Search

• To develop a model for the efficient retrieval of information using Query Expansion. • The model applies fuzzy logic to remove uncertainties and semantic filtering to remove noise. • The model employs the novelty of using fuzzy logic and semantic filtering and BERTscore for extracting more similar terms. • To study and evaluate the suggested model's performance on a variety of datasets. Several factors hinder information retrieval in the medical profession. Consumers (layman people) often struggle to learn medical terms. Because medical terms are more evident to professionals, it is difficult for consumers to construct a query using medical terms. Consumers would find it easier to access relevant medical information if medical words relevant to their query were automatically added. Various kiosks use approaches using machine vision to form the user queries and monitor their health. This work proposes a hybrid approach to term selection by expanding user queries with medical terms relevant to the medical query. The selection of terms is based on fuzzy similarity reasoning based on two primary term selection strategies. WordNet semantic filtering is applied for preventing query drift, followed by the calculation of BERTScore. The retrieved documents were ranked using Okapi-BM25. For evaluation purposes, six benchmark datasets have been used: CACM, CISI, MEDLINE, ADI, FIRE, and TREC (Covid-19). The results indicate that the suggested technique outperforms the current state-of-the-art. [ABSTRACT FROM AUTHOR]

Digital technology and social media have delivered many advantages in understanding human psychology, which is essential to industrial growth. Skytrax is an online social media platform for travellers to write reviews on airlines. In online reviews, consumer recommendations are critical indicators for service providers to improve their consumer policies as well as service quality. It is also helpful for future customers to help get information on future purchases prior to making them. Therefore, previous consumer recommendations based on online reviews play a vital role in airline recommendations. Our main goal is to use our proposed cuckoo optimized machine learning model to predict airline recommendations. Experimental analysis was implemented using data scraped from the website https://www.airlinequality.com. Our results show that the proposed eXtreme gradient boosting classifier optimized by Cuckoo Search (CS-XGB) outperforms other state-of-the-art techniques. [Display omitted] • Propose a consumer recommendation prediction system using online review. • Acquired consumer online reviews and ratings from Skytrax. • Applied various pre-processing schemes to input data and feed it to the model. • Create a Cuckoo Search (CS) based parameter optimization model. • Proposed CS-XGB outperforms the other state-of-the-art techniques. [ABSTRACT FROM AUTHOR]