Browsing by Author "Karunananda, A.S."

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An approach to develop Multi Techniques Integrated Expert System for Diagnosis of Human Constitutions
(2008) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.
This paper presents a multi techniques integrated expert system for diagnosis of Prakurthi in Ayurvedic medicine. The present mechanisms used for diagnosis of Prakurthi, which is considered, as classification of human constitutions and is inconsistent about its findings. Therefore a research has been conducted to reduce such inconsistencies using an expert system. In this issue multi techniques integrated expert system has been implemented for addressing the problem. Statistical technique and fuzzy logic have been described as multi techniques involved in the expert system. Tacit knowledge has always been influential to change the directions and emphasis of explicit models of knowledge. All explicit knowledge is rooted in tacit knowledge. Due to these reasons modelling of tacit knowledge is of great interest. 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. 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. As such, fuzzy logic has been used to further model the Ayurvedic sub-domain. The result of the modelling of Ayurvedic domain using fuzzy logic has been compatible with the experiences of the Ayurvedic experts. A framework for diagnosis of human constitutions has been integrated with an expert system shell thereby enabling the development of expert systems for domains with tacit knowledge. Currently, it has been integrated with FLEX expert system shell.
An approach to the development of commonsense knowledge modeling system for disaster management
(Springer, 2007) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.; Rathnayake, U.
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 modelling is the key to disaster management in the process of information gathering into a formalized way. Modelling 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 modelling approach. At the initial stage commonsense knowledge is converted into a questionnaire. Removing dependencies among the questions are modelled 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 modelling systems for land selection
(2012) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.
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.
Creative fusion of scientific methods with mathematics and computing.
(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Karunananda, A.S.
The birth of the Scientific Method in the 17th century is considered as an unprecedented breakthrough by humankind in the discovery of new knowledge and inventions. The scientific method primarily emphasizes on proving the validity of a hypothesis through a systematically designed experiment. In the process of conducting scientific experiments, the results generated are analyzed statistically, to substantiate a hypothesis thereby forming a scientific theory. In the 17th century, Francis Bacon identified induction as the method of reasoning in science, and Newton extended the idea of induction with the hypothetico-deductive reasoning. Since then, Newton’s approach to Scientific Method has been practiced. From its inception, the scientific method has been influenced by developments in fields such as mathematics, statistics and computing. The penetration of developments in other disciplines into science has primarily been required by the challenging, hazardous, costly, and time consuming nature of experimental design in Scientific Method. In other words, developing experiments to prove multiple hypothesis has become a challenge. One classic example is the challenge faced by Faraday and Wright brothers when they experimented and proved their hypotheses on electricity and flying machines. Historically, Mathematics happened to be the first giant to address practical issues in experimental designs. Developments in Mathematics in 18th and 19th centuries greatly influenced the Scientific Method and paved the way for Theoretical Physics. In simple terms, theoretical physics can formulate and verify theories by mathematical techniques such as differential equations. Many theories in Quantum Mechanics and Theory of Relativity were developed under the umbrella of theoretical physics. Such theories have been subsequently proven by experiments. Emergence of the field of computing in the latter part of the 20th century has marked yet another breakthrough in knowledge discovery and inventions. Undisputedly, computing is identified as a subject area in which significant developments have been reported in 60 years. The latest advancements in all other disciplines including natural sciences, medicine, engineering, entertainment, and social sciences have been influenced by the developments in computing. More importantly, computing has introduced new means of designing experiments as computer-based simulations in research in almost all disciplines. Computer-based experiments have been cost effective, time saving, hazard free, and even provided insight for experiments in unseen dimensions. It has now been a tradition to go for computer based simulation (in-silico) before going into actual experiments (wet-lab). For example, designing of complex machines such as aircrafts and ships are now modeled and tested in computer-based simulators before being tested through real world experiments. Presently, the computer has become the most versatile laboratory for research in all disciplines. More importantly, computing can facilitate not only experimental design but also hypothesis development, sampling, data collection, data analysis and presentations in scientific research. In conclusion, it is emphasized that creative fusion of mathematics and computing with scientific method has discovered a new dimension for research and development for humankind.
