2,636 research outputs found
An Intelligent QoS Identification for Untrustworthy Web Services Via Two-phase Neural Networks
QoS identification for untrustworthy Web services is critical in QoS
management in the service computing since the performance of untrustworthy Web
services may result in QoS downgrade. The key issue is to intelligently learn
the characteristics of trustworthy Web services from different QoS levels, then
to identify the untrustworthy ones according to the characteristics of QoS
metrics. As one of the intelligent identification approaches, deep neural
network has emerged as a powerful technique in recent years. In this paper, we
propose a novel two-phase neural network model to identify the untrustworthy
Web services. In the first phase, Web services are collected from the published
QoS dataset. Then, we design a feedforward neural network model to build the
classifier for Web services with different QoS levels. In the second phase, we
employ a probabilistic neural network (PNN) model to identify the untrustworthy
Web services from each classification. The experimental results show the
proposed approach has 90.5% identification ratio far higher than other
competing approaches.Comment: 8 pages, 5 figure
Outlier-Resilient Web Service QoS Prediction
The proliferation of Web services makes it difficult for users to select the
most appropriate one among numerous functionally identical or similar service
candidates. Quality-of-Service (QoS) describes the non-functional
characteristics of Web services, and it has become the key differentiator for
service selection. However, users cannot invoke all Web services to obtain the
corresponding QoS values due to high time cost and huge resource overhead.
Thus, it is essential to predict unknown QoS values. Although various QoS
prediction methods have been proposed, few of them have taken outliers into
consideration, which may dramatically degrade the prediction performance. To
overcome this limitation, we propose an outlier-resilient QoS prediction method
in this paper. Our method utilizes Cauchy loss to measure the discrepancy
between the observed QoS values and the predicted ones. Owing to the robustness
of Cauchy loss, our method is resilient to outliers. We further extend our
method to provide time-aware QoS prediction results by taking the temporal
information into consideration. Finally, we conduct extensive experiments on
both static and dynamic datasets. The results demonstrate that our method is
able to achieve better performance than state-of-the-art baseline methods.Comment: 12 pages, to appear at the Web Conference (WWW) 202
A Location Based Value Prediction for Quality of Web Service
The number of web services with functionality increases, the service users usually depends on web recommendation systems. Now a days the service users pay more importance on non functional properties which are also known as Quality of Service (QoS) while finding and selecting appropriate web services. Collaborative filtering approach predicts the QoS values of the web services effectively. Existing recommendation systems rarely consider the personalized influence of the users and services in determining the similarity between users and services. The proposed system is a ranking oriented hybrid approach which integrates user-based and item-based QoS predictions. Many of the non-functional properties depends on the user and the service location. The system thus employs the location information of users and services in selecting similar neighbors for the target user and service and thereby making personalized service recommendation for service users
Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications
Wireless sensor networks monitor dynamic environments that change rapidly
over time. This dynamic behavior is either caused by external factors or
initiated by the system designers themselves. To adapt to such conditions,
sensor networks often adopt machine learning techniques to eliminate the need
for unnecessary redesign. Machine learning also inspires many practical
solutions that maximize resource utilization and prolong the lifespan of the
network. In this paper, we present an extensive literature review over the
period 2002-2013 of machine learning methods that were used to address common
issues in wireless sensor networks (WSNs). The advantages and disadvantages of
each proposed algorithm are evaluated against the corresponding problem. We
also provide a comparative guide to aid WSN designers in developing suitable
machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial
A Location-sensitive and Network-aware Broker for Recommending Web Services
Collaborative Filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real ”quality” of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web Services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, andWeb services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations
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