Mobile applications in the area of human-centered applications are based on fuzzy logic have exhibited their effectiveness in managing intelligent environments, however the deployment of mobile fuzzy logic systems has been usually associated with dedicated hardware and software packages. Introducing openness for fuzzy logic systems offers exciting features such as system independence, simplicity, load balancing, and controlled resource allocation. On the other hand, while major cloud service providers support readymade commercial services for AI techniques such as for deep neural networks, there is no similar services for fuzzy logic systems. This study aims to develop a cloud-based fuzzy logic system under Microsoft Azure, employing Simpful as the cloud-side Python library and FML as data exchange standard. The developed cloud service is shown to effectively serve mobile phone applications for human monitoring purposes. Also in the present study, two types of fuzzy inference systems namely Mamdani and TSK have been utilized wherein both these systems have been compared on the basis of their processing time and accuracy of result. Results indicated that Mamdani fuzzy inference system outperformed TSK fuzzy inference system in terms of processing time by 0.456 seconds. Moreover, the detection accuracy of Mamdani system was found to be higher than that of TSK system by 6.82%.
An Integrated Fuzzy Logic System under Microsoft Azure using Simpful / Pandya, B.; Pourabdollah, A.; Lotfi, A.; Acampora, G.. - 2022-:(2022), pp. 1-9. (Intervento presentato al convegno 2022 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2022 tenutosi a ita nel 2022) [10.1109/FUZZ-IEEE55066.2022.9882612].
An Integrated Fuzzy Logic System under Microsoft Azure using Simpful
Acampora G.
2022
Abstract
Mobile applications in the area of human-centered applications are based on fuzzy logic have exhibited their effectiveness in managing intelligent environments, however the deployment of mobile fuzzy logic systems has been usually associated with dedicated hardware and software packages. Introducing openness for fuzzy logic systems offers exciting features such as system independence, simplicity, load balancing, and controlled resource allocation. On the other hand, while major cloud service providers support readymade commercial services for AI techniques such as for deep neural networks, there is no similar services for fuzzy logic systems. This study aims to develop a cloud-based fuzzy logic system under Microsoft Azure, employing Simpful as the cloud-side Python library and FML as data exchange standard. The developed cloud service is shown to effectively serve mobile phone applications for human monitoring purposes. Also in the present study, two types of fuzzy inference systems namely Mamdani and TSK have been utilized wherein both these systems have been compared on the basis of their processing time and accuracy of result. Results indicated that Mamdani fuzzy inference system outperformed TSK fuzzy inference system in terms of processing time by 0.456 seconds. Moreover, the detection accuracy of Mamdani system was found to be higher than that of TSK system by 6.82%.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
