An approach to the development of commonsense knowledge modeling systems for land selection

The land use methods which are ergonomically and environmentally appropriate are determined first and foremost by characteristics and location. For instance, land selection in architectural construction domain is considered as an area in land use methods, which involves commonsense knowledge of architects. This is because land selection criteria are very personal and there is no theory behind how it should be done. Sometime, there are too many redundancies in the process selection of lands. In this paper we present an approach to modeling commonsense knowledge in a sub field of architecture domain of land selection to come up with land classifications as psychological, physical and social events. This gives three-phase knowledge modeling approach for modeling commonsense knowledge in, which enables holistic approach for land selection. 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. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy

Development of Autonomous Multi Agent System for Multi-Hazard Risk Assessment

Developing autonomous multi agent systems are to be considered anadvancement of multi agent systems can be applied in both the physical and the logicalworld. Constructions of multi hazard risk assessment using spatial data for disastermanagement have a problem of effective communication because of implicitknowledge. Risk assessment is the determination of quantitative or qualitative value ofrisk related to a concrete situation and a recognized hazard. Multi hazard riskassessment requires commonsense knowledge related with the hazard. This complicatesthe effective communication of data to the user in real-time machine processing insupport of disaster management. The aim of the approach is to identify the influences ofdeveloping autonomous multi agent systems for risk assesmnet in disaster management.The objectives should a) contribute to a better understanding of the transformationprocesses in commonsense knowledge related with a hazard and b) provide effectivecommunication of data to the user in real-time machine processing in support of disastermanagement.In this paper we present a metodology to modeling commonsenseknowledge in Multi hazard risk assessment using Autonomous multi agent system. Thisgives three-phase knowledge modeling approach for modeling commonsenseknowledge in, which enables holistic approach for disaster management. At the initialstage autonomous agents are initialized to convert commonsense knowledge based onmulti hazards into a questionnaire. Removing dependencies among the questions aremodeled using principal component analysis. Classification of the knowledge isprocessed through fuzzy logic agent, which is constructed on the basis of principalcomponents. Further explanations for classified knowledge are derived by agent basedon expert system technology. We have implemented the system using FLEX expertsystem shell, SPSS, XML and VB. This paper describes one such approach usingclassification of human constituents in Ayurvedic medicine. Evaluation of the systemhas shown 77% accuracy.Key words: Autonomous multi agent systems, Multi hazards, risk assessment,commonsense knowledge, Fuzzy logi

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases

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 FUZZY EXPERT SYSTEMS FOR TACIT KNOWLEDGE MODELING IN STRATEGIC DECISION-MAKING

Knowledge modelling gives the intention of knowledge engineering which is applicable for managing information systems. Tacit knowledge is the key issue of knowledge modelling aspect because all knowledge is rooted in tacit knowledge. In recognizing knowledge as a new resource in gaining organizational competitiveness, knowledge management suggests a method in managing and applying knowledge for improving organizational performance. Much knowledge management research has focused on identifying, storing, and disseminating process related knowledge in an organized manner. Applying knowledge to decision making has a significant impact on organizational performance than solely processing transactions for knowledge management. This paper presents a research that incorporates modelling of tacit knowledge for strategic decision-making. Here we have used fuzzy expert system for developing an approach for modelling tacit knowledge. We primarily used fuzzy logic together with statistical technique of principal component analysis as techniques for modelling tacit domains. Tacit knowledge in Ayurvedic sub-domain of individual classification has been acquired through a questionnaire and analysed to identify the dependencies, which lead to make tacit knowledge in the particular domain. It has shown 77% accuracy in using the tacit knowledge for reasoning in the relevant domain. Keywords: Fuzzy Expert System, Tacit Knowledge, Principal Component Analysis, and Strategic Decision-making, Ayurvedic MedicineFor full Paper: [email protected]

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

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

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

A FUZZY EXPERT SYSTEM FOR BUSINESS INTELLIGENCE

Business Intelligence (BI) is recognized as an increasingly important support for business decision making in emerging business environment, where a huge amount of data is growing fast and scattered around. Explicit knowledge can be presented formally and capable of effective (fast and good quality) communication of data to the user where as commonsense knowledge can be represented in informal way and further modeling needed for BI. Acquiring useful Business Intelligence (BI) for decision-making is a challenging task in dynamic business environment. In this paper we present an approach for modeling commonsense knowledge in Business Intelligence. A fuzzy expert system based on principal component analysis (PCA) and statistical fuzzy inference system for modeling Business Intelligence in commonsense knowledge is introduced in, which enables holistic approach for disaster management. This paper describes one such approach using classification of human constituents in Ayurvedic medicine. Evaluation of the system has shown 77% accuracy. Key words: Business Intelligence, Statistical inference system, Common sense knowledge, Principal component analysis and Ayurvedic medicineFor full Paper: [email protected]