Simple multi attribute rating technique (SMART) method on employee promotions

Oktavianti, Euis; Komala, N.; Nugrahani, Fitria

doi:10.1088/1742-6596/1193/1/012028

Cited by 16 publications

(12 citation statements)

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“…The SMART technique is a method used for solving multi-criteria decisionmaking (MCDM) issues. It is founded on the concept that each alternative is composed of a number of criteria with values, and each criterion has a weight that indicates its relevance in comparison to other criteria [36,37]. Figure 4 shows how the SMART calculates the trust value.…”

Section: Trust Value Calculationmentioning

confidence: 99%

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

Alghofaili

Rassam

2022

Sensors

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Recently, Internet of Things (IoT) technology has emerged in many aspects of life, such as transportation, healthcare, and even education. IoT technology incorporates several tasks to achieve the goals for which it was developed through smart services. These services are intelligent activities that allow devices to interact with the physical world to provide suitable services to users anytime and anywhere. However, the remarkable advancement of this technology has increased the number and the mechanisms of attacks. Attackers often take advantage of the IoTs’ heterogeneity to cause trust problems and manipulate the behavior to delude devices’ reliability and the service provided through it. Consequently, trust is one of the security challenges that threatens IoT smart services. Trust management techniques have been widely used to identify untrusted behavior and isolate untrusted objects over the past few years. However, these techniques still have many limitations like ineffectiveness when dealing with a large amount of data and continuously changing behaviors. Therefore, this paper proposes a model for trust management in IoT devices and services based on the simple multi-attribute rating technique (SMART) and long short-term memory (LSTM) algorithm. The SMART is used for calculating the trust value, while LSTM is used for identifying changes in the behavior based on the trust threshold. The effectiveness of the proposed model is evaluated using accuracy, loss rate, precision, recall, and F-measure on different data samples with different sizes. Comparisons with existing deep learning and machine learning models show superior performance with a different number of iterations. With 100 iterations, the proposed model achieved 99.87% and 99.76% of accuracy and F-measure, respectively.

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Section: Trust Value Calculationmentioning

confidence: 99%

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

Alghofaili

Rassam

2022

Sensors

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“…Each parameter consists of some criteria that have a range of scores based on importance of resulting values [24].…”

Section: Defining Criteriamentioning

confidence: 99%

A novel trust management model for edge computing

Latif

Ahmed

Tahir

et al. 2021

Complex Intell. Syst.

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Edge computing is a distributed architecture that features decentralized processing of data near the source/devices, where data are being generated. These devices are known as Internet of Things (IoT) devices or edge devices. As we continue to rely on IoT devices, the amount of data generated by the IoT devices have increased significantly due to which it has become infeasible to transfer all the data over to the Cloud for processing. Since these devices contain insufficient storage and processing power, it gives rise to the edge computing paradigm. In edge computing data are processed by edge devices and only the required data are sent to the Cloud to increase robustness and decrease overall network overhead. IoT edge devices are inherently suffering from various security risks and attacks causing a lack of trust between devices. To reduce this malicious behavior, a lightweight trust management model is proposed that maintains the trust of a device and manages the service level trust along with quality of service (QoS). The model calculates the overall trust of the devices by using QoS parameters to evaluate the trust of devices through assigned weights. Trust management models using QoS parameters show improved results that can be helpful in identifying malicious edge nodes in edge computing networks and can be used for industrial purposes.

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“…Computing the deprivation scores requires the allocation of weight to each indicator. In this study, weights were derived using the entropy method (Gao & Sun, 2020), although alternative weighting methods such as the equal weighting method (Santos & Alkire, 2011), the CRITIC weighting method (Wang, Huang, Chen, Liu, & Xue, 2018), the SMART weighting method (Oktavianti, Komala, & Nugrahani, 2019), etc., may be considered. The deprivation scores were computed as a function of indicators as follows.…”

Section: Computation Of Multidimensional Deprivation Scoresmentioning

confidence: 99%

Multidimensional Poverty Modeling for Namibia Using the Beta Distribution

Mafale¹,

Ntirampeba²,

Ong'ala³

2021

IJSP

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Despite global efforts in alleviating poverty, many people are still living in poverty. Different methods were employed to estimate poverty with many researchers moving from monetary to multidimensional poverty modeling approach. In Namibia, very few studies have been conducted to estimate poverty in a multidimensional sense. The 2015/2016 Namibia household income and expenditure survey dataset was employed to develop multidimensional poverty indices (MPIs) using beta distribution. We showed that the MPI is equivalent to the mean of the left truncated beta distribution. The results revealed that the northern regions of Namibia are the most affected by multidimensional poverty. The results from this study can be used to identify areas that are severely affected by poverty and consequently form a basis to develop appropriate measures intended to alleviate poverty.

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Simple multi attribute rating technique (SMART) method on employee promotions

Cited by 16 publications

References 0 publications

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

A Trust Management Model for IoT Devices and Services Based on the Multi-Criteria Decision-Making Approach and Deep Long Short-Term Memory Technique

A novel trust management model for edge computing

Multidimensional Poverty Modeling for Namibia Using the Beta Distribution

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