The Generalized Constraint Language (GCL), introduced by Zadeh, is the essence of Computing with Words (CW). It provides an genda to represent the meaning of imprecise words and phrases in natural language and introduces advanced techniques to perform reasoning on imprecise knowledge. Despite its fundamental role, the definition of GCL has remained informal since its introduction by Zadeh and, to our knowledge, no attempt has been made to formalize GCL or to build a working GCL deduction system. In this dissertation, two main interrelated objectives are pursued: First, the syntax and semantics of GCL are formalized in a logical setting. The notion of soundness of a GCL argument is defined and Zadeh's inference rules are proven sound in the defined language. Second, a CW Expert System Shell (CWSHELL) is implemented for the realization of a GCL deduction system. The CWSHELL software allows users to express their knowledge in terms of GCL formulas and pose queries to a GCL knowledge base. The richness of GCL language allows CWSHELL to greatly surpass current fuzzy logic expert systems both in its knowledge representation and reasoning capabilities. While many available fuzzy logic toolboxes can only represent knowledge in terms of fuzzy-if-then rules, CWShell goes beyond simple fuzzy conditional statements and performs a chain of reasoning on complex fuzzy propositions containing generalized constraints, fuzzy arithmetic expressions, fuzzy quantifiers, and fuzzy relations. To explore the application of CWSHELL, a realistic case study is developed to compute the auto insurance premium based on an imprecise knowledge base. The alpha version of CWSHELL along with the case study and documentation is available for download at http://cwjess.cs.siu.edu/.

The goal of this dissertation is to organize a probabilistic expert system with use of the Shell Spirit in order to evaluate the default risk of borrowers in a financial institution or minimize the risk that it represents. The study was limited to private individuals, since the variables used to analyse the risk in the concession of credit to legal entities are different, and also to the fact that this area is less explored by academics. The applied methodology was inserted in the context of a quantitative empirical research, which aimed to bring the model developed as close to reality as possible. However, in this step it was necessary to collect inside information of the institution used to study, referring to risk analysis, credit policies, profile of borrowers, and also to extract knowledge from the experts with the purpose of selecting the relevant variables for the system and to make the interaction of these when composing rules along with the definition of their weights. Consecutive to the conclusion of the model, tests occurred with some favorable situations and/or unfavorable to the granting of credit, considering that in this phase were instantiated the pertinent variables to each situation at hand. The main idea of the system is to manage and reduce the credit risk of bank institutions, for SPIRIT, an expert system is able to work with uncertainties and manipulates data, being fed with information that indicates the no default probability. The results obtained with the tests were satisfactory as they were able to identify the probability of a borrower turn out to be a no default or minimize the risk, even before the credit was released. / O objetivo desta dissertação é estruturar um sistema especialista probabilístico com utilização da Shell Spirit para avaliar o risco de inadimplência de tomadores de crédito em uma instituição financeira ou de reduzir o risco que ele representa. O estudo foi limitado a pessoas físicas, uma vez que, as variáveis utilizadas para a análise de risco na concessão de créditos se diferem das pessoas jurídicas e, também, pelo fato de esta ser uma área menos explorada pelos acadêmicos. A metodologia aplicada foi inserida no contexto de uma pesquisa empírica quantitativa, na qual se buscou aproximar o máximo possível o modelo elaborado à realidade. Contudo, nesta etapa foi necessário coletar informações internas da instituição utilizada para o estudo, referentes à análise de riscos, políticas de crédito, perfis de tomadores, bem como extrair conhecimentos de especialistas com a finalidade de selecionar as variáveis relevantes para o sistema e de fazer a interação destas ao compor regras juntamente com a definição dos seus pesos. Consecutivo à conclusão do modelo, ocorreram testes de algumas situações favoráveis e/ou desfavoráveis a concessão de créditos, considerando que nesta fase foram instanciadas as variáveis pertinentes a cada situação em questão. O sistema é capaz de gerenciar e reduzir os riscos de crédito de instituição bancária, pois o SPIRIT trabalha com incertezas e manipula dados, sendo alimentado com informações que indiquem a probabilidade de inadimplência não . Os resultados obtidos com os testes foram satisfatórios à medida que estes possibilitaram identificar a probabilidade de um tomador de crédito vir a torna-se um não inadimplente ou de reduzir o risco, antes mesmo da liberação do crédito.

