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Electronic medical record (EMR) is currently a popular topic in e-health. EMR includes the health-related information of patients and forms the main factor of e-health applications. Moreover, EMR contains the legal records that are created in the medical centre and ambulatory environments. These records serve as the data source for electronic health record. Although hospitals utilise the EMR system, healthcare professionals experience difficultly in trusting this system. Studies devoted to EMR acceptance in hospitals are lacking, particularly those on the EMR system in the contexts of privacy and security concerns based on multi-criteria perspective. Thus, the current study proposes a decision support examination framework on how individual, security and privacy determinants influence the acceptance and use of EMR. The proposed framework is based on a multi-criteria perspective derived from healthcare professionals in Malaysia as frame of reference. The framework comprises four phases. The sub-factors of individual, security and privacy determinants were investigated in the two initial phases. Thereafter, the sub-factors were identified with uniform multi-criteria perspective to establish a decision matrix. The decision matrix used individual uniform as basis to cluster the sub-factors and user perspectives. Subsequently, a new ‘multi-criteria decision-making (MCDM) approach’ was adopted. Integrated technique for order of preference by similarity (TOPSIS) and analytic hierarchy process (AHP) were used as bases in employing the MCDM approach to rank each group of factors. K-means clustering was also applied to identify the critical factors in each group. Healthcare professionals in Malaysia were selected as respondents and 100 questionnaires were distributed to those employed in 5 Malaysian public hospitals. A conceptual model adapted from Unified theory of acceptance and use of technology 2 (UTAUT2) was employed to clarify the connection between individual, privacy and security determinants and EMR system acceptance and use in the selected context. After collecting the data sets (363), structural equation modelling was used to analyse data related to EMR acceptance and use. Results are as follows. (1) Five determinants (i.e. data integrity, confidentiality, non-repudiation, facilitating conditions and effort expectancy) exerted an explicit and important positive effect on EMR acceptance and use. (2) Three determinants (i.e. unauthorised, error and secondary use) exerted a direct and significant negative effect on EMR acceptance and use. (3) Three other determinants (i.e. authentication, performance expectancy and habit) insignificantly affected the behavioural intention of healthcare experts in Malaysia to use EMR.

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.

Efficient evaluation for machine learning (ML)-based intrusion detection systems (IDSs) for federated learning (FL) in the Internet of Medical Things (IoMTs) environment falls under the standardisation and multicriteria decision-making (MCDM) problems. Thus, this study is developing an MCDM framework for standardising and benchmarking the ML-based IDSs used in the FL architecture of IoMT applications. In the methodology, firstly, the evaluation criteria of ML-based IDSs are standardised using the fuzzy Delphi method (FDM). Secondly, the evaluation decision matrix (DM) is formulated based on the intersection of standardised evaluation criteria and a list of ML-based IDSs. Such formulation is achieved using a dataset with 125,973 records, and each record comprises 41 features. Thirdly, the integration of MCDM methods is formulated to determine the importance weights of the main and sub standardised security and performance criteria, followed by benchmarking and selecting the optimal ML-based IDSs. In this phase, the Borda voting method is used to unify the different ranks and perform a group benchmarking context. The following results are confirmed. (1) Using FDM, 17 out of 20 evaluation criteria (14 for security and 3 for performance) reach the consensus of experts. (2) The area under curve criterion has the lowest set of weights, whilst the CPU time criterion has the highest one. (3) VIKOR group ranking shows that the BayesNet is a best classifier, whilst SVM is the last choice. For evaluation, three assessments, namely, systematic ranking, computational cost and comparative analysis, are used.

A new framework for explainable artificial intelligence in the context of multimodal triage for autism spectrum disorders (ASD) using a fuzzy approach-based multi-criteria decision-making (MCDM) is proposed in this study. The framework consists of five phases. In the first phase, a real ASD dataset of 538 autistic patients is obtained and diagnosed based on 42 medical and sociodemographic criteria. In the second phase, an ASD methodology for triaging the 538 autistic patients into three levels (i.e., minor, moderate, and urgent) is presented using fuzzy approach-based MCDM techniques, namely, fuzzy Delphi method and fuzzy-weighted zero-inconsistency, followed by the processes for triaging autism patients. In the third phase, cost-sensitive learning is employed to balance two ASD datasets: one labeled based on the ASD triage methodology and the other labeled by specialized psychologists. Two multimodal of artificial intelligence are developed in the fourth phase using nine machine-learning algorithms for the balanced ASD datasets. The evaluation of the two multimodal setups is carried out using nine metrics. In the fifth phase, the local interpretable model-agnostic explanations (LIME) model is used to interpret the models using two scenarios. Four new algorithms are presented in the developed framework.

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.

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.

