Qos-Based Web Service Discovery And Selection Using Machine Learning

In service computing, the same target functions can be achieved by multiple Web services from different providers. Due to the functional similarities, the client needs to consider the non-functional criteria. However, Quality of Service provided by the developer suffers from scarcity and lack of reliability. In addition, the reputation of the service providers is an important factor, especially those with little experience, to select a service. Most of the previous studies were focused on the user's feedbacks for justifying the selection. Unfortunately, not all the users provide the feedback unless they had extremely good or bad experience with the service. In this vision paper, we propose a novel architecture for the web service discovery and selection. The core component is a machine learning based methodology to predict the QoS properties using source code metrics. The credibility value and previous usage count are used to determine the reputation of the service.Comment: 8 Pages, 3 Figure

Web service QoS prediction using improved software source code metrics

Due to the popularity of Web-based applications, various developers have provided an abundance of Web services with similar functionality. Such similarity makes it challenging for users to discover, select, and recommend appropriate Web services for the service-oriented systems. Quality of Service (QoS) has become a vital criterion for service discovery, selection, and recommendation. Unfortunately, service registries cannot ensure the validity of the available quality values of the Web services provided online. Consequently, predicting the Web services' QoS values has become a vital way to find the most appropriate services. In this paper, we propose a novel methodology for predicting Web service QoS using source code metrics. The core component is aggregating software metrics using inequality distribution from micro level of individual class to the macro level of the entire Web service. We used correlation between QoS and software metrics to train the learning machine. We validate and evaluate our approach using three sets of software quality metrics. Our results show that the proposed methodology can help improve the efficiency for the prediction of QoS properties using its source code metrics

QoS-Based Architecture for Discovery and Selection of suitable Web Services Using Non-functional properties

Web Services are the most emerging distributed applications published in the public registries. Web service are can be discovered by both functional properties and non functional properties. Due to the rapid Web development, there are number of functionally similar Web Services published by different vendors. The functional property based web service discovery is cannot be done with accuracy. So client can find the best Web Services by taking the non-functional criteria such as Quality of Service (QoS). However, most of clients are not experienced enough to acquire the best selection of Web Service based on its described QoS properties. In this paper we are proposing a client request message structure and broker architecture to find the best Web Service. First the broker will get Web Service client's requirement message along with QoS criteria, and then it will retrieve the functionally similar web service. The broker will use an efficient mechanism to rank the Web Services based on the client’s message as well as the QoS properties which being confirmed by the broker architecture, If any tie situation happens in the ranking of the web service we will use the previous usage history of the web service to select the best web service which is matching with the client’s request message

Scalable Architecture for Personalized Healthcare Service Recommendation Using Big Data Lake

Presented in Sixth Australasian Symposium on Service Research and Innovation 2017 (This collection of papers also includes: 5th Australasian Symposium, ASSRI 2015, Sydney, NSW, Australia, November 2–3, 2015) Title in Libraries Australia: Service research and innovation : 5th and 6th Australian Symposium, ASSRI 2015 and ASSRI 2017, Sydney, NSW, Australia, November 2-3, 2015, and October 19-20 2017 ; revised selected paper

Strategies for classifying water quality in the Cauvery River using a federated learning technique

Artificial intelligence methods are emerging techniques used in the field of environmental protection, especially in the analysis of air, water, and soil quality. AI analyzes vast amounts of environmental data to predict pollution and provide decision-makers with the information they need to develop efficient policies. One of the most important problems in environmental analysis is data security, and many organizations are actively working to ensure the secure collection, storage, and utilization of sensitive environmental data. In addition, organizations are focusing on developing strategies to protect their data from malicious attacks, such as cyber-attacks, as well as from accidental misuses, like unauthorized access. For this purpose, we have introduced a novel water quality prediction using the Federated Learning Technique. Federated learning enables multiple parties to collaborate and train a model on their local data without sharing it with others, thereby preserving data privacy. The proposed method is applied to a Cauvery River dataset of water quality parameters, and the results demonstrate that the PSO-optimized federated learning process achieves better prediction accuracy of 87%, a precision of 85%, a recall of 93%, and an 89% F1 score