Mobile cloud services recommendation: a soft set-based approach

Hao, Fei; Pei, Zheng; Park, Doo-Soon; Vilakone, Phonexay; Seo, Hyung-Seok

doi:10.1007/s12652-017-0572-7

Cited by 15 publications

(3 citation statements)

References 34 publications

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“…To overcome the problem of effective IoT device recommendation of edge computing IoT devices in the IIT, in this study, the soft set is adopted and applied [20]. However, it is important to deeply study the problem of IIoT device selection in the opportunistic network environment.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique

Shafiq

Tian

Cengiz

et al. 2020

Wireless Communications and Mobile Computing

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The Internet of Things (IoT) is growing day by day, and new IoT devices are introduced and interconnected. Due to this rapid growth, IoT faces several issues related to communication in the edge computing network. The critical issue in these networks is the effective edge computing IoT device selection whenever there are several edge nodes to carry information. To overcome this problem, in this paper, we proposed a new framework model named SoftSystem based on the soft set technique that recommends useful IIoT devices. Then, we proposed an algorithm named Softsystemalgo. For the proposed system, three different parameters are selected: IoT Device Security (IDSC), IoT Device Storage (IDST), and IoT Device Communication Speed (IDCS). We also find out the most significant parameters from the given set of parameters. It is evident that our proposed system is effective for the selection of edge computing devices in the IoT network.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique

Shafiq

Tian

Cengiz

et al. 2020

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…e soft set method was first proposed by Molodtsov [7] to handle the uncertainty of data or traditional mathematical tools unable to handle fuzzy information in the process of information fusion, and following the development of soft sets, numerous studies used the soft set method to handle different decisionmaking problems [7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Novel Contractor Selection Technique Using the Extended PROMETHEE II Method

Chang

2021

Mathematical Problems in Engineering

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Selecting suitable contractors directly influences product quality, corporate profits, and even sustainable development. The selection problem of contractors is, therefore, a critical issue for the sustainable development of an enterprise. However, traditional contractor selection techniques are unable to handle information regarding the relative importance of criteria or handle nonexistent or missing data in the assessment process of contractor selection. In order to effectively address this problem, this study proposes a new contractor selection technique that integrates the concept of soft set and the PROMETHEE II method to select suitable contractors. Three numerical examples are applied to prove the correctness and effectiveness of the proposed technique. This study also compares the simulation results achieved using the proposed method with those achieved using the traditional weighted arithmetic averaging method and the data envelopment analysis (DEA) technique. The simulation results show that the proposed method is a more general contractor selection technique for handling incomplete information than the traditional weighted arithmetic averaging method and the DEA method.

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“…Inside the methodology, there are various methods, such as content-based filtering, collaborative filtering and hybrid filtering [6]. Collaborative filtering is the most powerful and widely used technique in the recommendation system [7][8][9]. Collaborative filtering can be divided into two categories: memory-based and model-based [10,11].…”

Section: Introductionmentioning

confidence: 99%

The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System

Vilakone

Park

2020

Applied Sciences

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This article investigates the efficiency of a doParallel algorithm and a formal concept analysis (FCA) network graph applied to recommendation systems. It is the first article using the FCA method to create a network graph and apply this graph to improve the accuracy of a recommendation system. According to the fundamental knowledge about users who have similar feature information, they may like the same items. This idea was used to create an FCA network graph. In the proposed process, the k-clique method was used to divide this network graph into various communities. A combination of the k-nearest neighbor and the betweenness centrality methods was used to find a suitable community for a new user based on the feature information similarities between the new user and an existing user in each community. Finally, a data mining method created a list of items from suitable communities and recommended them to the new user. In essence, the execution in this article uses a doParallel algorithm as a mechanism in parallel processing technology. The result of the implementation is satisfactory. It proved that the proposed method could resolve the cold-start problem in a recommendation system and may overcome the vast time consumption when a huge dataset is involved.

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Mobile cloud services recommendation: a soft set-based approach

Cited by 15 publications

References 34 publications

SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique

SoftSystem: Smart Edge Computing Device Selection Method for IoT Based on Soft Set Technique

A Novel Contractor Selection Technique Using the Extended PROMETHEE II Method

The Efficiency of a DoParallel Algorithm and an FCA Network Graph Applied to Recommendation System

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