Fuzzy and Machine Learning based Multi-Criteria Decision Making for Selecting Electronics Product

Abstract

INTRODUCTION: In this study, we have considered electronics product as laptop one of the essential items in digital era. The decision-making and buying processes for laptops are time consuming and fraught with competing priorities. OBJECTIVES: Through a questionnaire that provided them with many choices for the newest features and essential components they desire in their devices, the participants' replies were sought. METHODS: The participants' responses were elicited from eighteen independent input variables: processor, ram capacity, gpu, graphics card, laptop brand, type of storage, storage size, ports, screen size, backlit keyboard, pc body, category, screen display, weight, webcam, battery life, operating system, and price range. Each of the input variables was quantified using a scale using the terms very low, low, medium, high, and very high. RESULTS: Five input and one output processes were designed using the Mamdani technique, a conventional fuzzy reasoning tool (FLC). Furthermore, machine learning is used to pick and purchase laptops using a variety of strategies. CONCLUSION: To arrive at a more precise knowledge of the procedure for choosing a laptop in accordance with the user's requirements, standard fuzzy systems were employed.