Abstract: Federated Learning (FL) has emerged as a cutting-edge paradigm in machine learning, showcasing remarkable advancements in recent years. This research paper delves into the dynamic landscape ...
This repository contains the implementation, benchmarks, and supporting tools for my MSc dissertation project: Self-learning Variational Autoencoder for EEG Artifact Removal (Key code only). Benchmark ...
This project was developed as part of my Master's programm at Heilbronn University. The goal is to classify different oil samples (e.g. olive oil, sunflower oil) based on their fluorescence and ...
This Research Topic explores the application of Machine Learning (ML) and Deep Learning (DL) methods in Neuromarketing and Consumer Neuroscience. Rather than following the prevailing “AI trend” ...
Artificial intelligence has become a buzzword in today's world, with nearly every smartphone launched in the previous two years basing its marketing around AI in some shape or form. It's gotten to a ...
Predicting thermodynamic properties of mixtures is a cornerstone of chemical engineering, yet conventional group-contribution (GC) methods like modified UNIFAC (Dortmund) remain limited by incomplete ...
A study published last week used a wave flume to evaluate the effects of waves on small-diameter pipelines. The experimental data was interpreted statistically and using machine learning. The ...
20+ Machine Learning Methods in Groundbreaking Periodic Table From MIT, Google, Microsoft Your email has been sent A new “periodic table for machine learning” is reshaping how researchers explore AI, ...
ABSTRACT: Background and Theoretical Dilemma: The United States of America (USA) is the world’s largest consumer of crude oil in the world. Ensuring the sustainability of the role of crude oil in the ...
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