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Neutrosophic multi-criteria decision making approach for iot-based enterprises
Faculty
Computer Science
Year:
2019
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohammed Abdel Basset Metwally Attia
Staff Zu Site
Abstract In Staff Site
Journal:
IEEE Access IEEE
Volume:
Keywords :
Neutrosophic multi-criteria decision making approach , iot-based
Abstract:
The Internet of Things (IoT) connects billions of devices to afford inventive opportunities between things and people. The rapid development of products related to the IoT is a new challenge to keep security issues, lack of confidence, and understanding of the IoT. Analytical hierarchy process (AHP) is a classic multi-criteria decision making (MCDM) method used to analyze and scale complex problems and to obtain weights for the selected criteria. The vague and inconsistent information in real situations can lead to the decision maker's confusion. The decision makers cannot determine accurate judgments for all situations due to the conditions of uncertainty factors in real life; in addition to the limited knowledge and experience of decision makers. In this research, we present a neutrosophic AHP of the IoT in enterprises to help decision makers to estimate the influential factors. The estimation of influential factors can affect the success of the IoT-related enterprise. This study combines AHP methods with neutrosophic techniques to effectively present the criteria related to influential factors. The recommended alternatives are presented based on neutrosophic techniques satisfying the estimated influential factors for a successful enterprise. A case study is applied in Smart Village, Cairo, Egypt, to show the applicability of the proposed model. The smart village' consistency rate is measured after applying neutrosophic methodologies to reach to nearest optimum results. Additional case studies on the smart city in the U.K. and China have been presented to justify that our proposal can be used and replicated in different environments.
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Saber Mohamed, "Training and Testing a Self-Adaptive Multi-Operator Evolutionary Algorithm for Constrained Optimization", ELSEVEIR, 2015
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