A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization
The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on C...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10382510/ |
| _version_ | 1827308318260985856 |
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| author | Thandra Jithendra Mohammad Zubair Khan S. Sharief Basha Raja Das A. Divya Chiranji Lal Chowdhary Abdulrahman Alahmadi Ahmed H. Alahmadi |
| author_facet | Thandra Jithendra Mohammad Zubair Khan S. Sharief Basha Raja Das A. Divya Chiranji Lal Chowdhary Abdulrahman Alahmadi Ahmed H. Alahmadi |
| author_sort | Thandra Jithendra |
| collection | DOAJ |
| description | The adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance. |
| first_indexed | 2024-04-24T18:55:17Z |
| format | Article |
| id | doaj.art-802ae19e45fc4aa5a3767fc175c1067c |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:55:17Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj.art-802ae19e45fc4aa5a3767fc175c1067c2024-03-26T17:35:06ZengIEEEIEEE Access2169-35362024-01-01126993700810.1109/ACCESS.2024.335064210382510A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT OptimizationThandra Jithendra0https://orcid.org/0000-0003-1103-9050Mohammad Zubair Khan1https://orcid.org/0000-0002-2409-7172S. Sharief Basha2https://orcid.org/0000-0002-3866-246XRaja Das3https://orcid.org/0000-0001-9945-5969A. Divya4Chiranji Lal Chowdhary5https://orcid.org/0000-0002-5476-1468Abdulrahman Alahmadi6https://orcid.org/0000-0001-6039-7730Ahmed H. Alahmadi7https://orcid.org/0000-0001-6613-6822School of Advanced Sciences, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Computer Science, Taibah University, Medina, Saudi ArabiaDepartment of Mathematics, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Mathematics, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Mathematics, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaSchool of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, IndiaDepartment of Computer Science, Taibah University, Medina, Saudi ArabiaDepartment of Computer Science, Taibah University, Medina, Saudi ArabiaThe adoption of adaptive neuro-fuzzy inference systems (ANFIS) and metaheuristic optimization approaches has been widely observed in recent research. Even so, integrating these methods improves the model’s capability to solve complex problems. A novel enhanced prediction method based on COOT bird optimization was developed for selecting the optimal parameters of ANFIS in the current study. This method combines COOT optimization with ANFIS to model the quality of service (QoS) characteristics of web services by using the adaptive neuro-fuzzy inference system COOT (ANFIS-COOT). In this instance, the quality of the web service (QWS) dataset was obtained from the GitHub database, which consists of 120 web services data, and then evaluated using the presented model on the dataset for estimating response time and throughput of web services. As significant evidence of ANFIS-COOT’s efficiency, the similar QWS data set is analyzed using four different prediction models: ANFIS, ANFIS-Beetle Antennae Search (ANFIS-BAS), ANFIS-Reptile Search Algorithm (ANFIS-RSA), and ANFIS-Snake Optimizer (ANFIS-SO). Moreover, the exploratory study used statistical benchmarks such as root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and determination coefficient (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) to emphasize the accuracy of the proposed model. Based on analysis results, the presented model achieved optimal values of RMSE (59.7473), MAE (15.8531), MAPE (0.0705), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and <inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 97.12 % for modelling response time and throughput of web services, compared to other models. Eventually, this report demonstrates the viability of the ANFIS-COOT while tackling a complex problem and improving predictive performance.https://ieeexplore.ieee.org/document/10382510/Web serviceQoS attributesANFISCOOT optimizationprediction models |
| spellingShingle | Thandra Jithendra Mohammad Zubair Khan S. Sharief Basha Raja Das A. Divya Chiranji Lal Chowdhary Abdulrahman Alahmadi Ahmed H. Alahmadi A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization IEEE Access Web service QoS attributes ANFIS COOT optimization prediction models |
| title | A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization |
| title_full | A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization |
| title_fullStr | A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization |
| title_full_unstemmed | A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization |
| title_short | A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization |
| title_sort | novel qos prediction model for web services based on an adaptive neuro fuzzy inference system using coot optimization |
| topic | Web service QoS attributes ANFIS COOT optimization prediction models |
| url | https://ieeexplore.ieee.org/document/10382510/ |
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