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Mathematics, Computer Programming, and Artificial Intelligence in 2D and 3D...
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Mathematics, Computer Programming, and Artificial Intelligence in 2D and 3D Software

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Mathematics and Computer Science".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 1343

Special Issue Editor

Dr. Tihomir Dovramadjiev

E-Mail Website
Guest Editor
Faculty of Shipbuilding, Technical University of Varna, 9010 Varna, Bulgaria
Interests: PLM; CAD; CAE; CAM; graphs in computer science
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Modern society is rapidly entering Industry 5.0 and Society 5.0. In them, artificial intelligence plays a significant role, where the comparison with human factors is increasingly more tangible. The present edition aims to capture this dynamic in the Special Edition on "Mathematics, Computer Programming, and Artificial Intelligence in 2D and 3D Software". The Special Edition aims to attract all stakeholders interested and working in the field of building applications, platforms, and global concepts, developing a digital 3D world and digital people. Often, these developments are related to ergonomics and human factors, where in a 3D digital universe, working, learning, and training activities and others are investigated. The subject of the publication is focused on advanced technical means, which are the main components in the construction of 3D digital worlds based on mathematical principles and materialized through 2D and 3D software. The publication welcomes innovative ideas related to the subject, while at the same time also accepting in-depth research that, despite the promotion of new digital technologies, digitalization, and artificial intelligence, also considers human factors and the preservation of human rights as affirmed by the United Nations community.

This Special Issue invites researchers who employ mathematics, computer programming, and AI techniques in practice to present their high-quality work related to:

  • Correlation of AI with 2D and 3D open-source software for developing automatically specialized addons;
  • Implementation of AI with 3D open-source software for creating digital humans;
  • Implementation of AI with 3D software for ergonomics needs;
  • Connecting of AI based tools with 3D open-source software to digitize interactive applications to enhance the 3D digital world.

Dr. Tihomir Dovramadjiev
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mathematics
  • programming
  • artificial intelligence
  • coding
  • industry 5.0
  • 2D and 3D software
  • open-source
  • digital
  • addons

Published Papers (1 paper)

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Research

16 pages, 461 KiB  
Article
LASSO and Elastic Net Tend to Over-Select Features
by Lu Liu, Junheng Gao, Georgia Beasley and Sin-Ho Jung
Mathematics 2023, 11(17), 3738; https://doi.org/10.3390/math11173738 - 30 Aug 2023
Cited by 1 | Viewed by 1053
Abstract
Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The [...] Read more.
Machine learning methods have been a standard approach to select features that are associated with an outcome and to build a prediction model when the number of candidate features is large. LASSO is one of the most popular approaches to this end. The LASSO approach selects features with large regression estimates, rather than based on statistical significance, that are associated with the outcome by imposing an L1-norm penalty to overcome the high dimensionality of the candidate features. As a result, LASSO may select insignificant features while possibly missing significant ones. Furthermore, from our experience, LASSO has been found to select too many features. By selecting features that are not associated with the outcome, we may have to spend more cost to collect and manage them in the future use of a fitted prediction model. Using the combination of L1- and L2-norm penalties, elastic net (EN) tends to select even more features than LASSO. The overly selected features that are not associated with the outcome act like white noise, so that the fitted prediction model may lose prediction accuracy. In this paper, we propose to use standard regression methods, without any penalizing approach, combined with a stepwise variable selection procedure to overcome these issues. Unlike LASSO and EN, this method selects features based on statistical significance. Through extensive simulations, we show that this maximum likelihood estimation-based method selects a very small number of features while maintaining a high prediction power, whereas LASSO and EN make a large number of false selections to result in loss of prediction accuracy. Contrary to LASSO and EN, the regression methods combined with a stepwise variable selection method is a standard statistical method, so that any biostatistician can use it to analyze high-dimensional data, even without advanced bioinformatics knowledge. Full article
(This article belongs to the Special Issue Mathematics, Computer Programming, and Artificial Intelligence in 2D and 3D Software)
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