Managing behavioural academic self-esteem using FuzzyXteem

Behavioural Akademic Self-Esteem (BASE) has been used with children of preschool, elementary; and junior high school classes both individually and in groups.In this study,BASE is used to estimate the factor structures and determine the levels of academic self-esteem of the student.The current practice of the existing system using BASE scale may be scored by hand or by computer based on the rigid crisp values to represent rating number one through five.Since BASE requires the ability for estimating the factor structure and also the ability to exlpain how the conclusion is derived,therefore artificial intelligent techniques that are required to perform BASE must be able to perform estimation and provide reasoning.For this purpose fuzzy logic and expert system have been integrated in a web-based environment to demonstrate the use of hybrid system on BASE factor structure and levels of academic self-esteem.For each BASE factor, the sub score is provided based on the classifications of Academic Self-Esteem and their respective ranges.In FuzzyXteem,users in particular teachers, counselors, or parent are allowed to measure students' self-esteem at early age using real time computation.FuzzyXteem facilitates user by automatically evaluating BASE factors and help the user diagnoses their students' level of academic self-esteem in 3 ratings:low,moderate and high.It is also able to provide explanation and describe how the conclusion can be derived.The system has been successfully tested by the counselors and conforms to the BASE factor rating scale and sub-scores.FuzzyXteem can be use as an aid to decision making in improving a person's self esteem, and indirectly increases an individual for productivity.The same system functions can be applied to business organization for managing and improving the organizations performance

Evaluation of structured questions using modified BLEU algorithm

This paper describes and exemplifies an application of a Structured Exam Questions Test Bank and Evaluation Using Modified Bilingual Evaluation Understudy (BLEU) Algorithm, a software system developed in pursuit of robust computerized marking of free-text answers to open-ended questions.It employs the Information System Development Research Methodology with modified BLEU Algorithm and Expert System for similar words.The system was developed to facilitate in managing and administrating structured questions for client/server architecture based on intranet. The system incorporates a number of processing modules specifically aim at providing an automated marking to reduce spelling errors, calculating scores, managing and administrating an exam.The system was trial-run by a group of students and lecturers, and modifications particularly on the interface have been modified and implemented. Problems and limitations discovered were then discussed and recommendations made to overcome the limitations for the future development of the research

Penggunaan WWW sebagai medium pendidikan interaktif

World Wide Web (atau WWW) merupakan media komunikasi tanpa sempadan yang semakin mendapat perhatian ramai. WWW berpontensi digunakan untuk pelbagai aktiviti yang memerlukan capaian dan maklum balas yang pantas dan berkesan.Pelbagai sumber boleh didapati di WWW. Sumber tersebut boleh dieksploitasi dan digunakan bagi tujuan pengajaran dan pembelajaran. Penggunaan WWW sebagai medium pendidikan, telah lama mendapat perhatian ahli akademik. Walau bagaimanapun, WWW sahaja tidak menjamin pembelajaran dan pengajaran yang interaktif. Oleh sebab itu, kertas kerja ini mencadangkan satu model pendidikan interaktif melalui WWW. Model tersebut menggabungkan Sistem Tutor Pintar dan antaramuka yang interaktif bagi menghubungkan pelajar, pendidik dan isi pembelajaran

Fuzzy logic controller for roof sprinkler cooling system / Faizzudin Sahazudin , Nooraini Yusoff and Fadzilah Siraj

In Malaysia, a cooling system is very efficient in reducing the heat from outside temperature. Normally, for those who afford, the best equipment will be utilized to make homes in a cool and comfort temperature. This may take an equipment such as air-conditioner or super insulation to their house. However, those equipments are costly for their installation, maintenance and energy. In this study, we propose a design of roof sprinkler cooling system using fuzzy logic (FL). The water sprinkler works by triggering the controller to open the valves to release and spray water. FL could benefit the cooling system by intelligently controlling the actual water quantity based on the observed daily weather condition captured from a sensor. This technique is the key approach to saving both water and energy. The system has been simulated and tested accordingly with a number of conditions and parameters, and it has been shown that the proposed design is feasible and practical to be implemented

