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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.

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 (

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.

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.

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 ([Formula: see text]), response selection ([Formula: see text]) and IDS evaluation ([Formula: see text]). 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.

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, [Formula: see text]-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 [Formula: see text]-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 [Formula: see text]-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean [Formula: see text]-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.

This study aims to analyze the value of character education in the Hajj verses to produce an extract of character education value. It is expected as an offer of thought from the Al-Qur’an as an authentic source to shape personal, family and community character. This type of study was qualitative literature, the primary data obtained through written and digital literature. It used an interpretive descriptive data analysis method with a thematic approach to describe and analyse the Hajj verses about the value of character education. The results of this study indicated that the verse related to Hajj is in QS Al-Baqarah/2: 114. This verse conceived the value of character education, and the author divided it into four categories; first: the characters related to Allah, such as taqwa, istiqamah, gratitude, sincere, patient, not negligent heart, second: the characters related to humans, such as tolerance, favoring deliberation, collaborative, hardworking, honest, keeps promises, fair, compassionate, iffah, third: the character of the social environment sensitivity such as loving the environment and animals, science lover, social care, fourth: the character of the strength of belief: visionary, optimism, mission maturity and strength of belief.

The aim of the research is to develop a Scratch Quiz Game using AppsGeyser. The method used in this research is Design and Creation, which is a research method that describes and creates a new product. The steps in this method are Awareness, Suggestion, Development, Evaluation and Conclusion. It is known that the research results have succeeded in designing a Scratch Quiz game using AppsGeyser. The stages carried out from the initial stages of research, development by applying the GDLC (Game Development Life Cycle) development method starting from the initiation, pre-production, production, testing, beta, and release stages until testing has been carried out. The test results show that 57% can understand the meaning of the game being tested without the need for direction by the game maker, 82% agree that the display is attractive, 64% of every game question has the correct answer, 71% of students agree that the application can replace class exams conventional, but on the question of whether students can make applications similar to AppsGeyser, they answered % stating that students still do not understand in making similar game applications.

This study was compiled based on library data and field research. In addition, many books related to the issues discussed are also used as primary and secondary reading material. All data are explored, categorized and analyzed using recursive and interpretive approaches to obtain a more complete understanding. The results of this paper show that tips for teachers in implementing a religious education curriculum in MIN Sabang can help students' parents and students by applying moral values, worship, and other religious precepts to their daily behavior. It shows that reaching out to Then make these values ​​a habit. The curriculum applied to the MIN Sabang is a competency-based curriculum that is designed to be a MIN education process that is conducive to the development of the potential of students. So that they are able to live independently as well as being able to live in the midst of a pluralistic society. The curriculum focuses more on competency targets than material mastery. The lack of quality teachers and the lack of supporting facilities are challenges faced by teachers in implementing the curriculum and because of this, religious teachers at MIN Sabang have not found effective tips as an effort to foster religious education at MIN Sabang.

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.

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.

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.

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.

Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast background of knowledge. This annotation process is costly, time-consuming, and error-prone. Usually, every DL framework is fed by a significant amount of labeled data to automatically learn representations. Ultimately, a larger amount of data would generate a better DL model and its performance is also application dependent. This issue is the main barrier for many applications dismissing the use of DL. Having sufficient data is the first step toward any successful and trustworthy DL application. This paper presents a holistic survey on state-of-the-art techniques to deal with training DL models to overcome three challenges including small, imbalanced datasets, and lack of generalization. This survey starts by listing the learning techniques. Next, the types of DL architectures are introduced. After that, state-of-the-art solutions to address the issue of lack of training data are listed, such as Transfer Learning (TL), Self-Supervised Learning (SSL), Generative Adversarial Networks (GANs), Model Architecture (MA), Physics-Informed Neural Network (PINN), and Deep Synthetic Minority Oversampling Technique (DeepSMOTE). Then, these solutions were followed by some related tips about data acquisition needed prior to training purposes, as well as recommendations for ensuring the trustworthiness of the training dataset. The survey ends with a list of applications that suffer from data scarcity, several alternatives are proposed in order to generate more data in each application including Electromagnetic Imaging (EMI), Civil Structural Health Monitoring, Medical imaging, Meteorology, Wireless Communications, Fluid Mechanics, Microelectromechanical system, and Cybersecurity. To the best of the authors’ knowledge, this is the first review that offers a comprehensive overview on strategies to tackle data scarcity in DL.