Development of commonsense knowledge modeling system for Psychological Assessment in Clinical Psycho
(2014) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.; Rathnayake, U.
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.
Development of fuzzy expert systems for Tacit knowledge modeling in strategic decision -making
(2012) Mendis, D.S.K.; Karunananda, A.S.; Samarathunga, U.
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.
A fuzzy expert system for business intelligence
(2013) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.; Rathnayake, U.
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.
Girimananda Sutta: Holistic Approach for development of commonsense knowledge system in clinical psychology
(2012) Mendis, D.S.K.; Karunananda, A.S.; Samarathunga, U.
The Buddhists believe that Girimananda sutta has some unseen power in healing the sick. In one of the discourses known as Girimananda Sutta, the Buddha talks about the causes of sickness and disease as originating from an imbalance of bile (pita), phlegm (kapha), wind (vata), from conflict of the humours, from changes of weather, from adverse condition (which here mearls faulty deportment), from devices, fi'om the result of kamma (kamma-vipaka); cold, heat, hunger, thirst, excrement, and urine. Ayurvedic medicine is prepared on the grounds of ill-balance ofthese constituents in a person. Ayurvedic medicine has a.very strong bearing on the concept of Prakurthi, which means nature (natural fo.m) of the build and constitution of the human body. Ayurvedic clinical psychology forms the clinical core of mental health counseling. According Ayurvedic medical practises, a person is dominatedon 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 ofconstituent type has a list ofprobable mental diseases. An important areaof 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 support of clinical psychology. The airn of the approach is to identify the influences of developing commonsense knowledge systems for psychological assessments in clinical psychology. The objectives should a) contribute to a better understanding of the transformation processes in commonsense knowledge related with concept of Prakurthi evolved by Girimananda Sutta and b) provide effective communication of data to the user in real-time machine processing in support of clinical psychology. In this paper we present a methodology to develop commonsense knowledge system in clinical psychology. At the initial stage commonsense knowledge relevant to dosa is converted into a questionnaire. Removing dependencies among the questions are modeled using principal component analysis. Classification for degre e of dasa is processed throughfuzry logic module, which is constructed on the basis of principal components" Further explanations for classified knowledge are derived by expert system technology. Evaluation of the system has shown 77% accuracy.
Modeling of Tacit Knowledge
(2003) Mendis D.S.K.; Karunananda, A.S.; Samarathunga, U.
Tacit knowledge has always been influential in changing the directions and emphasis of explicit models of knowledge. All explicit knowledge is rooted in tacit knowledge. Due to these reasons modelling of tacit knowledge is of great interest. A research has been conducted to develop an approach to model tacit knowledge. In this research, we have used Artificial Intelligence technique of fuzzy logic for developing an approach to model tacit knowledge. We have considered domain of “Ayurvedic” medicine as a case study domain with tacit knowledge. 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. In the first place analysis was done using statistical techniques of principle components and the results were not compatible with the experiences of Ayurvedic experts. As such, fuzzy logic has been used to further model the Ayurvedic subdomain. The result of the modelling of Ayurvedic domain using fuzzy logic has been compatible with the experiences of the Ayurveic experts. A framework for modelling tacit knowledge has been integrated with an expert system shell thereby enabling the development of expert systems for domains with tacit knowledge. Framework has been successfully applied for several tacit domains.
A Plug-in to Boost the Behaviour of a Rule-Based Expert System More Like a Human
(Faculty of Graduate Studies, University of Kelaniya, 2015) Weerakoon, W.A.C.; Karunananda, A.S.; Dias, N.G.J.
