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The objective of this paper is to propose a novel dual-level multisource information fusion approach for handling open multicriteria decision-making (MCDM) issues related to accurately determining the weights of criteria while considering the relative importance of expert opinions, handling heterogeneous information, and selecting an appropriate MCDM ranking method. The proposed approach includes two levels. The first integrates the "analytic hierarchy process (AHP)—full consistency method (FUCOM)", which comprises two phases. In the first phase, criteria weights are assigned using AHP, and FUCOM is employed to correct inconsistencies. In the second phase, the weights are computed for decision-makers. Triangular fuzzy numbers are employed due to their ability to encapsulate all the utilized heterogeneous data types. At the second level of the approach, three well-known distance-based methods, including "technique for order preference by similarity to ideal solution", "visekriterijumska optimizacija i kompromisno resenje", and "multiattributive border approximation area comparison", are fused to process a homogeneous decision matrix and provide comprehensive and robust rankings for alternatives. To develop our novel approach, a renewable energy source for Pakistan's electricity generation is adopted as a case study. The decision matrix contains four alternatives (i.e., hydropower, biomass energy, solar energy, and wind energy) and six main evaluation criteria (i.e., economic, quality of energy, social, political, environmental, and technical), with several subevaluation criteria under each criterion. The findings of the proposed approach were as follows: A2 was in the first rank with a score of 1.7889, and A4 was in the last rank with a score of 0.1199. The rest of the alternatives were distributed between them. The paper's implications include the advancement of decision-making methods, enhancement of decision-making outcomes, and addressing heterogeneous information to highlight the relative importance of experts. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

This study delves into the complex prioritization process for Autism Spectrum Disorder (ASD), focusing on triaged patients at three urgency levels. Establishing a dynamic prioritization solution is challenging for resolving conflicts or trade-offs among ASD criteria. This research employs fuzzy multi-criteria decision making (MCDM) theory across four methodological phases. In the first phase, the study identifies a triaged ASD dataset, considering 19 critical medical and sociodemographic criteria for the three ASD levels. The second phase introduces a new Decision Matrix (DM) designed to manage the prioritization process effectively. The third phase focuses on the new extension of Fuzzy-Weighted Zero-Inconsistency (FWZIC) to construct the criteria weights using Single-Valued Neutrosophic 2-tuple Linguistic (SVN2TL). The fourth phase formulates the Multi-Attributive Border Approximation Area Comparison (MABAC) method to rank patients within each urgency level. Results from the SVN2TL-FWZIC weights offer significant insights, including the higher criteria values "C12 = Laughing for no reason" and "C16 = Notice the sound of the bell" with 0.097358 and 0.083832, indicating their significance in identifying potential ASD symptoms. The SVN2TL-FWZIC weights offer the base for prioritizing the three triage levels using MABAC, encompassing medical and behavioral dimensions. The methodology undergoes rigorous evaluation through sensitivity analysis scenarios, confirming the consistency of the prioritization results with critical analysis points. The methodology compares with three benchmark studies, using four distinct points, and achieves a remarkable 100% congruence with these prior investigations. The implications of this study are far-reaching, offering a valuable guide for clinical psychologists in prioritizing complex cases of ASD patients. [ABSTRACT FROM AUTHOR]

Brain–computer interfacing (BCI) research, fueled by deep learning, integrates insights from diverse domains. A notable focus is on steady-state visual evoked potential (SSVEP) in BCI applications, requiring in-depth assessment through deep learning. EEG research frequently employs SSVEPs, which are regarded as normal brain responses to visual stimuli, particularly in investigations of visual perception and attention. This paper tries to give an in-depth analysis of the implications of deep learning for SSVEP-adapted BCI. A systematic search across four stable databases (Web of Science, PubMed, ScienceDirect, and IEEE) was developed to assemble a vast reservoir of relevant theoretical and scientific knowledge. A comprehensive search yielded 177 papers that appeared between 2010 and 2023. Thence a strict screening method from predetermined inclusion criteria finally generated 39 records. These selected works were the basis of the study, presenting alternate views, obstacles, limitations and interesting ideas. By providing a systematic presentation of the material, it has made a key scholarly contribution. It focuses on the technical aspects of SSVEP-based BCI, EEG technologies and complex applications of deep learning technology in these areas. The study delivers more penetrating reporting on the latest deep learning pattern recognition techniques than its predecessors, together with progress in data acquisition and recording means suitable for SSVEP-based BCI devices. Especially in the realms of deep learning technology orchestration, pattern recognition techniques, and EEG data collection, it has effectively closed four important research gaps. To increase the accessibility of this critical material, the results of the study take the form of easy-to-read tables just generated. Applying deep learning techniques in SSVEP-based BCI applications, as the research shows, also has its downsides. The study concludes that a radical framework will be presented which, includes intelligent decision-making tools for evaluation and benchmarking. Rather than just finding a comparable or similar analogy, this framework is intended to help guide future research and pragmatic applications, and to determine which SSVEP-based BCI applications have succeeded at responsibility for what they set out with. [ABSTRACT FROM AUTHOR]

In response to the burgeoning interest in the Metaverse—a virtual reality-driven immersive digital world—this study delves into the pivotal role of AI in shaping its functionalities and elevating user engagement. Focused on recent advancements, prevailing challenges, and potential future developments, our research draws from a comprehensive analysis grounded in meticulous methodology. The study, informed by credible sources including SD, Scopus, IEEE, and WoS, encompasses 846 retrieved studies. Through a rigorous selection process, 54 research papers were identified as relevant, forming the basis for a specific taxonomy of AI in the Metaverse. Our examination spans diverse dimensions of the Metaverse, encompassing augmented reality, virtual reality, mixed reality, Blockchain, Agent Systems, Intelligent NPCs, Societal and Educational Impact, HCI and Systems Design, and Technical Aspects. Emphasizing the necessity of adopting trustworthy AI in the Metaverse, our findings underscore its potential to enhance user experience, safeguard privacy, and promote responsible technology use. This paper not only sheds light on the scholarly interest in the Metaverse but also explores its impact on human behavior, education, societal norms, and community dynamics. Serving as a foundation for future development and responsible implementation of the Metaverse concept, our research identifies and addresses seven open issues, providing indispensable insights for subsequent studies on the integration of AI in the Metaverse. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become a crucial element of modern technology, especially in the healthcare sector, which is apparent given the continuous development of large language models (LLMs), which are utilized in various domains, including medical beings. However, when it comes to using these LLMs for the medical domain, there's a need for an evaluation platform to determine their suitability and drive future development efforts. Towards that end, this study aims to address this concern by developing a comprehensive Multi-Criteria Decision Making (MCDM) approach that is specifically designed to evaluate medical LLMs. The success of AI, particularly LLMs, in the healthcare domain, depends on their efficacy, safety, and ethical compliance. Therefore, it is essential to have a robust evaluation framework for their integration into medical contexts. This study proposes using the Fuzzy-Weighted Zero-InConsistency (FWZIC) method extended to p, q-quasirung orthopair fuzzy set (p, q-QROFS) for weighing evaluation criteria. This extension enables the handling of uncertainties inherent in medical decision-making processes. The approach accommodates the imprecise and multifaceted nature of real-world medical data and criteria by incorporating fuzzy logic principles. The MultiAtributive Ideal-Real Comparative Analysis (MAIRCA) method is employed for the assessment of medical LLMs utilized in the case study of this research. The results of this research revealed that "Medical Relation Extraction" criteria with its sub-levels had more importance with (0.504) than "Clinical Concept Extraction" with (0.495). For the LLMs evaluated, out of 6 alternatives, ( A 4 ) "GatorTron S 10B" had the 1st rank as compared to ( A 1 ) "GatorTron 90B" had the 6th rank. The implications of this study extend beyond academic discourse, directly impacting healthcare practices and patient outcomes. The proposed framework can help healthcare professionals make more informed decisions regarding the adoption and utilization of LLMs in medical settings. [ABSTRACT FROM AUTHOR]

