All Outputs (69)

Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. (2014)
Conference Proceeding
RATTADILOK, P. and PETROVSKI, A. 2014. Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence. In Proceedings of the 2014 IEEE symposium on evolving and autonomous learning systems (EALS 2014), part of the 2014 IEEE symposium series on computational intelligence (SSCI 2014), 9-12 December 2014, Orlando, USA. New York: IEEE [online], article number 7009508, pages 87-94. Available from: https://doi.org/10.1109/EALS.2014.7009508

A generic framework for evolving and autonomously controlled systems has been developed and evaluated in this paper. A three-phase approach aimed at identification, classification of anomalous data and at prediction of its consequences is applied to... Read More about Self-learning data processing framework based on computational intelligence enhancing autonomous control by machine intelligence..

Automated inferential measurement system for traffic surveillance: enhancing situation awareness of UAVs by computational intelligence. (2014)
Conference Proceeding
RATTADILOK, P. and PETROVSKI, A. 2014. Automated inferential measurement system for traffic surveillance: enhancing situation awareness of UAVs by computational intelligence. In Proceedings of the 2014 IEEE symposium on computational intelligence in control and automation (CICA 2014), part of the 2014 IEEE symposium series on computational intelligence (SSCI 2014), 9-12 December 2014, Orlando, USA. New York: IEEE [online], article number 7013256, pages 229-236. Available from: https://doi.org/10.1109/CICA.2014.7013256

An adaptive inferential measurement framework for control and automation systems has been proposed in the paper and tested on simulated traffic surveillance data. The use of the framework enables making inferences related to the presence of anomalies... Read More about Automated inferential measurement system for traffic surveillance: enhancing situation awareness of UAVs by computational intelligence..

Multi-objective particle swarm optimisation: methods and applications. (2014)
Thesis
AL MOUBAYED, N. 2014. Multi-objective particle swarm optimisation: methods and applications. Robert Gordon University, PhD thesis.

Solving real life optimisation problems is a challenging engineering venture. Since the early days of research on optimisation it was realised that many problems do not simply have one optimisation objective. This led to the development of multi-obje... Read More about Multi-objective particle swarm optimisation: methods and applications..

Anomaly monitoring framework based on intelligent data analysis. (2013)
Conference Proceeding
RATTADILOK, P., PETROVSKI, A. and PETROVSKI, S. 2013. Anomaly monitoring framework based on intelligent data analysis. In Yin, H., Tang, K., Gao, Y., Klawonn, F., Lee, M., Weise, T., Li, B. and Yao, X. (eds.) Proceedings of the 14th International conference on intelligent data engineering and automated learning (IDEAL 2013), 20-23 October 2013, Hefei, China. Lecture notes in computer science, 8206. Berlin: Springer [online], pages 134-141. Available from: https://doi.org/10.1007/978-3-642-41278-3_17

Real-time data processing has become an increasingly important challenge as the need for faster analysis of big data widely manifests itself. In this research, several Computational Intelligence methods have been applied for identifying possible anom... Read More about Anomaly monitoring framework based on intelligent data analysis..

Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning. (2013)
Conference Proceeding
RATTADILOK, P. and PETROVSKI, A. 2013. Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning. In Proceedings of the 2013 IEEE international conference on computational intelligence and virtual environments for measurement systems and applications (CIVEMSA 2013), 15-17 July 2013, Milan, Italy. New York: IEEE [online], article number 6617402, pages 93-98. Available from: https://doi.org/10.1109/CIVEMSA.2013.6617402

The paper proposes a generic approach to building inferential measurement systems. The large amount of data needed to be acquired and processed by such systems necessitates the use of machine learning techniques. In this study, an inferential measure... Read More about Inferential measurements for situation awareness: enhancing traffic surveillance by machine learning..

Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence. (2013)
Thesis
FOURNIER, F.A. 2013. Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence. Robert Gordon University [online], PhD thesis. Available from: https://openair.rgu.ac.uk

This work investigates the use of evolved Bayesian networks learning algorithms based on computational intelligence meta-heuristic algorithms. These algorithms are applied to a new domain provided by the exclusive data, available to this project from... Read More about Probabilistic modelling of oil rig drilling operations for business decision support: a real world application of Bayesian networks and computational intelligence..

Statistical optimisation and tuning of GA factors. (2005)
Conference Proceeding
PETROVSKI, A., BROWNLEE, A. and MCCALL, J. 2005. Statistical optimisation and tuning of GA factors. In Proceedings of the 2005 IEEE congress on evolutionary computation (CEC 2005), 2-5 September 2005, Edinburgh, UK. New York: IEEE [online], volume 1, article number 1554759, pages 758-764. Available from: https://doi.org/10.1109/CEC.2005.1554759

This paper presents a practical methodology of improving the efficiency of Genetic Algorithms through tuning the factors significantly affecting GA performance. This methodology is based on the methods of statistical inference and has been successful... Read More about Statistical optimisation and tuning of GA factors..

Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. (2001)
Conference Proceeding
PETROVSKI, A. and MCCALL, J. 2001. Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms. In Zitzler, E., Thiele, L., Deb, K., Coello Coello, C.A. and Corne, D. (eds.) Proceedings of the 1st International conference on evolutionary multi-criterion optimization (EMO 2001), 7-9 March 2001, Zurich, Switzerland. Lecture notes in computer science, 1993. Berlin: Springer [online], pages 531-545. Available from: https://doi.org/10.1007/3-540-44719-9_37

The main objectives of cancer treatment in general, and of cancer chemotherapy in particular, are to eradicate the tumour and to prolong the patient survival time. Traditionally, treatments are optimised with only one objective in mind. As a result o... Read More about Multi-objective optimisation of cancer chemotherapy using evolutionary algorithms..

An application of genetic algorithms to chemotherapy treatment. (1998)
Thesis
PETROVSKI, A. 1998. An application of genetic algorithms to chemotherapy treatment. Robert Gordon University, PhD thesis.

The present work investigates methods for optimising cancer chemotherapy within the bounds of clinical acceptability and making this optimisation easily accessible to oncologists. Clinical oncologists wish to be able to improve existing treatment reg... Read More about An application of genetic algorithms to chemotherapy treatment..