Accuracy of fast information retrieval of data for novel vehicle parking system using k-nearest neighbor algorithm compared over fuzzy algorithm.

The K-Nearest Neighbor approach outperforms the Fuzzy Algorithm in terms of accuracy during quick data retrieval for an innovative car parking system. The Components and Techniques: K-Nearest Neighbor Algorithm enables 20-sample database vehicle parking system, which improves innovative vehicle parking system's data retrieval accuracy and speed. The implementation was accomplished by developing a Python web application with the aid of the anaconda navigator. As a result, a car parking database is constructed using the K-Nearest Neighbor Algorithm, which improves efficiency by 87 percent in terms of secure accessibility compared to the FuzzyApproach, which improves efficiency by only 75 percent. The length of time it takes to log in and access the database has been analysed using SPSS (Statistical Package for the Social Sciences) with the independent variables of time and size and a significance level of p 0.05. K-Nearest Neighbor Algorithm, which is more significant than a Fuzzy Approach in terms of Accurate efficiency, has been employed in a protected system to improve the car parking database. When data is a dependent variable and sample size is an independent variable, SPSS analysis may show how reliable the data is. [ABSTRACT FROM AUTHOR]