Abstract: Data cleaning, also known as data cleansing or data scrubbing, is the process of identifying and correcting errors, inconsistencies, and inaccuracies in datasets. It is a crucial step in ...

Abstract: Data quality is the measure to which data satisfy the demands of its use in accuracy, completeness, consistency, timelines, and reliability. Data quality problems such as missing values, ...

Quantum machine learning is a hybrid approach that combines classical data with quantum computing methods. In classical computing, data is stored in bits encoded as a 0 or 1. Quantum computers use ...

Have you ever spent hours wrestling with messy spreadsheets, trying to clean up data that just won’t cooperate? Or maybe you’ve found yourself manually merging files, painstakingly copying and pasting ...

This project focuses on cleaning and standardizing a retail sales dataset that initially contained inconsistent data formats, missing values, duplicate entries, and formatting irregularities across ...

This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer ...

Department of Preventive Medicine, College of Basic Medicine and Forensic Medicine, Henan University of Science and Technology, Luoyang, China Background: Effective connectivity (EC) refers to the ...

Imagine this: you’ve just received a dataset for an urgent project. At first glance, it’s a mess—duplicate entries, missing values, inconsistent formats, and columns that don’t make sense. You know ...

The ability to rapidly regress the kinetic parameters from cyclic voltammograms is important for many laboratory automation endeavors. Inspired by how expert electrochemists can rapidly interpret ...