Application of Machine and Deep Learning in Cyber-Physical Systems (CPSs)

Dear Colleagues,

We invite submissions to our upcoming Special Issue focused on the innovative application of machine learning techniques in cyber-physical systems (CPSs). As CPSs become increasingly integral to a wide range of industries, there is a growing need for intelligent systems that can effectively integrate computational and physical processes. This conference aims to bring together researchers, practitioners, and industry experts to discuss cutting-edge advancements in the field.

Research Directions and Highlights

We are particularly interested in papers that address the following areas:

1. Data-Driven Applications to Real CPSs:
   We highly appreciate papers that apply data-driven methodologies to real-world CPSs, utilizing actual data to demonstrate practical applications. Submissions should focus on how machine learning techniques are employed to solve specific problems within CPSs, showcasing tangible benefits and improvements.
2. Leveraging Large Language Models (LLMs) for CPSs:
   We are interested in exploring how large language models (LLMs) can be harnessed to create more intelligent CPSs. Submissions in this area could include, but are not limited to, applications of LLMs for enhancing decision-making, improving human–machine interactions, and optimizing the integration of various CPS components.
3. Adaptive and Self-Learning CPSs:
   Research on adaptive systems that can learn and evolve over time is highly encouraged. Papers should explore how machine learning can be used to develop CPSs that adapt to changing environments and conditions, optimize performance, and maintain resilience.
4. Safety, Security, and Privacy in CPSs:
   Papers addressing the challenges of ensuring safety, security, and privacy in machine learning-driven CPSs are welcomed. Submissions could focus on innovative techniques for anomaly detection, threat modeling, secure data handling, and ensuring the robustness of CPSs in adversarial environments.
5. Resource-Efficient Machine Learning in CPSs:
   We welcome papers that explore methods for resource-efficient machine learning, including energy-efficient algorithms, low-power hardware solutions, and optimization techniques that reduce computational overhead while maintaining performance.
6. Explainability and Trustworthiness in CPSs:
   As machine learning models become more complex, the need for explainability and trustworthiness in CPSs is critical. We invite papers that focus on developing explainable AI models for CPSs, enhancing model transparency, and building trust in automated decision-making. Research that addresses how to balance model performance with interpretability, and methods for validating and verifying the reliability of learning systems in CPSs, are particularly encouraged.

Submission Guidelines

• All papers must be original and not under review at any other conference or journal.
• Authors are required to submit their code and datasets to an open-source repository and provide links within their abstracts.
• Please follow the provided template for formatting and submission guidelines.

We look forward to your submissions and to advancing the state of the art in the application of machine learning within cyber-physical systems.

Dr. Yongxin Liu, Embry-Riddle Aeronautical University
Dr. Jian Wang, University of Tennessee at Martin
Dr. Shuteng Niu, Bowling Green State University
Dr. Thomas Yang, Embry-Riddle Aeronautical University
Dr. Dahai Liu, Embry-Riddle Aeronautical University
Dr. Prashant Shekhar, Embry-Riddle Aeronautical University
Dr. Hong Liu, Embry-Riddle Aeronautical University
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. Big Data and Cognitive Computing 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 1800 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.

• cyber-physical systems (CPSs)
• Internet of Things
• data-driven applications
• explainable AI
• self-learning adaptive systems
• trustworthy machine learning
• efficient learning