Machine learning tests for data mining in engineering

Emil M. Oanta; Birol Menabil

doi:10.1117/12.2643115

2 March 2023 Machine learning tests for data mining in engineering

Emil M. Oanta, Birol Menabil

Proceedings Volume 12493, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI; 124930O (2023) https://doi.org/10.1117/12.2643115
Event: Advanced Topics in Optoelectronics, Microelectronics and Nanotechnologies 2022, 2022, Constanta, Romania

Abstract

Searching the intelligent aspects and identifying the rules in the huge amount of data automatically created nowadays is an appropriate method to explore and understand a world that is becoming more complex. Fortunately, the means of investigations are synchronous with the actual level of technological development. One might remark that, computer science being a rapid development field, it became an inspiring environment for other domains of science. The paper presents data mining techniques, original implementations of the linear/quadratic regression and of the K-means clustering methods and two machine learning models using the “Microsoft Machine Learning Studio (classic)” software. Some of the conclusions are: the data input process is simple and flexible, making it possible to access a wide range of data sources; there is an important library of algorithms that can be easily included in the experiment; data control may be thoroughly done using various methods, including visualizations; the machine learning application offers reliable results; the output consists of several types of reusable ‘objects’: trained models, data in various formats, web services. The paper is an important stage on the path of a trust development process which is intended to finish by considering machine learning instruments as common instruments in engineering.

Citation Download Citation

Emil M. Oanta and Birol Menabil "Machine learning tests for data mining in engineering", Proc. SPIE 12493, Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI, 124930O (2 March 2023); https://doi.org/10.1117/12.2643115

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