Many of the current fuzzy sets based on intuitionistic fuzzy concepts suffer from different limitations. Different approaches towards that end in the form of scientific works have been introduced, and many other fuzzy sets were extended to address such limitations. In this study, the Cubic Pythagorean fuzzy sets (CPFS) are introduced as one of the proposed solutions and among the most powerful tools to address uncertainty, especially in complex and difficult situations by using both interval-valued Pythagorean fuzzy set and Pythagorean fuzzy set to represent vagueness or ill-defined information. Considering all the merits of CPFS and its flexibility, this study aimed to integrate it with two profound multi-criteria decision-making methods. Fuzzy-weighted zero-inconsistency (FWZIC) and decision by opinion score method (FDOSM) are both extended based on CPFS, called CP-FWZIC and CP-FDOSM. Two methodological phases are performed in two phases. The first phase discusses all the necessary CP-FWZIC steps to determine the evaluation criteria weights, followed by alternatives ranking using CP-FDOSM. The second phase discusses a case study used in this research for sign language recognition systems. Finally, two methods were used to assess the extended multi-criteria decision-making methods, namely, systematic ranking assessment and sensitivity analysis. The assessment findings suggest that the ranking results are supported by both systematic ranking and high correlation results which were performed using different criteria weight-changing scenarios.

In the context of autism spectrum disorder (ASD) triage, the robustness of machine learning (ML) models is a paramount concern. Ensuring the robustness of ML models faces issues such as model selection, criterion importance, trade-offs, and conflicts in the evaluation and benchmarking of ML models. Furthermore, the development of ML models must contend with two real-time scenarios: normal tests and adversarial attack cases. This study addresses this challenge by integrating three key phases that bridge the domains of machine learning and fuzzy multicriteria decision-making (MCDM). First, the utilized dataset comprises authentic information, encompassing 19 medical and sociodemographic features from 1296 autistic patients who received autism diagnoses via the intelligent triage method. These patients were categorized into one of three triage labels: urgent, moderate, or minor. We employ principal component analysis (PCA) and two algorithms to fuse a large number of dataset features. Second, this fused dataset forms the basis for rigorously testing eight ML models, considering normal and adversarial attack scenarios, and evaluating classifier performance using nine metrics. The third phase developed a robust decision-making framework that encompasses the creation of a decision matrix (DM) and the development of the 2-tuple linguistic Fermatean fuzzy decision by opinion score method (2TLFFDOSM) for benchmarking multiple-ML models from normal and adversarial perspectives, accomplished through individual and external group aggregation of ranks. Our findings highlight the effectiveness of PCA algorithms, yielding 12 principal components with acceptable variance. In the external ranking, logistic regression (LR) emerged as the top-performing ML model in terms of the 2TLFFDOSM score (1.3370). A comparative analysis with five benchmark studies demonstrated the superior performance of our framework across all six checklist comparison points.

This study aims to perform a thorough systematic review investigating and synthesizing existing research on defense strategies and methodologies in adversarial attacks using machine learning (ML) and deep learning methods. A methodology was conducted to guarantee a thorough literature analysis of the studies using sources such as ScienceDirect, Scopus, IEEE Xplore, and Web of Science. A question was shaped to retrieve articles published from 2019 to April 2024, which ultimately produced a total of 704 papers. A rigorous screening, deduplication, and matching of the inclusion and exclusion criteria were followed, and hence 42 studies were included in the quantitative synthesis. The considered papers were categorized into a coherent and systematic classification including three categories: security enhancement techniques, adversarial attack strategies and defense mechanisms, and innovative security mechanisms and solutions. In this article, we have presented a systematic and comprehensive analysis of earlier studies and opened the door to potential future studies by discussing in depth four challenges and motivations of adversarial attacks, while three recommendations have been discussed. A systematic science mapping analysis was also performed to reorganize and summarize the results of studies to address the issues of trustworthiness. Moreover, this research covers a large variety of network and cybersecurity applications of defense in adversarial attack subjects, including intrusion detection systems, anomaly detection, ML-based defenses, and cryptographic techniques. The relevant conclusions well demonstrate what have achieved in defense mechanisms against adversarial attacks. In addition, the analysis revealed a few emerging tendencies and deficiencies in the area to be remedied through better and more dependable mitigation methods against advanced persistent threats. The findings of this review have crucial implications for the community of researchers, practitioners, and policy makers in network and cybersecurity using artificial intelligence applications.

In an effort to achieve energy efficiency, cost savings, and reduce carbon footprint, researchers are exploring ways to strategically redistribute energy use in cooling systems, particularly by flattening peak demand. This study proposes a novel approach using multiagent deep clustering reinforcement learning (MADCRL) to optimize load-shifting within multi-tank chilled water (MTCW) systems. Unlike traditional single-tank configurations, MTCW utilizes natural temperature stratification to store and distribute cool water, eliminating the need for physical barriers. The core of this approach lies in the MADCRL policy, which intelligently sequences chiller operations by taking advantage of cooler nighttime temperatures. By running chillers at full capacity while simultaneously charging the MTCW tanks, the system achieves substantial energy efficiency improvements. This dynamic control strategy ensures sufficient cooling capacity during peak periods while minimizing energy consumption during low-demand times. The effectiveness of this approach is validated through a complex MTCW plant simulation, highlighting the optimization of the value function by MADCRL, leading to improved decision-making and overall system efficiency. Implementing this reinforcement learning approach leads to significant energy savings and reduced power consumption. Compared to conventional methods using PID controllers, which struggle to shift loads efficiently, MADCRL achieves a superior coefficient of performance (COP), potentially exceeding energy savings by up to 25.47 %. This research demonstrates the promising potential of MADCRL in optimizing energy consumption within HVAC systems and beyond, offering substantial benefits in terms of energy efficiency and cost savings.

Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore®databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

Building a sustainable transportation system without involving international oil companies (IOCs) is an unrealistic feat. To date, no study has determined the best IOC and low-performing ones with respect to sustainable oil transportation, which is considered a benchmarking challenge requiring an urgent solution. Despite this limitation, the benchmarking of IOCs falls under the complex multicriteria decision making (MCDM) because of the use of several evaluation criteria and their varying datasets and the varying importance of these criteria. Moreover, the issues involving the use of imprecise, unclear, and ambiguous information remain unresolved in the existing multiattribute decision-making methods. The robustness of the multiobjective optimization on the basis of ratio analysis (MULTIMOORA, i.e., an updated version of MOORA) plus full-multiplicative form method and that of the fuzzy-weighted with zero inconsistency (FWZIC) method have been proven. Therefore, in this article, we propose a novel benchmarking of oil companies by extending the linear Diophantine fuzzy rough sets (LDFRSs) into the MCDM methods to help build a sustainable transportation industry. The proposed methodology consists of two phases. The initial phase involves assigning values to the evaluation criteria of IOCs to formulate the evaluation decision matrix. The second phase involves the development of two fuzzy MCDM methods, namely, the LDFRS with the FWZIC method (hereafter called LDFRS–FWZIC) for weighting the criterion of each IOC and the LDFRS with the MULTIMOORA method (hereafter called LDFRS–MULTIMOORA) for benchmarking the IOCs. The IOCs were evaluated based on 2 criteria, 9 subcriteria, and 47 measurement items by 483 experts from 11 IOCs. Results revealed the following: 1) LDFRS–FWZIC can effectively weigh the evaluation criteria of IOCs. The highest final weight of 0.2594 was for “cost leadership” (C2-1), whereas the lowest weights of 0.1148 was for “priority of other external matters” (C1-2) and “insufficient supply”(C1-4), and 2) LDFRS–MULTIMOORA can successfully benchmark the IOCs. IOC11 ranked first, followed by IOC10 and IOC3 in the second and third ranks, respectively. IOC4 ranked the lowest (rank = 11). A sensitivity analysis was conducted to determine the robustness of the developed fuzzy MCDM methods.

Pavement strategy is critical for achieving sustainable transportation. However, the presence of many evaluation criteria, criteria tradeoffs, criteria conflict, and criteria importance categorize the evaluation and selection of pavement strategies under complex multicriteria decision-making (MCDM) problems. To date, no study has presented an evaluation framework for selecting the most optimal pavement strategy to be utilized as a way to achieve sustainable transportation considering multicriteria evaluation of pavement strategies and sustainable solutions. This article presents a pavement strategy selection based on a new extension of fuzzy MCDM methods. The methodology is developed in two phases. First, the evaluation decision matrix is formulated on the basis of intersecting the “evaluation criteria” and “pavement strategies list.” Second, the proposed MCDM methods are developed: multilayer dual hesitant fuzzy weighted zero inconsistency (DH-FWZIC) to assign weights to the pavement's evaluation criteria followed by dual hesitant fuzzy decision by opinion score method (DH-FDOSM) for selecting the best pavement strategy. Four alternatives, namely, flexible/asphalt, rigid/concrete, reflective, and permeable alternatives pavement strategies, are evaluated on the basis of 30 criteria. Results show the following. 1) The multilayer DH-FWZIC method has weighted the pavement strategies’ evaluation criteria at three layers in a consistent manner, showing that a region's environment criterion has the highest final weight (0.1215) and the lowest importance value (0.0089) assigned for its windy criterion. 2) According to DH-FDOSM, the flexible/asphalt pavement achieved the first rank among the four pavement strategies. Finally, the robustness of the developed framework was assessed by conducting sensitivity analysis and comparison study.

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.

Cardiovascular diseases (CVDs) are conditions involving the heart or blood vessels which need specialised and urgent care. Centralised telemedicine is a client–server architecture fit for cardiovascular care, especially for monitoring health conditions, delivering healthcare services and providing other remote services by using mHealth. However, several challenges and unsolved issues remain, including (1) provision of healthcare services data in terms of hospital connectivity and continuous updates of all transactions occurring across distributed hospital networks for patient data, (2) lack of an accurate mHealth method to estimate time between patients with CVD and telemedicine hospitals for hospitalisation and (3) lack of investigation of important criteria for hospital evaluation. To develop a new mHealth framework for the evaluation and prioritisation of decentralised telemedicine hospitals based on integrated techniques Haversine-Global Positioning System (GPS) and analytical hierarchy process (AHP)-VIseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR). The framework can serve all health emergency levels (i.e. risk, urgent and sick) of patients with CVD. Three methodology phases were developed. First is the identification of important decentralised hospital criteria which affect hospital evaluation to create a new dataset for this context. Second is the development of a new mHealth framework phase consequent of four development sequences: new integrated distance measurement through Haversine-based on GPS for time estimation for the convenient remote interaction with hospitals, combination for new hospital datasets and development of three decision matrixes based on a crossover of (1) healthcare service packages/time of arrival of patient at the hospital criteria and (2) lists of hospitals for evaluation and prioritisation using integration AHP-VIKOR techniques. Third is the objective validation of the constructed results. In addition, the proposed framework is evaluated by using a checklist benchmarking procedure. Experimental results reveal that the new mHealth framework is effective in decentralised telemedicine architecture and verify the ability of all connected hospitals. The new integrated distance measurement technique boosts the overall methodology of hospital evaluation and supports the combination of the new hospital datasets for prioritisation configuration. The proposed mHealth framework offers healthcare services for all emergence levels of patients with CVD through the blockchain concept and decision making theory. Objective validation reveals significant differences between the scores of groups, indicating that the ranking results are valid for the three decision matrices. Evaluation results show that the proposed mHealth framework exhibits an advantage over the benchmark frameworks with a percentage of 66.67% intersection with six comparison points highlighted by the academic literature.

