Analyzing the Public Opinion as a Guide for Renewable-Energy Status in Malaysia: A Case Study

Qazi, Atika; Bhowmik, Chiranjib; Fayaz, H.; Yang, Shuiqing; Naseem, Usman; Abayomi‐Alli, Adebayo; Gumaei, Abdu; Al-Rakhami, Mabrook S.

doi:10.1109/tem.2020.3046749

Cited by 22 publications

(11 citation statements)

References 67 publications

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“…The population was randomly sampled from college students to record their opinions that are important to find the facts on a certain situation ( Qazi, 2014 , Qazi, 2017 , Qazi, 2021 ). A structured questionnaire was exploited to record the responses during the COVID-19 lockdown.…”

Section: Methodsmentioning

confidence: 99%

Adaption of distance learning to continue the academic year amid COVID-19 lockdown

Qazi

Naseer

et al. 2021

Children and Youth Services Review

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Section: Methodsmentioning

confidence: 99%

Adaption of distance learning to continue the academic year amid COVID-19 lockdown

Qazi

Naseer

et al. 2021

Children and Youth Services Review

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“…Machine learning (ML)-based methods are presented to address the shortcomings of the abovementioned techniques by making the machine learn and improve the performance [29]. Biomedical/clinical extraction can be classified as a labelling task sequence, referred to as a classification task in supervised learning of ML algorithms.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

A Comparative Analysis of Active Learning for Biomedical Text Mining

Naseem

Khushi

Khan

et al. 2021

ASI

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An enormous amount of clinical free-text information, such as pathology reports, progress reports, clinical notes and discharge summaries have been collected at hospitals and medical care clinics. These data provide an opportunity of developing many useful machine learning applications if the data could be transferred into a learn-able structure with appropriate labels for supervised learning. The annotation of this data has to be performed by qualified clinical experts, hence, limiting the use of this data due to the high cost of annotation. An underutilised technique of machine learning that can label new data called Active Learning (AL) is a promising candidate to address the high cost of the label the data. AL has been successfully applied to labelling speech recognition and text classification, however, there is a lack of literature investigating its use for clinical purposes. We performed a comparative investigation of various AL techniques using ML and deep learning (DL) based strategies on three unique biomedical datasets. We investigated Random Sampling (RS), Least confidence (LC), Informative diversity and density (IDD), Margin and Maximum representativeness-diversity (MRD) AL query strategies. Our experiments show that AL has the potential to significantly reducing the cost of manual labelling. Additionally, AL-assisted pre-annotations accelerates the de novo annotation process with less annotation time required.

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“…People's perceptions and attitudes concerning any situation can be assessed by sentiment analysis, which has become a prevalent information source (Qazi et al 2017a(Qazi et al , 2019. Sentiment analysis is often performed on public opinions that are available through a variety of domains and has been used extensively to gauge public sentiment towards ongoing situations (Qazi, Fayaz, and Raj 2014a;Qazi et al 2021a;Abo, Raj, and Qazi 2019). Therefore, in addition to SEM, we performed sentiment analysis on the 220 open-ended responses using a manual approach similar to card sorting (Nelson 1976), and classified the opinions into positive and negative sentiments (Figure 3).…”

Section: Sentiment Analysismentioning

confidence: 99%

Travel behaviour prediction amid covid-19 underlaying situational awareness theory and health belief model

Naseer

Qazi

et al. 2021

Behaviour & Information Technology

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Travel and tourism have been hard-hit by COVID-19, and people have been forced to cancel travel plans due to governments being forced to implement travel bans to curb infection dissemination. Therefore, this study investigates the influence of situational awareness for adopting healthprotective behaviours such as postponed travel plans. We used the theory of situational awareness (SAT) coupled with the health belief model (HBM) to analyze health-related behaviors. Data from a cross-sectional survey of 705 Pakistani and international adults were analyzed using Structural Equation Modelling, during the peak of the COVID-19. The results confirmed that perceived understanding of COVID-19 cause influences perceived severity, understanding of contracting COVID-19 influences perceived susceptibility, and perceived understanding of COVID-19 prevention influences both perceived barriers and perceived benefits. The results further showed that perceived understanding of COVID-19 prevention is a strong predictor of self-efficacy, which ultimately influences health-protective behaviour. In addition, the sentiment analysis revealed that as COVID-19 continues to rise, people are becoming more sentimental and avoiding travel, even during festivals. The outcome of this study demonstrates that situational awareness has an effect on the postulates of healthprotective measures and plays a key role in the implementation of protective measures such as canceling travel plans to ensure protection.

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Analyzing the Public Opinion as a Guide for Renewable-Energy Status in Malaysia: A Case Study

Cited by 22 publications

References 67 publications

Adaption of distance learning to continue the academic year amid COVID-19 lockdown

Adaption of distance learning to continue the academic year amid COVID-19 lockdown

A Comparative Analysis of Active Learning for Biomedical Text Mining

Travel behaviour prediction amid covid-19 underlaying situational awareness theory and health belief model

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