Service Recommendation Model Based on Trust and QoS for Social Internet of Things

Zhang, Shaozhong; Zhang, Dingkai; Wu, Yaohui; Zhong, Huihuang

doi:10.1109/tsc.2023.3274647

Cited by 7 publications

(1 citation statement)

References 61 publications

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“…Ding et al [55] implemented deep integration of features by taking environmental data into account in QoS to accomplish joint QoS prediction. Zhang et al [61] came up with the Levenberg Marquardt and Random Service System to assess predictions of trust and quality of service for the social Internet of Things and discovered that the resulting model demonstrates excellent reliability and quality. This study devised a prediction model using the ANFIS and COOT algorithms.…”

Section: Comparative Analysismentioning

confidence: 99%

A Novel QoS Prediction Model for Web Services Based on an Adaptive Neuro-Fuzzy Inference System Using COOT Optimization

Jithendra,

Khan,

Basha

et al. 2024

IEEE Access

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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 (R 2 ) 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 R 2 of 96.32 %, as well as RMSE (1.335), MAE (1.1255), MAPE (0.1818), and R 2 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.

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Section: Comparative Analysismentioning

confidence: 99%