Orientador: Jacques Wainer / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-14T04:18:48Z (GMT). No. of bitstreams: 1 Benedito_MarcusVinicius_M.pdf: 7380306 bytes, checksum: 7965cb287ddbdcc50899db7c7c4d7840 (MD5) Previous issue date: 2009 / Resumo: Esta dissertação apresenta um sistema de suporte à decisões que auxilia na diagnose de trinta e seis doenças de notificação compulsória as quais despertam interesse da Agência Nacional de Vigilância Sanitária Brasileira (ANVISA). O sistema é baseado no mecanismo de inferência abdutivo que usa a teoria das coberturas parcimoniosas (TCP) com algumas modificações. Ao invés de utilizar apenas as associações entre doenças e sintomas, como na TCP original, propomos associar também fatores relevantes que não são sintomas, conjuntos determinantes de informações que determinam a suspeita de uma doença, independentemente de outras informações, o conceito de sintomas quase obrigatórios e eliminamos a possibilidade de haver múltiplas disfunções simultâneas, para este cenário / Abstract: This work presents a decision support system that helps the diagnoses of thirty six infections diseases of interest to the Brazilian National Health Surveillance Agency (ANVISA). The system is based on an adductive inference mechanism that uses parsimonious covering theory (PCT) with some modifications. Instead of using only the diseases associated with symptoms as in PCT, we propose to associate relevant factors that are not symptoms, determinant information sets that, without any other information, determines that a patient is suspicious of having a disease. We also introduced the concept of almost obligatorily presence of some symptoms when a patient have a particular disease and we eliminate the possibility of having multiple dysfunctions simultaneously, for this scenario / Mestrado / Inteligencia Artificial / Mestre em Ciência da Computação

Orientador : Fernando Jose Von Zuben / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Eletrica e de Computação / Made available in DSpace on 2018-08-04T02:17:19Z (GMT). No. of bitstreams: 1 Lima_ClodoaldoAparecidodeMoraes_D.pdf: 5444612 bytes, checksum: 0172ca4143c2737bf19a3b2076c06b44 (MD5) Previous issue date: 2004 / Resumo: Os algoritmos baseados em métodos de kernel destacam-se entre as diversas técnicas de aprendizado de máquina. Eles foram inicialmente empregados na implementação de máquinas de vetores-suporte (SVMs). A abordagem SVM representa um procedimento de aprendizado não-paramétrico para classificação e regressão de alto desempenho. No entanto, existem aspectos estruturais e paramétricos de projeto que podem conduzir a uma degradação de desempenho. Na ausência de uma metodologia sistemática e de baixo custo para a proposição de modelos computacionais otimamente especificados, os comitês de máquinas se apresentam como alternativas promissoras. Existem versões estáticas de comitês, na forma de ensembles de componentes, e versões dinâmicas, na forma de misturas de especialistas. Neste estudo, os componentes de um ensemble e os especialistas de uma mistura são tomados como SVMs. O objetivo é explorar conjuntamente potencialidades advindas de SVM e comitê de máquinas, adotando uma formulação unificada. Várias extensões e novas configurações de comitês de máquinas são propostas, com análises comparativas que indicam ganho significativo de desempenho frente a outras propostas de aprendizado de máquina comumente adotadas para classificação e regressão / Abstract: Algorithms based on kernel methods are prominent techniques among the available approaches for machine learning. They were initially applied to implement support vector machines (SVMs). The SVM approach represents a nonparametric learning procedure devoted to high performance classification and regression tasks. However, structural and parametric aspects of the design may guide to performance degradation. In the absence of a systematic and low-cost methodology for the proposition of optimally specified computational models, committee machines emerge as promising alternatives. There exist static versions of committees, in the form of ensembles of components, and dynamic versions, in the form of mixtures of experts. In the present investigation, the components of an ensemble and the experts of a mixture are taken as SVMs. The aim is to jointly explore the potentialities of both SVM and committee machine, by means of a unified formulation. Several extensions and new configurations of committee machines are proposed, with comparative analyses that indicate significant gain in performance before other proposals for machine learning commonly adopted for classification and regression / Doutorado / Engenharia de Computação / Doutor em Engenharia Elétrica

