Development of a Decision Support System for Urban Planning by Using K-means ++ Algorithm

Abstract

This article depicts a decision support system (DSS) devoted to the coordinated administration of urban frameworks. This framework defines the information and related treatments normal to a few civil managers and characterizes the necessities and functionalities of the PC devices created to enhance the conveyance, execution, and coordination of metropolitan administrations to the populace. The cooperative framework called Decision Support System for Urban Planning (DSS-UP) is made out of a universal planning and coordination framework. So, it helps the decision-making process, a DSS was created as a learning-based framework gave derivation components that empower urban architect to settle on key decisions as far as specialized meditations on civil foundations. The learning-based framework stores experts_ information and additionally answers for past issues. Preparatory execution comes about demonstrate that DSS-UP viably and effectively underpins the decision-making process identified with overseeing urban foundations by using K-means++ data mining algorithm.

Adaptive Hybrid Learning for Websites Vulnerability prediction

“…The deliberate use of K-means clustering aids in distinguishing distinct clusters within the data, setting a stage for anomaly detection, and further honing the model's ability to predict vulnerabilities. The methodology behind the K-Means Clustering algorithm proceeds as follows [33]. , Where |𝑆 𝑐 𝑗 | is the number of points in the cluster 𝑆 𝑐 𝑗 , and the sum is over all points 𝑥 in 𝑆 𝑐 𝑗 .…”