Evaluating remote sensing techniques (RST)-based water quality monitoring systems in hydrological applications is a complex multi-attribute decision-making (MADM) problem due to several integrated issues. These issues include the need to consider 15 evaluation criteria, the importance of these criteria in relation to their application and network infrastructure, data variation, and trade-offs and conflicts between them. Therefore, this study proposes an MADM integrated modelling approach to weight these criteria and rank the selected RSTs using the "Fuzzy Weighted with Zero Inconsistency" (FWZIC) method coupled with the "Vlse-kriterijumska Optimizcija I Kaompromisno Resenje" (VIKOR) method. First, a decision matrix was developed for the evaluation criteria and their intersection with various RSTs in different network categories. Next, the assessment criteria were weighted using FWZIC, followed by ranking the RSTs for each category using VIKOR. The findings reveal that the criteria weighting varied across categories, as assigned by specialists-based FWZIC. For example, the "data acquisition system complexity" criterion received the maximum weight (w = 0.081) for mesh-based sensing and the lowest (w = 0.057) for cellular-based sensing. Using the obtained weights and based on the ascending order of the performance score value (Q), various RSTs were benchmarked using VIKOR. This includes (n = 22) RSTs in fixed star, (n = 8) RSTs in mesh sensing, (n = 6) RSTs in cellular sensing, (n = 3) RSTs in fixed cable sensing, and (n = 2) RSTs in movable sensing. Finally, the evaluation process for the benchmarked RSTs involved systematic ranking, sensitivity analysis, and comparison analysis, which confirmed the robustness of the proposed approach across all RST categories. [ABSTRACT FROM AUTHOR]

For optimal agricultural and other soil utilization, microbial structure analysis is essential. Sabkhat Madinah, Saudi Arabia, boasts high-salinity soil and thriving vegetation. The microbial makeup of the soil in this area is novel and has not been extensively studied. This study aimed to analyze the microbial structure of two locations in Sabkhat Madinah's soil and examine the correlation with soil properties. Soils microbiota were investigated using Illumina sequencing of bacterial 16S rRNA genes. The soil chemical properties were determined. The microbial composition of both sites was investigated at different taxonomic levels. There was a significant difference in salt concentration between the two sites derived from the same sabkha. The second sample had higher sodium content, leading to increased electrical conductivity (EC) measures. Moreover, the samples had different microbial compositions. The first sample was dominated by Bacteroidetes (18.37%), Firmicutes (13.57%), Proteobacteria (13.57%), and Actinobacteria (9.30%), while the second one contained Bacteroidetes (8.96%), Proteobacteria (25.01%), Actinobacteria (12.03%), and Firmicutes (11.36%). Firmicutes were newly recorded and found only in saline habitats in Saudi Arabia. Acidobacteria, Thermodesulfobacteria and Streptophyta were only in the first sample, while Verrucomicrobia sequences were identified only in the second sample which had higher salt content. On the genus level, 16 genera were found across both samples, with Bacillus being the most prevalent at 5.17% followed by Marinoscillum (4%), Fibrobacter (3.57%) and Rubrobacter (3.45%) in the first sample. The second soil sample had a dominant genus, Halomonas, making up 10.64% of the total sequences. Other genera present included Fibrobacter (3.96%), Nitrospira (3.92%), Rubrobacter (3.36%), and Methylophaga (3%). To conclude, the analysis of bacteria in the two sites revealed notable differences in soil properties and bacterial diversity. This study provides insight into the complex microbial compositions of saline soil and a foundation for future studies. Understanding these bacteria' genetics can improve soils or help crops thrive in saline environments. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cell (MSC) transfusion has shown promising results in treating COVID-19 cases despite the limited availability of these MSCs. The task of prioritizing COVID-19 patients for MSC transfusion based on multiple criteria is considered a multi-attribute decision-analysis (MADA) problem. Although literature reviews have assessed the prioritization of COVID-19 patients for MSCs, issues arising from imprecise, unclear and ambiguous information remain unresolved. Compared with the existing MADA methods, the robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy-weighted zero inconsistency (FWZIC) is proven. This study adopts and integrates FDOSM and FWZIC in a homogeneous Fermatean fuzzy environment for criterion weighting followed by the prioritization of the most eligible COVID-19 patients for MSC transfusion. The research methodology had two phases. The decision matrices of three COVID-19 emergency levels (moderate, severe, and critical) were adopted based on an augmented dataset of 60 patients and discussed in the first phase. The second phase was divided into two subsections. The first section developed Fermatean FWZIC (F-FWZIC) to weigh criteria across each emergency level of COVID-19 patients. These weights were fed to the second section on adopting Fermatean FDOSM (F-FDOSM) for the purpose of prioritizing COVID-19 patients who are the most eligible to receive MSCs. Three methods were used in evaluating the proposed works, and the results included systematic ranking, sensitivity analysis, and benchmarking checklist. [ABSTRACT FROM AUTHOR]