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.

A newly distributed fault-tolerant mHealth framework-based Internet of things (IoT) is proposed in this study to resolve the essential problems of healthcare service provision during the occurrence of frequent failures in a telemedicine architecture. Two models are presented to support the telehealth development of chronic heart disease (CHD) in a distant environment. In model-1, a new local multisensor fusion triage algorithm known as three-level localisation triage (3LLT) is proposed. In 3LLT, a group of heterogeneous sources is applied to triage patients as a clinical process, and the emergency levels inside/outside the home of a patient with CHD are determined. Failures related to sensor fusion can also be detected. In model-2, a centralised IoT connection towards distributed smart hospitals is employed by mHealth based on two attributes: (1) healthcare service packages and (2) time of arrival of a patient at a hospital. Three decision matrices have been used to overcome several issues on hospital selection based on multi-criteria decision-making by using an analytic hierarchy process. Two datasets are utilised: (1) a clinical CHD dataset, which includes 572 patients for testing model-1, and (2) a nonclinical dataset, which includes hospital healthcare service packages for testing model-2. Consequently, patients with CHD can be triaged into different emergency levels (risk, urgent and sick) with mHealth, and a final decision is made by selecting an appropriate hospital. Results are obtained through the clinical triage of patients, and different scenarios are provided for hospital selection. The verification of statistical results indicates that the proposed mHealth framework is systematically valid. The contribution of the mHealth framework is presented to provide an improved triage process, afford timely services and treatment for CVD patients and minimise the chances of error. These health sectors and policymakers can also recognise the evaluation benefits of smart hospitals by using the presented framework and move forward to fully automated mHealth applications.

Despite the wide range of research on pedestrian safety, previous studies have failed to analyse the real-time data of pedestrian walking misbehaviour on the basis of either pedestrian behaviour distraction or movements during specific activities to realise pedestrian safety for positive (normal) or aggressive pedestrians. Practically, pedestrian walking behaviour should be recognised, and aggressive pedestrians should be differentiated from normal pedestrians. This type of pedestrian behaviour recognition can be converted into a classification problem, which is the main challenge for pedestrian safety systems. In addressing the classification challenge, three issues should be considered: identification of factors, collection of data and exchange of data in the contexts of wireless communication and network failure. Thus, this work proposes a novel approach to pedestrian walking behaviour classification in the aforementioned contexts. Three useful phases are proposed for the methodology of this study. In the first phase involving factor identification, several factors of the irregular walking behaviour of mobile phone users are established by constructing a questionnaire that can determine users’ options (attitudes/opinions) about mobile usage whilst walking on the street. In the second phase involving data collection, four different testing scenarios are developed to acquire the real-time data of pedestrian walking behaviour by using gyroscope sensors. In the third phase involving data exchange, the proposed approach is presented on the basis of two modules. The first module for pedestrian behaviour classification uses random forest and decision tree classifiers part of machine learning techniques via wireless communication when a server becomes available. The developed module is then trained and evaluated using five category sets to obtain the best classification of pedestrian walking behaviour. The second module is based on four standard vectors for classifying pedestrian walking behaviour when a server is unavailable. Fault-tolerant pedestrian walking behaviour is identified and is initiated when failures occur in a network. Two sets of real-time data are presented in this work. The first dataset is related to the questionnaire data from 262 sampled respondents, and the second dataset comprises data on 263 sampled participants with pedestrian walking signals. Experimental results confirm the efficacy of the proposed approach relative to previous ones.

