K. Ramar

Bio: K. Ramar is an academic researcher. The author has contributed to research in topics: Web service & Quality of service. The author has an hindex of 3, co-authored 6 publications receiving 25 citations.

Web Service Recommendation Based on Semantic Analysis of Web Service Specification and Enhanced Collaborative Filtering

Abstract: With growing momentousness of Internet applications, digital world is overwhelmed with huge number of web services. To ease the job of selecting relevant WS in service composition process, recommendation system of Web Services is designed. It uses semantic analysis of WS along with enhanced collaborative filtering. Ontology based Semantic Analysis performed using Tversky Content Similarity Measure helps to identify most similar functionally relevant WS. The collaborative filtering process uses DBSCAN clustering and PCC similarity to identify highly collaborative WS, based on ratings given by experienced users. To overcome the existence of sparse data in WS ratings and to enhance filtering process, SVM Regression is implemented before collaborative filtering. Relative frequency method is applied to amalgamate collaborative and sematic similarity values of WS. The methodology is proved to produce more realistic, accurate and efficient WS recommendation. Future focus may be towards knowledge based filtering with real world contextual information.

Implementation of Adaptive Framework and WS Ontology for Improving QoS in Recommendation of WS

Abstract: With the advent of more users accessing internet for information retrieval, researchers are more focused in creating system for recommendation of web service(WS) which minimize the complexity of selection process and optimize the quality of recommendation. This paper implements a framework for recommendation of personalized WS coupled with the quality optimization, using the quality features available in WS Ontology. It helps users to acquire the best recommendation by consuming the contextual information and the quality of WS. Adaptive framework performs i) the retrieval of context information ii) calculation of similarity between users preferences and WS features, similarity between preferred WS with other WS specifications iii) collaboration of web service ratings provided by current user and other users. Finally, WS quality features are considered for computing the Quality of Service. The turnout of recommendation reveals the selection of highly reliable web services, as credibility is used for QoS predication.

Optimized QoS Prediction of Web Service using Genetic Algorithm and Multiple QoS Aspects

Abstract: In the present Internet era, with the seemingly insatiable growth in applications based on web services (WS), it is an arduous task for an user to select the foremost web service among a large number of competing services. Prediction of quality of service (QoS) ratings of WS helps to identify optimal WS. The ratings are appraised by an evaluation of different QoS factors. However, in the real world, missing values are often major problem in datasets of QoS factors, as they lead to imprecise prediction of QoS rating of a given web service. We present here a system designed for imputing the missing values in QoS datasets, by clustering, similarity checking and optimization techniques. The optimization of QoS values helps to obtain the more accurate dataset. Finally, QoS prediction of WS is calculated using imputed dataset. It helps to ascertain the quality and ranking of different WS. Experimental results show that the proposed method predicts web service QoS values more accurately and using these complete dataset an optimal web service is predicted/suggested to the user.

An integrated service recommendation approach for service-based system development

Abstract: With the wide adoption of service-oriented computing and cloud computing, service-based systems (SBSs), a kind of software systems that can offer certain functionalities by leveraging one or more Web services, become increasingly popular. A challenging issue in SBS development is to find suitable services from a variety of available (semantics different) services. Towards this issue, we propose a new service recommendation approach that can integrate diverse information of SBSs and their component services. In this research, SBSs, services, their respective attributes (e.g. content and categories) and SBS-service composition relations are modeled as a heterogeneous information network (HIN); and several semantic similarities between SBSs are measured on a set of meta-paths in the HIN. Particularly, a word embedding technique is used to learn word vectors from the content of SBSs and services, which contribute to better functional similarities between SBSs. Afterwards, the combinational weights of different similarities are optimized using a Bayesian personalized ranking algorithm. Services are finally recommended based on collaborative filtering. We identify two recommendation scenarios with different SBS requirements. By conducting a series of experiments on a real-world dataset crawled from the ProgrammableWeb, we validate the effectiveness of our approach and find out the optimal combinations of SBS similarities for those two scenarios.

Applying improved svm classifier for leukemia cancer classification using FCBF

Abstract: Classification of different tumor type are of great significance in problems cancer prediction. Choosing the most relevant qualities from huge microarray expression is very important. It is a most explored subject in bioinformatics because of its hugeness to move forward humans understanding of inherent causing cancer mechanism. In this paper, we aim to classify leukaemia cells. Our approach relies on selecting the predominant features from the dataset and classifying it using classification algorithm, Support Vector Machine (SVM). As the dataset is very large we need to reduce the size before classification, to decrease the computation time and increase the accuracy of the classifier. SVM-RFE removes only most irrelevant gene in each iteration. This algorithm does not differentiate the correlated genes. So before applying SVM-RFE for gene selection, a correlation-based feature selection is applied using FCBF (Fast Correlation-Based Filter) to select the most prominent not correlated genes. The resultant classifier generated using this set of genes yields more accurate result and reduce the computational time.

Web Service Recommendation Based on Semantic Analysis of Web Service Specification and Enhanced Collaborative Filtering

Abstract: With growing momentousness of Internet applications, digital world is overwhelmed with huge number of web services. To ease the job of selecting relevant WS in service composition process, recommendation system of Web Services is designed. It uses semantic analysis of WS along with enhanced collaborative filtering. Ontology based Semantic Analysis performed using Tversky Content Similarity Measure helps to identify most similar functionally relevant WS. The collaborative filtering process uses DBSCAN clustering and PCC similarity to identify highly collaborative WS, based on ratings given by experienced users. To overcome the existence of sparse data in WS ratings and to enhance filtering process, SVM Regression is implemented before collaborative filtering. Relative frequency method is applied to amalgamate collaborative and sematic similarity values of WS. The methodology is proved to produce more realistic, accurate and efficient WS recommendation. Future focus may be towards knowledge based filtering with real world contextual information.

Optimized Web Service Composition Using Evolutionary Computation Techniques

Abstract: In service computing, Quality of Service (QoS)-aware web service composition is considered as one of the influential traits. To embrace this, an optimal method for predicting QoS values of web service is implemented where credibility evaluation is computed by accumulating reputation and trustworthiness. An automatic approach for weight calculation is invoked to calculate the weight of QoS attributes; it improves WS QoS values. QoS value is optimized by using Genetic Algorithm. Services with high QoS values are taken as candidate services for service composition. Instead of just selecting services randomly for service composition, cuckoo-based algorithm is used to identify optimal web service combination. Cuckoo algorithm realizes promising combinations by replacing the best service in lieu of worst service and by calculating the fitness score of each composition. A comparative study proved that it can provide the best service to end-users, as cuckoo selects only service composition with high fitness score.