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This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.
This study focuses on four deep-learning models, which are Inception V3, MobileNet V2, ResNet152V2, and VGG19, aiming to enhance the accuracy of tumor Classification
FedAnil+ is a novel lightweight, and secure Federated Deep Learning Model to address non-IID data, privacy concerns, and communication overhead. This repo hosts a simulation for FedAnil+ written in Python.
The bignumber-utils package empowers developers to effortlessly perform accurate and reliable decimal and non-decimal arithmetic in JavaScript, leveraging the robust foundation of the bignumber.js library.
FedAnil is a secure blockchain-enabled Federated Deep Learning Model to address non-IID data and privacy concerns. This repo hosts a simulation for FedAnil written in Python.
A descriptive and inferential statistical analysis from the Kaggle database on the data collected by an IoT smoke detection device. Machine learning techniques were also used to help build this smart device, increasing its accuracy.
The Saxon XSLT Processor is using the accurate decimal-based floating-point arithmetic and half-up rounding for VAT rounding according to EU law. According to Europen Norm EN16931:2024
Text-based sentiment analysis plays a very important role in understanding customer opinions and preferences. But despite extensive research in sentiment and emotion analysis in text, a notable gap exists in understanding code-mixed texts. To address this, we propose an end-to-end transformer based model.