TMCnet

Data Normalization vs. Standardization: What's the Difference and When to Use Each

Data Normalization vs. Standardization is one of the most foundational yet often misunderstood topics in machine learning and data preprocessing. If you''ve ever built a predictive model, worked on a ...
InfoWorld

AI-augmented data quality engineering

This is where AI-augmented data quality engineering emerges. It shifts data quality from deterministic, Boolean checks to ...

WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification

Beijing, Feb. 06, 2026 (GLOBE NEWSWIRE) -- WiMi Releases Hybrid Quantum-Classical Neural Network (H-QNN) Technology for Efficient MNIST Binary Image Classification ...
IEEE

Seismic Data Sparse Representation Using Swin Transformers

Abstract: Seismic data preprocessing significantly benefits from advanced sparse representation and domain transformation techniques to enhance denoising, wavefield separation, and data reconstruction ...
IEEE

Unveiling Data Preprocessing Patterns in Computational Notebooks

Abstract: Data preprocessing, which includes data integration, cleaning, and transformation, is often a time and effort-intensive step due to its fundamental importance. This crucial phase is integral ...
GitHub

cpiVAE: Cross-Platform Proteomics Data Imputation

The cpiVAE learns a shared latent representation across different proteomics platforms. The model consists of platform-specific encoders and decoders connected through a shared latent space, allowing ...
GitHub

Spatial Data Management with DuckDB

Welcome to the official repository for Spatial Data Management with DuckDB: From SQL Basics to Advanced Geospatial Analytics. This repository contains all the code examples featured in the book, ...