As we live in a globally interconnected world and rapidly developing digital technology, cities are turning into big data machines. The challenge lies in collecting, storing, and analyzing these big data to take advantage of them both for the public and private good. The real problem is that cities have too little time and few resources to use these data to their full potential. Machine learning is a powerful data-driven approach that enables configurations of systems and devices to learn a target performance (quality) without requiring an in-depth understanding of the underlying theory behind it. It is a very applicable technology in the building, campus, and city deployments for smart operations. Machine learning is being used in many industries, including smart buildings and campuses, especially for smart lighting control, parking lot management, greenhouse control, energy management and traffic lights scheduling. By applying machine learning to optimize the usage of resources such as energy and water in a building or campus, a better environment can be established for human survival and life.

Machine Learning for Smart Systems provides a state-of-the-art overview of machine learning (ML) applications for smart systems. The core of the approach comprises four major parts covering the role of ML in smart environments, challenges and opportunities in the deployment of ML in smart systems, and methods of -ML system design. The former three parts are further broken down into nine chapters describing fundamental techniques of ML (e.g., regression analysis and classification), data science for smart systems (e.g., time series analysis/regression analysis and human activity recognition with wearable sensors), and ML for pervasive sensing (e.g., text to image generation and image retrieval). Research in smart cities is wide-ranging and includes traffic monitoring, surveillance, logistical service planning and management, urban transport systems, parking management and control, road safety applications on highways, street light control for better illumination at night for enhanced security and reduced energy cost, location-based services such as mobile device localization, consistent map updating, signage recognition or indoor mapping. The research issues such as data sharing, privacy protection, machine learning, and cloud computing for building automation should be addressed systematically.

This special issue will help understand the core ML building blocks and algorithms, use case applications and products, business opportunities, and develop a holistic approach to machine learning.

Recommended topics (but not limited to)

  • Role of artificial intelligence in smart buildings
  • Machine learning for smart systems
  • Challenges and opportunities of machine learning for smart systems
  • Innovations in machine learning for smart systems
  • Deep learning applications for smart systems
  • Artificial intelligence and machine learning for smart building and smart campus
  • Intelligent technologies for smart systems
  • Data-driven machine learning for smart systems
  • Safe and resilient machine learning for smart buildings
  • Understanding the interconnection between smart cities and machine learning for smart campus
  • Intelligent data analytics for smart systems
  • Trends in artificial intelligence and machine learning for smart buildings

Important dates

Submission deadline: 31 August, 2023

Authors notification: 30 October, 2023

Final version submission: 30 November, 2023

Submission guidelines

Original and unpublished works on any of the topics aforementioned or related are welcome. The SCPE journal has a rigorous peer-reviewing process and papers will be reviewed by at least two referees. All submitted papers must be formatted according to the journal's instructions, which can be found here.

Guest Editors

Dr. Achyut Shankar, Department of Cyber Security University of Warwick, United Kingdom, email:

Dr. Achyut Shankar is currently working with University of Warwick, United Kingdom. He has published more than 95 research papers in reputed international conferences & journals in which 75 papers are in SCIE journals. He is a member of ACM and has received research award for excellence in research for the year 2016 and 2017. He had organized many special sessions with Scopus Indexed International Conferences worldwide, proceedings of which were published by Springer, IEEE, Elsevier etc. He is currently serving as an Associate Editor in SAIEE Africa Research Journal(IEEE), Scientific Reports( Nature Journal, Q1), Human- Centric Computing and Information Sciences & SN applied sciences(SCOPUS & ESCI, Springer) and in year 2021 and 2022 handing few special issues as a Guest editor ACM transaction for TALIP, International Journal of Human Computer Interaction( Taylor and Francis) , International Journal of System Assurance Engineering and Management(Springer) and Journal of Interconnection networks( World Scientific journals). He is serving as reviewer of IEEE Transactions on Intelligent Transportation Systems, IEEE Sensors Journal, IEEE Internet of Things Journal, ACM Transactions on Asian and Low-Resource Language Information Processing and other prestigious conferences. His areas of interest include , Machine Learning, IOT, Network Security, Blockchain & Cloud computing.

Dr Zahid Akhtar, State University of New York Polytechnic Institute, USA, email:

Dr. Akhtar is an Assistant Professor in the Department of Network and Computer Security at State University of New York Polytechnic Institute (USA) since August 2020. Prior to this, he was a Research Assistant Professor at University of Memphis, USA (2018-2020) and a Postdoctoral Fellow at INRS-EMT-University of Quebec, Canada (2016-2018), University of Udine, Italy (2013-2016), Bahcesehir University, Turkey (2013), and University of Cagliari, Italy (2012-2013), respectively. He was a visiting Research Fellow at University of Cagliari, Italy, from 2008 to 2009. Between 2005 to 2008, he was Junior Research Fellow at University of Pune, India. In 2004, he worked as a summer intern at Tata Motors Limited, India. Dr. Akhtar received the Ph.D. degree in Electronic and Computer Engineering from University of Cagliari (Italy). He is an Associate Editor of IEEE Access journal. Moreover, he is a Senior Member of IEEE as well as a member of the Association for Computing Machinery (ACM). He is also a recipient of the Premium Award for Best Paper in IET Biometrics Journal (2014), Outstanding Paper Award at the International Conference on Internet (2017), Best Industry-Oriented research work award at National Seminar on Physics and Technology of Sensors (2007), Outstanding Contribution in Reviewing Award in the prestigious journal Pattern Recognition Letters (2016), and Thrice Best Reviewer Award at the International Conference on Vision, Image and Signal Processing (2017, 2018 & 2019).Dr. Akhtar’s current research interests are in the areas of Computer Vision, Machine Learning and Pattern Recognition with applications to Biometrics, Affect Recognition, Image and Video Processing, Perceptual-Based Audiovisual Multimedia Quality Assessment, and Cybersecurity.