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Vasileios Efthymiou
University of Luxembourg
Maria Koutraki
Foundation for Research and Technology Heraklion
Grigoris Antoniou
Foundation for Research and Technology Heraklion
Vol. 1 No. 1 (2012), Articles, pages 9-22


In this paper, we propose Bees Algorithm (BA) to enhance the performance in estimating the parameters for metabolic pathway data to simulate fermentation pathway for Saccharomyces cerevisiae. However, the parameter estimation of biological processes has always been a challenging task due to the complexity and nonlinear equations. Therefore, we present this algorithm as a new approach for parameter estimation for biological interactions to obtain more accurate parameter values. The result shows that BA outperforms other estimation algorithms as it produces the most accurate kinetic parameters, which contributes to the precision of simulated kinetic model.


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AAMODT A. A knowledge-intensive, integrated approach to problem solving and sustained learning. Dissertation, University of Trondheim, Norwegian Institute of Technology, Department of Computer Science, University Microfilms PUB 92-08460, 1991.

AAMODT A, PLAZA E. Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. AI Communications. IOS Press, vol. 7: 1, pp. 39-59, 1994.

ANTONIOU G, VAN HARMELEN F. A Semantic Web Primer, MIT Press, Cambridge, MA, 2004.

CHEN H, FININ T, JOSHI A, KAGAL L, PERICH F, CHAKRABORTY D. Intelligent agents meet the semantic web in smart spaces IEEE Internet Computing, 8 (6), pp. 69–79, 2004.

DE RAEDT L. Attribute-value learning versus Inductive Logic Programming: The missing links. In: Proc. ILP-98, Springer, 1998.

DEY AK. Understanding and Using Context. Personal and Ubiquitous Computing. vol. 5, issue 1, pp. 4-7, 2001.

EFTHYMIOU V. A real-time semantics-aware activity recognition system, Master Thesis, University of Crete, Computer Science Department, 2012.

GOLEMATI M, KATIFORI A, VASSILAKIS C, LEPOURAS G, HALATSIS C. Creating an Ontology for the User Profile: Method and Applications. First IEEE International Conference on Research Challenges in Information Science (RCIS), Morocco 2007.

GRAMMENOS D, ZABULIS X, ARGYROS A, STEFANIDIS C. FORTH-ICS Internal RTD Programme Ambient Intelligence and Smart Environments. Proceedings of the 3rd European Conference on Ambient Intelligence (AMI), 2009.

GRUBER T.R. A translation approach to portable ontology specifications. Knowledge

Acquisition, vol. 5, pp. 199-220, 1993.

HONG M, CHO D. Ontology context model for context-aware learning service in ubiquitous learning environments. International Journal of Computers, 2, July 2008.

INTILLE SS, LARSON K, BEAUDIN JS, NAWYN J, TAPIA EM, KAUSHIK, P. A Living Laboratory for the Design and Evaluation of Ubiquitous Computing Technologies. In Proceedings of CHI Extended Abstracts 1941-1944, 2005.

JAYASURYA K, FUNG G, YU S, DEHING-OBERIJE C, DE RUYSSCHER D, HOPE A, DE NEVE W, LIEVENS Y, LAMBIN P and DEKKERA ALAJ. Comparison of Bayesian network and support vector machine models for two-year survival prediction in lung cancer patients treated with radiotherapy. Med. Phys. 37 1401–7, 2010.

KNOX S, COYLE L, DOBSON S. Using ontologies in case-based activity recognition. In FLAIRS 2010 Proceedings, AAAI Press, 336-341, 2010.

KOFOD-PETERSEN A. Challenges in Case-Based Reasoning for Context Awareness in Ambient Intelligent Systems. In: 1st Workshop on Case-based Reasoning and Context Awareness (CACOA'06), 2006.

KOFOD-PETERSEN A, AAMODT A. Case-Based Reasoning for Situation-Aware Ambient Intelligence: A Hospital Ward Evaluation Study, Case-Based Reasoning Research And Development, Lecture Notes In Computer Science, 2009.

KRAMER, S. Relational learning vs. propositionalization, Investigations in inductive logic programming and propositional machine learning. AI Communications, vol. 3, pp. 275-276, IOS Press, 2000.

KRUMMENACHER R,STRANG T. Ontology-based Context Modeling. In Proceedings Third workshop on Context-Aware Proactive Systems (CAPS), 2007.

LEONIDIS A, MARGETIS G, ANTONA M, STEPHANIDIS C. ClassMATE: Enabling Ambient Intelligence in the Classroom. World Academy of Science, Engineering and Technology, issue 66, pp. 594 - 598, 2010.

MOHAMMAD RH, NGUYEN TTT, YOUNG-KOO L, BYEONG-SOO J, SUNGYOUNG L. Modeling an ontology for managing contexts in smart meeting space. In SWWS ’07: Proceedings of the 2007 International Conference on Semantic Web and Web Services, 2007.

O'DRISCOLL C, MOHAN M, MTENZI F, WU B. Deploying a Context Aware Smart Classroom. International Technology and Education Conference, INTED, 2008. Valencia

PISHVA D, NISHANTHA G. Smart Classrooms for Distance Education and their Adoption to Multiple Classroom Architecture. Journal of Networks (JNW) , 3 (5), 54-63, 2008.

RECIO-GARCIA JA, DIAZ-AGUDO B, GONZALEZ-CALERO P, SANCHEZ-RUIZ-GRANADOS A. Ontology based CBR with jCOLIBRI. Applications and Innovations in Intelligent Systems Xiva, 2007.

RECIO-GARCIA JA. jCOLIBRI: A multi-level platform for building and generating CBR systems. Dissertation, Universidad Complutense de Madrid, 2008.

RIBONI D, BETTINI C. COSAR: hybrid reasoning for context-aware activity recognition. In: Personal and Ubiquitous Computing, vol. 15 (3), pp. 271-289, 2011.

SHI YC, XIE WK, XU GY, SHI RT, CHEN EY, MAO YH, LIU F. The smart classroom: merging technologies for seamless tele-education. IEEE Pervasive Computing. (2), pp. 47–55, 2003.

STEFANIDIS C, ARGYROS A, GRAMMENOS D, ZABULIS X. Pervasive Computing @ ICS-FORTH. In: Proceedings of Pervasive 2008 Workshop, 119-124. 2008

TAPIA EM. Using Machine Learning for Real-time Activity Recognition and Estimation of Energy Expenditure. Dissertation, Massachusetts Institute of Technology, 2008.

WITTEN IH, FRANK E, HALL MA. Data Mining: Practical Machine Learning Tools and Techniques. 3rd ed.: Morgan Kaufmann, 2011.