Enhancing Vehicular-to-Everything Communication Efficiency with Fuzzy Machine Learning Techniques and Mathematical Modelling
Keywords:
Vehicle-to-Everything, Fuzzy logic, Supervised algorithms, Machine learning.Abstract
Vehicular communication networks perform a key role in intelligent transportation systems. Different kinds of communication networks exist to enhance the functioning of smart systems of transportation. The categorization of vehicular networks shall be generalized as vehicle-to-everything (V2X). The vehicular efficiency of V2X depends on diverse factors and this research work employs fuzzy machine learning techniques to determine the core features. This study explores all the possible factors persuading the vehicular efficiency of V2X. This work proposes a mathematical model incorporating fuzzy logic-based supervised machine learning approaches in handling uncertain and imprecise data. The qualitative features are decided based on the integrated approach of fuzzy and machine learning. The mathematical model developed in this research work is based on this integrated framework and the results of the model are more significant in the domain of smart intelligent systems. This model shall be further discussed with different categorizations of machine learning approaches to extend this work. This model presented in this work facilitates the automotive decision-makers and policymakers in identifying the crucial factors contributing to the efficiency of V2X.
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