The term Industry 4.0 is associated with the computerization of manufacturing Industries. In recent years, a number of trends such as the internet of things (IoT), digital manufacturing and cyber-physical systems have become key elements of the manufacturing innovations and Industry 4.0. Recent advancement in technologies have enabled to collect, transfer and analyze huge amounts of data very rapidly, which is at the core of this trend. Closely related, smart manufacturing is a concept that aims at developing smart factories that integrate these new technologies to rapidly adapt and respond to changes in the markets' demands for high-quality products. In practice, smart factories lie at the core of both, Industry 4.0 and smart manufacturing.

The aim of this special Issue of Journal IJATEE is to provide a platform for researchers to showcase findings and explore emerging technologies in the field of manufacturing and implementation of smart factories. Original research contributions and/or in depth reviews are invited for this special issue.

This Special Issue covers the Recent research in the field of advance manufacturing and Industry 4.0. The Industry 4.0 is the digital revolution involving Smart manufacturing, IoT, Machine learning, big data analysis and related fields. Industry 4.0 is the fourth industrial revolution involving a new stage in the organization and control of the industrial supply chain. Cyber-physical systems and smart machines plays key role in Industry 4.0. Objectives of this special issue to publish the novel research findings in the field of advanced manufacturing and Industry 4.0, in the evolving global scenario. It emphasizes on recent data and the contemporary issues in the field.

This Special issue will involve collection of high quality innovative research and review articles in the above fields. Themes of the Special Issue include but are not limited to:

  • Smart Manufacturing
  • IoT, Machine Learning, Artificial Intelligence and Industry 4.0
  • Cyber physical systems for the design and operation of smart manufacturing facilities
  • Use of cloud, distributed and digital manufacturing paradigms in cyber physical systems
  • Additive Manufacturing
  • Advances in Robotics and Mechatronics
  • Production and Automation Engineering
  • Advances in Industrial Engineering
  • Innovation in Industrial Design
  • Advanced Manufacturing Processes
  • Nanotechnology, Micro-engineering & MEMS
  • Micro and Nano Manufacturing
  • Advanced Materials for Engineering Exploration
  • Manufacturing of Advanced Composites

Submission Deadline: August 25, 2021

Guest Editor

  • Dr. Rajesh Purohit
    Professor, Mechanical Engineering
    MANIT, Bhopal, India
    rpurohit73@gmail.com
  • Dr. Manoj Gupta
    Associate Professor
    National University of Singapore (NUS), Singapore
    mpegm@nus.edu.sg
  • Dr. Anand Nayyar
    Professor, Researcher and Scientist
    Duy Tan University, Vietnam
    anandnayyar@duytan.edu.vn
  • Dr. R. S. Rana
    Associate Professor
    MANIT Bhopal, India
    ravindrarana74@gmail.com

Over the past decade, artificial intelligence has become part of our daily life and different domains such as telecommunication systems, transportation systems, machinery industries, medical instruments, web applications, etc. The modern AI is fueled by big data and deep neural network, it revolutionizes and modernizes the industries in IR4.0 with intelligent automations. The underlying intelligent software are not conventional and therefore do not that deserve the same conventional engineering treatment. The conventional software system behaviours are logic-driven or predetermined as software requirements and duly programmed. Even in systems with business intelligence, system behaviours are programmed according to known classified events derived from data. In contrast, the behaviours of modern AI systems are data-driven or determined by dynamic data generated by sensors, machines and humans during runtime.

The modern AI involves big data analytics (BDA) as well as deep learning (DL) which can be computing resource demanding and time consuming in which most software developers cannot afford. The modern AI also involves dynamic classification and regression based on live data and prescribed actions to make software systems intelligent. Some classifications may not be known during software development. Even though there are known, they may be invalid during runtime, so too, the prescribed actions by the system. Software development becomes even more challenging when the big data communities suggest companies adopting big data analytics should deem the pursue as a journey instead of a project. In other words, new insights will be discovered along the big data analytics journey, there is no deadline. It is unusual to run a conventional software projects without a deadline.

Moreover, the dynamic behaviour driven by data in some modern intelligent systems has also rendered the software quality challenging to measure. It remains an open question on how to measure the intelligence quotient (IQ) of intelligent software systems. For instance, although unmanned drones and cars had been tested in public areas in some countries, there are still safety concerns on how reliable the deployed AI on public road dealing with different ambient and sophisticated and ever dynamic contexts real time. Some fatal incidents in the recent years have also made the road to AI software system deployment in unmanned cars and drones even more rugged before public confidence is restored. This instance is just the tip of the iceberg, there are definitely other specific issues and challenges from other domains which are worth addressing. Humans on earth are now desperate for new models and tools which can really verify and validate these ever dynamic, mission critical, real-time intelligence software systems.

The nature of the modern AI software systems mentioned above could be a wakeup call for scientists and software practitioners around the world to find new ways to develop intelligent software systems. The software engineering and machine learning communities are desperate for new theories, pragmatic process model, scientific methods and techniques to support the development of reliable, cost effective, manageable, high quality modern AI software systems.

Scope (Topics)

  • Artificial Intelligence
  • Formal Methods for Artificial Intelligence
  • Model Checking for Intelligent Software
  • Metric for Intelligent Software
  • Intelligent Software System Quality
  • Intelligent Software Architecture
  • Software Engineering for Intelligent Systems
  • Intelligent Software Design
  • Artificial Intelligence Reuse
  • Internet of Things
  • Cyber Physical Software
  • Secure Intelligent Software
  • Affective Computing Software
  • Validation and Verification of Machine Learning Systems
  • Automated Machine Learning
  • Design of Safety-Critical Software
  • Integration of Intelligent Software Ecosystems
  • Software Tools for Intelligent Software Development
  • Application of Machine-Learning
  • Intelligent Software Project Management

Submission Deadline:31 August 2021

Guest Editor

  • Dr. Lee Lai Soon
    Laboratory of Computational Statistics and Operations Research
    Institute for Mathematical Research
    Universiti Putra Malaysia
    lls@upm.edu.my
  • Dr. Afnizanfaizal Bin Abdullah
    School of Computing, Faculty of Engineering
    Universiti Teknologi Malaysia (UTM)
    afnizanfaizal@utm.my
  • Dr. Ng Keng Yap
    Faculty of Computer Science and Information Technology
    Universiti Putra Malaysia (UPM)
    kengyap@upm.edu.my
  • Dr. Mamoun Jamous
    Techniaa Systems, Turkey
    mamoun@techniaa.com
  • Dr. Ashraf Osman
    Alzaim Alazhari University Khartoum, Sudan
    ashrafosman2@gmail.com
  • Dr. Zeyad Abd Algfoor Hasan
    Department of Computer Science
    College of Computer Science and Mathematics
    University of Mosul, Iraq
    drzeyad@uomosul.edu.iq
  • Dr Hongmei (Mary) He (SIEEE, FHEA)
    School of Computer Science and Informatics
    De Montfort University, United Kingdom
    maryhhe@icloud.com