Applications of AI and Data Engineering in Science

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (15 February 2024) | Viewed by 2440

Special Issue Editors


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Guest Editor Assistant
Department of Information Technology Systems and Networks, Faculty of Informatics, University of Debrecen, 4032 Debrecen, Hungary
Interests: sensory data classification; deep learning; feature extraction and prediction

Special Issue Information

Dear Colleagues,

Since around the middle of the last decade, artificial intelligence has emerged as one of the scientific subfields with the most rapid rate of advancement. However, while many researchers see this as an enormous opportunity for ever-faster product development, lower product manufacturing costs, and increased production efficiency, others are concerned about the possibility of massive layoffs and dependence on technologies, or even the possibility of people who are in possession of intelligent technologies seizing control of the entire world. Whoever is correct, one thing is certain: there is no turning back from the application of artificial intelligence to the majority of firms in the economy. This cannot be changed. A large amount of data on the execution of business processes in an organization are amassed as a result of the routine operational activities that take place in IT systems. In most cases, these are just logs, which certify the arrival of items or the starting up of machinery, as well as the time that employees clock in or leave. The use of such data is now feasible as a result of the development of algorithms. The use of algorithms enables the automatic identification of the process that is being carried out, as well as the analysis of that process. It enables the identification of bottlenecks, duplicate processes, and actions, as well as how the actual tasks completed in a corporation vary from those specified in authorized procedures, among other things. The dawn of the age of artificial intelligence heralds an increase in production and creativity. Artificial intelligence raises the bar in terms of speed, adaptability, and optimization. However, the most important fact is that businesses that automate their processes will gain a competitive advantage over their rivals and will maintain their position as market leaders in order to meet the ever-increasing expectations of their customers. This will be the case because these businesses will be able to meet their customers' needs more effectively.

Finally, I would like to thank Dr. Laith Baniata, Dr. Sulieman Ahmad Bani-Ahmad, Dr. Zoltán GÁL and their valuable work for assisting me with this Special Issue.

Dr. Fabrizio Marozzo
Guest Editor

Dr. Ashraf ALDabbas
Guest Editor Assistant

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.

Keywords

  • remote sensing
  • Internet of Things
  • artificial intelligence (AI)
  • cloud computing
  • big data
  • knowledge representation

Published Papers (1 paper)

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Research

16 pages, 4615 KiB  
Article
Quantum-Inspired Neural Network Model of Optical Illusions
by Ivan S. Maksymov
Algorithms 2024, 17(1), 30; https://doi.org/10.3390/a17010030 - 10 Jan 2024
Cited by 2 | Viewed by 1856
Abstract
Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model [...] Read more.
Ambiguous optical illusions have been a paradigmatic object of fascination, research and inspiration in arts, psychology and video games. However, accurate computational models of perception of ambiguous figures have been elusive. In this paper, we design and train a deep neural network model to simulate human perception of the Necker cube, an ambiguous drawing with several alternating possible interpretations. Defining the weights of the neural network connection using a quantum generator of truly random numbers, in agreement with the emerging concepts of quantum artificial intelligence and quantum cognition, we reveal that the actual perceptual state of the Necker cube is a qubit-like superposition of the two fundamental perceptual states predicted by classical theories. Our results finds applications in video games and virtual reality systems employed for training of astronauts and operators of unmanned aerial vehicles. They are also useful for researchers working in the fields of machine learning and vision, psychology of perception and quantum–mechanical models of human mind and decision making. Full article
(This article belongs to the Special Issue Applications of AI and Data Engineering in Science)
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