There has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed ...

ABSTRACT: In this paper, an Optimal Predictive Modeling of Nonlinear Transformations “OPMNT” method has been developed while using Orthogonal Nonnegative Matrix Factorization “ONMF” with the ...

ABSTRACT: The offline course “Home Plant Health Care,” which is available to the senior population, serves as the study object for this paper. Learn how to use artificial intelligence technologies to ...

Set between The Matrix and The Matrix Reloaded, Kid’s Story focuses upon a teenage boy named Michael Karl Popper (voiced in the English dub by Watson) who has long sensed something being off in the ...

Matrix factorization techniques, such as principal component analysis (PCA) and independent component analysis (ICA), are widely used to extract geological processes from geochemical data. However, ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The development of low-loss reconfigurable integrated optical devices enables further ...

As Machine Learning (ML) applications rapidly grow, concerns about adversarial attacks compromising their reliability have gained significant attention. One unsupervised ML method known for its ...

Abstract: Multi-layered graphs are popular in mobility studies because transportation data include multiple modalities, such as railways, buses, and taxis. Another example of a multi-layered graph is ...

Abstract: Non-negative Matrix Factorization (NMF) has been an ideal tool for machine learning. Non-negative Matrix Tri-Factorization (NMTF) is a generalization of NMF that incorporates a third ...

Factors are whole numbers that divide evenly into another number without leaving a remainder. For example, the factors of 24 are 1, 2, 3, 4, 6, 8, 12 and 24. These ...