Title: A novel approach for feature fatigue analysis using HMM stemming and adaptive invasive weed optimisation with hybrid firework optimisation method

Authors: J. Midhunchakkaravarthy; S. Selva Brunda

Addresses: Department of Computer Science, Bharathiar University, Coimbatore – 641046, India ' Cheran College of Engineering, Karur – 639111, India

Abstract: Due to the rapid growth of customer product reviews in e-commerce website makes the new online customer to analyse reviews to know about the features of the product that they want to buy. Integrating many features into a single product provides more attractive which makes the customer to buy that product, after worked with the high-feature product; the customer may get dissatisfied which eventually reduces the manufacturer's Customer Equity (CE). Thus, it is necessary to analyse the usability of the product. In this paper, k-optimal rule discovery technique with adaptive invasive weed optimisation is proposed to help designers to find an optimal feature that provides the decision supports for product designers to enhance the product usability using Hybrid firework optimisation. Then, the Feature Fatigue (FF) is alleviated efficiently. The proposed approaches are experimented and result shows that proposed work achieves 97% accuracy which is higher than existing work.

Keywords: feature fatigue; latent Dirichlet allocation; LDA; hybrid firework optimisation; HFO; differential evolution; hidden Markov model; HMM; invasive weed optimisation; IWO; k-optimal rule discovery; k-ORD.

DOI: 10.1504/IJCAET.2019.100442

International Journal of Computer Aided Engineering and Technology, 2019 Vol.11 No.4/5, pp.411 - 429

Received: 08 Oct 2016
Accepted: 27 Nov 2016

Published online: 29 Mar 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article