The design and development of intelligent university program ranking system using JEE frameworks

Web based systems have become the common medium of delivering software applications. Thus, developing web based systems has become a major concern for developers due to the large number of technologies, tools and techniques available today. Developers are often faced with the dilemma of choosing the best technology for their projects especially when developing a high quality system that must comply with certain standards. Enterprise developers often use development frameworks to simplify development job, and to insure that their code follows proven development methodologies. In this paper we describe our approach for developing an intelligent university program ranking system, using a JEE web application framework that has been developed utilizing industry standards and best practices. The development has been done using a well designed JEE web application framework and REpresentational State Transfer (REST) web services to minimize the development time and effort. The system which uses the powerful Adaptive Neuro-Fuzzy Inference System (ANFIS) algorithm for its AI engine is developed using the SEAM framework and RESTEasy web services framework. SEAM has been chosen due to its power and integration capabilities while RESTEasy framework is chosen for its seamless integration with JBoss SEAM. The developed system known as i-Selangkah is divided into two major components; a web based control panel to administer and modify the system as well as the programs fuzzy rules, and a web services interface to provide the existing Selangkah system with the ranking services. i-Selangkah has been successfully implemented, integrated and tested with the existing Selangkah system developed by UiTM. The tests showed that the system can effectively be used together with Selangkah to provide better program advising to these potential applicants with respect to their merit points, the specific and ever-changing program requirements and more significantly, the past intake trends.