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healthcare. Comput. Commun. 119, 138–155 (2018)
2. Almeida, A., L´ opez-de Ipi˜ na, D.: Assessing ambiguity of context data in intelligent
environments: towards a more reliable context managing system. Sensors 12(4),
4934–4951 (2012)
3. Alqudah, A.M.: Fuzzy expert system for coronary heart disease diagnosis in Jordan.
Health Technol. 7(2–3), 215–222 (2017)
4. Amirkalali, B., Hosseini, S., Heshmat, R., Larijani, B., et al.: Comparison of harris benedict and mifflin-st jeor equations with indirect calorimetry in evaluating
resting energy expenditure. Indian J. Med. Sci. 62(7), 283–290 (2008)
5. Chiang, T.C., Liang, W.H.: A context-aware interactive health care system based
on ontology and fuzzy inference. J. Med. Syst. 39(9), 105 (2015)
6. Huang, Y.P., Basanta, H., Kuo, H.C., Huang, A.: Health symptom checking system
for elderly people using fuzzy analytic hierarchy process. Appl. Syst. Innov. 1(2),
10 (2018)
7. Lee, C.S., Wang, L., Wang, M.-H.: A fuzzy expert system for diabetes decision
support application. IEEE Trans. Syst. Man Cybern.—Part B: Cybern. 41(1),
139–153 (2011)
8. Lopez-Perez, L., et al.: BD2Decide: big data and models for personalized head and
neck cancer decision support. In: 2019 IEEE 32nd International Symposium on
Computer-Based Medical Systems (CBMS), pp. 67–68. IEEE (2019)
9. Mulero, R., Urosevic, V., Almeida, A., Tatsiopoulos, C.: Towards ambient assisted
cities using linked data and data analysis. J. Ambient Intell. Humaniz. Comput.
9(5), 1573–1591 (2018). https://doi.org/10.1007/s12652-018-0916-y
10. Mumtaj, S., Umamakeswari, A.: Neuro fuzzy based healthcare system using IoT.
In: 2017 International Conference on Energy, Communication, Data Analytics and
Soft Computing (ICECDS), pp. 2299–2303. IEEE (2017)
11. Nazari, S., Fallah, M., Kazemipoor, H., Salehipour, A.: A fuzzy inference-fuzzy
analytic hierarchy process-based clinical decision support system for diagnosis of
heart diseases. Expert Syst. Appl. 95, 261–271 (2018)
12. Saad, S., Zafar, B.A., Mueen, A.: Developing a framework for e-healthcare applications using the semantic internet of things. Int. J. Comput. Appl. 975, 8887
(2018)
13. Selvan, N.S., Vairavasundaram, S., Ravi, L.: Fuzzy ontology-based personalized
recommendation for internet of medical things with linked open data. J. Intell.
Fuzzy Syst. 36(5), 4065–4075 (2019)
14. Sondes, T., Elhadj, H.B., Chaari, L.: An ontology-based healthcare monitoring system in the internet of things. In: 2019 15th International Wireless Communications
and Mobile Computing Conference (IWCMC), pp. 319–324. IEEE (2019)
15. Subramaniyaswamy, V., et al.: An ontology-driven personalized food recommendation in iot-based healthcare system. J. Supercomput. 75(6), 3184–3216 (2019)
16. Van Broekhoven, E., De Baets, B.: A comparison of three methods for computing
the center of gravity defuzzification. In: 2004 IEEE International Conference on
Fuzzy Systems (IEEE Cat. No. 04CH37542), vol. 3, pp. 1537–1542. IEEE (2004)
17. Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes:
estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–
1053 (2004)
18. Zhang, F., Cheng, J., Ma, Z.: A survey on fuzzy ontologies for the semantic web.
Knowl. Eng. Rev. 31(3), 278–321 (2016)