Numerous nations have prioritised the inclusion of citizens with disabilities, such as hearing loss, in all aspects of social life. Sign language is used by this population, yet they still have trouble communicating with others. Many sign language apps are being created to help bridge the communication gap as a result of technology advances enabled by the widespread use of smartphones. These apps are widely used because they are accessible and inexpensive. The services and capabilities they offer and the quality of their content, however, differ greatly. Evaluation of the quality of the content provided by these applications is necessary if they are to have any kind of real effect. A thorough evaluation like this will inspire developers to work hard on new apps, which will lead to improved software development and experience overall. This research used a systematic literature review (SLR) method, which is recognised in gaining a broad understanding of the study whilst offering additional information for future investigations. SLR was adopted in this research for smartphone-based sign language apps to understand the area and main discussion aspects utilised in the assessment. These studies were reviewed on the basis of related work analysis, main issues, discussions and methodological aspects. Results revealed that the evaluation of sign language mobile apps is scarce. Thus, we proposed a future direction for the quality assessment of these apps. The findings will benefit normal-hearing and hearing-impaired users and open up a new area where researchers and developers could work together on sign language mobile apps. The results will help hearing and non-hearing users and will pave the way for future collaboration between academicians and app developers in the field of sign language technology. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) presents challenges for accurate diagnosis, prompting researchers to search for an optimal diagnostic process. Feature selection (FS) approaches and classification methods considering medical tests and socio-demographic characteristics are crucial for diagnosing autism. However, evaluating and benchmarking hybrid diagnosis machine learning (ML) models in the presence of multiple evaluation performance metrics, criteria trade-offs, and varying criteria importance present complex multi-criteria decision-making (MCDM) problems. This study proposes a three-phase methodology integrating FS, ML, and fuzzy MCDM to develop and evaluate diagnosis models. Firstly, an ASD dataset combining medical tests and socio-demographic characteristics is identified and preprocessed. Secondly, 72 hybrid diagnosis models are developed by combining eight FS techniques and nine ML algorithms using an intersection process. Thirdly, the following steps are performed: (i) A decision matrix is formulated based on nine evaluation metrics, including classification accuracy (CA), specificity, precision, F1 score, recall, test time, train time, log loss, and area under the curve (AUC); (ii) a new extension of fuzzy-weighted zero inconsistency is developed using 2-tuple linguistic neutrosophic fuzzy sets (2TLNFSs) to assign weights to the evaluation metrics criteria and address related issues; (iii) a new extension of the fuzzy decision-by-opinion score method is developed using 2TLNFSs as well to benchmark the 72 models. Results indicate that the selected FS techniques vary in the number of features chosen, with the sets ranging from 19 to 46 out of the 48 available features. Socio-demographic features were predominantly selected over medical tests. Regarding the evaluation and benchmarking results, the weights constructed by three experts suggest that CA holds high importance, precision and recall are assigned equal weights, and AUC and test time carry moderate weights. At the same time, F1 and log loss are considered less crucial in the decision-making process. Specificity and train time are assigned relatively lower weights, indicating their lower importance. The best-performing hybrid model identified was sequential feature selection/logistic regression (SFS/LR)-decision tree, with a score value of 4.3964. Decision trees and gradient boosting consistently achieved high rankings, demonstrating their effectiveness in diagnosing ASD, while SVM, random forest, and logistic regression showed mixed results across different hybrid models. The sensitivity analysis assessments were conducted to verify the efficiency of the proposed evaluation and benchmarking methodology. We benchmarked the proposed framework against three other benchmark studies and achieved a score of 100% across five key areas. The developed methodology can potentially advance and accelerate the selection of diagnostic tools for ASD therapy, benefiting individuals with ASD. [ABSTRACT FROM AUTHOR]

In recent years, calls have increased for adherence to standards that ensure sustainability, including the global initiative presented by the United Nations with 17 Sustainable Development Goals (SDGs) to ensure a more sustainable future. Achieving these goals is extremely important, as institutions have sought to integrate technology, especially business intelligence, into their operations to ensure their achievement. This study aims to provide a systematic literature review of the intersection of information systems and sustainability in business intelligence. Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology was utilized to select high-quality studies from various databases, including ScienceDirect, IEEE Xplore, and Scopus, to be included in this review. The methodology resulted in 32 studies taxonomized into four main categories covering different aspects of the intersection of information systems and sustainability. This study discusses integrating information systems and sustainability in various sectors, such as tourism, health, urban, and other sectors, with different technologies, such as Blockchain, IoT, Industry 4.0, and other innovations. Moreover, the information system types implemented to support sustainability practices in different domains are highlighted. [ABSTRACT FROM AUTHOR]

The present study investigated the microbiological safety of the increasingly popular plant‐based milk alternatives. No (10/27) or only very low microbial counts (17/27) were detected in the tested products. These were mainly identified as spore formers via MALDI‐ToF‐MS. Three products contained Bacillus cereus group isolates, which were able to form considerable amounts of enterotoxins and exhibited cytotoxicity towards CaCo‐2 cells. Preliminary tests showed good growth of B. cereus, Listeria monocytogenes, and Salmonella enterica in all tested products (maximum bacterial counts: 5 × 1012 cfu/mL). These experiments also revealed strain‐, time‐, and temperature‐, but especially product‐specific enterotoxin production of B. cereus. In propagation and persistence tests according to DIN EN ISO 20976‐1:2019‐09, rapid bacterial proliferation was also detected in all products. B. cereus generally showed lower bacterial counts (106–107 cfu/mL) compared to L. monocytogenes and S. enterica (108–109 cfu/mL), but was detectable in a larger number of products over the test period of 6 weeks. pH values decreased (20/27) over time and visual and/or olfactory alterations (24/27) were observed. The present study provides information on the occurrence, growth and persistence of pathogenic bacteria in plant‐based drinking milk alternatives. It also points out that the accompanying changes in pH, odor, and appearance are not necessarily recognizable to the consumer. Practical Application: The present study contributes to the understanding of the microbial risk related to plant‐based drinking milk alternatives. It is crucial that the manufacturer ensures that particularly spore formers have been effectively eliminated from the products. Among them, especially toxin‐producing bacteria can pose a risk to the consumer, as these products promote proliferation and persistence of the bacteria. [ABSTRACT FROM AUTHOR]

Mobile apps for sign language are a fascinating area of research that merits a lot of attention. These apps are widely used due to their affordability and usability. Nevertheless, the quality of these apps needs to be evaluated if they are to have a significant influence. The outcomes of app quality assessment can also stimulate app development efforts and improvements to their functionality and accuracy. Therefore, to gain more in depth understanding, four academic databases (Science Direct, IEEE, Association for Computing Machinery, and Web of Science) were searched as part of a systematic literature review. The review highlighted that sign language apps require further development to support users in achieving the greatest possible usage and learning experience. We present a solution framework based on a quality assessment criterion for sign language mobile apps that could be adopted by academics and developers. Alternative identification, criteria, and development are the three primary aspects of the framework. The results demonstrate that the new assessment approach can establish a detailed set of criteria for sign language mobile app development. Future academics could gain a thorough understanding of mobile app development criteria from this study. The results are advantageous and open up a new field where researchers and developers could collaborate on sign language mobile app development. [ABSTRACT FROM AUTHOR]

