An effective method for clustering-based web service recommendation

Normally web services are classified by the quality of services; however, the term quality is not absolute and defined relatively. The quality of web services is measured or derived using various parameters like reliability, scalability, flexibility, and availability. The limitation of the methods employing these parameters is that sometimes they are producing similar web services in recommendation lists. To address this research problem, the novel improved clustering-based web service recommendation method is proposed in this paper. This approach is mainly dealing with producing diversity in the results of web service recommendations. In this method, functional interest, quality of service (QoS) preference, and diversity features are combined to produce a unique recommendation list of web services to end-users. To produce the unique recommendation results, we propose a varied web service classification order that is clustering-based on web services’ functional relevance such as non-useful pertinence, recorded client intrigue importance, and potential client intrigue significance. Additionally, to further improve the performance of this approach, we designed web service graph construction, an algorithm of various widths clustering. This approach serves to enhance the exceptional quality, that is, the accuracy of web service recommendation outcomes. The performance of this method was implemented and evaluated against existing systems for precision, and f-score performance metrics, using the research datasets

Quality of Service optimisation framework for Next Generation Networks

Within recent years, the concept of Next Generation Networks (NGN) has become widely accepted within the telecommunication area, in parallel with the migration of telecommunication networks from traditional circuit-switched technologies such as ISDN (Integrated Services Digital Network) towards packet-switched NGN. In this context, SIP (Session Initiation Protocol), originally developed for Internet use only, has emerged as the major signalling protocol for multimedia sessions in IP (Internet Protocol) based NGN. One of the traditional limitations of IP when faced with the challenges of real-time communications is the lack of quality support at the network layer. In line with NGN specification work, international standardisation bodies have defined a sophisticated QoS (Quality of Service) architecture for NGN, controlling IP transport resources and conventional IP QoS mechanisms through centralised higher layer network elements via cross-layer signalling. Being able to centrally control QoS conditions for any media session in NGN without the imperative of a cross-layer approach would result in a feasible and less complex NGN architecture. Especially the demand for additional network elements would be decreased, resulting in the reduction of system and operational costs in both, service and transport infrastructure. This thesis proposes a novel framework for QoS optimisation for media sessions in SIP-based NGN without the need for cross-layer signalling. One key contribution of the framework is the approach to identify and logically group media sessions that encounter similar QoS conditions, which is performed by applying pattern recognition and clustering techniques. Based on this novel methodology, the framework provides functions and mechanisms for comprehensive resource-saving QoS estimation, adaptation of QoS conditions, and support of Call Admission Control. The framework can be integrated with any arbitrary SIP-IP-based real-time communication infrastructure, since it does not require access to any particular QoS control or monitoring functionalities provided within the IP transport network. The proposed framework concept has been deployed and validated in a prototypical simulation environment. Simulation results show MOS (Mean Opinion Score) improvement rates between 53 and 66 percent without any active control of transport network resources. Overall, the proposed framework comes as an effective concept for central controlled QoS optimisation in NGN without the need for cross-layer signalling. As such, by either being run stand-alone or combined with conventional QoS control mechanisms, the framework provides a comprehensive basis for both the reduction of complexity and mitigation of issues coming along with QoS provision in NGN

ATM network impairment to video quality

Includes bibliographical reference

Implementation of Quality of Experience Prediction Framework through Mobile Network Data

Generally, a reliable method of analyzing the quality of experience is through the subjective method, which is time consuming, lacks usability, lacks repeatability in real-time and near real-time. Another method is the objective measurement that aims at predicting the subjective measurement based on the estimated mean opinion score. Therefore, this study adopted the objective measurement by implementing a quality of experience framework, which employed predictive analytics techniques to analyze the mobile internet user experience dataset gathered through the mobile network. The predictive analytics employed the use of multiple regression, neural network, decision trees, random forest, and decision forest to predict the mobile internet perceived quality of experience. Result from the study shows that decision forests performs better than other algorithms used for the predictive analytics. In addition, the result indicates that the predictive analytics can be used to enhance the allocation of network resources based on location and time constituted in the dataset

