A substantial impediment to widespread Coronavirus disease (COVID-19) vaccination is vaccine hesitancy. Many researchers across scientific disciplines have presented countless studies in favor of COVID-19 vaccination, but misinformation on social media could hinder vaccination efforts and increase vaccine hesitancy. Nevertheless, studying people's perceptions on social media to understand their sentiment presents a powerful medium for researchers to identify the causes of vaccine hesitancy and therefore develop appropriate public health messages and interventions. To the best of the authors' knowledge, previous studies have presented vaccine hesitancy in specific cases or within one scientific discipline (i.e., social, medical, and technological). No previous study has presented findings via sentiment analysis for multiple scientific disciplines as follows: (1) social, (2) medical, public health, and (3) technology sciences. Therefore, this research aimed to review and analyze articles.....

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The problem complexity of multi-criteria decision-making (MCDM) has been raised in the distribution of coronavirus disease 2019 (COVID-19) vaccines, which required solid and robust MCDM methods. Compared with other MCDM methods, the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) have demonstrated their solidity in solving different MCDM challenges. However, the fuzzy sets used in these methods have neglected the refusal concept and limited the restrictions on their constants. To end this, considering the advantage of the T-spherical fuzzy sets (T-SFSs) in handling the uncertainty in the data and obtaining information with more degree of freedom, this study has extended FWZIC and FDOSM methods into the T-SFSs environment (called T-SFWZIC and T-SFDOSM) to be used in the distribution of COVID-19 vaccines. The methodology was formulated on the basis of decision matrix adoption and development phases. The first phase described the adopted.....

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The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence.....

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As global median temperatures continue to rise, the demand for active cooling systems (ACs) is increasing. These systems are particularly prevalent in developed countries for maintaining comfort during hot weather. Various ACs technologies are available, and assessing their performance in multi-perspective settings is necessary to determine the best option for intended usage. This requires an evaluation platform for assessment. This paper presents a novel multi-criteria decision-making (MCDM) model based on a new integrated 2-tuple linguistic Pythagorean fuzzy-weighted zero-inconsistency (2 TLP-FWZIC) and modified 2-tuple linguistic Pythagorean fuzzy multi-attributive border approximation area comparison (2TLPF-MABAC). The former is used to determine the importance of assessment criteria, while the latter is employed for selecting the optimal ACs using the obtained weights. The first-level weighting results reveal that performance criteria were predominantly favored for assessment, wit.....

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Developing a comprehensive data-driven strategy for evaluating the organisational culture in companies to foster digital innovation involves a multi-criteria decision-making (MCDM) problem. This needs to consider various organisational culture characteristics that influence digital innovation success, assign significance weights to each characteristic, and recognise that distinct organisational cultures may excel in different aspects necessitates the proper handling of data variations. Hence, to provide organisations seeking to align cultural practises with digital innovation objectives with valuable insights, this study aims to develop an MCDM model for evaluating and benchmarking organisational culture in companies to foster digital innovation. The benchmarking decision matrix is formulated based on the intersection of evaluation characteristics and a list of organisational culture aspects in companies. The MCDM model is developed in two phases. Firstly, a new weighting model, q-rung.....

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Intrusion detection systems (IDSs) are commonly employed to mitigate network security threats in various fields, including federated learning applications within the Internet of Medical Things (IoMT). However, IDSs face challenges owing to the sheer volume of network traffic, high-dimensional datasets and the necessity for real-time detection. Although machine learning integration assists IDSs in overcoming these challenges, modelling difficulties persist due to varied evaluation criteria and levels of conflict and importance. Multi-criteria decision-making (MCDM) solutions have been utilised in IoMT and IDS, yet they fall short in capturing the subjective judgements of experts and rely on normalisation approaches, which can impact results. This study seeks to address these issues through the integration of robust MCDM methodologies, namely fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision by opinion score method (FDOSM). Utilising rough Fermatean fuzzy sets (RFFSs), this in.....

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The operation of autonomous underwater vehicles (AUVs) relies on three major motions: yaw, theta, and depth, each requiring its own set of proportional integral derivative (PID) controller gain criteria. Thus, different issues arise, including the availability of multiple criteria for optimization algorithm evaluation, the importance of these criteria, the trade-off between criterion performance, and criterion critical values. These issues make the evaluation of optimization algorithms for AUV motion control a complex multicriteria decision-making (MCDM) problem. This research proposes a novel selection-integrated approach for AUV optimization algorithms in different motions using two MCDM methods: fuzzy-weighted zero-inconsistency (FWZIC) for criteria weighting and fuzzy decision by opinion score method (FDOSM) for optimization algorithm selection. The approach comprises three main phases: development of PID, FWZIC-based criteria weighting, and FDOSM-based optimization algorithm selec.....

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The artificial intelligence (AI) trend to embrace Autism Spectrum Disorder (ASD) has dramatically transformed the landscape of medical diagnosis. People often exhibit fear and apprehension towards conditions they lack understanding of, and ASD being a complex affliction, poses challenges in comprehending its intricacies. Researchers have harnessed AI applications to improve the precision of disease diagnosis by utilizing Magnetic Resonance Imaging (MRI), Electroencephalography (EEG), genetic, sociodemographic, and medical data. However, the development of AI systems for early diagnosis and triage in healthcare is still in its nascent stages. In particular, studies have revealed a global increase in the prevalence of ASD, with an estimated 1 in 59 children being diagnosed. However, there is a lack of up-to-date information regarding the current status of ASD. This study aims to provide a systematic review of AI applications in early diagnosis and triage for ASD, supplementing the findin.....

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This study proposed a novel methodology of data acquisition systems (DASs) benchmarking based on fuzzy-weighted zero-inconsistency (FWZIC II) and fuzzy decision by opinion score method (FDOSM II), which are applied in an intuitionistic fuzzy set (IFS) context and account for hesitation when benchmarking DASs, to support industrial community characteristics in the design and implementation of advanced driver assistance systems in vehicles. The proposed methodology comprises two consecutive phases. The first phase involves constructing a decision matrix based on the intersection of the DAS alternatives and criteria. The second phase (development phase) proposes the formulation of a novel FWZIC II to weight the criteria and the formulation of a novel FDOSM II to benchmark DASs. Fourteen DASs were benchmarked based on the 15 DAS criteria, which included seven sub-criteria for comprehensive complexity assessment and eight sub-criteria for design and implementation, which had a significant e.....

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When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemics main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science a.....

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