This case report describes a new digital workflow for computer-assisted implant surgery in an edentulous patient using transitional implants to support a fixed surgical template and interim prosthesis. The accuracy of the final implant position using the described protocol was evaluated and compared to the outcomes obtained using other types of surgical templates. This novel digital approach appears to enhance the accuracy of implant positioning for edentulous patients and seems to be comparable to a tooth-supported surgical template. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition arising from a dysregulated host immune response to infection, leading to a substantial global health burden. The accurate identification of bacterial pathogens in sepsis is essential for guiding effective antimicrobial therapy and optimising patient outcomes. Traditional culture-based bacterial typing methods present inherent limitations, necessitating the exploration of alternative diagnostic approaches. This study reports the successful application of Fourier-transform infrared (FT-IR) spectroscopy in combination with chemometrics as a potent tool for the classification and discrimination of microbial species and strains, primarily sourced from individuals with invasive infections. These samples were obtained from various children with suspected sepsis infections with bacteria and fungi originating at different sites. We conducted a comprehensive analysis utilising 212 isolates from 14 distinct genera, comprising 202 bacterial and 10 fungal isolates. With the spectral analysis taking several weeks, we present the incorporation of quality control samples to mitigate potential variations that may arise between different sample plates, especially when dealing with a large sample size. The results demonstrated a remarkable consistency in clustering patterns among 14 genera when subjected to principal component analysis (PCA). Particularly, Candida, a fungal genus, was distinctly recovered away from bacterial samples. Principal component discriminant function analysis (PC-DFA) allowed for distinct discrimination between different bacterial groups, particularly Gram-negative and Gram-positive bacteria. Clear differentiation was also observed between coagulase-negative staphylococci (CNS) and Staphylococcus aureus isolates, while methicillin-resistant S. aureus (MRSA) was also separated from methicillin-susceptible S. aureus (MSSA) isolates. Furthermore, highly accurate discrimination was achieved between Enterococcus and vancomycin-resistant enterococci isolates with 98.4% accuracy using partial least squares-discriminant analysis. The study also demonstrates the specificity of FT-IR, as it effectively discriminates between individual isolates of Streptococcus and Candida at their respective species levels. The findings of this study establish a strong groundwork for the broader implementation of FT-IR and chemometrics in clinical and microbiological applications. The potential of these techniques for enhanced microbial classification holds significant promise in the diagnosis and management of invasive bacterial infections, thereby contributing to improved patient outcomes. [ABSTRACT FROM AUTHOR]

Plants are being researched as potential sources of novel drugs, which has led to a recent acceleration in the discovery of new bioactive compounds. Research on tissue culture technology for the synthesis and processing of plant compounds has skyrocketed, surpassing all expectations. These plants can be bought either raw or as extracts, where some of the chemicals are extracted by mashing the plant in water, alcohol, or another solvent. The use of herbal medicine may open new chances for reducing the onset of infections and treating different diseases including cancer. A perennial plant that blooms in the winter, Cyclamen, is one of the most widely used potted flowers in many nations. Alkaloids, flavonoids, phenols, tannins, saponins, sterols, and glycosides are the main active components of Cyclamen. Analgesic, cytotoxic, antioxidant, antimicrobial, and anti-inflammatory properties have all been demonstrated as potential effects of various extracts of Cyclamen tubers. However, the use of this medicinal plant in official medicine will require further research in the areas of pharmacology. Furthermore, it is necessary to create standard operating procedures for a crude herbal medication. In this regard, this review aims to highlight the key characteristics of the Cyclamen plant, such as its various parts, species, stages of development, and geographic range; pinpoint its intriguing bioactivities, its antioxidant, anti-inflammatory, and its anti-cancerous effects; and ascertain its potential medicinal uses and the main future perspectives. [ABSTRACT FROM AUTHOR]

Benchmarking brain–computer interface (BCI) applications, considering all available smart training environment (STE) criteria, is a challenging task due to the following issues: inconsistent weights, static ranks and ranking stability measurements. Therefore, this study aims to develop a dynamic decision-making framework for benchmarking BCI applications based on STE criteria through three integrated phases. In the first phase, the adaptivity of the decision matrix is identified concerning two dimensions: 27 BCI applications as alternatives and 10 STE criteria. In the second phase, the consistency of weights is evaluated and constructed to each STE criterion via the fuzzy-weighted zero-inconsistency (FWZIC) method and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method for benchmarking the BCI applications. In the third phase, four sensitive scenarios are developed for measuring the consistency of the STE criteria's weights and the ranking performance of the BCI applications. The experimental result shows that the 'ease of use' STE criterion obtains a high-affected weight with a value of 0.13, while other criteria, augmented reality, hybrid and desktop use (stationary), obtain less weight with a 0.075 value. Additionally, BCI applications A5 and A6 are robust and stable among the others based on the consistency of weights concerning the four scenarios, and they are further candidates to be deployed in real-life applications. The overall ranking results are stable and less affected when applied to the four sensitive scenarios due to the robustness of the integrated FWZIC-VIKOR method of the proposed dynamic framework. The outcome of this framework is objectively validated in terms of five groups, and the ranking results are reliable and the closest to the decision-makers' viewpoints. The proposed framework considers a good solution for choosing a dependable application to support the user and community of BCI systems with a stable STE environment. [ABSTRACT FROM AUTHOR]

Globally, cancer is one of the primary causes of both morbidity and mortality. To prevent cancer from getting worse, more targeted and efficient treatment plans must be developed immediately. Recent research has demonstrated the benefits of natural products for several illnesses, and these products have played a significant role in the development of novel treatments whose bioactive components serve as both chemotherapeutic and chemo-preventive agents. Phytochemicals are naturally occurring molecules obtained from plants that have potential applications in both cancer therapy and the development of new medications. These phytochemicals function by regulating the molecular pathways connected to the onset and progression of cancer. Among the specific methods are immune system control, inducing cell cycle arrest and apoptosis, preventing proliferation, raising antioxidant status, and inactivating carcinogens. A thorough literature review was conducted using Google Scholar, PubMed, Scopus, Google Patent, Patent Scope, and US Patent to obtain the data. To provide an overview of the anticancer effects of several medicinal plants, including Annona muricata, Arctium lappa, Arum palaestinum, Cannabis sativa, Catharanthus roseus, Curcuma longa, Glycyrrhiza glabra, Hibiscus, Kalanchoe blossfeldiana, Moringa oleifera, Nerium oleander, Silybum marianum, Taraxacum officinale, Urtica dioica, Withania somnifera L., their availability, classification, active components, pharmacological activities, signaling mechanisms, and potential side effects against the most common cancer types were explored. [ABSTRACT FROM AUTHOR]

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients' data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients' score and dose availability without sharing the patients' data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death. [ABSTRACT FROM AUTHOR]

Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results. [ABSTRACT FROM AUTHOR]

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 integration produces precise solutions with reduced uncertainty. Our methodology involves adopting a decision matrix for IDS classifiers based on integrated evaluation criteria, followed by deriving new formulations and developments for RFFSs-based FDOSM and FWZIC for the modelling and weighting of criteria, respectively. Evaluations using datasets involving 125,973 records and 41 features across 17 evaluation criteria revealed that accuracy–security and training time–performance weights yielded the highest scores, whereas false negative rate–security and CPU time–performance criteria received the lowest weights. The random forest emerged as the optimal IDS classifier. Systematic modelling, sensitivity analysis and comparative studies confirmed the robustness of our results. [ABSTRACT FROM AUTHOR]