Objectives: To describe the epidemiological characteristics of the first 69,382 patients with COVID-19 infection in Oman. Methods: A retrospective case series study of patients diagnosed with SARS-CoV-2 infection in Oman from 24 February to 23 July 2020. The data were obtained from the National surveillance COVID-19 network. Results: The overall mean age of patients was 34 (± 14) years, 74% (n= 51,391) were males, 40,859 (59%) were Omani citizens, and 28,523 (41%) were foreign-born individuals. The most common symptoms at presentation were fever 50% (n= 34,600), cough 46% (n= 32,062), sore throat 46% (n= 31,953) and shortness of breath (SOB) 35% (n= 24,567). Overall, 8,960 (12.9%) patients required hospitalization with 1189 (13.3%) individuals requiring admission to the intensive care unit (ICU) and mechanical ventilation (MV). Patients hospitalized with COVID-19 infection were mostly Omani nationals and males between 30 and 39 years old (p< 0.001). The mortality rate was 7.7 per 100,000 population (n= 359) with rates of 9.4 (n= 278) and 4.8 (n= 81) deaths per 100,000 population in Omani nationals and foreign-born individuals, respectively. Females hospitalized with COVID-19 had a mean age of 64 (± 18) years versus a mean age of 55 (± 17) years in males. Mean age of patients with COVID-19-related mortality was 58 (± 18) years with significant differences in mean age between females and males 64 (± 18) versus 55 (± 17) years, respectively. Conclusions: Young Omani males accounted for the highest incidence of COVID-19 infection and hospitalization, while mortality rates were higher among males and the older age (> 50 years). Identifying the epidemiological characteristics and outcomes of COVID-19-infected patients is essential for developing targeted intervention strategies and preparing for the resurgence of anticipated second and third waves of this pandemic.

Energy Systems Integration (ESI) involves coordinating and planning energy systems to provide reliable and affordable energy services while minimizing environmental harm. It optimizes interactions among different energy sources to achieve sustainability goals and promotes efficient resource usage. However, evaluating and benchmarking ESI frameworks to select the most suitable and transparent ones is a complex Multi-Criteria Decision-Making (MCDM) problem. This complexity arises from trade-offs, conflicts, and importance considerations of the six ESI evaluation characteristics: Multidimensional, Multivectoral, Systemic, Futuristic, Systematic, and Applied. Hence, this study aims to address this complexity by integrating Fuzzy-Weighted Zero-Inconsistency (FWZIC) and Multi-Attributive Border Approximation Area Comparison (MABAC). The proposed methodology consists of two phases. Firstly, the development of a Dynamic Decision Matrix (DDM) to handle 26 ESI frameworks as alternatives and the six ESI characteristics criteria. Secondly, the integration of mathematical processes is formulated based on the FWZIC-MABAC methods. Using the FWZIC technique, the ESI evaluation criteria were weighted based on the preferences of twelve experts. ESI-C2 (Multivectoral) and ESI-C1 (Multidimensional) criteria received the highest weights of 0.195 and 0.190, respectively, while the ESI-C5 (Systematic) criterion received the lowest weight of 0.110. The remaining criteria, namely ESI-C3 (Systemic), ESI-C6 (Applied), and ESI-C4 (Futuristic) obtained weights of 0.189, 0.168, and 0.147, respectively. The MABAC benchmarking results showed that A11 (Energy Security) and A15 (Energy Security under decarbonization) ranked first with the highest score value of 0.28081 for both. Conversely, A19 (EJM) had the lowest score value of −0.17022. The systematic rank and sensitivity analysis assessments were conducted to verify the efficiency of the proposed methodology. We benchmarked the proposed methodology against three other benchmark studies and achieved a score of 100 % across three key perspectives. This methodology offers valuable support in making informed and sustainable decisions in the energy sector.

With the widespread usage of smartphones, users not just using their smartphone for calls and messaging only, but they are using it for a variety of purposes such as banking transactions, e-mailing, chatting, online shopping, video conferencing, health monitoring, and many more. Most of these activities store personal and sensitive data of the user on the device, reaching this confidential information by unauthorized person may cause huge losses and bad consequences. Therefore, securing the smartphone’s accessibility from unauthorized people became an extremely essential. With the advanced development in the area of sensor-based smartphone authentication methods, different methods were developed to be easily used by users through specific criteria. However, many aspects haven’t been discovered yet in the area of sensor-based smartphone authentication methods. On the basis of that, this study aims to review the area of sensor-based smartphone authentication methods systematically to provide a comprehensive understanding of this new area and to discuss the current challenges and issues. To conduct this systematic review, four scientific digital databases were utilized; ScienceDirect, IEEE Xplore, Web of Science, and Scopus. The total number of studies found was n = 256 articles, only n = 46 articles were included based on our inclusion criteria. To provide a comprehensive understanding of the included studies, taxonomy was drawn. Also, this study provides a discussion on the current motivations, challenges and issues, and recommendations in sensor-based smartphone authentication methods. This review enlightens, encourage, and direct researchers to develop authentication solutions and reduce the current gaps in this area.

