A novel approach on Particle Agent Swarm Optimization (PASO) in semantic mining for web page recommender system of multimedia data: a health care perspective

Abstract

Recent decades have seen huge amounts of information collected in clinical databases for mining patients’ health states from multimedia data. Since on the web diverse type of web recommendation is made obtainable toward user every day with the purpose of consists of Image, Video, Audio, query suggestion and web page. Therefore, multimedia data accessible designed for patient-oriented decision making has improved significantly however is often scattered across various sites. In this perspective, Web page recommender systems with multimedia data might provide patients with further laymen-friendly information helping toward enhanced understand their health status as represented by their record. In this research work, Web Page health recommender systems are introduced via the use of certain agents in order to provide extremely appropriate web pages for patients. The main feature of Particle Agent Swarm Optimization (PASO) is that the creation of the algorithm is denoted by a set of Particle agents who cooperate in attaining the objective of the task under consideration. In the research method, two kinds of agents are presented: web user particle agent and semantic particle agent. PASO Based Web Page Recommendation (PASO-WPR) system is an intermediate program (or a particle agent) containing a user interface, which wisely produces a collection of info that suits an individual’s requirements. PASO-WPR is carried out dependent upon incorporating semantic info with data mining techniques on the web usage data as well as clustering of pages dependent upon similarity in their semantics. As the Web pages with multimedia files are viewed as ontology individuals, the pattern of patients’ navigation are like instances of ontology rather than the uniform resource locators, and with the help of semantic similarity, page clustering is carried out. For producing web page recommendations to users, the outcome is utilized. The recommender engine concentrates on the semantic info and as well exploits a particle agent to reform the outcomes of web pages recommendation. Consequently, the system response time is enhanced and as a result, creating the framework scalable. The outcomes recommend that the PASO-WPR system is improved in identifying the web page that a user is about to request while matched up other approaches. The outcome proves that the presented PASO-WPR system is carried out well in regard to the accuracy measures aspects such as accuracy, coverage and M-Metric are identified to contain greater values compared to the previous item based collaborative filtering recommendation systems.