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Choosing suitable IT courses for potential applicants or graduates based on their academic performance (GPA) and soft skills is a multicriteria decision-making (MCDM) problem due to three factors: multiple evaluation criteria, uncertain importance levels of these criteria and data variability. In this study, data from 80 Bachelor of Education (Information Technology) honour graduates (AT20) at Sultan Idris Education University’s School of Art, Computing and Creative Industry in 2017 was analysed. A decision matrix was constructed for each applicant based on the analysed data. The analytic hierarchy process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods were used to determine the criteria weights and rank the IT subjects for each applicant, respectively. Results indicated the following: (i) AHP effectively allocated weights to the evaluation criteria, with GPA receiving the highest weight of 0.708 and soft skills obtaining a weight of 0.292. (ii) The integration of AHP and VIKOR (intelligent approach) efficiently ranked each IT subject for each applicant and assigned the highest-ranked subject to the applicants. A systematic ranking was conducted to validate the proposed results. Selecting IT subjects for graduates based on GPA and soft skills can considerably affect the quality of education, the success of schools and the professional development of individual educators. By incorporating these factors into the hiring process, educational institutions can foster more diverse, adaptable and high-performing teaching staff applicants.

Abstract 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.

Driver behavior is a concerning issue in the area of intelligent transportation system (ITS). Driver behavior is a significant key player in a wide range of unpleasant events during the ride, such as accidents or crashes, traffic congestion, harsh braking, and acceleration/deceleration. Influencing factors of driver behavior have been explored in several studies. It is imperative to investigate these factors in order to provide a comprehensive analysis and to categorize them on the basis of a coherent taxonomy. With that, this study conducted a systematic review on prior studies that focused on bus driver behavior, particularly in the ITS. This study also established a taxonomy on the topic of driver behavior in multiple areas of ITS and their classifications. Different databases, namely ScienceDirect, Web of Science, and IEEE Explore, were utilized to obtain relevant articles from 2008 to 2021 (15 April). Several filtering and scanning stages were performed according to the exclusion/inclusion criteria on all 2,803 articles obtained; however, only 87 articles met the criteria. The final set of articles were categorized into a taxonomy. The first part of the taxonomy focuses on five main factors that influence driver behavior: environmental, demographic, habit, vehicle, and on-road routine factors. The second part of the taxonomy discusses the mapping of data collection methods on the basis of four categories: real-time data collection, survey, simulation, and benchmark. Discussion and analysis were provided to highlight the critical literature gaps on bus driver behavior in the ITS, involving the use of real-time data collection, which is imperative for acquiring highly accurate and sophisticated data. This multi-field systematic review has exposed new research opportunities, motivations, challenges, limitations, and recommendations and highlighted the need for the synergistic integration of interdisciplinary works. Overall, this study presented pathways solution in future direction on the basis of three sequenced phases, namely design, labeling and validation, and machine learning. This study can serve as a guide for future researchers, as it addressed the ambiguities in the ITS-driver behavior domain and provided valuable information on these ITS-driver behavior trends.

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.

The vehicle-to-infrastructure (V2I) communication system allows the exchange of information between vehicles and road infrastructures. It aims to avoid or reduce vehicular accidents, increase mobility, and provide other road safety benefits. This paper aimed to review and analyze the literature on data exchanges in the V2I communication system. The factors considered to improve the understanding of various contextual aspects and the characteristics of the field were motivations, open challenges, and recommendations from other researchers. We systematically searched all articles on data exchanges in the V2I communication system from the three main databases, namely ScienceDirect, Web of Science, and IEEE Xplore, from 2008 to 2018. These indices were sufficiently extensive to encompass our field of literature. A total of 70 articles were selected based on our inclusion and exclusion criteria. Most studies (42/70) covered a developed V2I communication system, while numerous articles (22/70) focused on general research on the V2I communication system. The smallest portion of articles (6/70) comprised reviews and surveys. The V2I system plays a key role in vehicular ad hoc networks but is less implemented than vehicle-to-vehicle communication owing to its deployment costs and maintenance requirements. However, numerous studies have been conducted on the V2I communication system to promote its utility. Research areas on V2I communication classification vary but are all equally vital. We expect this systematic review to help emphasize current research opportunities and thus extend and create additional research fields.

Data exchange in Vehicle-to-vehicle (V2V) communications systems is a field that requires automated solutions, tools and methods and the capability to facilitate early detection and even a prediction. Many studies have focused on V2V system and its classification to improve road safety, reduce traffic congestion and help streamline the vehicle flow on the road. This study aims to review and analyse literature related to data exchange in V2V communications systems. The factors considered to improve the understanding of the field's various contextual aspects were derived from published studies. We systematically searched all articles about the classification and detection of data exchange in vehicles, as well as their evaluation. Three main databases, namely, ScienceDirect, Web of Science and IEEE Xplore from 2008 to 2018, were used. These indices were considered sufficiently extensive to encompass our literature. On the basis of our inclusion and exclusion criteria, 140 articles were selected. Most articles (53/140) are studies conducted in a V2V communication system; a number of papers (51/140) covered the actual attempts to develop V2V communications; and few papers (18/140) comprised framework proposals and architectures. The last portion (18/140) of articles presented review and survey articles. V2V collision avoidance system, which is a field requiring automated solutions, tools and methods, entails the capability to facilitate early detection. Several studies have been performed on the automatic detection of V2V and their subtypes to promote accurate detection. The basic characteristics of this emerging field are identified from the aspects of motivations, open challenges that impede the technology's utility, authors' recommendations and substantial analysis of the previous studies are discussed based on seven aspect (devices, number of scenario, test location, types of sensors, number of vehicle, evaluation techniques used and types of software). A propose research methodology as new direction is provided to solve the gaps identified in the analysis. This methodology consists of four phases; investigation, develop a hardware system, study and analysis, and evaluation phases. However, research areas on V2V communication with the scope of data exchange are varied. This systematic review is expected to open opportunities for researchers and encourage them to work on the identified gaps.