ICTACT Journal on Soft Computing

Chellammal Surianarayanan ¹, Gopinath Ganapathy ¹

Affiliations
1 Department of Computer Science and Engineering, Bharathidasan University, IN

Abstract

The process of semantic service discovery using an ontology reasoner such as Pellet is time consuming. This restricts the usage of web services in real time applications having dynamic composition requirements. As performance of semantic service discovery is crucial in service composition, it should be optimized. Various optimization methods are being proposed to improve the performance of semantic discovery. In this work, we investigate the existing optimization methods and broadly classify optimization mechanisms into two categories, namely optimization by efficient reasoning and optimization by efficient matching. Optimization by efficient matching is further classified into subcategories such as optimization by clustering, optimization by inverted indexing, optimization by caching, optimization by hybrid methods, optimization by efficient data structures and optimization by efficient matching algorithms. With a detailed study of different methods, an integrated optimization infrastructure along with matching method has been proposed to improve the performance of semantic matching component. To achieve better optimization the proposed method integrates the effects of caching, clustering and indexing. Theoretical aspects of performance evaluation of the proposed method are discussed.

Keywords

Optimization Infrastructure, Service Cache, Service Cluster, Service Index, Semantic Service Discovery.

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Chellammal Surianarayanan ¹, Sharmila Rengasamy ², M. Baby Nirmala ³, Pethuru Raj Chelliah ⁴

Affiliations
1 Centre for Distance and Online Education, Bharathidasan University, India., IN
2 Department of Computer Science, Government Arts and Science College, Srirangam, Tiruchirappalli, India., IN
3 Department of Computer Applications, Holy Cross College, India., IN
4 Edge AI Division, Reliance Jio Platforms Ltd., Bangalore, India., IN

Abstract

Healthcare information systems typically collect, store and manage various kinds of data such as illness details, clinical history, essential body parameters, health insurance plans, and other related data towards enabling data processing and analytics to arrive at better decision making with all the clarity and alacrity. To reduce the mortality rate due to heart diseases, it is essential to predict the presence of disease in its budding stage itself. Manual extraction of the useful knowledge from historical data is practically tedious and timeconsuming. Machine learning (ML) algorithms are being used to detect and predict something useful out of both historical and current data. Despite the applicability of machine learning algorithms for prediction, the accuracy of prediction is significantly influenced by features used for prediction. Moreover, to meet the needs of evolving data sizes, suitable technologies for data storage also become essential. Based on these two aspects, a comparative analysis has been performed for feature selection using four filter methods, namely, correlation measure, information gain, gain ratio and relief. Further, a scalable architecture using Hadoop framework has been proposed to enable the machine learning algorithms to handle larger datasets while performing prediction task. The impact of the proposed architecture on the performance of machine learning algorithm has been evaluated with benchmark dataset and found to have improved scalability and accuracy.

Keywords

Disease Prediction, Hadoop Distributed File System, Machine Learning, Random Forest, Support Vector Machine, Scalable Architecture.

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