It is challenging to test machine learning (ML) applications, which are intended to learn properties of data sets where the correct answers are not already known. In the absence of a test oracle, one approach to testing these applications is to use metamorphic testing, in which properties of ...
An approach to software testing of machine learning applications - Murphy, Kaiser, et al. - 2007 () Citation Context ...are engineers developing software for scientists in the field of molecular biology X X PS39 [55] A framework for randomly generating large data sets for testing machine ...
As a complementary route, artificial intelligence and machine learning (ML) approaches are establishing the fourth paradigm (data-driven science; Fig.1)in concrete research and offering fresh perspectives and practical solutions for accelerating innovations in the design and development of cementitious mate...
However, researchers have yet to produce an automated functional testing approach, resulting in many developers relying on a resource consuming manual testing. In this paper, we present a novel approach for the automation of functional testing in mobile software by leveraging machine learning techniques...
An increasing array of tools is being developed using artificial intelligence (AI) and machine learning (ML) for cancer imaging. The development of an optimal tool requires multidisciplinary engagement to ensure that the appropriate use case is met, as well as to undertake robust development and t...
This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance in terms of pro...
Figure 4. Machine Learning process flow. As mentioned before, the idea of this paper is to give a different use of ML compared to a normal flow. A common approach follows the standard way that a dataset is divided into Training Set, Testing Set, and Validation Set; in this case, the ...
Machine learning (ML)-based approaches to system development employ a fundamentally different style of programming than historically used in computer science. This approach uses example data to train a model to enable the machine to learn how to perform a task. ML training is highly iterative with...
and better understanding of the behaviour of electronic circuits and devices. Machine learning algorithms and artificial neural networks can model electronic circuits and solve complex problems. They are also applied in the field of measurement, testing, and diagnostics. Methodologies and models for proce...
Simultaneous monitoring of the process parameters in a multivariate normal process has caught researchers’ attention during the last two decades. However, only statistical control charts have been developed so far for this purpose. On the other hand, machine-learning (ML) techniques have rarely been...