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Algorithms
Special Issues
Generative AI Algorithms and Their Applications to Real-World Problems
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Generative AI Algorithms and Their Applications to Real-World Problems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: closed (20 October 2024) | Viewed by 3768

Special Issue Editors

Dr. Sandra Ortega-Martorell

E-Mail Website
Guest Editor
School of Computer Science and Mathematics, Liverpool John Moores University, Liverpool L3 5UG, UK
Interests: machine learning; artificial intelligence
Dr. Ivan Olier-Caparroso

E-Mail Website
Guest Editor
Data Science, Liverpool John Moores University, Liverpool L3 5UG, UK
Interests: statistical and machine learning methods, with focus on modelling large-scale; highly structured and/or relational data (multivariate time series, graphs, networks, etc.); drug development; bioinformatics; healthcare

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the area of generative modelling to this Special Issue “Generative AI Algorithms and Their Applications to Real-World Problems”.

Generative AI algorithms learn the underlying distribution of a given dataset, which allows them to generate new data samples that are similar to the original data distribution or create meaningful representations that capture the essential features of the data.

Generative AI algorithms are widely used for data representation and visualisation tasks. They can learn compact and meaningful representations of high-dimensional data, which can be visualised in lower-dimensional spaces. Notable generative models specifically used for data representation and visualisation include autoencoders and Generative Topographic Mapping (GTM). Some generative algorithms used for generating new data include Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Autoregressive Models.

Generative AI algorithms have found a wide range of applications in various real-world problems across different domains such as text generation like GPT-3, image generation like DALL-E 2, audio generation, video generation, and molecule generation for drug discovery. As the field of generative modelling continues to evolve, new applications and use cases are likely to emerge, driving innovation in various industries.

We invite you to submit high-quality papers to this Special Issue, with subjects covering the whole range from theory to applications.

Dr. Sandra Ortega-Martorell
Dr. Ivan Olier-Caparroso
Guest Editors

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. Algorithms is an international peer-reviewed open access monthly 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 1600 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.

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

18 pages, 6736 KiB  
Article
A Dilemma-Based Learning-to-Rank Approach for Generative Design in Urban Architectural Regeneration
by Ioannis Kavouras, Ioannis Rallis, Dimitra Zouli, Emmanuel Sardis, Eftychios Protopapadakis, Anastasios Doulamis and Nikolaos Doulamis
Algorithms 2024, 17(12), 538; https://doi.org/10.3390/a17120538 - 23 Nov 2024
Viewed by 682
Abstract
Continuous urbanization and climate change degrade urban living conditions. Nature-based solutions in architectural and urban design offer promising remedies but are often hindered by time, cost, and early design phase challenges. To address this, we present a Generative Design System framework utilizing AI-generated [...] Read more.
Continuous urbanization and climate change degrade urban living conditions. Nature-based solutions in architectural and urban design offer promising remedies but are often hindered by time, cost, and early design phase challenges. To address this, we present a Generative Design System framework utilizing AI-generated images and learning-to-rank algorithms. This system generates numerous image solutions to inspire architects and urban planners, significantly accelerating early design stages. To manage the overwhelming volume of images, we introduce a dilemma-based learning approach that employs learning-to-rank and smart bubble sorting algorithms to prioritize images based on user preference. A case study demonstrates the framework’s potential, providing valuable insights into its application, benefits, and limitations in urban design. Full article
(This article belongs to the Special Issue Generative AI Algorithms and Their Applications to Real-World Problems)
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16 pages, 1017 KiB  
Article
Efficient Estimation of Generative Models Using Tukey Depth
by Minh-Quan Vo, Thu Nguyen, Michael A. Riegler and Hugo L. Hammer
Algorithms 2024, 17(3), 120; https://doi.org/10.3390/a17030120 - 13 Mar 2024
Viewed by 1842
Abstract
Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most commonly used alternative strategy [...] Read more.
Generative models have recently received a lot of attention. However, a challenge with such models is that it is usually not possible to compute the likelihood function, which makes parameter estimation or training of the models challenging. The most commonly used alternative strategy is called likelihood-free estimation, based on finding values of the model parameters such that a set of selected statistics have similar values in the dataset and in samples generated from the model. However, a challenge is how to select statistics that are efficient in estimating unknown parameters. The most commonly used statistics are the mean vector, variances, and correlations between variables, but they may be less relevant in estimating the unknown parameters. We suggest utilizing Tukey depth contours (TDCs) as statistics in likelihood-free estimation. TDCs are highly flexible and can capture almost any property of multivariate data, in addition, they seem to be as of yet unexplored for likelihood-free estimation. We demonstrate that TDC statistics are able to estimate the unknown parameters more efficiently than mean, variance, and correlation in likelihood-free estimation. We further apply the TDC statistics to estimate the properties of requests to a computer system, demonstrating their real-life applicability. The suggested method is able to efficiently find the unknown parameters of the request distribution and quantify the estimation uncertainty. Full article
(This article belongs to the Special Issue Generative AI Algorithms and Their Applications to Real-World Problems)
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