Machine Learning and Artificial Intelligence in Engineering Applications

Dear Colleagues,

Artificial intelligence is at our doorstep with machine learning 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 data. As a result, information has become an essential commodity. Furthermore, the development of artificial intelligence and deep learning models with 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 cloud big data concentrations 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.

Many engineering systems and applications will benefit from such unsupervised intelligent processes. In addition, natural language processing capabilities and the extensive exploitation of neural networks will provide new human–machine interactions for robotics, agriculture, process and manufacturing, and the transportation industry, while further promoting the extensive use of augmented, virtual, and mixed reality applications.

This Special Issue aims to emerge new distributed or cloud-based engineering applications, which involve smart algorithms and services targeting holistic, innovative, and sustainable systems. 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):

• Machine learning and deep learning algorithm services, and processes for logistic, manufacturing, industrial, and safety applications;
• Smart cities and smart home automation services and applications;
• Smart transportation systems and services;
• Smart medical systems and services;
• Smart agricultural decision support systems and services;
• Human–machine interactive and cognitive services;
• Augmented reality, virtual reality, and mixed reality systems, services, and applications;
• Internet of Things, smart algorithms, provided as services over distributed and cloud-based decision support systems;
• Design, evaluation, and implementation of novel Internet of Things solutions incorporating machine learning, deep learning, and data mining logic.

We look forward to receiving your contributions.

Dr. Sotirios Kontogiannis
Dr. Myrto Konstantinidou
Guest Editors

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