Prof. Rangith Baby Kuriakose
Prof. Baby Kuriakose

NRF RATING
C
POSITION
Associate Professor
QUALIFICATIONS
DENG: ELECTRICAL ENGINEERING
PORTFOLIO
Prof. Rangith Baby Kuriakose is an accomplished Associate Professor and NRF C-rated researcher at the Central University of Technology (CUT), with 17 years of experience in higher education, research, postgraduate supervision and academic leadership. His research is positioned at the forefront of Smart Manufacturing and the Fourth and Fifth Industrial Revolutions, with expertise in Digital Twins, Industrial Internet of Things (IIoT), Blockchain Technology, 5G technologies, intelligent manufacturing systems and production optimisation.
Prof Kuriakose has developed a strong and growing international res.earch profile, with more than 40 accredited publications, over 400 Google Scholar citations and an h-index of 11. His research has attracted approximately R40 million in funding, including significant international support through the AVIAT-STEM (EIT) programme.
His research leadership extends to the development of the next generation of researchers. He has supervised and co-supervised multiple PhD and Master’s students, with several graduates achieving cum laude. His current research supervision includes emerging applications of Digital Twins, human-centred intelligent manufacturing, augmented reality and machine learning for advanced manufacturing.
Beyond research, Prof Kuriakose has made significant contributions to academic leadership at CUT, including serving as Acting Assistant Dean: Teaching and Learning from April 2025 to June 2026, as well as contributing to curriculum development and institutional academic initiatives.
He is a member of ECSA and IEEE and contributes to the international academic community through journal peer review, external examination and international research dissemination.
AREAS OF EXPERTISE
Smart Manufacturing
Industry 4.0/5.0
Digital Twins
Artificial Intelligence & Machine Learning
Manufacturing Optimisation
5G Industrial Communication
Human–Technology Interaction
PUBLICATIONS
  1. Contextual Awareness in a Communication Architecture for Smart Manufacturing, Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J. Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-9191-3_33.
  2. Developing a Reinforcement Learning Model for Optimizing Pump Speed in a Water Bottling Plant
    Kuriakose, R.B. Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-8793-0_31.
  3. Experimental Setup and Results Analysis of Tactile Internet Implemented Using 5G Technology
    Mokotjo, H.J.; Kuriakose, R.B. Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-9048-0_17.
  4. State of the Art in Human–Machine Intelligence in Manufacturing Systems with Augmented Reality: A Smart Manufacturing Perspective Mohoje, K.; Kuriakose, R.B.; Tshabalala, P. Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-8901-9_27.
  5. Using Cognitivist Theory to Effect Learning and Engagement in Higher Education: A Process Control Engineering Case Study Kuriakose, R.B.
    Smart Innovation, Systems and Technologies. DOI: 10.1007/978-3-032-12999-4_5.
  6. Mushroom Farming, Smart Farming Techniques and Challenges: A Review Lemphane, N.J.; Kotze, B.; Kuriakose, R.B. Lecture Notes in Networks and Systems, Vol. 1660, pp. 476–486. DOI: 10.1007/978-3-032-07109-5_32.
  7. Digital Twins and Challenges in Mushroom Farming: A Review Lemphane, N.J.; Kotze, B.; Kuriakose, R.B.
    Artificial Intelligence: Theory and Applications. pp. 156–166.
  8. Object Centric Process Mining with a Communication Architecture Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J.
    ICT: Applications and Social Interfaces.
  9. Developing a Machine Learning Algorithm to Ensure Geometric Fidelity in Laser Powder Based Fusion Platinum Products Mpata, R.; Dzogbewu, T.C.; Kuriakose, R.B.
    Lecture Notes in Networks and Systems. DOI: 10.1007/978-3-032-19675-0_16.
2025
  1. Adaptation of 5G Technology in Programmable Logic Controller Automated Smart Manufacturing Plants to Improve Network Factors that Affect Production Time
    Kuriakose, R.B.; Mokotjo, H.J. Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-1744-9_34.
  2. Designing a Digital Twin for a Mixed-Model Stochastic Assembly Line for the Reduction of Cycle Time
    Tshabalala, P.; Kuriakose, R.B. DOI: 10.1007/978-981-96-6929-5_16.
  3. Designing and Testing an Experimental Setup for Incorporating 5G Wireless Network in a PLC Automated Smart Manufacturing Plant Mokotjo, H.J.; Kuriakose, R.B.
    DOI: 10.1007/978-3-031-99965-9_32.
  4. Examining Different Artificial Intelligence Techniques Used in a Mixed Model Stochastic System to Increase Production Efficiency Tsumake-Kabuya, O.P.; Kuriakose, R.B.; Coetzer, J.DOI: 10.1007/978-981-96-1744-9_39.
