messages with AI. [100 STARS TO ENABLE AUTO CHAT WITH AI]

This project aims to emulate some of the advanced reasoning capabilities seen in models like OpenAI's o1. It does not claim to replicate the exact functionality or performance of o1.

Auto connect wallet, referrals, chat with AI.

Fact-checking system for textual and visual inputs.

A server-side TypeScript and JavaScript library immune to Regular Expression Denial of Service (ReDoS) attacks by using Rust and linear RegEx under the hood. Regolith has a linear worst case time complexity, compared to the default RegExp found in TypeScript and JavaScript, which has an exponential worst case.

A machine learning program, that detects denial of service attack using machine learning technique.

Automation for Pixai.art auto claim credits

Python implementation of the MIDAS algorithm.

Auto do tasks, play games, referrals, checkin.

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

Computers & Security

Insurance claim fraud detection using machine learning algorithms.

GLM, Neural Network and Gradient Boosting for Insurance Pricing, Part 1: Claim Frequency

Successfully established a machine learning model which can estimate the net health insurance claim of an individual based on a set of characteristics of that individual to an appreciable level of accuracy.

A retrieval-augmented generation-based framework for automatically constructing a hierarchy of aspects typically considered when addressing a nuanced claim and enriching them with corpus-specific perspectives.

The platform features: Provide 24/7, personalized guidance on policy selection and claims via voice or chat. Ensure clear understanding of policy terms. Automate form-filling with Al for error-free applications and higher claim acceptance rates.