Web service selection based on QoS using tModel working on feed forward network

Karande, Aarti M.; Kalbande, Dhananjay

doi:10.1109/icicict.2014.6781247

Cited by 5 publications

(2 citation statements)

References 3 publications

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“…However, it is difficult for users to select appropriate web services themselves, as a result of a shortage of professional knowledge and numerous web services. Therefore, how to efficiently and accurately recommend services based on user preference has become a challenging issue for both industry and academia [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation

et al. 2020

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The numbers of web services are growing rapidly in recent years. One of the most challenging issues in service computing is the personalized recommendation of Web services. Most of the current research recommends services based on Quality of Service (QoS)-aware data with few considerations of service-side factors, such as service functions. In this paper, a new QoS-aware Web service recommendation model based on user and service clustering (RMUSC) is proposed to gain an advance in recommended accuracy. Firstly, similar users are clustered together by a Top-N similarity algorithm through the user QoS records. Secondly, a K-means++ based filtering service cluster is established. Finally, a user and services collaborative scheme is exploited and obtains potential user QoS preferences to generate recommendations. The experimental results show that when the density of the service invocation matrix is 5%, 10% and 20%. the average absolute error (MAE) and root mean square error (RMSE) of RMUSC are lower than those of other methods.

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Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Clustering Model for QoS-Aware Service Recommendation

et al. 2020

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“…Karande & Kalbande [7] addressed the choosing of web services through tModel of SOA that is formulated through feed forward network. The designing is carried out through XML language.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Particle Swarm Optimization Multi Layer Perceptron for Web-Services Classification

Mustafa¹,

Kumaraswamy²

2016

Int. Jour. Info. Sci. Comp.

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The Web services are applications that perform specific tasks and are accessible via the network through a communication protocol. QoS plays an important role in finding out the performance of web services. Multi-layer perceptron neural network (MLP) is the most popular and widely used nonlinear network for solving many practical problems in applied science, biology, and engineering. In this paper, hybrid Particle Swarm Optimization (PSO) with MLP is performed for medical web-service classification. MLP performance is based on initial weights setting. Conventional training algorithms like Back propagation (BPP) and Levenberg-Marquardt (LM) have slow convergence and local minima problems. Results show that the proposed method performs better accuracy, average precision, average recall and RMSE.

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