Stress and deformation of optimally shaped silicon microneedles for transdermal drug delivery

In this study, we demonstrated the fabrication of the concave conic shape microneedle with the aid of COMSOL Multiphysics simulation. The stress and buckling of the microneedle structure were simulated by applying various loads ranging from 50 to 800 g perpendiculars to the tip in order to predict the occurrence of microneedles structure deformation. The simulation study indicated that the surface buckling deformation does not occur to the microneedle structure with the increment of the load. The microneedles with dimensions of height and diameter tip ranging from 60 to 100 μm and 1 to 4 μm, respectively had been fabricated via an etching process in a mixture of hydrofluoric acid, nitric acid, and acetic acid. Three optimized microneedles but different in the structures were fabricated via the acidic etching process. The reproducibility of three different microneedle structures was 15, 20, and 60%, respectively. Stress and buckling analyses of the fabricated microneedles were further carried out on the rat skin. The obtained experimental results show promising applications for the deep dermis, stratum corneum to epidermis layer penetration

WEB ADMINISTRATION SUGGESTION BY MEANS OF MISUSING AREA AND QOS DATA

Web administrations are incorporated programming segments for the backing of interoperable machine-to-machine association over a system. Web administrations have been broadly utilized for building administration situated applications in both industry and the educated community in late years. The quantity of freely accessible Web administrations is consistently expanding on the Internet. Be that as it may, this multiplication makes it hard for a client to choose an appropriate Web administration among a lot of administration competitors. An improper administration determination may bring about numerous issues (e.g., illsuited execution) to the subsequent applications. In this paper, we propose a novel community separating based Web administration recommender framework to help clients select administrations with ideal Quality-of-Service (QoS) execution. Our recommender framework utilizes the area data and QoS qualities to bunch clients and administrations, and makes customized administration proposal for clients in view of the grouping results. Contrasted and existing administration suggestion techniques, our methodology accomplishes impressive change on the proposal precision. Extensive tests are led including more than 1.5 million QoS records of true Web administrations to exhibit the adequacy of our methodology

Composition de services basée sur les relations sociales entre objets dans l’IoT Service composition based on social relations between things in IoT

With the rapid development of service-oriented computing applications and social Internet ofthings (SIoT), it is becoming more and more difficult for end-users to find relevant services to create value-added composite services in this big data environment. Therefore, this work proposes S-SCORE (Social Service Composition based on Recommendation), an approach for interactive web services composition in SIoT ecosystem for end-users. The main contribution of this work is providing a novel recommendation approach, which enables to discover and suggest trustworthy and personalized web services that are suitable for composition. The first proposed model of recommendation aims to face the problem of information overload, which enables to discover services and provide personalized suggestions for users without sacrificing the recommendation accuracy. To validate the performance of our approach, seven variant algorithms of different approaches (popularity-based, user-based and item-based) are compared using MovieLens 20M dataset. The experiments show that our model improves the recommendation accuracy by 12% increase with the highest score among compared methods. Additionally it outperforms the compared models in diversity over all lengths of recommendation lists. The second proposed approach is a novel recommendation mechanism for service composition, which enables to suggest trustworthy and personalized web services that are suitable for composition. The process of recommendation consists of online and offline stages. In the offline stage, two models of similarity computation are presented. Firstly, an improved users’ similarity model is provided to filter the set of advisors for an active user. Then, a new service collaboration model is proposed that based on functional and non-functional features of services, which allows providing a set of collaborators for the active service. The online phase makes rating prediction of candidate services based on a hybrid algorithm that based on collaborative filtering technique. The proposed method gives considerable improvement on the prediction accuracy. Firstly, it achieves the lowest value in MAE (Mean Absolute Error) metric and the highest coverage values than other compared traditional collaborative filtering-based prediction approaches