Wan Hussain Wan Ishak

Identifying student and organization matching pattern using Apriori algorithm for practicum placement

Students placement in industry for the practicum training is difficult due to the large number of students and organizations involved. Further the matching process is complex due to the various criteria set by the organization and students. This paper will discuss the results of a pattern extraction process using association rules of data mining technique where Apriori algorithm was chosen. The data use consists of Bachelor of Information Technology and Bachelor in Multimedia students of University Utara Malaysia from the year 2004 till 2005. Two experiments were conducted using undirected data and directed data. The pattern extracted gave information on the previous matching process done by University -Industry Linkage Centre of University Utara Malaysia.

Intelligent Decision Support Model Based on Neural Network to Support Reservoir Water Release Decision

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good.