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Editorial
Editorial to the Inaugural Issue
andreas pester*
British University in Egypt (BUE), 11837 Cairo, Egypt
Acadlore Transactions on AI and Machine Learning
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Volume 1, Issue 1, 2022
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Page 1-1
Received: 10-19-2022,
Revised: 10-19-2022,
Accepted: 10-19-2022,
Available online: 11-19-2022
View Full Article|Download PDF

Dear editors, reviewers, authors, readers, and other members of the whole scientific and professional community,

Welcome to this inaugural issue of the Acadlore Transactions on AI and Machine Learning (ATAIML).

Artificial intelligence and machine learning changed in the last 10 years to emerging technologies, which are in the focus of academic and applied research as powerful applications in industries, agriculture, health, service and culture sectors as well. Together with next-generation computing, metaverse and other areas, they shape our digital Future.

According to the Gartner report about emerging AI trends

- Democratizing AI

- Combining different AI techniques to improve the efficiency of learning

- Using AI techniques embedded in Internet of Things (IoT) technologies

- Making appropriate business and ethical choices when adopting AI and

- Developing AI techniques that learn a representation of artifacts from the data and using them to generate completely original artifacts are the emerging trends for AI development in the next years.

Acadlore Transactions on AI and Machine Learning (ATAIML) is a new open access journal of computer science, artificial intelligence, machine and deep learning, graph neural networks, synthetic data and other related fields. It covers theory, methods and interdisciplinary applications, algorithms, data and implementations on different platforms. It is embedded in a family of publication platforms which support the above-mentioned trends and offers an advanced meeting place for studies related to AI and machine learning topics and their applications. The journal will have a special focus on the relation between AI and extended reality, to synthetic data and to graph neural networks.

This new journal focuses on (in an alphabetical order)

 AI and design, fashion and arts

 AI and IoT

 AI and mixed reality and multimedia data

 AI and psychology

 AI and smart food, agriculture and forest

 Data-centric AI and synthetic data

 Ethical and law issues of AI

 Graph neural networks

 Machine Learning in medicine, biology, chemistry, physics

 Statistical and topological methods in machine learning

but not only.

In our first issue we present seven papers, related to deep learning applications in medicine, traffic security, cinematography and real estate business.

The used algorithms are well known in the community and adapted according to the requirements of the related application. They cover semantic analysis, feature selection, image segmentation with U-nets and computer vision.

The authors are coming from India, China, Cameroon, Indonesia. We see this as a contribution to develop our journal as a world open journal, supporting scientists especial also from emerging countries.

We commend these articles to the readership and hope for much feedback.


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Pester, A. (2022). Editorial to the Inaugural Issue. Acadlore Trans. Mach. Learn., 1(1), 1-1. https://doi.org/10.56578/ataiml010101
A. Pester, "Editorial to the Inaugural Issue," Acadlore Trans. Mach. Learn., vol. 1, no. 1, pp. 1-1, 2022. https://doi.org/10.56578/ataiml010101
@editorial{Pester2022EditorialTT,
title={Editorial to the Inaugural Issue},
author={Andreas Pester},
journal={Acadlore Transactions on AI and Machine Learning},
year={2022}
}
Andreas Pester, et al. "Editorial to the Inaugural Issue." Acadlore Transactions on AI and Machine Learning, v 1, pp 1-1. doi: https://doi.org/10.56578/ataiml010101
Andreas Pester. "Editorial to the Inaugural Issue." Acadlore Transactions on AI and Machine Learning, 1, (2022): 1-1. doi: https://doi.org/10.56578/ataiml010101
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©2022 by the authors. Licensee Acadlore Publishing Services Limited, Hong Kong. This article can be downloaded for free, and reused and quoted with a citation of the original published version, under the CC BY 4.0 license.
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