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.

Software similarity in different programming codes is a rapidly evolving field because of its numerous applications in software development, software cloning, software plagiarism, and software forensics. Currently, software researchers and developers search cross-language open-source repositories for similar applications for a variety of reasons, such as reusing programming code, analyzing different implementations, and looking for a better application. However, it is a challenging task because each programming language has a unique syntax and semantic structure. In this paper, a novel tool called Cross-Language Software Similarity (CroLSSim) is designed to detect similar software applications written in different programming codes. First, the Abstract Syntax Tree (AST) features are collected from different programming codes. These are high-quality features that can show the abstract view of each program. Then, Methods Description (MDrep) in combination with AST is used to examine the relationship among different method calls. Second, the Term Frequency Inverse Document Frequency approach is used to retrieve the local and global weights from AST-MDrep features. Third, the Latent Semantic Analysis-based features extraction and selection method is proposed to extract the semantic anchors in reduced dimensional space. Fourth, the Convolution Neural Network (CNN)-based features extraction method is proposed to mine the deep features. Finally, a hybrid deep learning model of CNN-Long-Short-Term Memory is designed to detect semantically similar software applications from these latent variables. The data set contains approximately 9.5K Java, 8.8K C#, and 7.4K C++ software applications obtained from GitHub. The proposed approach outperforms as compared with the state-of-the-art methods.

This study evaluated the benchmarking process of active queue management (AQM) methods, which consider a multicriteria decision-making (MCDM) problem using multidimensional criteria. Academic studies have benchmarked the AQM methods using MCDM techniques. However, these studies have used existing MCDM techniques, which face considerable theoretical challenges. The latest MCDM method called fuzzy decision by opinion score (FDOSM) was published in the Journal of Applied Soft Computing in 2020 to address the theoretical challenges of the existing MCDM methods. However, FDOSM continues to encounter serious issues. That is, it exclusively depends on the direct aggregation MCDM approach based on arithmetic mean (AM) operator. However, performing other operators (i.e., geometric mean, harmonic mean, and root mean square), in addition to applying other MCDM approaches (i.e., distance measurement and compromise rank), may result in different ranking results. Hence, this study mainly proposes an extension of FDOSM through the following aspects: (1) application of different aggregation techniques in the direct aggregation MCDM approach, (2) discussion of the effectiveness of each type on the final AQM benchmarking, and (3) use of varying MCDM approaches on FDOSM to reach the optimum result when benchmarking the AQM methods. The current research methodology is based on two sequential phases. The first phase provides the decision matrix used in benchmarking the AQM methods. The decision matrix was constructed based on the AQM evaluation criteria and a list of AQM methods. The second phase presents two stages, namely, data transformation unit and data processing. Findings of the AQM benchmarking are as follows. (1) In the individual FDOSM, two main configurations are recommended when using the AQM benchmarking: direct aggregation MCDM approach with AM operator and compromise rank approach. Benchmarking results of both configurations based on six decision makers are nearly similar, with the AQM BLUE method being ranked the best. The exception is for the results of the compromise rank approach based on the third decision maker, which revealed that the AQM ERED method is the best. (2) Results of the group FDOSM showed a relatively similar order for the AQM methods in both configurations, with the AQM BLUE method being the best. (3) Lastly, significant differences were found among the groups' scores, thereby indicating the validity of the FDOSM-based AQM benchmarking results.

The benchmarking of smart e-tourism data management applications falls under the problem of multicriteria decision-making (MCDM). This claim is supported by three issues: 12 smart key concepts need to be considered in the evaluation, criteria importance, and data variation among these criteria. Thus, an MCDM solution is essential to overcome problem complexity. To end this, this study presents a decision-making framework on the basis of the extension of interval type 2 trapezoidal-fuzzy weighted with zero inconsistency (IT2TR-FWZIC) integrated with the Vlsekriterijumska Optimizcija I Kaompromisno Resenje (VIKOR) method for evaluating and benchmarking the smart e-tourism data management applications. Our methodology comprises two consecutive phases. In the first phase, a decision matrix is constructed using the intersection between the 12 key concepts and smart e-tourism data management applications of each category and subcategory in smart e-tourism. In the second phase, the integration of the IT2TR-FWZIC formulation and VIKOR is presented to compute the weights for the 12 key concepts and benchmark the smart e-tourism data management applications for each category. The results are as follows: (1) A clear difference is found among the criteria weights (12 smart key concepts). Specifically, the real-time criterion achieves the highest importance weight (0.098), whereas augmented reality obtains the lowest weight (0.068). The context-awareness and recommender systems have the same weight value (0.087), and the other eight criteria are distributed in between. (2) The smart e-tourism data management applications are evaluated and benchmarked effectively per category and subcategories. (3) Benchmarked applications in each category are subjected to a systematic ranking in the evaluation process. The sensitivity analysis has shown high correlation outcomes to the systematic ranking results over the 31 scenarios of criteria weight changing. Moreover, a comparative analysis of the proposed work with other existing studies is also discussed.