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.

Applying improved svm classifier for leukemia cancer classification using FCBF

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.

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

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.

Optimized Web Service Composition Using Evolutionary Computation Techniques

Web service composition is a novel methodology to integrate diverse business solutions to accomplish complex business logic. In this fast-paced digital era with diverse categories of users, intelligent service composition based on the contextual information is highly essential to satisfy individual users requirements. For intelligent composition, ontology based declarative knowledge model is used to describe the business rules, commercial directions and personalized user environment in machine comprehensible way. This paper depicts healthcare service composition with domain ontology to encompass ontology manipulation, contextual information gathering, selecting services and dynamic composition. E-healthcare system automates service composition which invokes hospital, ambulance, scan-laboratory and pharmacy services. It provide valuable information like nearest hospital, best ambulance, scan-laboratory facilities, appropriate department and doctor details by gathering the contextual inforamtion of users like location, severity, symptoms, etc. Thus, elaborare search process is eliminated which ultimately helps the user to save time and life in case of emergency situations. thod.

Contextual Aware Dynamic Healthcare Service Composition Based on Semantic Web Ontology

Web Services are materialized as a major technology carried out for automated interaction between distributed and miscellaneous applications. It is defined as a software service that provides business solutions consumed by different service requester. It can be accessed by a standard web protocol. Service composition is the mechanism used for selecting, reusing and combining existing web services to build new web services. With immense increase in web services, quality assessment plays an essential role in the selection approach. QoS is defined as the ability to guarantee the requirements like latency, reliability, bandwidth, etc. in order to satisfy a service level agreement between an application provider and end-user. QoS based dynamic service composition leads to the upward growth of an organization that implements multiple services to provide its business solution. The proposed research work elaborates the research work on different methodologies of QoS prediction with its implications to provide a proper road map for future research on efficient service composition.

Methodologies for selection of Quality Web Services to Develop Efficient Web Service Composition

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.

R. Renjitha

Papers

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