Research on statistical arbitrage in U.S. equities (1997–2007) shows that PCA-based strategies achieved average annual Sharpe ...

A Python script with an AI algorithm that solves a 2D maze using the A* search algorithm - but, with specific movement constraints, to really force the AI earn its way out of the maze. This time, ...

All PPG, filtering and face detection combinations were tested on the UBFC2 dataset [2], comparing to the ground truth and values generated by the python library pyVHR [1], a library for studying ...

Learn how to build the RREF algorithm step-by-step in Python—great for math and code enthusiasts!

Learn how the Adadelta optimization algorithm really works by coding it from the ground up in Python. Perfect for ML enthusiasts who want to go beyond the black box!

This work aims to compare two different Feature Extraction Algorithms (FEAs) viz. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), using a K-Nearest Neighbor (KNN) classifier ...

ABSTRACT: The integration of artificial intelligence into the development and production of mechatronic products offers a substantial opportunity to enhance efficiency, adaptability, and system ...

Results show that the PCA-PSO-LSSVM fault diagnosis model has a maximum fault recognition efficiency that is 10.4% higher than the other three models, the test sample classification time is reduced by ...

Principal component analysis (PCA) is a multivariate statistical method that can help overcome these challenges by extracting relevant information from complex datasets and providing new dimensions ...