Comprehensive Assessment of Distance Learning Modules by Fuzzy AHP-TOPSIS Method

Bekešienė, Svajonė; Vasiliauskas, Aidas Vasilis; Hošková-Mayerová, Šárka; Vasilienė-Vasiliauskienė, Virgilija

doi:10.3390/math9040409

Cited by 24 publications

(14 citation statements)

References 51 publications

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“…The attributes' weights that were obtained by AHP are provided to the TOPSIS method under HF conditions to achieve the ranking order for the alternatives. The normalized fuzzy decision-matrix for 8 attributes and 10 competitive alternatives was determined through some intermediary operations by incorporating point 10 and Equation (18) and is depicted in Table 5. By incorporating Equations ( 19) and ( 22), the normalized fuzzy decision-matrix cell values (performance-values) are multiplied by each attribute weight value, yielding a weighted fuzzy normalized decision-matrix, as shown in Table 6.…”

Section: Numerical Calculations and Resultsmentioning

confidence: 99%

“…. X K was taken, and construction of summarized decision matrix X = [x ij ] was completed with the help of Equation (18).…”

Section: Adopted Methodologymentioning

confidence: 99%

“…Almotiri (2021) proposed a study to assess the effectiveness of malicious traffic detention systems. In this study, he used AHP-TOPSIS under fuzzy environment to evaluate six malicious traffic detention systems [18]. The Host-based malicious traffic detection strategy (MTD4) was shown to be the most successful and long-lasting malicious traffic detection mechanism among the six alternatives in this investigation.…”

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confidence: 96%

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Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

et al. 2021

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Machine learning (ML) is one of the dominating technologies practiced in both the industrial and academic domains throughout the world. ML algorithms can examine the threats and respond to intrusions and security incidents swiftly in an instinctive way. It plays a critical function in providing a proactive security mechanism in the cybersecurity domain. Cybersecurity ensures the real time protection of information, information systems, and networks from intruders. Several security and privacy reports have cited that there has been a rapid increase in both the frequency and the number of cybersecurity breaches in the last decade. Information security has been compromised by intruders at an alarming rate. Anomaly detection, phishing page identification, software vulnerability diagnosis, malware identification, and denial of services attacks are the main cyber-security issues that demand effective solutions. Researchers and experts have been practicing different approaches to address the current cybersecurity issues and challenges. However, in this research endeavor, our objective is to make an idealness assessment of machine learning-based intrusion detection systems (IDS) under the hesitant fuzzy (HF) conditions, using a multi-criteria decision making (MCDM)-based analytical hierarchy process (AHP) and technique for order of preference by similarity to ideal-solutions (TOPSIS). Hesitant fuzzy sets are useful for addressing decision-making situations in which experts must overcome the reluctance to make a conclusion. The proposed research project would assist the machine learning practitioners and cybersecurity specialists in identifying, selecting, and prioritizing cybersecurity-related attributes for intrusion detection systems, and build more ideal and effective intrusion detection systems.

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Section: Numerical Calculations and Resultsmentioning

confidence: 99%

“…. X K was taken, and construction of summarized decision matrix X = [x ij ] was completed with the help of Equation (18).…”

Section: Adopted Methodologymentioning

confidence: 99%

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confidence: 96%

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Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

et al. 2021

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“…It is feasible and effective to use MADM methodologies to discuss the comprehensive assessment of alternatives [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

A Hybrid Z-Based MADM Model for the Evaluation of Urban Resilience

Huang

2021

Mathematical Problems in Engineering

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Natural disasters and man-made incidents have many negative impacts on major cities, including casualties, economic losses, disruption of social order, and environmental contamination. Cities need to be resilient in order to protect people’s lives and property. Although research on urban resilience has been rapidly emerging in recent years, there are still some research gaps. The interplay of attributes for assessing urban resilience has not been explored, and the Multiple Attribute Decision Making- (MADM-) based framework for evaluating urban resilience is rarely studied. Therefore, this study proposes a novel model to evaluate urban resilience, the Z number-based Decision-Making Trial, and Evaluation Laboratory (Z-DEMATEL), to identify the mutual influential relationships and the weights of the attributes. In addition, the Z number-based Reference Ideal Method (Z-RIM) is used to determine the resilience capacity of cities and to suggest improvements for decision makers to develop appropriate strategies. In this study, we not only use trapezoidal fuzzy numbers to reflect the uncertainty of information but also measure the reliability/confidence of experts in the assessment. The integrated methodology is presented for the first time in this study, and we use the firmness data of six major metropolitan cities in Taiwan as an example of model demonstration. The results of the study show that population density, value of business activity, healthcare facilities, electricity supply, and number of business registrations are the most important attributes influencing the resilience of cities. Taipei City and Taichung City are the two major cities with better resilience in Taiwan based on the analysis of this study.

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“…The method of index superposition also includes the grey system clustering evaluation model with fixed or variable weights, the fuzzy comprehensive evaluation method, the TOPSIS model, etc. [18][19][20]. These kinds of methods easily ignore possible linear relationships, coupling relationships or causality between indicators.…”

Section: Introductionmentioning

confidence: 99%

Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai

2021

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Taking Shanghai as an example, this paper uses remote sensing (RS) and geographical information systems (GIS) technology to conduct multisource data fusion and a spatial pattern analysis of urban carrying capacity at the micro scale. The main conclusions are as follows: (1) based on the “production, living and ecology” land functions framework and land use data, Shanghai is divided into seven types of urban spaces to reveal their heterogeneity and compatibility in terms of land use functions. (2) We propose an urban carrying capacity coupling model (UCCCM) based on multisource data. The model incorporates threshold and saturation effects, which improve its power to explain urban carrying capacity. (3) Using the exploratory spatial data analysis (ESDA) technique, this paper studies the spatial pattern of carrying capacity in different urban spaces of Shanghai. (4) We analyse the causes of the cold spots in each urban space and propose strategies to improve the urban carrying capacity according to local conditions.

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Comprehensive Assessment of Distance Learning Modules by Fuzzy AHP-TOPSIS Method

Cited by 24 publications

References 51 publications

Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

Analyzing the Impact of Cyber Security Related Attributes for Intrusion Detection Systems

A Hybrid Z-Based MADM Model for the Evaluation of Urban Resilience

Research on Urban Carrying Capacity Based on Multisource Data Fusion—A Case Study of Shanghai

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