Sabang City has the characteristics of diverse natural tourism and can be used as a tourist spot for the community. In choosing a tourist spot, it is certainly not easy, apart from distance, cost, transportation and the number of family members, it is also necessary to consider so that the selection of tourist attractions visited by the community is in accordance with their wishes and financial conditions. The purpose of this research is to apply a recommendation system using the fuzzy logic of the Tsukamoto method and a web-based programming language as the language that will be used in making a decision support system application program to determine the best tourist attraction in Aceh. The several stages of work consist of; data collection, analysis, application design and design, implementation, and testing and evaluation. The results of research and testing of the Decision Support System for Determining the Best Tourist Attractions Using the Fuzzy Tsukamoto Method in the City of Sabang Web-Based that have been carried out by the author, several conclusions can be drawn, namely; 1) The expert system application to find the best tourist attraction is an application based on rules to solve problems to determine the best tourist attraction, especially in Sabang City with a high level of accuracy and is used as a tourist reference to determine the tourist attraction to be selected, 2) With the Expert System In this case, tourists or people who need it as a reference about the best tourist objects in Sabang City and as a reference for the Sabang City Government, and 3) The results of the analysis generated from this system are the same as the results of manual calculations using the theory of the Fuzzy Tsukamoto Method so that the accuracy The results are in accordance with the calculations obtained from the trial.

Incoming and outgoing mail information systems have an important role as a source of information and documentation media. As a source of information, archives are material/data for making the right decisions, so that archives can be said as a system that is interrelated with each other in a unified bond, because archives can support a program of organizational activities, both in terms of planning, implementation and controlling the tasks of the organization concerned, from leading activities to decision-making activities. Archives as documentation media, archives are official evidence that can be accounted for for government activities, therefore it is an obligation for every employee who works in the archives unit in this organization to carry out their duties properly, so that correspondence services can be more effective. and efficient, while the purpose of this study is to analyze the Archives Management System at the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University. In this study, researchers used descriptive research methods with a qualitative approach. This study aims to develop concepts and facts in depth to answer how the Archives Management System in the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University. The archive management system at the Department of Earth Sciences, Faculty of Engineering, Syiah Kuala University uses a number system and a dating system that is still less effective but has been running well, because it uses a combined procedure of centralization and decentralization. The Prototyping method is used as a software development model and evaluation activities are also carried out as an assessment of the results of the prototype built. Based on the evaluation of the appearance of the current desktop application. With the prototype design, it is known that each category has increased usability values. Content, Organization and Readability previously had a value of 0.64 and increased to 0.72. The Navigation category was previously 0.69 to 0.71. The previous User Interface Design category was 0.58 to 0.74 and the previous Performance and Effectiveness category was 0.62 to 0.72.

Village development has a very important and strategic role in the context of National Development and Regional Development. In village development, the village government is located as a subsystem of the government administration system in Indonesia, so that the village has the authority, duties and obligations to regulate and manage the interests of its own community. In carrying out the authority, duties, and obligations of the village in the administration of government and development, a source of village income is needed. In the process of managing the Village (Gampong) Fund Allocation, Peukan Bada District, Aceh Besar District, the village government is faced with the condition of the community's education level being still weak. Based on previous research, this research has a purpose, namely to describe and analyze the Management of Village Fund Allocation in empowering rural communities; the driving and inhibiting factors for the management of Village Fund Allocation in empowering rural communities in Peukan Bada District, Aceh Besar District. The objectives to be achieved from this research are; create an online budget data processing information system design, and improve all aspects of data forms or expand the relationship or relationship of each data. The design method used is the waterfall model taking basic activities such as specification, development, validation, and evolution and representing them as phases such as requirements specification, software design, implementation, testing and so on. Based on the results of the analysis and design of the village fund allocation information system, it can be seen that until now it is still done manually. The author can conclude including; the design of the village fund allocation application can provide convenience in carrying out the implementation process of gampong development, and the use of a system that is still manual has limitations in the data storage process, data search and report generation process.

