A fuzzy-based expert system to analyse purchase behaviour under uncertain environment

Thắng, Đoàn Văn; Mangla, Monika; Satpathy, Suneeta; Pattnaik, Chinmaya Ranjan; Mohanty, Sachi Nandan

doi:10.1007/s41870-021-00615-z

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Subsequently, the output is fuzzified gets transformed into a singular crisp value. The phenomenon of effecting a transition from a fuzzy set to a crisp set is commonly referred to as defuzzification [6,7].…”

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confidence: 99%

Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning

Parida,

Pande,

Challa

et al. 2024

EAI Endorsed Trans IoT

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This research paper aims to investigate the significance of electrical products, specifically earbuds and headphones, in the digital world. The processes of decision-making and purchasing of audio accessories are often characterized by a significant investment of time and effort, as well as a complex interplay of competing priorities. In addition, various methodologies are employed for the selection and procurement of audio equipment through the utilization of machine learning algorithms. This study aimed to gather responses from a diverse group of participants regarding their preferences for the latest functionalities and essential components in their gadgets. The data was collected through a questionnaire that provided multiple options about the specifications of the audio accessories for the participants to choose from. The study employed seven distinct input factors to elicit responses from participants. These factors included brand, type, design, fit, price, noise cancellation, and folding design. The quantification of each input parameter was executed through the utilization of a scaling function in the Fuzzy Logic Interface, which assigned the labels “Yes” or “No” to each parameter. In this study, the Mamdani approach, which is a widely used fuzzy reasoning tool, was employed to develop a fuzzy logic controller (FLC) consisting of seven input and one output processes. In this study, standard fuzzy algorithms were employed to enhance the accuracy of the process of selecting an audio accessory in accordance with the user's specific requirements on the basis of Fuzzy threshold where “Yes” signifies about the availability of such audio accessory and “No” refers to the non-availability and readjustment of the input parameters.

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Section: Figure 2 Frbs Illustrative Viewmentioning

confidence: 99%

Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning

Parida,

Pande,

Challa

et al. 2024

EAI Endorsed Trans IoT

View full text Add to dashboard Cite

show abstract

“…The output that has been fuzzified is subsequently transformed into a single crisp value. Defuzzification is the term for this transformation [26,27].…”

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confidence: 99%

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

Agarwal,

Suthar,

Panda

et al. 2023

ICST Transactions on Scalable Information Systems

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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. Furthermore, machine learning is used to pick and purchase laptops using a variety of strategies. 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. 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. Five input and one output processes were designed using the Mamdani technique, a conventional fuzzy reasoning tool (FLC). 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.

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“…Therefore, vocational interest is basically just another aspect of personality and interest inventories (tests) ( Skarpaas & Hellekjær, 2021 ). People are divided into 6 different types, each corresponding to a work environment, which are: conventional, entrepreneurial, investigative, artistic, social and realistic ( Alfaro-Barquero & Chinchilla-Brenes, 2017 ; Van Thang et al, 2021 ). Each person projects their views about occupations, about themselves and about the work environment they prefer.…”

Section: Introductionmentioning

confidence: 99%

Improving vocational guidance through an expert system: Temporary decreasing and enhancing student self-awareness

Huayta-Gómez,

Pacheco

2024

F1000Res

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Background The vocational guidance process in educational institutions faces important challenges in managing trials and errors in diagnoses. Technological tools are identified as an effective solution to address these problems. This research seeks to improve career guidance in educational institutions through the implementation of an expert system. The main objective is to reduce test processing time and achieve greater efficiency in students’ self-knowledge regarding their interests, based on the personalities of the Holland Test. Methods The development of the expert system followed a six-model approach. First, an organisational model was created to assess the scope and feasibility of the project. Next, a task and agent model was developed to investigate the impact and look for improvements. A knowledge model was then developed to analyse the relevant knowledge bases. A communication model was also developed to evaluate the communication interface of the system. Next, a design model was created to provide guidelines for the implementation of the system. Finally, the implementation of the knowledge system was carried out to ensure its correct functioning. Results The implementation of the expert system has shown significant improvements in the vocational guidance process. It was possible to reduce the time needed to apply the test, thus optimising the psychologist’s time and allowing a greater capacity for analysis. In addition, an improvement in the effectiveness of the students’ self-knowledge in relation to their vocational interests based on the personalities of the Holland Test was observed. Conclusions This study contributes to career guidance in educational institutions by introducing an innovative expert system. This technological solution optimizes the career guidance process, benefiting psychologists administering tests and students seeking self-knowledge about their career interests.

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A fuzzy-based expert system to analyse purchase behaviour under uncertain environment

Cited by 6 publications

References 10 publications

Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning

Enhancing Audio Accessory Selection through Multi-Criteria Decision Making using Fuzzy Logic and Machine Learning

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

Improving vocational guidance through an expert system: Temporary decreasing and enhancing student self-awareness

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