Recent years have shown significantly pervasive interest in mobile applications (hereinafter “apps”). The number and popularity of these apps are dramatically increasing. Even though mobile apps are diverse, countless ones are available through many platforms. Some of these apps are not useful nor do they possess rich content, which benefits end users as expected, especially in medical-related cases. This research aims to review and analyze articles associated with medical app assessment across different platforms. This research also aimed to provide the best practices and identify the academic challenges, motivations and recommendations related with quality assessments. In addition, a methodological approach followed in previous research in this domain was also discussed to give some insights for future comers with what to expect. We systematically searched articles on topics related to medical app assessment. The search was conducted on five major databases, namely, Science Direc.....

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Missing data is a common problem in real-world data sets and it is amongst the most complex topics in computer science and many other research domains. The common ways to cope with missing values are either by elimination or imputation depending of the volume of the missing data and its distribution nature. It becomes imperative to come up with new imputation approaches along with efficient algorithms. Though most existing imputation methods focus on a moderate amount of missing data, imputation for high missing rates over 80% is still important but challenging. Even with the existence of some works in addressing high missing volume issue, they mostly rely on imputing reference dataset (Complete Datasets for evaluation) after they create artificial missing values and impute it to measure the accuracy of their proposed techniques. So far, the option of imputing high proportions of missing values with no reference comparison dataset (Original Dataset with highly missing values) have been.....

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Researchers have been inspired to use technology to enable people with hearing and speech impairment to communicate and engage with others around them. Sensory approach to recognition facilitates real-time and accurate recognition of signs. Thus, this study proposes a Malaysian Sign Language (MSL) recognition framework. The framework consists of three sub-modules for the recognition of static isolated signs based on data collected from a DataGlove. The first module focuses on the characteristics of signs, yielding sign recognition system requirements. The second module describes the different steps required to develop a wearable sign-capture device. The third module discusses the real-time SL recognition approach, which uses a template-matching algorithm to recognize acquired data. The final design of the DataGlove with 65 data channel fulfils the requirement identified from an analysis of MSL. The DataGlove is able to record data for all of the signs (both dynamic and static) of MSL d.....

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During the coronavirus disease (COVID-19) pandemic, different technologies, including telehealth, are maximised to mitigate the risks and consequences of the disease. Telehealth has been widely utilised because of its usability and safety in providing healthcare services during the COVID-19 pandemic. However, a systematic literature review which provides extensive evidence on the impact of COVID-19 through telehealth and which covers multiple directions in a large-scale research remains lacking. This study aims to review telehealth literature comprehensively since the pandemic started. It also aims to map the research landscape into a coherent taxonomy and characterise this emerging field in terms of motivations, open challenges and recommendations. Articles related to telehealth during the COVID-19 pandemic were systematically searched in the WOS, IEEE, Science Direct, Springer and Scopus databases. The final set included (n = 86) articles discussing telehealth applications with respe.....

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Due to the vast volumes of newly streamed data on the Internet and social media, the use of sentiment analysis (SA) to extract information and analyze people's opinions has become a trendy topic. Yet, the majority of research are attributed to the English language, despite the fact that other languages, such as Arabic, are among the most popular on the Internet. Considering the availability of numerous dialects of this language and how their data were annotated and processed, the absence of research in this field is evident. Understanding these initiatives merits a great deal of attention in Arabic SA research. To the best of our knowledge, this domain has not been considered before, and thus the aim of this study is to perform a systematic review with regard to SA and data annotations for Arabic dialects published between 2015 and 2023. The outcomes of this research offer a refined taxonomy of data annotation methods classified into three categories: (1) manual, (2) automatic, and (3).....

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The technology deployment in smart e-tourism brings high potential in terms of customer data, events, reservations, and others. It acts as an effective and personalized guide to aid travelers. There is an increasing variety of smart e-tourism apps with multiple categories and criteria, but in terms of decision making, this presents a multicriteria complex problem to determine the best app from a group of available options with high criteria subjectivity. Literature reviews have evaluated and modeled the existing smart e-tourism apps alternatives, but informational uncertainty remains. The fuzzy sets and Multi-Attribute Decision Analysis (MADA) were used to handle the subjectivity issue. However, this process includes levels of uncertainty, which affects the decisions made and still an open issues. Spherical fuzzy rough sets (SFRSs) environment are useful in this situation for resolving fuzziness and ambiguity. This paper proposed a decision modeling approach for smart E-Tourism data ma.....

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In the last few years, the trend in health care of embracing artificial intelligence (AI) has dramatically changed the medical landscape. Medical centres have adopted AI applications to increase the accuracy of disease diagnosis and mitigate health risks. AI applications have changed rules and policies related to healthcare practice and work ethics. However, building trustworthy and explainable AI (XAI) in healthcare systems is still in its early stages. Specifically, the European Union has stated that AI must be human-centred and trustworthy, whereas in the healthcare sector, low methodological quality and high bias risk have become major concerns. This study endeavours to offer a systematic review of the trustworthiness and explainability of AI applications in healthcare, incorporating the assessment of quality, bias risk, and data fusion to supplement previous studies and provide more accurate and definitive findings. Likewise, 64 recent contributions on the trustworthiness of AI in.....

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In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncert.....

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This research proposes a novel mobile health-based hospital selection framework for remote patients with multi-chronic diseases based on wearable body medical sensors that use the Internet of Things. The proposed framework uses two powerful multi-criteria decision-making (MCDM) methods, namely fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score method for criteria weighting and hospital ranking. The development of both methods is based on a Q-rung orthopair fuzzy environment to address the uncertainty issues associated with the case study in this research. The other MCDM issues of multiple criteria, various levels of significance and data variation are also addressed. The proposed framework comprises two main phases, namely identification and development. The first phase discusses the telemedicine architecture selected, patient dataset used and decision matrix integrated. The development phase discusses criteria weighting by q-ROFWZIC and hospital ranking by q-ROFDOSM.....

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An intelligent remote prioritization for patients with high-risk multiple chronic diseases is proposed in this research, based on emotion and sensory measurements and multi-criteria decision making. The methodology comprises two phases: (1) a case study is discussed through the adoption of a multi-criteria decision matrix for high-risk level patients; (2) the technique for reorganizing opinion order to interval levels (TROOIL) is modified by combining it with an extended fuzzy-weighted zero-inconsistency (FWZIC) method over fractional orthotriple fuzzy sets to address objective weighting issues associated with the original TROOIL. In the first hierarchy level, chronic heart disease is identified as the most important criterion, followed by emotion-based criteria in the second. The third hierarchy level shows that Peaks is identified as the most important sensor-based criterion and chest pain as the most important emotion criterion. Low blood pressure disease is identified as the most i.....

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