This study presents a benchmarking and evaluation approach for active queue management (AQM) network congestion control methods, which are considered as a problem of multi-criteria decision-making (MCDM). In recent years, the development of MCDM methods has been studied from various perspectives. The latest one called fuzzy decision by opinion score method (FDOSM) has proved its efficiency in solving the concerns faced by other methods. However, the approach of FDOSM and its extension is based on fuzzy type-1, which suffers from issues, especially minimising the effect of data uncertainties. Therefore, this study extended FDOSM into a fuzzy type-2 environment that utilises interval type-2 trapezoidal (IT2T) membership, and then discusses the effectiveness of such membership on AQM method benchmarking. The methodology of this study involves two consecutive phases. The first phase is the construction of a decision matrix utilised in AQM method benchmarking based on a list of AQM methods .....

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Context and background: Corona virus (COVID) has rapidly gained a foothold and caused a global pandemic. Particularists try their best to tackle this global crisis. New challenges outlined from various medical perspectives may require a novel design solution. Asymptomatic COVID-19 carriers show different health conditions and no symptoms; hence, a differentiation process is required to avert the risk of chronic virus carriers. Objectives: Laboratory criteria and patient dataset are compulsory in constructing a new framework. Prioritisation is a popular topic and a complex issue for patients with COVID-19, especially for asymptomatic carriers due to multi-laboratory criteria, criterion importance and trade-off amongst these criteria. This study presents new integrated decision-making framework that handles the prioritisation of patients with COVID-19 and can detect the health conditions of asymptomatic carriers. Methods: The methodology includes four phases. Firstly, eight important lab.....

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A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several is.....

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The benchmarking of smart e-tourism data management applications falls under the problem of multicriteria decision-making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision-making framework on the basis of the extension of interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e-tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e-tourism data management applications of each category and subcategory in smart e-tourism. In the second phase, the integratio.....

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The brain?computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current top.....

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