A <span style="mso-fareast-font-family: 'MS Mincho'; mso-fareast-language: JA;">Fuzzy Logic and Deep Learning for a Knowledge-Driven Modeling-Based Recommendation System

The fuzzy- logic (FL) based recommendation system is a research subject that studies and develops technological systems capable of solving complex tasks typically requiring human intelligence, as well as creating intelligent recommendation systems. Fuzzy logic (FL) and deep learning (DL) are techniques for handling variables that allow multiple values to be processed through the same variable. To resolve issues with an open, inexact spectrum of information that makes it feasible to obtain an array of specific. Our proposed model is a FL based recommendation system, characterized by a hybrid fuzzy detection (HFD) and loss computation model. Furthermore, the FL based model is developed using DL, along with a FL algorithm designed for detecting and preventing attacks. Configuration techniques results such as positive and negative attacks, which are attributed to the type of attack or normal behavior. The frequency results are attributed to the type of attack or normal behavior. Evolution analysis refers to the description and modeling of regularities or trends for objects whose count changes over time. The distribution includes a positive peak of 77 and a negative peak of over 100 categories, forming the foundation of the FL based recommendation system. In engineering finding the proposed FL and DL recommendation system has the potential to provide valuable support and help users achieve their financial goals.