Cooperation between expert knowledge and data mining discovered knowledge: Lessons learned

Expert systems are built from knowledge traditionally elicited from the human expert. It is precisely knowledge elicitation from the expert that is the bottleneck in expert system construction. On the other hand, a data mining system, which automatically extracts knowledge, needs expert guidance on the successive decisions to be made in each of the system phases. In this context, expert knowledge and data mining discovered knowledge can cooperate, maximizing their individual capabilities: data mining discovered knowledge can be used as a complementary source of knowledge for the expert system, whereas expert knowledge can be used to guide the data mining process. This article summarizes different examples of systems where there is cooperation between expert knowledge and data mining discovered knowledge and reports our experience of such cooperation gathered from a medical diagnosis project called Intelligent Interpretation of Isokinetics Data, which we developed. From that experience, a series of lessons were learned throughout project development. Some of these lessons are generally applicable and others pertain exclusively to certain project types

Mejoramiento de Imágenes Luminosas utilizando el Modelo de Intersección Cortica

El uso de imágenes digitales va en aumento, sin embargo, se pueden ver afectadas por diversos factores, que degradan su calidad lo que dificulta su correcto análisis. Las imágenes luminosas son un claro ejemplo de ello. En este trabajo se implementa una Red Neuronal Pulso-Acoplada para mejorar las imágenes luminosas, utilizando el Modelo de Intersección Cortical y una Matriz de Tiempo para modificar el valor de los pixeles y conseguir una imagen de mejor calidad en menor tiempo

Red Neuronal Pulsante Adaptada al Problema del Camino más Corto

La solución eficiente del problema del camino más corto tiene aplicaciones en áreas tan importantes y actuales como la robótica, las telecomunicaciones, la investigación de operaciones, la teoría de juegos, las redes de computadoras, internet, diseño industrial, fenómenos de transporte, diseño de circuitos electrónicos y otros, por lo que es un tema de gran interés en el área de optimización combinatoria. En el presente trabajo se describe una Red Neuronal Artificial Pulsante capaz de atacar eficientemente el problema del camino más corto entre dos nodos. Una vez que la Red Pulsante encuentra el nodo meta a costo mínimo, se realiza una extracción o Explicitación de Conocimiento de esta Red para recuperar la trayectoria final. Debido al diseño en paralelo de la Red Neuronal aquí presentada, este enfoque de solución puede resultar altamente competitivo, según se observó en la etapa de experimentación a partir de los buenos resultados obtenidos, aún en casos con miles de nodos

SÚPER RESOLUCIÓN Y MEJORA DEL ALGORITMO CANNY PARA LA DETECCIÓN DE BORDES EN IMÁGENES MÉDICAS

La detección de bordes en imágenes es de suma importancia para encontrar objetos de interés en estas, especialmente si se trata de imágenes médicas, donde la búsqueda de cuerpos extraños es la actividad crítica para el médico al momento de analizar los estudios en imágenes del paciente. Sin embargo, normalmente las imágenes a examinar, presentan ruido que dificulta el proceso de diagnóstico. Por ello, en este artículo, se propone un sistema de Tratamiento Digital de Imágenes que mejore la resolución de imágenes de resonancia magnética (MRI) con técnicas de Súper Resolución (SR) y un algoritmo de detección de bordes, denominado CannySu, que consiste en el algoritmo de Canny en combinación con el algoritmo de Otsu para establecer valores de umbral al detector de bordes. Los resultados obtenidos demuestran la efectividad de esta propuesta

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

FEATURE SELECTION CONSIDERING ATTRIBUTE INTER-DEPENDENCIES

Abstract. With the increasing size of databases, feature selection has become a relevant and challenging problem for the area of knowledge discovery in databases. An effective feature selection strategy can significantly reduce the data mining processing time, improve the predicted accuracy, and help to understand the induced models, as they tend to be smaller and make more sense to the user. Many feature selection algorithms assumed that the attributes are independent between each other given the class, which can produce models with redundant attributes and/or exclude sets of attributes that are relevant when considered together. In this paper, an effective best first search algorithm, called buBF, for feature selection is described. buBF uses a novel heuristic function based on n-way entropy to capture interdependencies among variables. It is shown that buBF produces more accurate models than other state-ofthe-art feature selection algorithms when compared on several synthetic and real datasets

Libro de Proyectos Finales 2021 primer semestre

PregradoIngeniero CivilIngeniero de SistemasIngeniero ElectricistaIngeniero ElectrónicoIngeniero IndustrialIngeniero Mecánic