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 findings of previous studies and offering a comprehensive overview of the evidence. To achieve this, a rigorous literature search method and selection criteria were employed, resulting in the identification of 46 recent contributions on the applications of AI in ASD from various databases, including ScienceDirect (SD), IEEE Xplore digital library (IEEE), Web of Science (WOS), PubMed, and Scopus. The selected papers were categorized into three main categories: ASD triage levels, clinical diagnosis for ASD, and diagnosis based on telemedicine, with further subcategories under the clinical diagnosis category. Theoretical and practical aspects of AI methods used for ASD diagnosis, as well as the presentation utilizing data analytics, were presented. The paper presents a systematic and comprehensive analysis of previous studies, examining the challenges, motivations, and recommendations, thereby paving the way for potential future research. Additionally, the work provides decisive evidence for the use of AI in ASD healthcare diagnosis and triage, offering nine critical analyses of the current state-of-the-art and addressing relevant research gaps. To the best of our knowledge, this study is innovative in exploring the feasibility of using AI in ASD medical diagnosis and triage. It highlights essential pieces of information, including Explainable AI (XAI), Auto machine learning (AutoML), Internet of Things (IoT)-based AI, robot-assisted therapy-based AI, telemedicine, data fusion techniques, and available ASD datasets with different aspects. The analysis of the revised contributions reveals crucial implications for academics and practitioners. The paper also proposes potential methodological aspects to enhance the triage and prioritization of autistic patients using AI applications in the medical sector, as well as addressing theoretical and practical application aspects and five methodology phases using fuzzy Multi-Criteria Decision Making (MCDM) methods in ASD triage and prioritization. [ABSTRACT FROM AUTHOR]

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 effect on the DAS design when implemented by industrial communities. A systematic ranking and sensitivity analysis were conducted to demonstrate that the benchmarking results were subject to systematic ranking, as indicated by the high correlations across all described scenarios of changing criteria weight values. [ABSTRACT FROM AUTHOR]

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 pandemic's 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 along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID‐19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters. [ABSTRACT FROM AUTHOR]

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 uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, whereas 'misclassification error' has the lowest weight. In the ranking results, a variation of ranking is scored by each expert, and group decision-making is applied to solve the individual ranking variety. The robustness of the formulated methods is evaluated using systematic ranking, sensitivity analysis and comparison analysis. [ABSTRACT FROM AUTHOR]

Pesticides have significant adverse effects on human health such as carcinogenesis, immunotoxicity, birth defects, neurological toxicity, and endocrine disruption. Therefore, it is very important to monitor pesticides in fruits and vegetables and to assess if they cause a risk to human health. The present study is to isolate and detect the content of pesticides residue in the locally grown vegetables by using sensitive liquid chromatography-mass spectrometry (LC–MS). Four vegetable samples were collected from local farms of Salalah and Al Sharqiyah and were cleaned and chopped. The chopped sample of each vegetable sample was taken in a heavy-duty tube and then mixed with ethyl acetate, magnesium sulfate, and sodium sulfate. The extracts were evaporated and injected into the LC–MS injector. A total of 382 pesticides were analyzed by using the same. The results showed that all the collected samples from Salalah and Al Sharqiyah contained pesticides from the concentration range of 0.011 to 0.595 mg/kg. The range of pesticides concentrations of vegetables collected from Salalah was 0.011 to 0.099 mg/kg, and the range of Al Sharqiyah's vegetables was 0.011 to 0.595 mg/kg. Among the collected vegetables, the highest content of acetamiprid pesticide was in lettuce and the lowest content of acetamiprid pesticide was in cucumber and the lowest to highest order was cucumber > tomato > eggplants > lettuce. In conclusion, the concentration of pesticides in all vegetable samples were within the permissible limit. Therefore, the results of this study showed that the detected pesticides were not exposed to potential health risks through the consumption of fruits and vegetables. [ABSTRACT FROM AUTHOR]

Existing methods for scoring student presentations predominantly rely on computer-based implementations and do not incorporate a robotic multi-classification model. This limitation can result in potential misclassification issues as these approaches lack active feature learning capabilities due to fixed camera positions. Moreover, these scoring methods often solely focus on facial expressions and neglect other crucial factors, such as eye contact, hand gestures and body movements, thereby leading to potential biases or inaccuracies in scoring. To address these limitations, this study introduces Robotics-based Presentation Skill Scoring (RPSS), which employs a multi-model analysis. RPSS captures and analyses four key presentation parameters in real time, namely facial expressions, eye contact, hand gestures and body movements, and applies the fuzzy Delphi method for criteria selection and the analytic hierarchy process for weighting, thereby enabling decision makers or managers to assign varying weights to each criterion based on its relative importance. RPSS identifies five academic facial expressions and evaluates eye contact to achieve a comprehensive assessment and enhance its scoring accuracy. Specific sub-models are employed for each presentation parameter, namely EfficientNet for facial emotions, DeepEC for eye contact and an integrated Kalman and heuristic approach for hand and body movements. The scores are determined based on predefined rules. RPSS is implemented on a robot, and the results highlight its practical applicability. Each sub-model is rigorously evaluated offline and compared against benchmarks for selection. Real-world evaluations are also conducted by incorporating a novel active learning approach to improve performance by leveraging the robot's mobility. In a comparative evaluation with human tutors, RPSS achieves a remarkable average agreement of 99%, showcasing its effectiveness in assessing students' presentation skills. [ABSTRACT FROM AUTHOR]

Brain-computer interfaces (BCIs) based on motor imagery (MI) face challenges due to the complex nature of brain activity, nonstationary and high-dimensional properties, and individual variations in motor behaviour. The identification of a consistent "golden subject" in MI-based BCIs remains an open challenge, complicated by multiple evaluation metrics and conflicting trade-offs, presenting complex Multi-Criteria Decision Making (MCDM) problems. This study proposes a hybrid brain signal decoding model called Hybrid Adaboost Feature Learner (HAFL), which combines feature extraction and classification using VGG-19, STFT, and Adaboost classifier. The model is validated using a pre-recorded MI-EEG dataset from the BCI competition at Graz University. The fuzzy decision-making framework is integrated with HAFL to allocate a golden subject for MI-BCI applications through the Golden Subject Decision Matrix (GSDM) and the Fuzzy Decision by Opinion Score Method (FDOSM). The effectiveness of the HAFL model in addressing inter-subject variability in EEG-based MI-BCI is evaluated using an MI-EEG dataset involving nine subjects. Comparing subject performance fairly is challenging due to complexity variations, but the FDOSM method provides valuable insights. Through FDOSM-based External Group Aggregation (EGA), subject S5 achieves the highest score of 2.900, identified as the most promising golden subject for subject-to-subject transfer learning. The proposed methodology is compared against other benchmark studies from various key perspectives and exhibits significant novelty in several aspects. The findings contribute to the development of more robust and effective BCI systems, paving the way for advancements in subject-to-subject transfer learning for BCI-MI applications. [ABSTRACT FROM AUTHOR]

