A Commonsence knowledge modeling systems for qualitaive risk assessment

Knowledge is the fundamental resource that enhances to function intelligently. Knowledge can be defined into two types such as explicit and implicit. Commonsense knowledge is one type of in implicit knowledge. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as implicit knowledge can be represented in informal way and further modeling needed for gaining effective communication. Constructions of risk assessment using spatial data for disaster management have a problem of effective communication because of implicit knowledge. Risk assessment is a step in a risk management process. Risk assessment is the determination of quantitative or qualitative value of risk related to a concrete situation and a recognized hazard. Quantitative risk assessment requires commonsense knowledge related with the hazard. This complicates the effective ommunication of data to the user in real-time machine processing in support of disaster management. In this paper we present an approach to modeling commonsense knowledge in Quantitative risk assessment. This gives three-phase knowledge modeling approach for modeling commonsense knowledge in, which enables holistic approach for disaster management. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy

A Statistical fuzzy inference system for classifying human constituents

In this paper, statistical fuzzy inference system based on principal component analysis (PCA) and Fuzzy Expert system for diagnosis of human constituents is introduced. This statistical fuzzy inference system deals with combination of the filtering and lassification from measured PCA and Fuzzy expert system technology. This intelligent system has three phases. In acquiring tacit knowledge phase, the model refinement and reasoning for diagnosis of human constituents performed. Tacit knowledge in Ayurvedic subdomain of individual classification has been acquired through a questionnaire and analyzed to identify the dependencies, which lead to make tacit knowledge in the particular domain. In the first place analysis was done using statistical techniques of principal components and the results were not compatible with the experiences of Ayurvedic experts. The result of the modeling of Ayurvedic domain using fuzzy logic has been compatible with the experiences of the Ayurvedic experts. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. The development has been done using Visual basic, FLEX expert system shell and the system runs on Windows platform

Development of commonsense knowledge modeling system for psychological assessment in clinical psycho

According to the Buddhist philosophy, hatred (dosa) is considered as one of the three unwholesome roots which determine the actual immoral quality of volitional states and a conscious thought with its mental factors. Hatred, then, comprises all degrees of repulsion from the faintest trace of ill-humour up to the highest pitch of hate and wrath. Thus, ill-will, evil intention, wickedness, corruption and malice are various expressions and degrees of dosa. A hateful temperament is said to be due to a predominance of the type of dosa, apo, vayu and semha. Vedic psychology forms the clinical core of mental health counseling in the Ayurvedic medical tradition. According to Ayurvedic medical practises, a person is dominated on one of constitutes type (type of dosa) namely vata {vayu), pita {apo) or kapha {semha). This is known as prakurthi pariksha. Important aspect of identification of constitute type is for diagnosis of mental diseases, because each of constituent type has a list of probable mental diseases. An important area of expertise for many clinical psychologists is psychological assessment. Constructions of information systems using psychological assessment in clinical psychology have a problem of effective communication because of implicit knowledge. This complicates the effective communication of clinical data to the psychologist. In this paper, it presents an approach to modeling commonsense knowledge in clinical psychology in Ayurvedic medicine. It gives three-phase an approach for modeling commonsense knowledge in psychological assessment which enables holistic approach for clinical psychology. Evaluation of the system has shown 77% accuracy

An approach to the development of commonsense knowledge modeling systems for disaster management

Knowledge is the fundamental resource that allows us to function intelligently. Similarly, organizations typically use different types of knowledge to enhance their performance. Commonsense knowledge that is not well formalized modeling is the key to disaster management in the process of information gathering into a formalized way. Modeling commonsense knowledge is crucial for classifying and presenting of unstructured knowledge. This paper suggests an approach to achieving this objective, by proposing a three-phase knowledge modeling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification of the knowledge is processed through fuzzy logic module, which is constructed on the basis of principal components. Further explanations for classified knowledge are derived by expert system technology. We have implemented the system using FLEX expert system shell, SPSS, XML, and VB. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy