Bariah Yusob

Developing Student Model Using Kohonen Network in Adaptive Hypermedia Learning System

This paper presents a study on method to develop student model by identifying the students’ characteristics in an adaptive hypermedia learning system. The study involves the use of student profiling techniques to identify the features that may be useful to help the researchers have a better understanding of the student in an adaptive learning environment. We propose a supervised Kohonen network with hexagonal lattice structure to classify the student into 3 categories: beginner, intermediate and advance to represent their knowledge level while using the learning system. An experiment is conducted to see the proposed Kohonen network’s performances compared to the other types of Kohonen networks in term of learning algorithm and map structure. 10-fold cross validation method is used to validate the network performances. Results from the experiment shows that the proposed Kohonen network produces an average percentage of accuracy, 81.3889% in classifying the simulated data and 51.6129% when applied to the real student data.

Banking Deposit Number Recognition Using Neural Network

During normal cash deposit process, the bank customer will fill in the account number, amount of cash and name of the account holder at the bank in slip, then key in the account number and amount manually into the computer. If there are numbers of customer at one time, the process will take times and sometime the banker will make errors during reading or keying the data. The recognition process was executed using integration of Artificial Intelligent techniques: image preprocessing and Neural Network. Image processing techniques were used to extract the written character on the slip. After that, the extracted characters were passed to the recognition phase, where Neural Network will identify the input character patterns. Results: We tested the proposed method using 40 cash deposit slip written with numbers to be tested. 3 neural networks with 40, 50 and 60 training data particularly were used to test the success rate of recognition. Through experiment, the proposed system had successfully recognizes at least 90% of the written character on cash deposit slips. Using the proposed approach, we developed an automatic banking deposit number recognition system which is able to recognize the handwritten account number and amount number on the cash deposit slip and thus automate the cash deposit process at bank counter.