A Multi-tenant database (MTD) is a way of deploying a Database as a Service (DaaS). A multi-tenant database refers to a principle where a single instance of a Database Management System (DBMS) runs on a server, serving multiple clients organisations (tenants). This technology has helped to discard the large-scale investments in hardware and software resources, in upgrading them regularly and in expensive licences of application software used on in-house hosted database systems. This is gaining momentum with significant increase in the number of organisations ready to take advantage of the technology. The benefits of MTD are potentially enormous but for any organisation to venture into its adoption, there are some salient factors which must be well understood and examined before venturing into the concept. This research examines these factors, different models of MTD, consider the requirements and challenges of implementing MTDs. Investigation of the degree of impact each of these factors has on the adoption of MTD is conducted in this research which focused mainly on public organisations. The methodology adopted in undertaking this study is a mixed method which involved both qualitative and quantitative research approaches. These strategies are used here to cover statistics (quantifiable data) and experts’ knowledge and experiences (abstract data) in order to satisfactorily achieve the aim and objectives and complete the research. Following the involvement of these strategies, a framework was developed and further refined after a second survey was carried out with a quantitative approach. This framework will help prospective tenants to make informed decisions about the adoption of the concept. The research also considers the direction of decisions about MTDs in situations where two or more factors are combined. A new MTD framework is presented that improves the decision making process of MTD adoption. Also, an Expert System (ES) is developed from the framework which was validated via a survey and analysed with the aid of SPSS software. The findings from the validation indicated that the framework is valuable and suitable for use in practice since majority of respondents accepted the research findings and recommendations for success. Likewise, the ES was validated with majority of participants accepting it and embracing the high level of its friendliness.

Major bridge stakeholders such as federal and provincial transportation agencies are in dire need for objective knowledge-based systems that assist decision-makers in the selection of bridge type. Besides that, estimating bridge construction costs at the conceptual design stage is an increasing necessity for accurate budgeting and effective allocation of funding. Whilst multiple bridge management systems have already been developed; they still possess major drawbacks pertaining to interoperability and integration with complex time and cost optimization-related problem solving. In another perspective, infrastructure restoration has been backlogged with multifaceted factors that have captured the attention of municipal and federal authorities. Several successful integrations of bridge information management systems (BrIMS) with decision support systems and computer-aided engineering design solutions have significantly leveraged downstream processes of bridge maintenance operations and inspired many researchers. The subjective nature of evaluating bridge conditions and deteriorations is the main factor that influences bridge maintenance, repair, and replacement decisions. In order to overcome this shortcoming, the objectives of this study are intended to demonstrate the viability of integrating a decision support system with a stochastic gamma deterioration model utilizing a probabilistic fuzzy logic strategic approach at the conceptual design stage. In summary, this study presents a systematic multi-objective knowledge-based approach for selecting bridge type, forecasting elemental deteriorations, linear scheduling, and estimating construction costs at the conceptual design stage. The proposed methodology comprises a framework to deploy a system that automatically generates conceptual cost estimates by integrating objective functions with bridge information modeling (BrIM) through an external data interchange protocol in synchrony with interoperability standards. Deployment of the developed system shall minimize the degree of subjectivity involved while decision makings pertaining to bridge projects and assists designers and cost engineers obtain results in an integrated quantitative, qualitative, and systematic manner. The successful deployment of the expert system signifies a technological achievement of novelty pertaining to the integration of bridge information modeling (BrIM) concept with probabilistic fuzzy logic strategic approaches at the conceptual design stage of bridges.