The brain–computer interface (BCI) technique represents one of the furthermost active interdisciplinary study domains and includes a wide knowledge spectrum from a different disciplines such as medicine, neuroscience, machine learning and rehabilitation. The motor imagery (MI) technique based on BCI has been broadly applied in rehabilitation especially for upper limb motor movement where people with disabilities need to restore or improve their walking capability. Nowadays, virtual reality is a beneficial scheme for BCI users because it proposes a relatively cost-effective, safe way for BCI users to train and explain themselves in using BCI in a computer-generated environment earlier than in a real-life scenario. Depicting the whole picture for signal processing techniques and methods utilised in MI-based BCI training environments is difficult. In addition, numerous challenges and open issues regarding signal processing and pattern recognition exist in the literature of the current topic; however, to the best of our knowledge, this is the first attempt to highlight these challenges and open issues in signal processing methods, techniques and pattern recognition in smart BCI training environments. This work illustrates the effect of the theoretical perspectives associated with BCI works for research development in smart training environments. Consequently, this research copes with these issues via a systematic review protocol to help the large community of BCI users, especially people with disabilities. Fundamentally, four substantial databases, namely, IEEE, ScienceDirect, Scopus and PubMed contain a considerable amount of technical and scientific articles relevant to smart BCI training systems. A set of 375 articles is collected from 2010 to 2020 to reveal a clear picture and a better understanding of all the academic literature through a final set of 25 articles. In addition, this research provides the state of the art for signal processing, feature extraction, classification techniques and smart training environment characteristics for MI-based BCI applications. This study also reports the challenges and issues identified by the researchers as well as recommended solutions to solve the persistent problems. This study introduces the state-of-the art virtual and augmented reality environments as a smart platform and the neurofeedback schemes used for MI-based smart BCI training systems. Moreover, this study highlights for the first time 10 concepts of smart training in a virtual environment applied in MI and BCI, and investigates the evaluation of these concepts against the literature to gain only 45.55%. Collectively, the implication of this study will offer the opportunity of deploying an efficient smart BCI training system in terms of data acquisition and recording, pattern recognition and smart environment for BCI users and rehabilitation programmes.

Medical institutions face serious problems, such as growing elderly population and lack of doctors. Telemedicine and remote health monitoring system (RHMS) intend to tackle these problems by slightly shortening hospital stays. RHMS reduces the burden on patients with primary care and improves communication among different health units to reduce the burden on emergency departments. Several healthcare studies have attempted to replace hospital visits with RHMS to deliver triage and prioritisation for patients because of considerable advances in wireless information communication and signal-processing technology. The process of medical triage determines the severity of a patient’s situation, whilst prioritisation is carried out to provide healthcare services for patients in due course to save their lives. An essential investigation is required to highlight the drawbacks of the current situation of patient triage and prioritisation over telemedicine environment. In this paper, a systematic review of medical emergency triage and patient prioritisation in a telemedicine environment was presented on the basis of two critical directions. Firstly, previous studies on patient triage and prioritisation in such an environment were collected, analysed and categorised. Secondly, many standards and guidelines of triage and different methods and techniques of prioritisation were presented and reviewed in detail. The following results were obtained: (1) The limitations and problems of existing patient triage and prioritisation methods were presented and emphasised. (2) The combination of triage and prioritisation of patients with chronic heart disease was not presented. (3) A framework based on evidence theory and integration of multilayer analytical hierarchy process and technique for order of preference by similarity to ideal solution methods can be used in the future in order to triage chronic heart disease patients into different emergency levels and prioritise many patients to receive emergency and treatment-based services.

Group-based systems, such as e-health systems, have been introduced since the last few decades. E-health systems can be used anytime and anywhere for patient monitoring. Wireless networks are continuously used to monitor patients’ conditions and recovery progress. The confidentiality, integrity and authenticity of patients’ health records are important to secure in such applications. Efficient key management and distribution are required to secure e-health applications in a wireless mobile environment. However, existing key management protocols cannot route e-health applications securely due to the resource-constrained architecture of the wireless mobile environment. A novel and enhanced key management scheme which aims to identify the challenges related to the security and privacy issues of patients’ sensitive information through a strong encryption management is proposed in this study. The proposed model also aims to provide a well-organised and lightweight key management mechanism. This system requires few computations of keys and offers a null rekeying mechanism to ensure forward and backward secrecies. As a result, a secure and privacy-preserving key management scheme for e-health systems, which is known as the healthcare key management (HCKM) framework and aims to decrypt the ciphertext of the same plain text with different keys, is acquired. HCKM minimises the rekeying overhead of group members and the overhead expressed in terms of the number of exchanged messages whilst achieving a sufficiently high security level. The proposed protocol also can operate on dynamic scenarios with a large number (thousands) of nodes and exhibits an excellent performance under the assumption of low rate of evictions.

This research aims to review the attempts of researchers in response to the new and disruptive technology of skin cancer applications in terms of evaluation and benchmarking, in order to identify the research landscape from the literature into a cohesive taxonomy. An extensive search was conducted for articles dealing with ‘skin cancer’, ‘apps’ and ‘smartphone’ or ‘mHealth’ in different variations to find all the relevant articles in three main databases, namely, “Web of Science”, “Science Direct”, and “IEEE explore”. These databases are considered wide enough to cover medical and technical literature. The final classification scheme outcome of the dataset contained 110 articles that were classified into four classes: development and design; analytical; evaluative and comparative; and review and survey studies. Afterwards, another filtering process was achieved based on the evaluation criteria error rate within the dataset, time complicity and reliability, which are used in skin cancer applications. The final classification scheme outcome of the dataset contained 89 articles distributed in mapping and crossover with four sections concluded from 110 articles. Development and design studies, analytical studies, evaluative and comparative studies and articles of reviews and surveys comprised of 48.3146%, 22.4719%, 16.8539% (15), and 12.3595% (11) of the reviewed articles, respectively. The basic features of this evolving approach were identified in these aspects. We also determined open issues in terms of evaluation and benchmarking that hamper the utility of this technology. Furthermore, with the exception of the 89 papers reviewed, the new recommendation pathway solution was described in order to improve the measurement process for smartphone-based skin cancer diagnosis applications.