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.

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.

[Abstract] The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations. Taiwan. Ministry of Science and Technology; 108-2511-H-224-007-MY3

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.

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

This study presents a benchmarking and evaluation approach for active queue management (AQM) network congestion control methods, which are considered as a problem of multi-criteria decision-making (MCDM). In recent years, the development of MCDM methods has been studied from various perspectives. The latest one called fuzzy decision by opinion score method (FDOSM) has proved its efficiency in solving the concerns faced by other methods. However, the approach of FDOSM and its extension is based on fuzzy type-1, which suffers from issues, especially minimising the effect of data uncertainties. Therefore, this study extended FDOSM into a fuzzy type-2 environment that utilises interval type-2 trapezoidal (IT2T) membership, and then discusses the effectiveness of such membership on AQM method benchmarking. The methodology of this study involves two consecutive phases. The first phase is the construction of a decision matrix utilised in AQM method benchmarking based on a list of AQM methods and multiple evaluation criteria. The second phase is regarding the new method (IT2T-FDOSM), which illustrated two main stages, namely, data transformation unit and data processing. The findings of this study are the following: (1) Individual results of benchmarking which used six decision-makers are almost similar, with the AQM fuzzy GRED method ranked as the best. (2) The group benchmarking results show that a relatively similar order and fuzzy GRED method is the best as well. (3) IT2T-FDOSM can deal with the uncertainty problem properly. (4) The results show significant differences amongst the groups’ scores, which indicate the validity of the IT2T-FDOSM results.

This paper aims to propose a grouping framework for benchmarking the active queue management (AQM) methods of network congestion control based on multicriteria decision-making (MCDM) techniques to assist developers of AQM methods in selecting the best AQM method. Given the current rapid development of the AQM techniques, determining which of these algorithms is better than the other is difficult because each algorithm performs better in a specific metric(s). Current benchmarking studies benchmark the AQM methods from a single incomplete prospective. In each proposed AQM method, the benchmarking was achieved with reference to some evaluation measures that are relatively close to the desired goal being followed during the development of the AQM methods. Furthermore, the benchmarking frameworks of AQM methods are complicated and challenging because of the following reasons: (1) the technical details of the AQM methods are adapted and the input parameters are selected according to the sensitivity of the AQM methods; and (2) a framework is developed and designed for simulating AQM methods, the simulated network and the collected results. For this purpose, a set of criteria for AQM comparison are determined. These criteria are performance, processing overhead and configuration. The benchmarking framework is developed based on the crossover of three groups of multi-evaluation criteria and several AQM methods as a proof of concept. The AQM families that are implemented and utilized in experiments to generate the data that are used as a proof of concept of our proposed framework are the parameter-based (pars) and fuzzy-based AQM methods. Accordingly, constructing the decision matrix (DM) that will be used to generate the final results is necessary. Subsequently, the underlying AQM methods are benchmarked and ranked using MCDM techniques, namely, integrated analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal solution (TOPSIS). The validation was performed objectively. The [Formula: see text] deviation was computed to ensure that the AQM methods ranking undergo systematic ranking. Results illustrate that (1) the integration of AHP and TOPSIS solves the AQM method benchmarking problems; (2) results of the individual TOPSIS context clearly show variances among the ranking results of the six experts; (3) the ranks of the AQM methods obtained from internal and external TOPSIS group decision-making are nearly similar, with random early detection method being ranked as the best one; and (4) in the objective validation, significant differences were found between the groups’ scores, thereby indicating that the ranking results of internal and external TOPSIS group decision-making were valid.