Background: Telemedicine is becoming an integral part of healthcare. Training medical students in telemedicine is encouraged by many medical organizations. However, in the United Arab Emirates in particular, most medical schools have not incorporated it into their curriculum. Therefore, this study aims to assess medical students' perceptions and interest in telemedicine teaching at the University of Sharjah, UAE. Methods: A questionnaire-based survey was built based on the current literature and was distributed to all medical students at the University of Sharjah between February and March 2023. The questionnaire assessed the participants for their demographic data, access to and use of digital devices, exposure to and beliefs related to telemedicine, and their medical school experience with distance learning and telemedicine. The data were analyzed via simple statistics, and the Chi-square test was used to assess the associated factors affecting the participants' interest in receiving telemedicine teaching. Results: The questionnaire had a 70.4% (547/777) response rate. The mean age (SD) of the participants was 20.7 years (1.57), and the majority were female (68.4%). Over 98% of the students reported having easy access to and being comfortable with using computers and the internet. Most students (90.5%) believed that the medical school curriculum should include teaching in telemedicine; however, 78.2% of these students stated that it should be included as an elective course. The participants' interest in receiving teaching in telemedicine had a statistically significant association with the following factors: being female, being familiar with telemedicine, having read literature on telemedicine, having beliefs that telemedicine is an opportunity to improve current medical practice, that its use should be encouraged, that it has an important role to play in healthcare, that it does not pose greater threat to current medical practice, having a preference to continue distance learning at medical school and having an interest in incorporating telemedicine in their future careers. Conclusions: It is an ideal time to incorporate telemedicine into the medical curriculum at the University of Sharjah with most students expressing interest in it. However, further research is needed to assess its applicability to other medical schools in the country and elsewhere. [ABSTRACT FROM AUTHOR]

The significance of deep learning techniques in relation to steady-state visually evoked potential- (SSVEP-) based brain-computer interface (BCI) applications is assessed through a systematic review. Three reliable databases, PubMed, ScienceDirect, and IEEE, were considered to gather relevant scientific and theoretical articles. Initially, 125 papers were found between 2010 and 2021 related to this integrated research field. After the filtering process, only 30 articles were identified and classified into five categories based on their type of deep learning methods. The first category, convolutional neural network (CNN), accounts for 70% (n = 21 / 30). The second category, recurrent neural network (RNN), accounts for 10% (n = 3 / 30). The third and fourth categories, deep neural network (DNN) and long short-term memory (LSTM), account for 6% (n = 30). The fifth category, restricted Boltzmann machine (RBM), accounts for 3% (n = 1 / 30). The literature's findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. A comprehensive mapping analysis was also conducted, which identified six categories. Current challenges of ensuring trustworthy deep learning in SSVEP-based BCI applications were discussed, and recommendations were provided to researchers and developers. The study critically reviews the current unsolved issues of SSVEP-based BCI applications in terms of development challenges based on deep learning techniques and selection challenges based on multicriteria decision-making (MCDM). A trust proposal solution is presented with three methodology phases for evaluating and benchmarking SSVEP-based BCI applications using fuzzy decision-making techniques. Valuable insights and recommendations for researchers and developers in the SSVEP-based BCI and deep learning are provided. [ABSTRACT FROM AUTHOR]

Autism spectrum disorders (ASD) are a diverse group of conditions characterized by difficulty with social interaction and communication. ASD is expected to be a high‐risk disease. Recent studies have focused on the diagnosis based on sociodemographic and family characteristics factors. The development of a diagnosis model, which is primarily based on machine learning methods, has been carried out to alleviate the detection of autism. However, they neglected the importance of ASD features in a training dataset, especially because some features have different levels of contributions to the processing data and possess more relevancies to the classification information than others. Such limitations use preprocessing techniques for the construction of the machine learning model, but the role of the physician's experience towards feature contributions remains limited. However, for certain autism datasets, the relevancies of sociodemographic and family characteristic feature concerning the given class labels should be considered. Accordingly, this study developed a new machine learning model for the diagnosis of ASD based on multi‐criteria decision‐making (MCDM). By using three methodology phases, the model combines two representative theories, namely, MCDM and machine learning. The identification phase for imbalance ASD dataset and application of pre‐possessing stages by imputing missing values, feature selection of sociodemographic and family characteristics, and data imbalanced approach resulted in balanced ASD dataset, including 107,573 cases. The development phase for the new model was achieved by the proposed complex T‐spherical fuzzy‐weighted zero‐inconsistency (CT‐SFWZIC) method. CT‐SFWZIC was developed based on a new fuzzy set (i.e., complex T‐spherical fuzzy) for weighting affected features, and then applied for training and testing the machine learning model considering various complex T‐spherical fuzzy membership functions (i.e., T = 1, 2, 3, 5, 7, and 10). The results obtained from a 10‐fold cross‐validation test for all T values by using nine machine learning classifiers were measured under seven evaluation metrics, namely AUC, accuracy, F1, precision, recall, training time (s), and test time (s). Performance evaluation results reveal that AdaBoost can be used to boost the ASD diagnosis as the best machine learning algorithm for all T values based on all metrics to improve the diagnosis based on physician's assessment. Under the most extreme evaluation metric, which is accuracy, the results of the AdaBoost classifiers for T = 1, 2, 3, 5, 7, 10 have obtained 0.99948, 0.99934, 0.99930, 0.99939, 0.99910, and 0.99930, respectively. [ABSTRACT FROM AUTHOR]

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 and their sub-associated processes. Weighting results by q-ROFWZIC indicate that the time of arrival criterion is the most significant across all experimental scenarios with (0.1837, 0.183, 0.230, 0.276, 0.335) for (q = 1, 3, 5, 7, 10), respectively. Ranking results indicate that Hospital (H-4) is the best-ranked hospital in all experimental scenarios. Both methods were evaluated based on systematic ranking and sensitivity analysis, thereby confirming the validity of the proposed framework. [ABSTRACT FROM AUTHOR]

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 important criterion for patient prioritization, with the most severe cases being prioritized. The results are evaluated using systematic ranking and sensitivity analysis. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) employ sophisticated security techniques to detect malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security of computer and network systems. However, numerous evaluation and selection issues related to several cybersecurity aspects of IDSs were solved using a decision support approach. The approach most often utilized for decision support in this regard is multi-attribute decision-making (MADM). MADM can aid in selecting the most optimal solution from a huge pool of available alternatives when the appropriate evaluation attributes are provided. The openness of the MADM methods in solving numerous cybersecurity issues makes it largely efficient for IDS applications. We must first understand the available solutions and gaps in this area of research to provide an insightful analysis of the combination of MADM techniques with IDS and support researchers. Therefore, this study conducts a systematic review to organize the research landscape into a consistent taxonomy. A total of 28 articles were considered for this taxonomy and were classified into three main categories: data analysis and detection (n = 4), response selection (n = 7) and IDS evaluation (n = 1 7). Each category was thoroughly analyzed in terms of a variety of aspects, including the issues and challenges confronted, as well as the contributions of each study. Furthermore, the datasets, evaluation attributes, MADM methods, evaluation and validation and bibliography analysis used by the selected articles are discussed. In this study, we highlighted the existing perspective and opportunities for MADM in the IDS literature through a systematic review, providing researchers with a valuable reference. [ABSTRACT FROM AUTHOR]

