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Home > Archives > Volume 20, No 8 (2022) > Article

DOI: 10.14704/nq.2022.20.8.NQ44169

Deep neural network-based consumer behavior analysis in business management using Aumann Agreement Theorem

Damodharan Palaniappan*, K.Sridhar, Mohammad Salameh Almahairah, Vijaya Lakshmi V, Nita Sanjay Patil, Sunil Devidas Bobade


The limit of connectionist models to figure out consumer behavior is analyzed in this assessment, with an emphasis on the feed forward neural association model, and the chance of developing the speculative design of the Behavior based Perspective Model to wrap connectionist parts is examined. Numerous neural organization modelling of changed intricacy are being made to foresee consumer dedication, which is a significant piece of consumer behavior. The exhibition of profound neural organization models for consumer buy behavior examination is researched according to three viewpoints: the basic hypothesis of profound neural organization models, model development and execution, and model improvement, and observational investigation is led through exploratory outcomes. These techniques provide significant benefits to commercial activities because of their flexibility in dealing with diverse forms of data and high accuracy in creating predictions. Based on information from traditional surveys, this research studies how customer behavior might be recognized using artificial neural networks. The results show that neural networks have good discriminatory power, providing generally better results than traditional discriminant analysis. The general consumer is the primary focus of this investigation. In this study, 500 questionnaires were distributed, with an 84% response rate. The relapse concentrate on found that the more prominent the consumer item association, the more prominent the item understanding and drive purchasing behavior.


Machine Learning, Neural Networks, Consumer Behavior, Business Management

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