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.

Ranking the strengths and weaknesses of software engineering students in software development life cycle (SDLC) process level is a challenging task owing to (1) data variation, (2) multievaluation criteria, (3) criterion importance and (4) alternative member importance. According to the existing literature, no specified procedure can rank the ability of software engineering students based on SDLC process levels to figure out the strengths and weaknesses of each student. This study aims to present a novel triplex procedure for ranking the ability of software engineering students to address the literature gap. The methodology of the proposed work is presented on the basis of three phases. In the identification phase, four steps are implemented, namely, processing dataset, identifying the criteria, distributing the courses to the software engineering body of knowledge and proposing the pre-decision matrix (DM). The data comprise the GPA and soft skills from 60 software engineering students who graduated from Universiti Pendidikan Sultan Idris in 2016. In the pre-processing phase, three steps are involved as follows. Analytic hierarchy process (AHP) is first used to assign weights to the courses and then multiply the assigned weight by courses, which is the first procedure in the proposed work. In this phase, the construction of DM is presented based on multimeasurement criteria (GPA and soft skills), with SDLC process levels as alternatives. In the development phase, AHP is used again to weight the multimeasurement criteria, and this is the second procedure. In such case, the coordinator and head of the software engineering department are consulted to obtain subjective judgments for each criterion. Technique for order performance by similarity to ideal solution (TOPSIS) is then used to rank the students, which is the third procedure. In the validation, statistical analysis is performed to validate the results by checking the accuracy of the systematic ranking. Results show that (1) integrating AHP and group TOPSIS is suitable for ranking the ability of students. (2) The 60 students are categorized into five ranking groups based on their strength level: 14 collector requirements, 13 designers, 5 programmers, 13 testers and 15 maintenances. (3) Significant differences are observed between the groups’ scores for each level of SDLC, indicating that the ranking results are identical for all levels.

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.

Pendidikan kecerdasan emosional adalah pendidikan untuk mampu mengenali. Mengekspresikan dan mengelola emosi, baik emosi dirinya sendiri maupun emosi orang lain. Dengan tindakan konstruktif, yang mempromosikan kerja sama sebagai Tim yang mengacu pada produktivitas dan bukan pada konflik. Dalam penelitian ini penulis menjelaskan bagaimana strategi guru dalam mendidik kecerdasan emosional siswa. Tujuan penelitian penelitian ini adalah untuk mengetahui strategi apa yang digunakan guru dalam mendidik kecerdasan emosionas siswa di SMA Jaya Langsa dan untuk mengetahui kendala-kendala yang dihadapi guru dalam mendidik kecerdasan emosional siswa di SMA Jaya Langsa. Adapun metode penelitian penelitian ini bersifat deskriptif yaitu suatu kajian yang membahas masalah yang sedang terjadi dengan menggunakan penelitian kepustakaan dan penelitian lapangan yaitu dengan cara observasi wawancara dan angket dengan sampelnya para murid di SMA Jaya Langsa yang jumlahnya sebanyak 45 orang dan Kepala sekolah serta guru. Berdasarkan hasil penelitian dapat diambil suatu kesimpulan bahwa Strategi guru dalam mendidik kecerdasan emosional siswa di SMA Jaya Langsa adalah berupa penetapan kedisiplinan. Penyediaan buku-buku paket yang berisi tentang kecerdasan emosional dan penerapan kegiatan ekstrakulikuler berupa pemantapan mental emosional melalui kegiatan Pramuka dan kegiatan itu berlangsung bersama dengan kegiatan proses belajar-mengajar. melalui penerapan strategi khusus dengan pembinaan pengajaran kepada para guru dan menciptakan kegiatan yang khusus di luar sekolah. kendala-kendala yang dihadapi guru dalam pelaksanaan strategi mendidik kecerdasan emosional siswa yaitu kurangnva pemahaman guru terhadap pentingnya kecerdasan emosional, kurangnya pengetahuan guru tentang strategi khusus dalam mendidik kecerdasan emosional siswa ,dan kurangnya media dalam membantu guru untuk mendidik kecerdasan emosional siswa,untuk itu diharapkan kepada para guru agar dapat mendalami pengetahuannya tentang kecerdasan emosional agar dapat melahirkan murid--murid yang tidak hanya cerdas intelektual tetapi juga cerdas emosional.

