A new intelligent modelling two-stage of hybrid fuzzy prediction approach by using computation software

• Published in: JOURNAL OF INTELLIGENT & FUZZY SYSTEMS
• Main Authors: Shafi, Muhammad Ammar; Rusiman, Mohd Saifullah; Jacob, Kavikumar; Musa, Aisya Natasya
• Format: Article
• Language: English
• Published: IOS PRESS 2023
• Subjects: Computer Science
• Online Access: https://www-webofscience-com.uitm.idm.oclc.org/wos/woscc/full-record/WOS:001120921100113

With relevant computational software, fuzzy prediction, a new intelligent modelling technique, is utilised to resolve unclear phenomena in various disciplines. Excellent software risk prediction is essential for effective prediction, such as risk management, case planning, and control. We provide an intelligent modelling strategy for software risk prediction in this research. We are applying a support vector machine model and two phases of hybrid fuzzy linear regression clustering (SVM). This method may produce the most accurate risk predictions for various continuous data. The best model with even less error value, acceptable interpretability, and imprecise uncertainty inputs is a fuzzy linear regression with symmetric parameter clustering with a support vector machine (FLRWSPCSVM), a new intelligent modelling technique. The model's predictive accuracy is demonstrably higher than other prediction models, according to validation utilising simulation data and four software packages such as SPSS, MATLAB and Weka Explorer.