The research is conducted in the area of Software Engineering, with emphasis on the design phase of the Software Development Life Cycle (SDLC). The object-oriented paradigm is the point of departure. The investigation deals with the problem of creating support for the design phase of object-oriented system development. This support must be able to guide the system designer through the design process, according to a sound design method, highlight opportunities for prototyping and point out where to re-iterate a design step, for example. A solution is proposed in the form of a knowledge-based support system. In the prototype this support guides a designer partially through the first step of the System Design task for object-oriented design. The intention is that the knowledge-based system should capture the know-how of an expert system designer and assist an inexperienced system designer to create good designs. / Computing / M. Sc. (Information Systems)

The research is conducted in the area of Software Engineering, with emphasis on the design phase of the Software Development Life Cycle (SDLC). The object-oriented paradigm is the point of departure. The investigation deals with the problem of creating support for the design phase of object-oriented system development. This support must be able to guide the system designer through the design process, according to a sound design method, highlight opportunities for prototyping and point out where to re-iterate a design step, for example. A solution is proposed in the form of a knowledge-based support system. In the prototype this support guides a designer partially through the first step of the System Design task for object-oriented design. The intention is that the knowledge-based system should capture the know-how of an expert system designer and assist an inexperienced system designer to create good designs. / Computing / M. Sc. (Information Systems)

Indiana University-Purdue University Indianapolis (IUPUI) / Proteins are nano-machines that work inside every living organism. Functional disruption of one or several proteins is the cause for many diseases. However, the functions for most proteins are yet to be annotated because inexpensive sequencing techniques dramatically speed up discovery of new protein sequences (265 million and counting) and experimental examinations of every protein in all its possible functional categories are simply impractical. Thus, it is necessary to develop computational function-prediction tools that complement and guide experimental studies. In this study, we developed a series of predictors for highly accurate prediction of proteins with DNA-binding, RNA-binding and carbohydrate-binding capability. These predictors are a template-based technique that combines sequence and structural information with predicted binding affinity. Both sequence and structure-based approaches were developed. Results indicate the importance of binding affinity prediction for improving sensitivity and precision of function prediction. Application of these methods to the human genome and structure genome targets demonstrated its usefulness in annotating proteins of unknown functions and discovering moon-lighting proteins with DNA,RNA, or carbohydrate binding function. In addition, we also investigated disruption of protein functions by naturally occurring genetic variations due to insertions and deletions (INDELS). We found that protein structures are the most critical features in recognising disease-causing non-frame shifting INDELs. The predictors for function predictions are available at http://sparks-lab.org/spot, and the predictor for classification of non-frame shifting INDELs is available at http://sparks-lab.org/ddig.

<p dir="ltr">Axial spondyloarthritis (axSpA), a disease that due to its complexity and rarity, presents challenges in diagnosis. With a focus on integrating expert knowledge into an intelligent diagnostic system, the research explores the intricate nature of axSpA, emphasizing the challenges associated with its diverse clinical presentation. By leveraging a comprehensive knowledge base curated by domain experts, encompassing insights into pathophysiology, genetic factors, and evolving diagnostic criteria of axSpA, the expert system strives to provide a nuanced understanding of the disease. The methodology employs a hybrid reasoning approach, combining both forward and backward chaining techniques. Forward chaining iteratively processes clinical data and available evidence, applying logical rules to infer potential diagnoses and refine hypotheses, while backward chaining starts with the desired diagnostic goal and works backward through the knowledge base to validate or refute hypotheses. Additionally, certainty theory is incorporated to manage uncertainty in the diagnostic process, assigning confidence levels to conclusions based on the strength of evidence and expert knowledge. By integrating knowledge base, forward and backward chaining, and certainty theory, the research aims to enhance diagnostic precision for this less common yet impactful inflammatory rheumatic condition, emphasizing the importance of early and accurate identification for effective management and improved patient outcomes. The results indicate a significant improvement in diagnostic accuracy, sensitivity, and specificity compared to traditional methods. The system's potential to enhance early diagnosis and treatment outcomes is discussed, along with suggestions for future research to further refine and expand the system.</p>