This project utilizes advanced data analysis and machine learning techniques to predict equipment failures before they occur. The goal is to detect anomalies and possible defects in equipment and processes to enable preemptive maintenance, thereby reducing downtime and costs.

Explaining black boxes with a SMILE: Statistical Mode-agnostic Interpretability with Local Explanations

Qt-DAB, a general software DAB (DAB+) decoder with a (slight) focus on showing the signal

TeleXGI: Explainable Gastrointestinal Image Classification for TeleSurgery Systems

Se realiza una recopilación de todos los archivos desarrollados y utilizados en cada una de las actividades realizadas a lo largo del semestre ABR-AGO2024 en la materia de Radio Definida por Software de la Carrera de Ingeniería en Telecomunicaciones de la Universidad Técnica del Norte

This repository focuses on Explainable AI techniques for explaining IOT attack detection models and image classification models.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

C# LIME protocol implementation

The Fraud Detection project aims to improve identification of fraudulent activities in e-commerce and banking by developing advanced machine learning models that analyze transaction data, employ feature engineering, and implement real-time monitoring for high accuracy fraud detection.

Explainable AI for underwater acoustic models.

Lite version of lime buy the full on discord

Developing delivery time estimation model using Neural Networks

A V-slice fork/engine made to have modding in mind. Built off of V-Slice's Source Code

Multicycles.org aggregates on one map, more than 300 share vehicles like bikes, scooters, mopeds and cars. Demo APP for the Data Flow API, see https://flow.fluctuo.com

A port of Friday Night Funkin' v0.2.8 made by rebuilding the code via reverse engineering.

Interpreting Black-Box Time Series Classifiers using Parameterised Event Primitives

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

The main idea for this project is explore a Kaggle dataset about ChatGPT reviews using a NLP approach in order to apply ML models for score reviews predictions. I applied LIME algorithm to evaluate explainability to get text and features explanations. I realised a Docker container to set up a Django web application.