In the ever-growing field of machine learning, one of the most significant challenges is making complex models interpretable and accessible. Enter AutoXplainAI, an innovative framework developed by ...

It can be relatively cheap to gather a lot of bio-signal data. To teach a machine-learning algorithm to find a relationship between bio-signals and health outcomes, however, you need to teach the ...

Workload Discovery on AWS is a solution to visualize AWS Cloud workloads. With it you can build, customize, and share architecture diagrams of your workloads based on live data from AWS. The solution ...

Deep Learning (DL), particularly Convolutional ... this field by enhancing the accuracy of classification tasks. In this work, a novel approach combining a transformerbased Swin-Unet architecture with ...

This article proposes a novel two-stage method for UAV detection and classification using a scanning frequency-modulated continuous wave (FMCW) radar system and machine-learning (ML) techniques. In ...

School of Sustainable Chemical, Biological and Materials Engineering, The University of Oklahoma, Norman, Oklahoma 73019, United States ...

School of Mechanical and Equipment Engineering, Hebei University of Engineering, 19 Taiji Road, Congtai District, HanDan 056038, P. R. China ...

… the machine learning methods for estimation of the nuisance functions, … the resampling schemes, … the double machine learning algorithm, … the Neyman ...

This study combines machine learning and a phase diagram to accelerate the design of a cobalt-based superalloy with a composition of Co-30Ni-10Al-6Ta (at%). The results show that Co-30Ni-10Al-6Ta ...

Due to the hierarchical nature of deep learning models, complex functions can be learned to solve difficult classification problems that were previously unsolvable by classic machine learning ... an ...