Improving generalization of neural network using length as discriminant

This paper discusses the empirical evaluation of improving generalization performance of neural networks by systematic treatment of training and test failures. As a result of systematic treatment of failures, a discrimination technique using LENGTH was developed. The experiments presented in this paper illustrate the application of discrimination technique using LENGTH to neural networks trained to solve supervised learning tasks such as the Launch Interceptor Condition 1 problem. The discriminant LENGTH is used to discriminate between the predicted "hard-to-learn" and predicted "easy-to-learn" patterns before these patterns are fed into the networks. The experimental results reveal that the utilization of LENGTH as discriminant has improved the average generalization of the networks increased

Investigating the effect of data representation on neural network and regression

In this research the impact of different data representation on the performance of neural network and regression was investigated on different datasets that has binary or Boolean class target.In addition, the performance of particular predictive data mining model could be affected with the change of data representation.The seven data representations that have been used in this research are As_Is, Min Max normalization, standard deviation normalization, sigmoidal normalization, thermometer representation, flag representation and simple binary representation.Moreover, all data representations have been applied on two datasets which are Wisconsin breast cancer and German credit dataset. As a result, the neural network performance is better than logistic regression on both datasets if we exclude the thermometer and flag representations.For datasets having a binary or Boolean target class, flag or thermometer binary representation is recommended to be used if logistic regression analysis is performed. Meanwhile, As_is representation, min max normalization,standard deviation normalization or sigmoidal normalization is recommended for neural network analysis on datasets having binary or Boolean target class

Exploring hidden relationships within students' data using neural network and logistic regression

Considerable attention has been given to the development of sophisticated techniques for exploring data sets.One of the most commonly used techniques is neural networks that have the abilities to detect nonlinear effects and/or interactions.Due to the reduced interpretability of the output model of neural networks the some data set has been analyzed using logistic regression.In this study both techniques have been applied to education data set.The study aims to provide some insight into fist year students undertaking undergraduate programs namely Bachelor of Information Technology (BIT),Bachelor of Multimedia (BMM) and Bachelor in Management of Technology (BMoT) at University Utara Malaysia. The Holland Personality Model was used to indicate the students personality traits in conjunction with students academic achievement of accuracies in both methods the methods were used in this exploratory study in a complementary manner

Fuzzy logic approach to corporate strategy mapping

Corporate strategy mapping involves an analysis of company's present situation based on strategic factors known as SWOT factors that represent Strengths, Weaknesses, Opportunities, and Threats. The company survival analysis aims to forecast appropriate strategies to undertake.For this purpose, Internal-External Matrix (I-E Matrix) is used to map a company's external and internal factors' scores to determine the overall corporate strategy of a company. Based on both scores, IE matrix recommends a company with three types of strategy; Grow and Build, Hold and Maintain, and Harvest and Divest. In allocating the strategies, there are regions whereby the coordinates of mapped IFA and EFA scores are not able to immediately indicate the appropriate strategy to be undertaken by a company.When such cases arise, an analyst opinion is required in order to determine which strategy implementation is most appropriate. Different analyst may provide different opinion based on his or her assumption, `market driven' or `resource-based'. There is no exact solution for the scores that fall in the ambiguous regions. As a solution, one possible approach is to integrate Fuzzy Logic technique with I-E Matrix in producing the automatic strategy formulation.This is due to the fact that Fuzzy Logic has shown to have ability to improve the intelligence of systems on uncertain, imprecise and noisy environment. In this study, Fuzzy Logic has been developed and tested on real cases data.The result shows that the proposed technique is able to forecast the strategic choice for the ambiguous locations that exists in the company

Model peramalan pinjaman pendidikan Negeri Kedah menggunakan pendekatan rangkaian neural

Lembaga Biasiswa Negeri Kedah (LBNK) is a state government body that helps to provide a scholarship or educational loan to the individual who are born in Kedah.To approve an educational loan application is not an easy task and it will take a longer time if the manpower in not sufficient. To assist LBNK in reducing cost and processing time, this study aims to develop a forecasting model that can be used by the management to accelerate the loan application processing.In this study, the neural network based forecasting is selected.Such a technique has been chosen since this technique is able to recognize non linear patterns with the data set.For this purpose, a multilayer perceptron with backpropagation learning was employed to predict whether an application is suitable to be awarded a scholarship or loan by LBNK.A total of 1062 applications for the year 2002 was used to test the identified neural network model.The training and test results indicate that the highest result achieved is 99.06%.This indicates that neural network has the potential to be used as a forecasting model in education