Machine Learning and Artificial Intelligence in Engineering Applications: 2nd Edition

Dear Colleagues,

Artificial intelligence is at our doorstep with deep learning processes supported by services and applications incorporated in industrial, agricultural, energy, financial, healthcare, manufacturing, transportation, and logistic systems. These technological capabilities bring about tremendous world changes, boosting the economy, increasing productivity, and providing new opportunities. Moreover, all applications now rely heavily on IoT sensory data and large language models (LLMs). As a result, information has become an essential commodity. Furthermore, the development of artificial intelligence and deep learning models following the ever-increasing human–machine interactions in everyday applications is a crucial aspect of the next Industrial Revolution.

The rapid deployment of the Internet of Things (IoT) and the integration of big data repositories lead to the ever-increasing engagement of information, which requires training in new intelligent algorithms, protocols, and processes. The growth of AI with the incorporation of machine learning and deep learning in engineering applications has enabled developers to create machines that can carry out complex manufacturing tasks. The ultimate goal is to develop systems that can learn and improve without human intervention.

Such unsupervised and reinforced learning processes will benefit many engineering systems and applications. In addition, advances in natural language processing and the extensive exploitation of neural networks provide new human–machine interactions for robotics, agriculture, process manufacturing, and the transportation industry while further promoting the extensive use of augmented and virtual reality applications.

This Special Issue aims to emerge new distributed or cloud-based engineering applications, which involve AI algorithms and services targeting holistic, innovative, and sustainable systems supported by deep learning models. We encourage contributors to publish their work related to intelligent information systems, decision support systems, incident response systems, distributed data collection processes, and deep learning/machine learning architectures and algorithms provided as a service, associated with (but not limited to) the following:

• Deep learning algorithms, services, and processes for logistics, manufacturing, industrial, and safety applications;
• Smart cities and smart home automation models, algorithms, services, and applications;
• Transportation and incident response processes and services utilizing AI;
• Smart medical systems and services;
• Smart agricultural decision support systems and services;
• Human–machine interactive and cognitive services;
• Augmented reality, virtual reality services, and applications supported by deep learning processes or LLMs;
• Internet of Things, smart algorithms, and deep learning models, provided as services over distributed and cloud-based decision support systems;
• Design, evaluation, and implementation of novel Internet of Things models and algorithms that incorporate supervised, reinforcement, self-supervised, or ensemble learning.

Dr. Sotirios Kontogiannis
Dr. Myrto Konstantinidou
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.

• intelligent information systems
• deep learning frameworks, processes, and methods
• deep learning algorithms services and applications
• intelligent engineering applications
• engineering processes incorporating natural language processing models and LLMs
• distributed AI information systems
• Industry 4.0 processes supported by AI systems
• Industry 5.0 human–machine collaboration AI processes
• cloud-based decision support systems offering deep learning services
• smart IoT applications and smart grids
• augmented and virtual reality services supported by AI models

• Machine Learning and Artificial Intelligence in Engineering Applications in Algorithms (7 articles)