  5. Implementing Human–Machine Collaboration in an Industry 5.0 Setting—A Case Study of an Automated Water Bottling Plant Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-96-6935-6_13.
  6. Improving Vehicle Dynamics in Road-to-Rig Testing by Integrating Model-Based Simulation and LSTM Predictions
    Mangaluru Ramananda, A.; König, T.; Zimmermann, S.; Tshabalala, P.; Kuriakose, R.B.; Schwarzer, S.; Kley, M.
    Lecture Notes in Networks and Systems. DOI: 10.1007/978-981-96-1747-0_19.
  7. Key Considerations for Appropriate Information in a Communication Architecture for Smart Manufacturing
    Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J.
    DOI: 10.1007/978-981-96-6929-5_9.
  8. Quantitative Assessment of a Communication Architecture for Smart Manufacturing Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J. Intelligent Systems Conference 2025, pp. 60–74.
  9. Mushroom Farming, Smart Farming Techniques and Challenges: A Review Lemphane, N.J.; Kotze, B.; Kuriakose, R.B. Lecture Notes in Networks and Systems, Vol. 1660.
2024
  1. A Communication Architecture Approach for Mitigating Complexities in Smart Manufacturing Units by Means of Information Appropriateness Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-99-8346-9_8.
  2. A Performance Comparison Between a Digital Shadow and a Digital Twin in a Mixed Model Stochastic System
    Tshabalala, P.; Kuriakose, R.B. DOI: 10.1007/978-981-97-3559-4_6.
  3. Designing an Experimental Setup for Incorporating Data Provenance into Blockchain Smart Contracts in a Smart Manufacturing Environment Mokalusi, O.L.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-3-031-52303-8_14.
  4. Implementing Tactile Internet Using 5G Network for Cloud Manufacturing in a PLC-Driven Water Bottling Plant
    Kuriakose, R.B.; Mokotjo, H.J. DOI: 10.1007/978-981-99-8346-9_29.
  5. Using the OEE Score to Enable Collaborative Decision-Making for Human–Machine Interaction in an Industry 5.0 Setting Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J.
    DOI: 10.1007/978-981-99-8349-0_22.
  6. Developing a Digital Twin Model for Improved Pasture Management at Sheep Farm to Mitigate the Impact of Climate Change Lemphane, N.J.; Kotze, B.; Kuriakose, R.B.
    International Journal of Advanced Computer Science and Applications, 15(6). DOI: 10.14569/IJACSA.2024.0150627.
2023
  1. A Comparison of Transaction Fees for Various Data Types and Data Sizes of Blockchain Smart Contracts on a Selection of Blockchain Platforms Mokalusi, O.L.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-19-5221-0_67.
  2. Designing a Digital Shadow for Pasture Management to Mitigate the Impact of Climate Change Lemphane, N.J.; Kuriakose, R.B.; Kotze, B. DOI: 10.1007/978-981-19-0095-2_35.
  3. Designing an Experimental Setup for Digital Twins in Modern Manufacturing—A Case Study Using a Water Bottling Plant Tshabalala, P.; Kuriakose, R.B.
    DOI: 10.1007/978-981-19-5221-0_58.
  4. Factors Influencing the Selection of a Blockchain Platform for Incorporating Data Provenance into Smart Contracts
    Mokalusi, O.L.; Kuriakose, R.B.; Vermaak, H.J.DOI: 10.1007/978-981-19-2394-4_47.
  5. Integrating IoT Sensors to Setup a Digital Twin of a Mixed Model Stochastic System for Real-Time Monitoring
    Tshabalala, P.; Kuriakose, R.B.DOI: 10.1007/978-981-99-3243-6_24.
  6. The Impact of Collaborative Decision-Making in a Smart Manufacturing Environment: Case Study Using an Automated Water Bottling Plant Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J.; Nel, G.DOI: 10.1007/978-981-19-2394-4_30.
2022
  1. A Review on Current IoT-Based Pasture Management Systems and Applications of Digital Twins in Farming
    Lemphane, N.J.; Kotze, B.; Kuriakose, R.B.
    DOI: 10.1007/978-981-16-4538-9_18.
  2. Analyzing the Performance of a Digital Shadow for a Mixed-Model Stochastic System Tshabalala, P.; Kuriakose, R.B. DOI: 10.1007/978-981-19-2130-8_50.
  3. Creating a Decentralized Communication Protocol for SMART Manufacturing Units Within Industry 4.0
    Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J.; Madsen, O.
    DOI: 10.1007/978-981-16-5120-5_55.
  4. Developing an Improved Software Architecture Framework for Smart Manufacturing Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-16-9416-5_7.