Children with autism spectrum disorders (ASDs) tremendously impact people's lives, and the incidence and prevalence of ASDs are increasing globally. Global health organisations and other autism-treatment centres specialising in autism diagnosis and detection are encountering challenges on how to provide an appropriate ASD diagnosis system that enables accurate analyses and early detection of autism. Information about ASD detection is affected by unknown aetiology of the disease, and an urgent solution is required to investigate its aetiological factors. Accordingly, increasing the opportunities to provide evidence of the 'sociodemographic and family characteristics' risk factors in predicting ASD is a scientific complex problem that needs to be solved. This study developed an early prediction model for diagnosing and detecting children with ASD based on effective sociodemographic and family characteristic features related to ASD using the machine learning (ML) model. The proposed methodology involves three phases. The identification phase is first accomplished by identifying a large-scale ASD dataset and preprocessing stages: 1-NN model for imputing missing data, feature-selection methods using Chi2 and Relief, and adaptive balancing data approach using Synthetic Minority Oversampling Technique. Chi2 and Relief are applied to determine the most effective sociodemographic and family characteristic features and produce a new balanced ASD dataset. The second development phase trains and tests the newly prepared ASD dataset through eight ML methods: decision tree, random forest, Naive Bayes, kNN, SVM, logistic regression, AdaBoost, and neural network multilayer perceptron (MLP). The developed model is evaluated in the third phase using five metrics: accuracy, precision, recall, F1 and AUROC, and test time in seconds. Results indicated the following: (1) Out of 10 highly effective sociodemographic and family characteristic features, seven related to autism cases are extracted. (2) Correlation sensitivity analysis reveals that the 'Mom_age_at_child_birth' has the highest positive correlation with 'Father_age_at_child_birth,' with an r-value of 0.751. Moreover, 'child_birth_month' and 'Birth_number' have the highest negative correlation with 'Ses_points_1_10', with an r-value of (− 0.07). (3) AdaBoost, neural network, K-nearest neighbour, and decision tree methods show higher accuracy results (0.9995, 0.9925, 0.9834, and 0.9786, respectively), whereas random forest, logistic regression, and Naive Bayes methods show relatively lower accuracy (0.8297, 0.8199 and 0.8002, respectively). However, the support vector machine method shows the lowest accuracy (0.7105). AdaBoost obtained the highest accuracy on the basis of four other evaluation metrics (AUC = 0.9999, F1 = 0.9995, precision = 0.9995 and recall = 0.9995). Accordingly, the new preprocessed and balanced ASD dataset can be utilised as a data source for autism research. The preprocessing stages can be considered correct and successfully perform better results than the original ASD dataset. Similar results from Chi2 and Relief in the feature-selection approaches substantially improved the classification accuracy. The study confirms the efficacy of the proposed prediction model compared with previous models in different comparative points. Early prediction of autism is possible through this proposed model. [ABSTRACT FROM AUTHOR]

Indoor air quality (IAQ) refers to the conditions found within buildings that can impact respiratory health. Good IAQ conditions for hospital facilities are essential, especially for patients and medical staff. Recently, several concerns have been outlined and require an urgent solution in identifying IAQ pollutants and related thresholds and ways to provide a knowledge-based method for labelling pollution levels. To this end, a systematic review should be conducted first to construct new taxonomy research on internet of things-based IAQ sensory technology in hospital facilities to identify a research gap. Thus, the present study aims to develop an IAQ methodology that includes the recommended nine pollutants for hospitals and facilities: Carbon monoxide, Carbon dioxide, Nitrogen Dioxide, Ozone, Formaldehyde, Volatile organic compound, particulate matter (PM) and air humidity and temperature. The developed methodology utilised actual and simulated IAQ pollutant datasets to predict the pollution levels within hospital facilities in three distinct phases. In the first phase, two IAQ datasets (real and large-scale simulated datasets) are identified. The second phase includes the following: First is utilising the Interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) method from the Multi-Criteria Decision Making theory for providing the required weights to the nine pollutants. Second is developing a new method, the Unified Process for Labelling Pollutants Dataset (UPLPD), consisting of six processes based on the IT2TR-FWZIC method. The UPLPD can classify the pollution levels into four levels and assign the required labels within the two datasets. Third is applying the labelled datasets to the developed machine learning model using eight algorithms. The third phase includes the model evaluation using five metrics in terms of accuracy, Area under the Curve, F1-score, precision and recall. For the actual dataset, the best three algorithms' results are Support Vector Machine, Logistic Regression and Decision Tree (DT), which achieved the highest accuracy of 99.813, 99.259 and 98.182%, respectively, with performance metrics. The simulated dataset, the Random Forest, DT and AdaBoost achieved the highest accuracy of 90.094, 88.964 and 87.735%, respectively, with performance metrics. The results satisfied the challenges and overcame the issues, and experimental results confirmed the efficacy of the predictive model. [ABSTRACT FROM AUTHOR]

Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented. [ABSTRACT FROM AUTHOR]

Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications. [ABSTRACT FROM AUTHOR]

Hybridised classification and prioritisation of patients with chronic heart diseases (CHDs) can save lives by categorising them on the basis of disease severity and determining priority patients. Such hybridisation is challenging and thus has not been reported in the literature on telemedicine. This paper presents an intelligent classification and prioritisation framework for patients with CHDs who engage in telemedicine. The emergency status of 500 patients with CHDs was evaluated on the basis of multiple heterogeneous clinical parameters, such as electrocardiogram, oxygen saturation, blood pressure and non-sensory measurements (i.e. text frame), by using wearable sensors. In the first stage, the patients were classified according to Dempster–Shafer theory and separated into five categories, namely, at high risk, requires urgent care, sick, in a cold state and normal. In the second stage, hybridised multi-criteria decision-making models, namely, multi-layer analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS), were used to prioritise patients according to their emergency status. Then, the priority patients were queued in each emergency category according to the results of the first stage. Results demonstrated that Dempster–Shafer theory and the hybridised MLAHP and TOPSIS model are suitable for classifying and prioritising patients with CHDs. Moreover, the groups' scores in each category showed remarkable differences, indicating that the framework results were identical. The proposed framework has an advantage over other benchmark classification frameworks by 33.33% and 50%, and an advantage over earlier benchmark prioritisation by 50%. This framework should be considered in future studies on telemedicine architecture to improve healthcare management. [ABSTRACT FROM AUTHOR]

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19. [ABSTRACT FROM AUTHOR]