A review is conducted to deeply analyse and map the research landscape of current technologies in finger vein (FV) biometric authentication in medical systems into a coherent taxonomy. This research focuses on articles related to the keywords `biometrics', `finger veins' and `verification' and their variations in three major databases, namely, Web of Science, ScienceDirect and IEEE Xplore. The final set of collected articles related to FV biometric authentication systems is divided into software- and hardware-based systems. In the first category, software development attempts are described. The experiment results, frameworks, algorithms and methods that perform satisfactorily are presented. Moreover, the experiences obtained from conducting these studies are discussed. In the second category, hardware development attempts are described. The final articles are discussed from three aspects, namely, (1) number of publications, (2) problem type, proposed solutions, best results and evaluation methods in the included studies and (3) available databases containing different scientific work collected from volunteers, such as staff and students. The basic characteristics of this emerging field are identified from the following aspects: motivations of using FV biometric technology in authentication systems, open challenges that impede the technology's utility, authors' recommendations and future research prospects. A new solution is proposed to address several issues, such as leakage of biometrics that leads to serious risks due to the use of stolen FV templates and various spoofing and brute-force attacks in decentralised network architectures in medical systems, including access points and various database nodes without a central point. This work contributes to literature by providing a detailed review of feasible alternatives and research gaps, thereby enabling researchers and developers to develop FV biometric authentication medical systems further. Insights into the importance of such a technology and its integration into different medical applications and fields are also provided.

Telemedicine is increasingly used in the modern health care system because it provides health care services to patients amidst distant locations. However, the prioritisation process for patients with multiple chronic diseases (MCDs) over telemedicine is becoming increasingly complex due to diverse and big data generated from multiple disease conditions. To solve such a problem, massive datasets must be collected, and high velocity must be acquired, specifically in real-time processing. This process requires decision-making (DM) regarding the emergency degree of each chronic disease for every patient. Multi-criteria decision-making (MCDM) approaches (i.e. direct aggregation, distance measurement and compromise ranking) are the main solutions for dealing with this complex situation. However, each MCDM approach provides a unique rank from those of other methods. By far, the preferred DM approach that can provide an ideal rank better than other approaches has not been established. This study proposes an extension of the technique for reorganising opinion order to interval levels (TROOIL). Such an extension is called Hybrid DM and Voting Method (HDMVM) which is based on different DM approaches (i.e. direct aggregation, distance measurement and compromise ranking). HDMVM is used to prioritise big data of patients with MCDs in real-time through the remote health-monitoring procedure. In this paper, we propose a methodology that is based on three sequential stages. The first stage illustrates how the big data of patients with MCDs can be recognised in the telemedicine environment and identifies the target telemedicine tier in this study. The second stage describes the steps of the proposed HDMVM sequentially. The third stage applies the proposed method by prioritising the case study of big data of patients with MCDs based on the above DM approaches. Moreover, dataset of remote patients with MCDs (n = 500 ) is adopted, which contains three diseases, namely, chronic heart diseases and high and low blood pressures. The prioritisation results vary among direct aggregation, distance measurement and compromise approaches. The proposed HDMVM effectively provides a uniform and final ranking result for big data of patients with MCDs. A statistical method (i.e. mean) is performed to objectively validate the ranking results. Significant differences between the scores of the groups are identified in the objective validation, signifying identical ranking results. The evaluation of the proposed work with the benchmark study indicates that this study has tackled issues relevant to big data and diversity of MCDM approaches in the prioritisation of patients with MCDs.

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.