MEASURING SERVICE QUALITY IN SERVICE-ORIENTED ARCHITECTURES USING A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM AND ARTIFICIAL NEURAL NETWORK (PSO-ANN)

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Zavvar Mohammad; Garavand Shole; Sabbagh Esmaeel; REZAEI MEYSAM; Khalili Hajar; Zavvar Mohammad Hossein; MOTAMENI HOMAYUN

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Seminar Paper

Paper Information

Seminar: INTERNATIONAL CONFERENCE ON WEB RESEARCH
Year:2017 | Issue:3

Download Full-Text

View:

113

Download:

Information Seminar Paper

Title

MEASURING SERVICE QUALITY IN SERVICE-ORIENTED ARCHITECTURES USING A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM AND ARTIFICIAL NEURAL NETWORK (PSO-ANN)

Author(s)

Keywords

COMPONENT

MEASURING

QUALITY

SERVICE-ORIENTED ARCHITECTURE

ARTIFICIAL NEURAL NETWORK

PARTICLE SWARM OPTIMIZATION

Abstract

WEB SERVICE COMBINATION IS AN IMPORTANT TASK PERFORMED IN DIFFERENT PHASES OF THE SERVICE-ORIENTED ARCHITECTURE LIFECYCLE. MEASURING SERVICE QUALITY BASED ON THE NON-FUNCTIONAL CHARACTERISTICS IS AN EXCEEDINGLY DIFFICULT TASK. THEREFORE, THIS PAPER PRESENTS A MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORK (MLPANN) TO PROVIDE A METHOD FOR MEASURING QUALITY OF SERVICE IN A SERVICE-ORIENTED ARCHITECTURE AND TO IMPROVE NETWORK PERFORMANCE. PARTICLE SWARM OPTIMIZATION (PSO) IS USED TO OPTIMIZE THE WEIGHTS OF THE NETWORK. FINALLY, OUR RESULTS ARE COMPARED TO THOSE OF A COMBINATION OF DIFFERENT EVOLUTION (DE) ALGORITHM AND MLPANN IN TERMS OF MEAN SQUARE ERROR (MSE), ROOT MEAN SQUARE ERROR (RMSE) AND STANDARD DEVIATION (STD). THE RESULTS DEMONSTRATE THE SUPERIORITY OF THE PROPOSED METHOD.

Cites

No record.

References

No record.

Cite

APA: Copy

Zavvar, Mohammad, Garavand, Shole, Sabbagh, Esmaeel, REZAEI, MEYSAM, Khalili, Hajar, Zavvar, Mohammad Hossein, & MOTAMENI, HOMAYUN. (2017). MEASURING SERVICE QUALITY IN SERVICE-ORIENTED ARCHITECTURES USING A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM AND ARTIFICIAL NEURAL NETWORK (PSO-ANN). INTERNATIONAL CONFERENCE ON WEB RESEARCH. SID. https://sid.ir/paper/946872/en

Vancouver: Copy

Zavvar Mohammad, Garavand Shole, Sabbagh Esmaeel, REZAEI MEYSAM, Khalili Hajar, Zavvar Mohammad Hossein, MOTAMENI HOMAYUN. MEASURING SERVICE QUALITY IN SERVICE-ORIENTED ARCHITECTURES USING A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM AND ARTIFICIAL NEURAL NETWORK (PSO-ANN). 2017. Available from: https://sid.ir/paper/946872/en

IEEE: Copy

Mohammad Zavvar, Shole Garavand, Esmaeel Sabbagh, MEYSAM REZAEI, Hajar Khalili, Mohammad Hossein Zavvar, and HOMAYUN MOTAMENI, “MEASURING SERVICE QUALITY IN SERVICE-ORIENTED ARCHITECTURES USING A HYBRID PARTICLE SWARM OPTIMIZATION ALGORITHM AND ARTIFICIAL NEURAL NETWORK (PSO-ANN),” presented at the INTERNATIONAL CONFERENCE ON WEB RESEARCH. 2017, [Online]. Available: https://sid.ir/paper/946872/en

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans

No record.

Recommended Workshops