Artificial Intelligence (AI) is one major aspect of Computer Science. Among the applications of AI, expert systems are predominant. There are expert systems built for variety of subject domains such as education, medicine, and engineering, and were built by imitating the human experts with the ability to make accurate decisions by resolving the proper set of rules and facts stored in a knowledgebase to solve more complex problems. When it comes to systems, it is expected to be more accurate, reliable, efficient and complete. The current expert systems consists of many facilities such as user interfaces, reasoning of the system, knowledgebase, working memory, making inferences, prioritizing and an automatic way for the user to enter knowledge, with compared to the human experts. Even though, the expert systems are still behind and much specific in some aspect such as the abilities in generalizing concepts, drawing associations among knowledge entities depending on the causal relationships, adding new knowledge, removing irrelevant knowledge, prioritizing knowledge entities for the execution as per the input to gain improvements over generations of execution as human experts do. Among the technical categories of the expert systems such as rulebased, frame-based and induction-based, our concern is to improve the rule-based expert systems by solving the said problem by constructing a processing model which consists of the processing states such as Origin, Classified, Pre-State, Resolve and Terminate with newly introduced multiple sub-processes such as Input/Identify knowledge entities, Classify facts/rules depending on the causal relationships crafting the generalizing facility and Termination. When the system executes over generations, it produces outputs and gains improvements using the above mentioned processing model as per the input/queries. For this processing model, newly introduced sub-processes will be implemented using C programming language and will integrate to the current expert systems written in ‗C Language Integrated Production System‘ as a plug-in. The system will be able to evaluate by comparing its states With-Plug-In and Without-Plug-In for the quality using a non-parametric test such as Mann-Whitney-U-test and for the time using a paired-t-test. As a result we are capable of providing an expert system which is more like a human expert.
A statistical fuzzy inference system for analyzing temperamental groups in neuro-linguistic programming
(Gampaha Wickramarachchi Ayurveda Institute, University of Kelaniya, Sri Lanka, 2016) Mendis, D.S.K.; Ratnayake, U.; Karunananda, A.S.; Samaratunga, U.
Neuro-Linguistic Programming describes the fundamental dynamics between mind (neuro) and language (linguistic) and how their interplay affects our body and behavior (programming). Neuro-Linguistic Programming (NLP) is about self-discovery, exploring identity and mission. It also provides a framework for understanding and relating to the 'spiritual' part of human experience. The immediate problem that this poses for a full understanding of human functioning is that the inner subjective experiences of consciousness based in NLP. Manas prakurthi in Ayuverda contributes to the study of personality. Tamas-Rajas-Sattva temperamental groups give rise to the framework of Space-Time-Causation when evolution starts in association with Consciousness Principle in manas prakrti. The objectives should contribute to a better analyzing of the temperamental groups in manas prakrti and to analyze the gap between current state of work and values of NLP. This paper attempts to present a tool to analyze Tamas-Rajas-Sattva temperamental groups that are found in manas prakrti by using a statistical fuzzy inference system. At the initial stage common sense knowledge based on manas prakrti is converted into a questionnaire. Removal of dependencies among the questions in the questionnaire is modelled using principal component analysis. Classification of Tamas-Rajas-Sattva temperamental groups is processed through fuzzy logic module, which is constructed on the basis of principal components. Effective decision making for type of manas prakrti has been derived from sugeno defuzzification technique based on an integrated Principal Component Analysis approach. The statistical fuzzy inference system facilitates an approach to identify the influences to understand the nature of human personality in Neuro-Linguistic Programming.
Using intelligent techniques for widening scope of real world problem solving
(2002) Mendis, D.S.K.; Karunananda, A.S.; Samaratunga, U.
This paper presents a real world application where intelligent techniques have given promising results when statistical methods fail. Here we have considered the domain of Ayurvedic classification of individuals. Ayurvedic classification on individuals is based on analysis of a questionnaire. The questionnaire has been used over many years without any research into its improvement. So, classification using this method is still vague and subjective. An intelligent hybrid system has been developed to recognize human constituents. The system consists of modules using two intelligent techniques, namely, Fuzzy logic and Expert system. The statistical analysis on questionnaire of pilot study shows that principal component (PC) are not significant to consider. However, according to intelligent system developed, there is a significant difference between what we conclude with PC analysis and without PC analysis, provided that intelligent technique of Fuzzy logic is used. This is an interesting result that shows limitation of statistical techniques and how intelligent systems can be used for improving decision making.