Deep learning detects foodborne bacteria within three hours by eliminating debris misclassifications

Researchers have significantly enhanced an artificial intelligence tool used to rapidly detect bacterial contamination in ...
The Lancet

Machine learning prediction of 1-year mortality in older patients with heart failure: a nationwide, multicenter, prospective cohort study

A machine learning model incorporating functional assessments predicts one-year mortality in older patients with HF and improves risk stratification beyond established scores. Functional status at ...
FinanceFeeds

Crypto Machine Learning Algorithms Explained for Beginners

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions ...
Proceedings of the National Academy of Sciences (PNAS)

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Machine learning holds great promise for classifying and identifying fossils, and has recently been marshaled to identify trackmakers of dinosaur ...
Frontiers

Advances in machine and deep learning for ECG beat classification: a systematic review

The electrocardiogram (ECG) is an important tool for exploring the structure and function of the heart due to its low cost, ease of use, efficiency, and non-invasive nature. With the rapid development ...
Scientific Research Publishing

Pedamonti, D. (2018) Comparison of Non-Linear Activation Functions for Deep Neural Networks on MNIST Classification Task. arXiv.

ABSTRACT: Artificial deep neural networks (ADNNs) have become a cornerstone of modern machine learning, but they are not immune to challenges. One of the most significant problems plaguing ADNNs is ...
C&EN

Machine Learning-Guided Design of High-Performance Antifungal Polymers against Candida albicans

School of Chemical Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia Australian Centre for NanoMedicine, University of New South Wales, Sydney, New South Wales 2052, ...
IEEE

Multi-Class Cancer Classification Using Gene Expression and Machine Learning

Abstract: This study evaluates the performance of eight machine learning models such as Gradient Boosting, Logistic Regression, Naïve Bayes, Linear Discriminant Analysis (LDA), Random Forest, Support ...