Hybrid Cuckoo Optimized Multi Layer Perceptron for Hospital Management Information System Web-Services Quality Classification

摘要

Web services are a key notion in Service-Oriented Architecture (SOA) and provides various functionalities accessible through a network. In a large hospital the software is closely integrated with the pharmacy, vendor and all other associated support system. Large-scale distributed systems like medical SOA are challenging to develop due to its complex and decentralized nature. The World Wide Web has evolved since its days of mere static pages to a space of dynamic web services. This allows integration of applications with ease and less cost than ever before. Web Services faces the issue of finding required services. With the explosion of Web Services accessible on the web, automatic categorization of the services to organize the data becomes essential. In Web Services classification the tasks are automatically sorted into categories from a predefined set. In the current paper, we focus on the classification of medical Web services for managing medical services. A hybrid Tabu Search (TS) optimization with Cuckoo Search (CS) is proposed to improve Multi-Layer Perceptron Neural Network (MLPNN) learning for improving web service classification.