Utilizing a Hybrid Geometric Operator for AIFS Data in Renewable-Energy-Source Classification
- Resource Type
- Conference
- Authors
- Joshi, Bhagawati Prasad; Kumar, Dinesh; Joshi, Dheeraj Kumar; Oli, Sanjay; Rayal, Ashish; Giri, Shailendra
- Source
- 2024 2nd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) Intelligent Data Communication Technologies and Internet of Things (IDCIoT), 2024 2nd International Conference on. :298-302 Jan, 2024
- Subject
- Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Fuzzy sets
Renewable energy sources
Uncertainty
Fuses
Decision making
Internet of Things
Data communication
Information fusion
Renewable energy sources (RESs)
Ambiguous Intuitionistic fuzzy set
Hybrid operator
geometric operator
multi-criteria decision-making (MCDM)
- Language
In this study, a new operator is announced in the domain of Ambiguous Intuitionistic Fuzzy Set (AIFS) theory. As AIFS handle uncertainties more effectually than Fuzzy Sets (FS) and intuitionistic FS (IFS). Generating a scheme for classifying Renewable Energy Sources (RESs) in the AIFS background is the main goal. A new aggregation operator called Ambiguous-Intuitionistic-Hybrid-Geometric (AIFHG) operator is offered to fuse the AIFS information in decision science. With the help of this operator, a decision method for effectively addressing the RES-selection problem within the context of AIFS is established and successfully implemented. The comparison analysis is also included to validate the efficacy of the proposed framework.