  5. Exploring the Means and Benefits of Including Blockchain Smart Contracts to a Smart Manufacturing Environment: Water Bottling Plant Case Study Mokalusi, O.L.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-16-6369-7_27.
  6. Using a Single Group Experimental Study to Underpin the Importance of Human-in-the-Loop in a Smart Manufacturing Environment Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J.; Nel, G. DOI: 10.1007/978-981-16-4538-9_37.
2021
  1. Devising a Novel Means of Introducing Collaborative Decision-Making to an Automated Water Bottling Plant to Study the Impact of Positive Drift Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J. DOI: 10.1007/978-981-15-8354-4_66.
  2. Designing a Digital Shadow for Pasture Management to Mitigate the Impact of Climate Change Lemphane, N.J.; Kuriakose, R.B.; Kotze, B. Lecture Notes in Networks and Systems.
 
2020
  1. Collaborative Decision-Making for Human-Technology Interaction—A Case Study Using an Automated Water Bottling Plant Coetzer, J.; Kuriakose, R.B.; Vermaak, H.J.
    Journal of Physics: Conference Series. DOI: 10.1088/1742-6596/1577/1/012024.
  2. Designing a Simulink Model for a Mixed Model Stochastic Assembly Line: A Case Study Using a Water Bottling Plant
    Kuriakose, R.B.; Vermaak, H.J. Journal of Discrete Mathematical Sciences and Cryptography, 23(2), 329–336.
  3. IoT Water Monitor Implementation Strategy
    Gericke, G.A.; Kuriakose, R.B. Journal of Physics: Conference Series. DOI: 10.1088/1742-6596/1577/1/012045.
  4. Machine to Machine Communication Protocol for SMART Manufacturing Units Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J.; Madsen, O. Journal of Physics: Conference Series. DOI: 10.1088/1742-6596/1577/1/012047.
  5. The Impact of Communication Protocols Within SMART Manufacturing and Their Benefits Gericke, G.A.; Vermaak, H.J.; Kuriakose, R.B.; Madsen, O. International Journal of Simulation: Systems, Science & Technology, 21(2). DOI: 10.5013/IJSSST.a.21.02.22.
  6. Performance Analysis of a Real-Time Optimization Model for a Mixed Model Stochastic Assembly Line Kuriakose, R.B.; Vermaak, H.J. International Journal of Simulation: Systems, Science and Technology.
2019
  1. Customized Mixed Model Stochastic Assembly Line Modelling Using Simulink Kuriakose, R.B.; Vermaak, H.J.
    International Journal of Simulation: Systems, Science & Technology, 20(1), 6.1–6.5. DOI: 10.5013/IJSSST.a.20.S1.06.
  2. Optimization of a Real Time Web Enabled Mixed Model Stochastic Assembly Line to Reduce Production Time
    Kuriakose, R.B.; Vermaak, H.J. Communications in Computer and Information Science. DOI: 10.1007/978-981-15-0108-1_5.
  3. Optimization of a Customized Mixed Model Assembly Using MATLAB/Simulink Kuriakose, R.B.; Vermaak, H.J.
    Journal of Physics: Conference Series.
  4. Design of Digital Twins for Optimization of a Water Bottling Plant Kuriakose, R.B.; Vermaak, H.J.
  5. Wireless SMART Product Tracking Using Radio Frequency Identification Jardine, N.; Gericke, G.A.; Kuriakose, R.B.; Vermaak, H.J.
BOOK(S) OR CHAPTER(S) IN BOOK(S)
Binson, V. A., Alex, S. B., & Kuriakose, R. B. (2025). Comparative analysis of supervised and unsupervised learning algorithms in the detection of Alzheimer’s disease. In Computational Intelligence Algorithms for the Diagnosis of Neurological Disorders (pp. 219–238). CRC Press/Taylor & Francis. https://doi.org/10.1201/9781003520344-18.
PRESENTED CONFERENCES, SEMINARS, WORKSHOPS
Prof Kuriakose has delivered over 20 national and international conferences along with being invited keynote and conference addresses on engineering, sustainability and emerging technologies. His keynote engagements include “The Role of Engineers in Achieving the Sustainable Development Goals (SDGs)” at Phase Shift 2022 in Bangalore, India, and “Digital Twins in the Manufacturing Industry: Applications, Benefits, Implications, and the Way Forward” at the SAIEE KZN Leadership Conference 2024.
EXTERNAL PROFILES
LinkedIn
ResearchGate
Google Scholar
ORCID

Contact Us
  • Prof. Baby Kuriakose
  • Tel: +27(0)51 507 3257
  • rkuriako@cut.ac.za