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

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