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Updated: Feb 4, 2026
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Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
Published on: November 26, 2019
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An improved robust heteroscedastic probabilistic neural network based trust prediction approach for cloud service
Nivethitha Somu1, Gauthama Raman M R1, Kalpana V1
1Centre for Information Super Highway (CISH), School of Computing, SASTRA Deemed University, Thanjavur, Tamil Nadu, India.
Summary
This study introduces a new method, HC-RHRPNN, for predicting cloud service trustworthiness. It uses hypergraph coarsening to improve accuracy and reduce training time for better service quality applications.
Area of Science:
- Computer Science
- Artificial Intelligence
- Cloud Computing
Background:
- Trustworthiness is crucial for assessing services in service-oriented environments.
- Predicting cloud service trust is complex due to non-linear relationships between Quality of Service (QoS) attributes and trust.
- Artificial Neural Networks (ANNs) show promise but face challenges in training and kernel functions.
Purpose of the Study:
- To propose a novel method for predicting cloud service trustworthiness.
- To enhance the accuracy and efficiency of trust prediction models.
- To facilitate the development of high-quality service applications.
Main Methods:
- A multi-level Hypergraph Coarsening based Robust Heteroscedastic Probabilistic Neural Network (HC-RHRPNN) was developed.
- Hypergraph coarsening was employed to identify informative samples for training.
- The proposed model was trained and evaluated using the Quality of Web Service (QWS) dataset.
Main Results:
- The HC-RHRPNN model demonstrated improved prediction accuracy.
- The method effectively reduced the runtime for trust prediction.
- Evaluations included classifier accuracy, precision, recall, and F-Score.
Conclusions:
- The HC-RHRPNN model offers a robust approach to cloud service trust prediction.
- Hypergraph coarsening is effective in improving ANN-based trust prediction efficiency and accuracy.
- This work contributes to building more reliable service applications.
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