La presente tesis se desarrolló en el Centro de Salud Juan Parra del Riego de Huancayo, en el cual se elaboró y se aplicó un Sistema Experto en EXSYS PRO, con el objetivo de identificar si un niño menor de 5 años tiene neumonía. Es por eso que el objetivo principal de este informe de Tesis es reemplazar el conocimiento humano por un sistema informático, que ayude al diagnóstico de neumonía, ya que este tipo de sistemas tiene la capacidad de trabajar con grandes cantidades de información y bases de conocimiento, evitando el escenario de toma de decisiones de manera errónea puesto que un sistema procesa y analiza toda la información a gran velocidad dando como resultado una decisión más objetiva y sólida, por dicha razón se desarrolló este sistema para reducir la deficiencia existente al momento de diagnosticar (Diagnóstico no confiable) y reducir el tiempo de respuesta en el diagnóstico de neumonía en niños menores de 5 años de acuerdo a los síntomas que presente, evitando mayores complicaciones y poniendo en riesgo su vida, a fin de disponer su inmediata referencia a un servicio de mayor complejidad para ser evaluado y recibir el tratamiento necesario. / Tesis

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The bioreactor landfill is an emerging concept for solid waste management that has gained significant attention in the last decade. This technology employs specific operational practices to enhance the microbial decomposition processes in landfills. However, the unsupervised management and lack of operational guidelines for the bioreactor landfill, specifically leachate manipulation and recirculation processes, usually results in less than optimal system performance. Therefore, these limitations have led to the development of SMART (Sensor-based Monitoring and Remote-control Technology), an expert control system that utilizes real-time monitoring of key system parameters in the management of bioreactor landfills. SMART replaces conventional open-loop control with a feedback control system that aids the human operator in making decisions and managing complex control issues. The target from this control system is to provide optimum conditions for the biodegradation of the refuse, and also, to enhance the performance of the bioreactor in terms of biogas generation. SMART includes multiple cascading logic controllers and mathematical calculations through which the quantity and quality of the recirculated solution are determined. The expert system computes the required quantities of leachate, buffer, supplemental water, and nutritional amendments in order to provide the bioreactor landfill microbial consortia with their optimum growth requirements. Soft computational methods, particularly fuzzy logic, were incorporated in the logic controllers of SMART so as to accommodate the uncertainty, complexity, and nonlinearity of the bioreactor landfill processes. Fuzzy logic was used to solve complex operational issues in the control program of SMART including: (1) identify the current operational phase of the bioreactor landfill based on quantifiable parameters of the leachate generated and biogas produced, (2) evaluate the toxicological status of the leachate based on certain parameters that directly contribute to or indirectly indicates bacterial inhibition, and (3) predict biogas generation rates based on the operational phase, leachate recirculation, and sludge addition. The later fuzzy logic model was upgraded to a hybrid model that employed the learning algorithm of artificial neural networks to optimize the model parameters. SMART was applied to a pilot-scale bioreactor landfill prototype that incorporated the hardware components (sensors, communication devices, and control elements) and the software components (user interface and control program) of the system. During a one-year monitoring period, the feasibility and effectiveness of the SMART system were evaluated in terms of multiple leachate, biogas, and waste parameters. In addition, leachate heating was evaluated as a potential temperature control tool in bioreactor landfills. The pilot-scale implementation of SMART demonstrated the applicability of the system. SMART led to a significant improvement in the overall performance of the BL in terms of methane production and leachate stabilization. Temperature control via recirculation of heated leachate achieved high degradation rates of organic matter and improved the methanogenic activity.

The main point of the work is an identification with NPS32 expert system, the describtion of the ways of getting knowledges. By choosing a suitable district for the expert system aplication, the suggestion of the knowledge base for the district will have been the result of the work.

This work consists in development of expert system intended for choosing appropriate method of waste processing. The software is created in VisiRule software which is built on Prolog language and is part of WinProlog 4.900 development tool. It also deals with problematics of creating of knowledge base for applications of this type and judging of suitability of possible approaches to creating an expert system for given purpose.

The master's theses deal with concept and creation of a new version of the compute kernel in expert system based on NPS32. This paper is focused on possibilities and requirements of the expert system's operation on the Internet, too. One part of the study is devoted to testing the designed SW and demonstrating of its facilities.