Attached are 80 Forever stamps. Please accept them as a donation since I am incarcerated and don't have the means to donate otherwise. Yaser Albahri, Mayo, FL We are so [...]

New correlations are developed to calculate the molecular weight, the carbon to hydrogen weight ratio, and the aniline point temperature for petroleum fractions and coal liquids over a wide range (70 < MW < 1,685 g/mol, 5.4 < C/H < 15.1, and 45.5 < AP < 106.4 °C), using the average boiling point and the standard specific gravity as input parameters. The developed correlations are more accurate than similar ones in the literature with squared correlation coefficients of 0.9853, 0.95 and 0.9452 for MW, C/H and AP, respectively. The proposed correlations have the advantage of combined accuracy, simplicity and convenience compared to similar methods. They cover a wider property range and apply to petroleum fuels and coal liquids as well. In addition, examples are provided to utilise the predictions of the developed correlations to calculate the average molecular formula for fuels and coal liquids. [Received: October 26, 2016; Accepted: May 24, 2017]

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.

Theoretical models have become increasingly complex, but the dual-phase structural equation modelling (SEM) and artificial neural network analysis can be used by scholars to unveil the causal interactions and nonlinear relationships between variables. However, not only a single open issue and challenge—but several of them—are encountered in the use of different multi-assessment types of measurement model to achieve the reliability and validity whilst implementing SEM, but the gaps have not been fully determined at present. The issues significantly impact the effectiveness process of selecting the most suitable method to assess the measurement model of SEM. Once the best sequence quality improvement is met, it then needs to present a recommendable solution. To this end, this study completes the literature by presenting a systematic review of all main advanced aspects of the SEM reliability and validity approaches. Firstly, the databases of ScienceDirect, IEEE Xplore, Web of Science and Scopus were checked for the retrospective studies. A total of 239 papers were gathered for the period covering 2016 to June 2021. Then, the obtained articles were filtered according to the predefined inclusion criteria. Sixty articles were ultimately selected and divided into three categories (single, hybrid and other types) to enable a new representation of the crossover taxonomy amongst 'SEM reliability and validity' and 'multi-assessment methods for structural model' for the first time. The three categories had been matched with the SEM processes, and each of the detailed models were defined to determine the sets of principal criteria of the entire selected SEM approaches. Consequently, this multi-field interdisciplinary review was used to expose the state-of-the-art challenges and open issues (i.e. multiple-evaluation criteria, importance criteria and data variation) related to the sets of SEM criteria necessitating a selection process for deriving the best SEM method. Each issue entailed a 'wherefore', and multi-criteria decision making was adopted to handle the complexity problems in the different cases. Thus, a new three-phase decision-making methodology was constructed. In the first phase, a decision matrix (DM) was identified for the SEM approach; the composition of the decision alternatives and identified criteria were derived from the academic literature. In the second phase, the development methodology was achieved on the basis of the integrated multi-criteria DM techniques. The analytic hierarchy process was used for the subjective weighting of the criteria within the constructed DM, whereas the vlsekriterijumska optimizcija i kaompromisno resenje technique was used for ranking and selecting the best SEM methods. In the third phase, an objective validation approach was adopted to validate the proposed methodology. The outcome of this novel approach is intended to guide decision makers and policymakers on the easy evaluation of their goals of selecting the most suitable computing methods and the improvement of the reliability and validity of SEM. [ABSTRACT FROM AUTHOR]

A method is developed for simulating undefined multicomponent mixtures such as petroleum fractions, coal liquids, and gas condensates using a preselected set of pure components. The molecularly explicit characterization model (MECM) uses routinely made measurements of a petroleum fraction's bulk properties such as the ASTM D86 distillation, API gravity, Reid vapor pressure (RVP), and the paraffin, naphthene, and aromatic (PNA) content along with the pure components' properties in an optimization algorithm to define the molecular composition of the petroleum fuel suitable for simulation purposes. The model is tested using a set of 68 pure components to characterize petroleum naphtha and simulate its separation in a distillation column. This model is found to be of better accuracy than the characterization methods currently used. The method has the flexibility to be tailored to use any set of pure components as desired and may further be used to predict the properties of petroleum fractions.

A theoretical method for predicting the auto ignition temperature (AIT) of pure components is presented. Artificial neural networks were used to investigate several structural group contribution (SGC) methods available in the literature. The networks were used to probe the structural groups that have significant contribution to the overall AIT property of pure components and arrive at the set of groups that can best represent the auto ignition temperature for about 490 substances. The 58 single and binary structural groups listed were derived from the Ambrose, Joback, and Chueh−Swanson definitions of group contributions and modified to account for the location of the functional groups in the molecule. The proposed method can predict the auto ignition temperature of pure components, from only the knowledge of the molecular structure, with an average error of 2.8% and a correlation coefficient of 0.98. The results are further compared to the more traditional approach of the SGC method along with other methods in the literature, and shown to be far more accurate. The method is notable for the absence of any method which has previously been used to estimate pure component AIT from their molecular structure alone.

