How AI decides what your content means and why it gets you wrong

AI systems label and score content before ranking. Annotation determines how you’re understood — and whether you compete at all.
Yahoo

Amazon shopper frustrated after learning packaging labels were misleading: 'How can Amazon get away with [this]?'

A Redditor took to the platform to complain about Amazon's allegedly recyclable packaging after they made attempts to recycle mailers. Posting in r/infuriatingasf***, the user shared that they had ...
Phys.org

Mapping cell development with mathematics-informed machine learning

The development of humans and other animals unfolds gradually over time, with cells taking on specific roles and functions via a process called cell fate determination. The fate of individual cells, ...
Visual Studio Magazine

Tsetlin Machine Binary Classification Using C#

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...
IEEE

A Comparative Analysis of Machine Learning and Deep Learning Approaches for Phonocardiogram Classification Using Dataset Integration

Abstract: The worldwide increase in cardiovascular diseases and related deaths necessitates advanced, non-invasive diagnostic methods that allow for timely detection and intervention to mitigate the ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...
Scientific Research Publishing

Comparative Research in Sentiment Analysis Using Machine Learning Technique ()

In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a ...
IEEE

Learning With Partial-Label and Unlabeled Data: Contrastive With Negative Example Separation

Abstract: Semi-Supervised Partial Label Learning (SSPLL) is an important branch of weakly supervised learning, where the data consists of both partial label examples and unlabeled ones. In SSPLL, the ...