The task of benchmarking smart e-tourism applications based on multiple smart key concept attributes is considered a multi-attribute decision-making (MADM) problem. Although the literature review has evaluated and benchmarked these applications, data ambiguity and vagueness continue to be unresolved issues. The robustness of the fuzzy decision by opinion score method (FDOSM) and fuzzy weighted with zero inconsistency (FWZIC) is proven compared with that of other MADM methods. Thus, this study extends FDOSM and FWZIC under a new fuzzy environment to address the mentioned issues whilst benchmarking the applications. The neutrosophic fuzzy set is used for this purpose because of its high ability to handle ambiguous and vague information comprehensively. Fundamentally, the proposed methodology comprises two phases. The first phase adopts and describes the decision matrices of the smart e-tourism applications. The second phase presents the proposed framework in two sections. In the first section, the weight of each attribute of smart e-tourism applications is calculated through the neutrosophic FWZIC (NS-FWZIC) method. The second section employs the weights determined by the NS-FWZIC method to benchmark all the applications per each category (tourism marketing and smart-based tourism recommendation system categories) through the neutrosophic FDOSM (NS-FDOSM). Findings reveal that: (1) the NS-FWZIC method effectively weights the applications' attributes. Real time receives the highest importance weight (0.402), whereas augmented reality has the lowest weight (0.005). The remaining attributes are distributed in between. (2) In the context of group decision-making, NS-FDOSM is used to uniform the variation found in the individual benchmarking results of the applications across all categories. Systematic ranking, sensitivity analysis and comparison analysis assessments are used to evaluate the robustness of the proposed work. Finally, the limitations of this study are discussed along with several future directions. [ABSTRACT FROM AUTHOR]

A key concern in agriculture is how to feed the expanding population and safeguard the environment from the ill effects of climate change. To feed a growing global population, food production and security are significant problems, as food output may need to double by 2050. Thus, more innovative and effective approaches for increasing agricultural productivity (hence, food production) are required to meet the rising demand for food. The world's most widely cultivated grains include corn, wheat, and rice, which serve as the foundation for basic foods. This review focuses on some of the key most up-to-date approaches that boost wheat, rice, corn, barley, and oat yields with insight into how molecular technology and genetics may raise the production and resource-efficient use of these important grains. Although red light management and genetic manipulation show maximal grain yield enhancement, other covered strategies including bacterial-nutrient management, solar brightening, facing abiotic stress through innovative agricultural systems, fertilizer management, harmful gas emissions reduction, photosynthesis enhancement, stress tolerance, disease resistance, and varietal improvement also enhance grain production and increase plant resistance to harmful environmental circumstances. This study also discusses the potential challenges of the addressed approaches and possible future perspectives. [ABSTRACT FROM AUTHOR]

Brain-Computer Interface (BCI) research is considered one of the significant interdisciplinary fields. It assists people with severe motor disabilities to recover and improve their motor actions through rehabilitation sessions using Motor Imagery (MI) based BCI systems. Several smart criteria, such as virtual reality, plays a significant role in training people for motor recovery in a virtual environment. Accordingly, Smart Training Environments (STEs) based on virtual reality for MI-BCI users provide a safe environment. They are cost-effective for real-life conditions and scenarios with severe motor disabilities. Fundamentally, the literature presents a lack of comparison of the STE applications considering the smart and effective criteria of the developed applications. Accordingly, three key issues faced the comparison process: importance, multi-evaluation criteria, and data variation, which falls under complex Multi-Criteria Decision Making (MCDM). Performance issues increased comparison complexity caused by the rapidly changing market demands of the MI-BCI. Therefore, this study developed two methodology phases for evaluating and benchmarking the STE applications for the MI-BCI community; making effective decisions is vital. In the first phase, formulate the STE Decision Matrix (DM) based on two main dimensions: the evaluation of ten smart criteria of STE and the alternatives (27 STE applications) developed in the literature for MI-BCI. In the second phase, integration methods of MCDM have been formulated: Analytic Hierarchy Process (AHP) for weighting the ten smart criteria and Fuzzy Decision by Opinion Score Method (FDOSM) for benchmarking STE applications based on constructed AHP weights. The evaluation results show importunity in the obtained weights among the ten STE criteria to distinguish the greatest and lowest important weights. Through the benchmarking performance, FDOSM processes prioritized all STE applications. The ranking results were objectively validated based on five groups of alternatives, and the results were systematically ranked. Finally, this study argued three important summary points concerning the STE dataset, formulated a DM of STE applications, and smart criteria for STE applications to support the MI-BCI community and market. Developing the appropriate STE application for MI-BCI is a better choice to support a large BCI community by identifying the ten smart criteria and considering the presented methodology to establish a robust, practical, cost-efficient, and reliable BCI system. [ABSTRACT FROM AUTHOR]

Mesenchymal stem cells (MSCs) have shown promising ability to treat critical cases of coronavirus disease 2019 (COVID-19) by regenerating lung cells and reducing immune system overreaction. However, two main challenges need to be addressed first before MSCs can be efficiently transfused to the most critical cases of COVID-19. First is the selection of suitable MSC sources that can meet the standards of stem cell criteria. Second is differentiating COVID-19 patients into different emergency levels automatically and prioritising them in each emergency level. This study presents an efficient real-time MSC transfusion framework based on multicriteria decision-making(MCDM) methods. In the methodology, the testing phase represents the ability to adhere to plastic surfaces, the upregulation and downregulation of specific surface protein markers and finally the ability to differentiate into different kinds of cells. In the development phase, firstly, two scenarios of an augmented dataset based on the medical perspective are generated to produce 80 patients with different emergency levels. Secondly, an automated triage algorithm based on a formal medical guideline is proposed for real-time monitoring of COVID-19 patients with different emergency levels (i.e. mild, moderate, severe and critical) considering the improvement and deterioration procedures from one level to another. Thirdly, a unique decision matrix for each triage level (except mild) is constructed on the basis of the intersection between the evaluation criteria of each emergency level and list of COVID-19 patients. Thereafter, MCDM methods (i.e. analytic hierarchy process [AHP] and vlsekriterijumska optimizcija i kaompromisno resenje [VIKOR]) are integrated to assign subjective weights for the evaluation criteria within each triage level and then prioritise the COVID-19 patients on the basis of individual and group decision-making(GDM) contexts. Results show that: (1) in both scenarios, the proposed algorithm effectively classified the patients into four emergency levels, including mild, moderate, severe and critical, taking into consideration the improvement and deterioration cases. (2) On the basis of experts' perspectives, clear differences in most individual prioritisations for patients with different emergency levels in both scenarios were found. (3) In both scenarios, COVID-19 patients were prioritised identically between the internal and external group VIKOR. During the evaluation, the statistical objective method indicated that the patient prioritisations underwent systematic ranking. Moreover, comparison analysis with previous work proved the efficiency of the proposed framework. Thus, the real-time MSC transfusion for COVID-19 patients can follow the order achieved in the group VIKOR results. [ABSTRACT FROM AUTHOR]

Considering the coronavirus disease 2019 (COVID‐19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision‐making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID‐19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID‐19 by presenting a systematic literature review of the state‐of‐the‐art COVID‐19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID‐19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field. [ABSTRACT FROM AUTHOR]