A theoretical method for predicting the octane number of pure hydrocarbon liquids is presented. The method is based on a structural group contribution approach and requires no experimental procedure or knowledge of the physical or chemical properties only the chemical structure of the molecule. The proposed model is simple and can predict the research and motor octane numbers of more than 200 pure hydrocarbon liquids with an average deviation of 4 and 5.7, respectively. The results of two different sets of structural groups derived from the Joback group contribution approach are tested and compared. The method is notable for the absence of any theoretical procedure which has previously been used to estimate the pure-component octane number. In addition, the method has the potential advantage of synthesis of additional hydrocarbons with knock measurements as a major objective.

The Muon g-2 Experiment at Fermi National Accelerator Laboratory (FNAL) has measured the muon anomalous precession frequency ωma to an uncertainty of 434 parts per billion (ppb), statistical, and 56 ppb, systematic, with data collected in four storage ring configurations during its first physics run in 2018. When combined with a precision measurement of the magnetic field of the experiment's muon storage ring, the precession frequency measurement determines a muon magnetic anomaly of aµ(FNAL)=116592040(54)×10-11 (0.46 ppm). This article describes the multiple techniques employed in the reconstruction, analysis, and fitting of the data to measure the precession frequency. It also presents the averaging of the results from the 11 separate determinations of ωma, and the systematic uncertainties on the result. [ABSTRACT FROM AUTHOR]

Background and objectivesClassical homocystinuria (CHU) due to Cystathionine Beta-Synthase (CBS) deficiency is rare autosomal recessive inborn error of amino acids metabolism causing accumulation of methionine and homocysteine. The estimated prevalence of CHU is between 1/20,000 and 344,000. However, in Qatar it is approximately 1/1,800 births, the highest in the world. Brain white matter lesions unrelated to thromboembolic events are not commonly described in CHU. We present four patients with pyridoxine non-responsive CHU, who were found to have white matter changes on brain MRI, with a review of their clinical characteristics and neurological outcome.MethodsRetrospective review of medical records for four patients previously diagnosed with CHU. All patients had an underlying homozygous mutation of c.1006C>T (p.R336C) in the CBS gene and were diagnosed before the implementation of the National Metabolic Newborn Screening Program in Qatar.ResultsCerebral white matter changes were detected by brain MRI in all the four patients with pyridoxine non-responsive CHU whom had poor compliance to medication and diet restriction. In case (1) brain MRI was requested after signs of upper motor neuron lesion. Case (2) brain MRI was done due to history of headaches. In case (3), MRI brain was requested as a follow up for abnormal EEG findings. Finally, in case (4) brain MRI was done to investigate the complications of a road traffic accident. Hypermethioninemia was documented in all our patients with levels ranging between 600->1000 umol/L. We observed in two of our patients, case (2) and (3), that when blood methionine levels dropped because of better compliance, previously seen brain white matter changes were reversible. This supports the hypothesis of hypermethioninemia rather than Betaine treatment as the cause of the white matter changes.ConclusionOur report confirms the finding of reversible cerebral white matter changes in poorly controlled patients with CHU. We stress on the importance of family/patient education to improve compliance with treatment and the dietary restriction to prevent serious complications. Further research is recommended to improve our understanding of the correlation between methionine levels the appearance of cerebral white matter changes, their progression and resolution.

• A soft computing approach to address non-reference missing data at large volumes. • An approach to reconstruct dataset with huge amount of missing. • Making a representative portions of data for missing data imputation. Missing data is a common problem in real-world data sets and it is amongst the most complex topics in computer science and many other research domains. The common ways to cope with missing values are either by elimination or imputation depending of the volume of the missing data and its distribution nature. It becomes imperative to come up with new imputation approaches along with efficient algorithms. Though most existing imputation methods focus on a moderate amount of missing data, imputation for high missing rates over 80% is still important but challenging. Even with the existence of some works in addressing high missing volume issue, they mostly rely on imputing reference dataset (Complete Datasets for evaluation) after they create artificial missing values and impute it to measure the accuracy of their proposed techniques. So far, the option of imputing high proportions of missing values with no reference comparison dataset (Original Dataset with highly missing values) have been often ignored or overlooked. Therefore, we propose a missing data imputation approach for high volumes of missing values with no reference comparison dataset. The approach makes use of pre-processing measures and breaking the dataset into small continuous non-missing portions then using Multi Criteria Decision-making analysis to select a portion of data which is representative of the entire broken datasets. This portion helps to create reference comparisons and expands the missing dataset through artificial missing-making procedures with different percentages and imputation using different machine learning techniques. This study conducted two experiments using BMI datasets with more than 80% of missing values, derived from the National Child Development Centre (NCDRC) at Sultan Idris Education University (UPSI), Malaysia. The results show that our approach capability in reconstructing datasets with huge missing values. [ABSTRACT FROM AUTHOR]

Several letters to the editor are presented in response to the articles published in previous issues including "West Bank Pieds Noirs" by John Finnessey in the January/February 2017 issue, an article on designating Middle East as Nuclear Weapon-Free Zone (NWFZ), and Israeli-Palestinian conflict.