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Various models used in stock market forecasting presented have been classified according to the data preparation, forecasting methodology, performance evaluation, and performance measure. However, these models have not sufficiently discussed in data preparation to overcome randomness, as well as uncertainty and volatility of stock prices issues in achieving high forecasting accuracy. Therefore, the focus of this paper is the data preparation procedure of triangular fuzzy number to build an improved fuzzy random auto-regression model using non-stationary stock market data for forecasting purposes. The improved forecasting model considers two types of input, which are data with low-high and single point values of stock market prices. Even though, low-high data present variability and volatility in nature, the single data has to be form in symmetry left-right spread to present variability and standard error. Then, expectations and variances, confidence-intervals of fuzzy random data are constructed for fuzzy input-output data. By using the input-output data and simplex approach, parameters of the model can be estimated. In this study, some real data sets were used to represent both types of inputs, which are the Kuala Lumpur stock exchange and Alabama University enrollment. The study found that variability and spread adjustment are important factors in data preparation to improve accuracy of the fuzzy random auto-regression model. [ABSTRACT FROM AUTHOR]

Determining the weight values is crucial in developing problem's mathematical model. The value of the model's weight must be determined before the model is solved. Nevertheless, the developed mathematical model is troublesome when the weight values are not exactly known, as relevant data are sometimes not given or difficult to obtain or estimate. Since numerous researches focusing in finding the solution of the model, this paper focuses on determining the weight value and a weighting method specifically for linear fractional programming to solve possibilistic programming of the multi-objective decision-making problem. Fuzzy random regression approach is applied to estimate the multi-objective model's weight value. Meanwhile, the minimal and maximal values of the objective function are utilised in determination for objective function weight value. Since most of the weight values in the developed model discusses in this paper are estimated from real data, assessment to these weights value in the objective function is executed. The weight value assessment uses weight absolute percentage error of fuzzy demand (WAPE_FD). This analysis concludes that it is worthwhile to pursue proposed solution approach to the multi-objective evaluation scheme, which addresses some limitation to determine and assess the weight values within fuzzy circumstances.

Real-life applications face simultaneously hybrid uncertainty namely fuzziness and randomness, or ambiguous and vague information that makes the existing decision-making model incapable of handling such uncertainties. This paper presents the possibilistic programming for decision-making using a fractile approach. Some real world problems are formulated as a necessity measure model to deal with the uncertainties, which come from vague aspiration and ambiguous coefficients. Thus, the proposed methodology is important in building the model and finding the solution. The vagueness and ambiguity are properly treated in the paper and the fractile approach is used to solve fuzzy linear programming problem. An illustrative example explains the proposed model. The analytical results of the proposed method reveal the improvement of conventional decision-making approaches to appropriately handle inherent uncertainties contained in the real world situation.

This paper demonstrates a satisfaction-based method for solving multi-objective programming problems in a multi-level decision structure through the renewed use of a weighted additive fuzzy goal programming (weighted additive-FGP) approach. The weighted additive-FGP enables us to indicate decision maker's preferences toward the unit's goals and decision vectors that are controlled by the decision maker in the pth-level. This paper presents the algorithm reflecting the influence of a higher-level decision on the decision of a lower level, with the respective decision maker having the authority to place emphasis on certain decision variables. A numerical example clearly illustrates the proposed solution procedure.

Heart attack is one of the serious illnesses and reported as the main killer disease. Early prevention is significant to reduce the risk of having the disease. The prevention efforts can be strengthen through awareness and education about risk factor and healthy lifestyle. Therefore the knowledge dissemination is needed to play role in order to distribute and educate public in health care management and disease prevention. Since the knowledge dissemination in medical is important, there is a need to develop a knowledge based system that can emulate human intelligence to assist decision making process. Thereby, this study utilized hybrid artificial intelligence (AI) techniques to develop a Fuzzy Expert System for Diagnosing Heart Attack Disease (HAD). This system integrates fuzzy logic with expert system, which helps the medical practitioner and people to predict the risk and as well as diagnosing heart attack based on given symptom. The development of HAD is expected not only providing expert knowledge but potentially become one of learning resources to help citizens to develop awareness about heart-healthy lifestyle.

The role and importance of healthcare systems to improve quality of life and social welfare in a society have been well recognized. Attention should be given to raise awareness and implementing appropriate measures to improve health care. Therefore, a computer based system is developed to serve as an alternative for people to self-diagnose their health status based on given symptoms. This strategy should be emphasized so that people can utilize the information correctly as a reference to enjoy healthier life. Hence, a Web-based Community Center for Healthcare Diagnosis system is developed based on expert system technique. Expert system reasoning technique is employed in the system to enable information about treatment and prevention of the diseases based on given symptoms. At present, three diseases are included which are arthritis, thalassemia and pneumococcal. Sets of rule and fact are managed in the knowledge based system. Web based technology is used as a platform to disseminate the information to users in order for them to optimize the information appropriately. This system will benefit people who wish to increase health awareness and seek expert knowledge on the diseases by performing self-diagnosis for early disease detection.