Emerging Topics in Artificial Intelligence (AI): Architectures and Techniques for Real-World Applications

Dear Colleagues,

Artificial Intelligence (AI) is a technological innovation that is transforming the world and changing the relationship between humans and machines. The past decade has seen tremendous progress in the area of AI, with giant leaps in the advancement of machine learning and deep learning. In the near future, most technology applications will harness or incorporate the output of some form of AI. AI turns autonomous vehicles and robots into reality and enables them to sense their environments, learn, adapt, and respond on their own. AI has transformed healthcare and become a critical part of the healthcare industry. AI will also disrupt business models and create new ways of working and facilitate digital transformation in many applications.

The techniques for the AI of the future are also changing to meet new requirements and applications. In a traditional approach, training a machine learning model requires building a large dataset locally and keeping it on the local machine or a data center. This is a centralized approach where data are gathered in a centralized server and machine learning models are trained over them. One future trend is distributed AI and performing AI on edge devices. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the location of the device. Edge AI is growing in popularity and is the next frontier of development for the Intelligent Internet of Things or artificial intelligence IoT (AIoT).

This Special Issues focusses on the recent development of AI and aims to collect the latest research works for emerging topics in AI, intelligent systems, machine learning, and deep learning. The potential topics include but are not limited to the following:

• AI and intelligent systems;
• Edge intelligence or Edge AI and computing;
• Distributed AI or distributed deep learning;
• On-device machine learning/deep learning/AI;
• AI and Internet of Things for smart cities;
• Embedded Intelligence on GPU/ FPGA/ SoC;
• Collaborative AI;
• Generative AI;
• AI in software engineering;
• Scalable AI for big data;
• Trusted AI or trustworthy AI;
• Explainable AI;
• Future AI threats and security;
• Future AI and digital health;
• Future AI data-driven technology;
• The future of AI in transportation;
• The future of mobile AI;
• The future of human-centered AI.

Prof. Dr. Kah Phooi Seng
Prof. Dr. Li-Minn Ang
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. Electronics 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 2400 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.

• artificial intelligence
• intelligent systems
• intelligent sensing
• deep learming
• edge AI
• generative AI
• trustworthy AI