S. Titi et al.
References
1. Ali, F., et al.: Type-2 fuzzy ontology-aided recommendation systems for IoT-based
healthcare. Comput. Commun. 119, 138–155 (2018)
2. Almeida, A., L´ opez-de Ipi˜ na, D.: Assessing ambiguity of context data in intelligent
environments: towards a more reliable context managing system. Sensors 12(4),
4934–4951 (2012)
3. Alqudah, A.M.: Fuzzy expert system for coronary heart disease diagnosis in Jordan.
Health Technol. 7(2–3), 215–222 (2017)
4. Amirkalali, B., Hosseini, S., Heshmat, R., Larijani, B., et al.: Comparison of harris benedict and mifflin-st jeor equations with indirect calorimetry in evaluating
resting energy expenditure. Indian J. Med. Sci. 62(7), 283–290 (2008)
5. Chiang, T.C., Liang, W.H.: A context-aware interactive health care system based
on ontology and fuzzy inference. J. Med. Syst. 39(9), 105 (2015)
6. Huang, Y.P., Basanta, H., Kuo, H.C., Huang, A.: Health symptom checking system
for elderly people using fuzzy analytic hierarchy process. Appl. Syst. Innov. 1(2),
10 (2018)
7. Lee, C.S., Wang, L., Wang, M.-H.: A fuzzy expert system for diabetes decision
support application. IEEE Trans. Syst. Man Cybern.—Part B: Cybern. 41(1),
139–153 (2011)
8. Lopez-Perez, L., et al.: BD2Decide: big data and models for personalized head and
neck cancer decision support. In: 2019 IEEE 32nd International Symposium on
Computer-Based Medical Systems (CBMS), pp. 67–68. IEEE (2019)
9. Mulero, R., Urosevic, V., Almeida, A., Tatsiopoulos, C.: Towards ambient assisted
cities using linked data and data analysis. J. Ambient Intell. Humaniz. Comput.
9(5), 1573–1591 (2018). https://doi.org/10.1007/s12652-018-0916-y
10. Mumtaj, S., Umamakeswari, A.: Neuro fuzzy based healthcare system using IoT.
In: 2017 International Conference on Energy, Communication, Data Analytics and
Soft Computing (ICECDS), pp. 2299–2303. IEEE (2017)
11. Nazari, S., Fallah, M., Kazemipoor, H., Salehipour, A.: A fuzzy inference-fuzzy
analytic hierarchy process-based clinical decision support system for diagnosis of
heart diseases. Expert Syst. Appl. 95, 261–271 (2018)
12. Saad, S., Zafar, B.A., Mueen, A.: Developing a framework for e-healthcare applications using the semantic internet of things. Int. J. Comput. Appl. 975, 8887
(2018)
13. Selvan, N.S., Vairavasundaram, S., Ravi, L.: Fuzzy ontology-based personalized
recommendation for internet of medical things with linked open data. J. Intell.
Fuzzy Syst. 36(5), 4065–4075 (2019)
14. Sondes, T., Elhadj, H.B., Chaari, L.: An ontology-based healthcare monitoring system in the internet of things. In: 2019 15th International Wireless Communications
and Mobile Computing Conference (IWCMC), pp. 319–324. IEEE (2019)
15. Subramaniyaswamy, V., et al.: An ontology-driven personalized food recommendation in iot-based healthcare system. J. Supercomput. 75(6), 3184–3216 (2019)
16. Van Broekhoven, E., De Baets, B.: A comparison of three methods for computing
the center of gravity defuzzification. In: 2004 IEEE International Conference on
Fuzzy Systems (IEEE Cat. No. 04CH37542), vol. 3, pp. 1537–1542. IEEE (2004)
17. Wild, S., Roglic, G., Green, A., Sicree, R., King, H.: Global prevalence of diabetes:
estimates for the year 2000 and projections for 2030. Diabetes Care 27(5), 1047–
1053 (2004)
18. Zhang, F., Cheng, J., Ma, Z.: A survey on fuzzy ontologies for the semantic web.
Knowl. Eng. Rev. 31(3), 278–321 (2016)