K-Dramas, algorithms and adolescence: When digital fantasy becomes an emotional shelter

From clinical psychosis to fatal fixation, fantasy worlds — amplified by algorithms, cheap data and unsupervised screen time ...

K-Dramas, algorithms and adolescence: How digital fantasy becomes an escape

From clinical psychosis to fatal fixation, fantasy worlds — amplified by algorithms, cheap data and unsupervised screen time — can become emotional shelters for vulnerable young minds.

Can AI Find Physics Beyond the Standard Model?

In 1930, a young physicist named Carl D. Anderson was tasked by his mentor with measuring the energies of cosmic ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Beebom

What is the Difference Between Deep Learning and Machine Learning

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
www.cs.cmu.edu

Efficient Learning Algorithms under (Heavy) Contamination

In this talk, I will present a series of new results in supervised learning from contaminated datasets, based on a general outlier removal algorithm inspired by recent work on learning with ...
Frontiers

The value of unsupervised machine learning algorithms based on CT and MRI for predicting sarcopenia

Objectives: This study aims to investigate the efficacy of unsupervised machine learning algorithms, specifically the Gaussian Mixture Model (GMM), K-means clustering, and Otsu automatic threshold ...
Scientific Research Publishing

Goldstein, M. and Uchida, S. (2016) A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data. PLOS ONE, 11, e0152173.

ABSTRACT: Purpose: The purpose of this study is to develop a scalable, risk-aware artificial intelligence (AI) framework capable of detecting financial fraud in high-throughput digital transaction ...
IEEE

An Unsupervised Learning Approach for Reconstructing 3T-Like Images From 0.3T MRI Without Paired Training Data

Abstract: Magnetic resonance imaging (MRI) is powerful in medical diagnostics, yet high-field MRI, despite offering superior image quality, incurs significant costs for procurement, installation, ...
IEEE

Gram–Schmidt Methods for Unsupervised Feature Extraction and Selection

Abstract: Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS) type ...