International Journal on Data Science and Technology
Volume 3, Issue 2, March 2017, Pages: 24-33
Received: Apr. 5, 2017;
Accepted: May 15, 2017;
Published: Jul. 27, 2017
Views 2022 Downloads 71
Eboka Andrew Okonji, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Yerokun Mary Oluwatoyin, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Okoba Ifeoma Patricia, Department of Computer Education, Federal College of Education Technical, Asaba, Nigeria
Scheduling tasks persist in our daily functioning and as academia, abounds more in University circle as hard-NP constraint satisfaction tasks. Many studies exist with the objective of resolving the many conflicted constraints that exists in a timetable schedule using various algorithms. Many of such algorithms simply search the domain space for a goal state that satisfies the problem constraints. Our study yields an outcome assignment that provides a complete, feasible and optimal academic schedule that satisfies medium cum hard constraints for the Federal University of Petroleum Resource Effurun in Delta State of Nigeria using memetic algorithm. Results showed that model converges after 4minutes and 29seconds; while its convergence time depends on use of belief space to ensure agents do not violate model bounds, encoding scheme used amongst others. The schedule considers both instructors and students’ preference as medium constraints of high priority.
Eboka Andrew Okonji,
Yerokun Mary Oluwatoyin,
Okoba Ifeoma Patricia,
Intelligence Classification of the Timetable Problem: A Memetic Approach, International Journal on Data Science and Technology.
Vol. 3, No. 2,
2017, pp. 24-33.
Academic Staff Union of Universities (2015). Downward review of the monetary allocation in defiance of FGN/ASUU 2009 MOU, Gaurdian Newspaper, Wed 18 November, 2015, pp 32 – 33.
Bolton, R. J and Hand, D. J., (2002). Statistical fraud detection: a review, Statistical Science, 17(3), pp235-255.
Chakraborty, R., (2010). Soft computing and fuzzy logic, Lecture notes, retrieved from http://www.myreaders.info/07_fuzzy_systems.pdf.
Delamaire, L and Abdou, H., (2009). Credit card fraud and detection techniques: a review, Banks and Bank Systems, 4(2), pp57.
Heppner, H and Grenander, U., (1990). Stochastic non-linear model for coordinated bird flocks, In Krasner, S (Ed.), The ubiquity of chaos pp.233–238. Washington: AAAS.
Kennedy, C and Porter, A., (2013). Fraud detection and prevention, [online]: retrieved July 2015 from www.moss-adam.com/fraud_reviews.
Khashei, M., Eftekhari, S and Parvizian, J (2012). Diagnosing diabetes type-II using a soft intelligent binary classifier model, Review of Bioinformatics and Biometrics, 1(1), pp9-23.
Kuan, C and White, H., (1994). Artificial neural network: econometric perspective, Econometric Reviews, Vol.13, Pp.1-91 and Pp.139-143.
Mandic, D and Chambers, J., (2001). Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability, Wiley & Sons: New York, pp56-90.
Michalewicz, Z., (1998). A survey of constraint handling techniques in Evolutionary computation methods, www.dhpc.adelaide.edu.au.
Minns, A., (1998).Artificial neural networks as sub-symbolic process descriptors, published PhD Thesis, Balkema, Rotterdam, Netherlands.
Ojugo, A., Eboka, A., Okonta, E., Yoro, R and Aghware, F., (2012). GA rule-based intrusion detection system, Journal of Computing and Information Systems, 3(8), pp 1182 - 1194.
Ojugo, A. A., Emudianughe, J., Yoro, R. E., Okonta, E. O and Eboka, A., (2013). Hybrid neural network gravitational search algorithm for rainfall runoff modeling, Progress in Intelligence Computing and Application, 2 (1), doi: 10.4156/pica.vol2.issue1.2, pp22–33.
Ojugo, A. A., Ben-Iwhiwhu, E., Kekeje, D. O., Yerokun, M. O and Iyawa, I. J. B., (2014). Malware propagation on time varying network, Int. J. Modern Edu. Comp. Sci., 8, pp25 – 33.
Ojugo, A. A., Ben-Iwhiwhu, E., R. E. Yoro., R. J. Ureigho., Yerokun, M. O and F. N. Efozia., (2014). Metamophic virus detection using profile hidden markov model, Technical Report: Centre for High Performance and Dynamic Computing, TRON-03-2013-01, Federal University of Petroleum Resources, Nigeria, p24-37.
Ojugo, A. A., A. O. Eboka., R. E. Yoro., M. O. Yerokun and F.N. Efozia (2015a). Framework design for statistical fraud detection, Mathematics and Computers in Sciences and Engineering Series, 50: 176-182, ISBN: 976-1-61804-327-6.
Ojugo, A. A., A. O. Eboka., R. E. Yoro., M. O. Yerokun and F. N. Efozia (2015b). Hybrid model for early diabetes diagnosis, Mathematics and Computers in Sciences and Engineering Series, 50: 207-217, ISBN: 976-1-61804-327-6.
Ojugo, A. A., D. Allenotor., D. A. Oyemade., O. B. Longe and C. N. Anujeonye (2015c). Hybrid model for early diabetes diagnosis, Mathematics and Computers in Sciences and Engineering Series, 50: 207-217, ISBN: 976-1-61804-327-6.
Ojugo, A. A., D. A. Oyemade and D. Allenotor (2016): Solving For Computational Intelligence the Timetable-Problem, Advances in Multidisciplinary Research Journal. Vol 2, No. 3, Pp 67-84.
Perez, M and Marwala, T., (2011). Stochastic optimization approaches for solving Sudoku, IEEE Transaction on Evol. Comp., pp.256–279.
Peter, S., (2014). An Analytical Study on Early Diagnosis and Classification of Diabetes Mellitus, Bonfring International Journal of Data Mining, 4(2), pp7-13.
Reynolds, R., (1994). Introduction to cultural algorithms, Transaction on Evolutionary Programming (IEEE), pp.131-139.
Saleh Elmohamed, M. A, Fox. G and Coddington, P., (1998). A comparison of annealing techniques for academic course scheduling, Notes on Intelligence Computing, DHCP-045, pp 1-20. www.dhpc.adelaide.edu.au.
Stolfo, S. J., Fan, D. W., Lee, W and Prodromidis, A. L., (2015). Credit card fraud detection using meta learning: issues and initial results, [online]: http://www.researchgate.net/publication/2282588.
Ursem, R., Krink, T., Jensen, M.and Michalewicz, Z., (2002). Analysis and modeling of controls in dynamic systems. IEEE Transaction on Memetic Systems and Evolutionary Computing, 6(4), pp.378-389.
Alowosile, O. Y., Oyeleye, C. A., Omidiora, E. O, Aborisade, D. O. & Odumosu, A.A. (2016): Comparative analysis of some selected cryptographic Algorithms. Computing, Information Systems, Development Informatics & Allied Research Journal. Vol 7 No 2. Pp 53-64, Available online at www.cisdijournal.net.