Document Type : Research Paper
1 Department of Computer Science, University of Uyo, Uyo, PMB 1017, Uyo, Akwa Ibom State, Nigeria.
2 Department of Information and Communication Technology, Mangosuthu University of Technology, P.O. Box 12363 Jacobs, 4026 Durban, South Africa.
Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) have shown popularity, superiority, and more accuracy in performance in a number of applications in the last decade. This is due to its ability to cope with uncertainty and precisions adequately when compared with its type-1 counterpart. In this paper, an Interval Type-2 Fuzzy Logic System (IT2FLS) is employed for remote vital signs monitoring and predicting of shock level in cardiac patients. Also, the conventional, Type-1 Fuzzy Logic System (T1FLS) is applied to the prediction problems for comparison purpose. The cardiac patients’ health datasets were used to perform empirical comparison on the developed system. The result of study indicated that IT2FLS could coped with more information and handled more uncertainties in health data than T1FLS. The statistical evaluation using performance metrices indicated a minimal error with IT2FLS compared to its counterpart, T1FLS. It was generally observed that the shock level prediction experiment for cardiac patients showed the superiority of IT2FLS paradigm over T1FLS.
- Zadeh, L. A. (1965). Fuzzy sets. Control, 8, 338–353.
- Negnevitsky, M. (2005). Artificial intelligence: a guide to intelligent systems. Pearson education.
- Chiu, S. (1997). Extracting fuzzy rules from data for function approximation and pattern classification. In D. Dubois, H. Prade, and R. Yager (Eds.) Fuzzy information engineering: a guided tour of applications. John Wiley & Sons.
- Mamdani, E. H. (1974, December). Application of fuzzy algorithms for control of simple dynamic plant. In Proceedings of the institution of electrical engineers(Vol. 121, No. 12, pp. 1585-1588). IET.
- Ramkumar, V., Mihovska, A. D., Prasad, N. R., & Prasad, R. (2010). Fuzzy-logic based call admission control for a heterogeneous radio environment. The 12th international symposium on wireless personal multimedia communications (WPMC 2009). Japan, Sendai, Japan. https://vbn.aau.dk/en/publications/fuzzy-logic-based-call-admission-control-for-a-heterogeneous-radi
- Selvi, M. P., & Sendhilnathan, S. (2016). Fuzzy based mobility management in 4G wireless Networks. Brazilian archives of biology and technology, 59(SPE2).
- G. U. (2017). Fuzzy based vertical handoff decision controller for future networks. International journal of advanced engineering, management and science (IJAEMS), 3(1), 111-119.
- Abbasi, R., Bidgoli, A., & Abbasi, M. (2012). A new fuzzy algorithm for improving quality of service in real time wireless sensor networks. International journal of advanced smart sensor network systems (IJASSN), 2(2), 1-14.
- Dogman, A., Saatchi, R., & Al-Khayatt, S. (2012). Quality of service evaluation using a combination of fuzzy C-means and regression model. World academy of science, engineering and technology, 6, 562-571.
- Umoh, U. A., & Inyang, U. G. (2015). A fuzzfuzzy-neural intelligent trading model for stock price prediction. International journal of computer science issues (IJCSI), 12(3), 36.
- Umoh, U., & Asuquo, D. (2017). Fuzzy logic-based quality of service evaluation for multimedia transmission over wireless ad hoc networks. International journal of computational intelligence and applications, 16(04), 1750023.
- Mendel, J. M., & John, R. B. (2002). Type-2 fuzzy sets made simple. IEEE transactions on fuzzy systems, 10(2), 117-127.
- Zadeh, L. A. (1975). The concept of a linguistic variable and its application to approximate reasoning—I. Information sciences, 8(3), 199-249.
- Hagras, H. (2007). Type-2 FLCs: A new generation of fuzzy controllers. IEEE computational intelligence magazine, 2(1), 30-43.
- Tan, W. W., & Chua, T. W. (2007). [Review of Uncertain rule-based fuzzy logic systems: introduction and new directions, by J. M. Mendel]. IEEE Computational intelligence magazine, 2(1), 72-73.
- Postlethwaite, B. E. (1993). [Review of Fuzzy control and fuzzy systems, by W. Pedrycz]. International journal of adaptive control and signal processing, 5(1), 87-153. https://doi.org/10.1002/acs.4480050208
- Hagras, H. A. (2004). A hierarchical type-2 fuzzy logic control architecture for autonomous mobile robots. IEEE transactions on fuzzy systems, 12(4), 524-539.
- Mendel, J. M., John, R. I., & Liu, F. (2006). Interval type-2 fuzzy logic systems made simple. IEEE transactions on fuzzy systems, 14(6), 808-821.
- Zimmermann, H. J. (2011). Fuzzy set theory—and its applications. Springer Science & Business Media.
- Castillo, O., & Melin, P. (2012). Recent advances in interval type-2 fuzzy systems. Springer.
- Isizoh, A. N., Okide, S. O., Anazia, A. E., & Ogu, C. D. (2012). Temperature control system using fuzzy logic technique. International journal of advanced research in artificial intelligence, 1(3), 27-31.
- Roy, N., & Mitra, P. (2016). Electric load forecasting: an interval type-ii fuzzy inference system based approach. International journal of computer science and information technologies (IJCSIT), 7(6), 2515-2522.
- Umoh, U. A., Inyang, U. G., & Nyoho, E. E. (2019). Interval type-2 fuzzy logic for fire outbreak detection. International journal on soft computing, artificial intelligence and applications (IJSCAI), 8(3), 1-20.
- Castillo, O., Melin, P., Kacprzyk, J., & Pedrycz, W. (2007, November). Type-2 fuzzy logic: theory and applications. 2007 IEEE international conference on granular computing (GRC 2007)(pp. 145-145). IEEE.
- Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13.
- Mamdani, E. H. (1976). Advances in the linguistic synthesis of fuzzy controllers. International journal of man-machine studies, 8(6), 669-678.
- Umoh, U. A., Nwachukwu, E. O., Obot, O. U., & Umoh, A. A. (2011). Fuzzy-neural network model for effective control of profitability in a paper recycling plant. American journal of scientific and industrial research (AJSIR), 2(4), 552-558. DOI: 5251/ajsir.2011.2.4.552.558
- Wu, H., & Mendel, J. M. (2002). Uncertainty bounds and their use in the design of interval type-2 fuzzy logic systems. IEEE Transactions on fuzzy systems, 10(5), 622-639.
- Wu, D. (2005). Design and analysis of type-2 fuzzy logic systems (Master’s Thesis, Department of Electrical and Computer Engineering, National University of Singapore). Retrieved from https://core.ac.uk/download/pdf/48633921.pdf
- Wu, D., & Tan, W. W. (2005, May). Computationally efficient type-reduction strategies for a type-2 fuzzy logic controller. The 14th IEEE international conference on fuzzy systems, 2005. FUZZ'05.(pp. 353-358). IEEE.
- Karnik, N. N., & Mendel, J. M. (2001). Centroid of a type-2 fuzzy set. Information SCiences, 132(1-4), 195-220.