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In real life situation, it is often difficult to judge the relative importance of different parameters being considered for evaluating some alternatives. In the context of fuzzy sets, it is a situation where it is difficult to define precise membership grades for attribute values. Here we require more generalized type of fuzzy sets which have a greater representational power than ordinary fuzzy sets. For this purpose we use “interval type-2 trapezoidal fuzzy preference relations (IT2TrFPRs)” in this article as a generalization of fuzzy preference relations and consider the environment discussed above, where there is no information on priority weights. A collective decision matrix will be constructed on the basis of hybrid averages using weighted averaging and signed distance based OWA operation. Then a least deviation model will be employed in order to determine the priority weight vectors. Finally, the alternatives will be ranked on the basis of weighted normalized signed distance of each alternative from the ideal solution. Moreover, a real life example of location selection is illustrated to elaborate the effectiveness of the proposed scheme.

Introduction. Taxonomy is an effective mean of managing and accessing a large amount of digital information. Various techniques have been developed to generate taxonomy automatically. The purpose of this study is threefold: (i) review methods and approaches adopted during taxonomy generation, (ii) identify the factors influencing the choice of a particular method or approach, (iii) highlight issues and open challenges. Method. This paper adopts a systematic literature review approach proposed by Kitchenham, and the nature of this review is qualitative. Analysis. A total of thirty techniques were reviewed and categorized into various categories and subcategories. An in-depth analysis of the existing techniques was performed based on this categorization. This ultimately helps in identifying factors influencing the choice of a particular method or approach, and also determines issues and challenges associated with the automatic taxonomy generation. Results. Four major factors influencing the choice of a particular method or approach for generating taxonomy have been identified. Moreover, five major challenges associated with the existing automatic taxonomy generation techniques have also been highlighted. Conclusions. This paper presents a comprehensive review of taxonomy generation so that taxonomy can be used effectively, and it highlights open challenges for future research in the area of taxonomy generation so that new and improved techniques can be developed.

With the emergence of green consumerism, the trend of “going green” has now prolonged to the Asian region, including Malaysia. Despite most of Malaysians have shown great concern for environmental issues; however, the green buying behavior among Malaysians is not encouraging. The study focuses on the barriers that trigger the green purchasing gap and; identify which barriers are perceived as the most relevant in acquiring eco-friendly home appliances. The paper examines the measurement instrument (questionnaire) for validity and reliability, for the purpose of data analysis. Statistical techniques used for validity and reliability were Exploratory Factor Analysis and Cronbach’s Alpha. Exploratory Factor Analysis was performed on individual dimension for the purpose of explaining variation among items and their reduction to form a meaningful construct.

Phytoremediation is a cost-effective and eco-friendly process to remove heavy metals from water. A small-scale experiment was conducted to determine the phytoremediation efficiency of two macrophytes, i.e. Pistia stratiotes and Eichhornia crassipes for the removal of chromium and copper from the prepared metal solutions of different concentrations (2, 4, 6, 8 mg/L) for a period of one month. Both aquatic macrophytes showed different efficiency for the removal and accumulation of Chromium (Cr) and Copper (Cu). The dry biomass of both macrophytes increased over the period of time. The average accumulation of chromium in roots and leaves of P. stratiotes was 85 mg, 56 mg and copper was 96 mg and 70 mg, respectively whereas the average accumulation of chromium in roots and leaves of E. crassipes was 90 mg, 53 mg and copper was 86 mg, 50 mg, respectively. Higher absorbance of metals was observed in roots as compared to the leaves of both macrophytes. The concentration of metals in the solution significantly decreased from day 1 to day 30. The results of one-way ANOVA showed that removal of Cr was significantly (p <.05) greater in E. crassipes and removal of Cu was significantly (p <.05) greater in P. stratiotes. [ABSTRACT FROM AUTHOR]

Sentiments are the key factors that lead to influence our behavior. Sentiment analysis is a technique that analyzes people's behaviors, attitudes, and emotions toward a service, product, topic, or event. Since 2020, no country has remained untouched by COVID-19, and the governing bodies of most countries have been applying several anti-pandemic countermeasures to combat it. In this regard, it becomes tremendously important to analyze people's sentiments when tackling infectious diseases similar to COVID-19. The countermeasures taken by any country to control the pandemic leave a direct and crucial impact on each sector of public life, and every individual reacts to them differently. It is necessary to consider these reactions to implement appropriate messaging and decisive policies. Pakistan has done enough to control this virus's spread like every other country. This research aims to perform a sentimental analysis on the famous microblogging social platform, Twitter, to get insights into public sentiments and the attitudes displayed towards the precautionary steps taken by the Government of Pakistan in the years 2020 and 2021. These steps or countermeasures include the closure of educational institutes, suspension of flight operations, lockdown of business activities, enforcement of several standard operating procedures (SOPs), and the commencement of the vaccination program. We implemented four approaches for the analysis, including the Valence Aware Dictionary and sEntiment Reasoner (VADER), TextBlob, Flair, and Bidirectional Encoder Representations from Transformers (BERT). The first two techniques are lexicon-based. Flair is a pre-trained embedding-based approach, whereas BERT is a transformer-based model. BERT was fine-tuned and trained on a labeled dataset, achieving a validation accuracy of 92%. We observed that the polarity score kept varying from month to month in both years for all countermeasures. This score was analyzed with real-time events occurring in the country, which helped understand the public's sentiment and led to the possible formation of a notable conclusion. All implemented approaches showed independent performances. However, we noticed from the classification results of both TextBlob and the fine-tuned BERT model that neutral sentiment was dominant in the data, followed by positive sentiment., Competing Interests: The authors declare that they have no competing interests., (© 2023 Ali et al.)