Nueva propuesta evolutiva para el agrupamiento de documentos en sistemas de recuperación de información

Texto en español y resumen en español e inglésFernández del Castillo Díez, José Raúl, codir.El conocimiento explicito de las organizaciones se encuentra recogido en colecciones documentales controladas, a disposición de sus usuarios. Cuándo el número de documentos es elevado se necesitan herramientas para organizar y mostrar los contenidos de la colección, que permitan y faciliten a los usuarios explorar la colección para conocer mejor su naturaleza y descubrir relaciones, patrones, tendencias, y otras características para poder así ?comprender? la información. La necesidad de usar conocimientos en los Sistemas de Recuperación de Información empujó a los investigadores a analizar los sistemas inteligentes que procuran incorporar y usar dichos conocimientos con la finalidad de optimizar el sistema. En la presente tesis, se muestra un Sistema Evolutivo (SEV), y los resultados obtenidos en la construcción de un sistema de esta naturaleza. En este trabajo hacemos una aportación en el área de Recuperación de Información (RI), proponiendo el desarrollo de un nuevo sistema que, utilizando técnicas evolutivas, implemente un sistema de aprendizaje del tipo no supervisado, para agrupar los documentos de un Sistema de Recuperación de Información (SRI); en donde los grupos y el número de ellos son desconocidos a priori por el sistema. El criterio para realizar el agrupamiento de los documentos estará basado por la similitud y distancia de los documentos, formando así de esta manera grupos ó clustering de documentos afines, permitiendo así agrupar los documentos de un SRI de una manera aceptable, presentándose como una alternativa válida a los métodos de agrupamiento tradicionales, pudiéndose contrastar sus resultados experimentalmente con algunos de los métodos clásicos. Los lexemas más relevantes de cada documento, obtenidos mediante la aplicación de técnicas de RI, permiten enriquecer la información asociada a los documentos de la colección y utilizarlos como valores de metadatos para el algoritmo evolutivo. De esta forma, el sistema funciona mediante una metodología de procesamiento de documentos que selecciona los lexemas de los documentos mediante criterios de recuperación de información. Los resultados obtenidos demuestran la viabilidad de la construcción de una aplicación a gran escala de estas características, para integrarla en un sistema de gestión de conocimiento que tenga que manejar grandes colecciones documentales controladas

Nueva propuesta evolutiva para el agrupamiento de documentos en sistemas de recuperación de información

Texto en español y resumen en español e inglésFernández del Castillo Díez, José Raúl, codir.El conocimiento explicito de las organizaciones se encuentra recogido en colecciones documentales controladas, a disposición de sus usuarios. Cuándo el número de documentos es elevado se necesitan herramientas para organizar y mostrar los contenidos de la colección, que permitan y faciliten a los usuarios explorar la colección para conocer mejor su naturaleza y descubrir relaciones, patrones, tendencias, y otras características para poder así ?comprender? la información. La necesidad de usar conocimientos en los Sistemas de Recuperación de Información empujó a los investigadores a analizar los sistemas inteligentes que procuran incorporar y usar dichos conocimientos con la finalidad de optimizar el sistema. En la presente tesis, se muestra un Sistema Evolutivo (SEV), y los resultados obtenidos en la construcción de un sistema de esta naturaleza. En este trabajo hacemos una aportación en el área de Recuperación de Información (RI), proponiendo el desarrollo de un nuevo sistema que, utilizando técnicas evolutivas, implemente un sistema de aprendizaje del tipo no supervisado, para agrupar los documentos de un Sistema de Recuperación de Información (SRI); en donde los grupos y el número de ellos son desconocidos a priori por el sistema. El criterio para realizar el agrupamiento de los documentos estará basado por la similitud y distancia de los documentos, formando así de esta manera grupos ó clustering de documentos afines, permitiendo así agrupar los documentos de un SRI de una manera aceptable, presentándose como una alternativa válida a los métodos de agrupamiento tradicionales, pudiéndose contrastar sus resultados experimentalmente con algunos de los métodos clásicos. Los lexemas más relevantes de cada documento, obtenidos mediante la aplicación de técnicas de RI, permiten enriquecer la información asociada a los documentos de la colección y utilizarlos como valores de metadatos para el algoritmo evolutivo. De esta forma, el sistema funciona mediante una metodología de procesamiento de documentos que selecciona los lexemas de los documentos mediante criterios de recuperación de información. Los resultados obtenidos demuestran la viabilidad de la construcción de una aplicación a gran escala de estas características, para integrarla en un sistema de gestión de conocimiento que tenga que manejar grandes colecciones documentales controladas

A Learning Health-Care System for Improving Renal Health Services in Peru Using Data Analytics

The health sector around the world faces the continuous challenge of improving the services provided to patients. Therefore, digital transformation in health services plays a key role in integrating new technologies such as artificial intelligence. However, the health system in Peru has not yet taken the big step towards digitising its services, currently ranking 71st according to the World Health Organisation (WHO). This article proposes a learning health system for the management and monitoring of private health services in Peru based on the three key components of intelligent health care: (1) a health data platform (HDP); (2) intelligent technologies (IT); and (3) an intelligent health care suite (HIS). The solution consists of four layers: (1) data source, (2) data warehousing, (3) data analytics, and (4) visualization. In layer 1, all data sources are selected to create a database. The proposed learning health system is built, and the data storage is executed through the extract, transform and load (ETL) process in layer 2. In layer 3, the Kaggle dataset and the decision tree (DT) and random forest (RF) algorithms are used to predict the diagnosis of disease, resulting in the RF algorithm having the best performance. Finally, in layer 4, the intelligent health-care suite dashboards and interfaces are designed. The proposed system was applied in a clinic focused on preventing chronic kidney disease. A total of 100 patients and six kidney health experts participated. The results proved that the diagnosis of chronic kidney disease by the learning health system had a low error rate in positive diagnoses (err = 1.12%). Additionally, it was demonstrated that experts were “satisfied” with the dashboards and interfaces of the intelligent health-care suite as well as the quality of the learning health system.Revisión por pare

Design of a predictive model of a rock breakage by blasting using artificial neural networks

Over the years, various models have been developed in the stages of the mining process that have allowed predicting and enhancing results, but it is the breakage, the variable that connects all the activities of the mining process from the point of view of costs (drilling, blasting, loading, hauling, crushing and grinding). To improve this process, we have designed and developed a computational model based on an Artificial Neural Network (ANN), the same that was built using the most representative variables such as the properties of explosives, the geomechanical parameters of the rock mass, and the design parameters of drill-blasting. For the training and validation of the model, we have taken the data from a copper mine as reference located in the north of Chile. The ANN architecture was of the supervised type containing: an input layer, a hidden layer with 13 neurons and an output layer that includes the sigmoid activation function with symmetrical properties for optimal model convergence. The ANN model was fed-back in its learning with training data until it becomes perfected, and due to the experimental results obtained, it is a valid prediction option that can be used in future blasting of ore deposits with similar characteristics using the same representative variables considered. Therefore, it constitutes a valid alternative for predicting rock breakage, given that it has been experimentally validated, with moderately reliable results, providing higher correlation coefficients than traditional models used, and with the additional advantage that an ANN model provides, due to its ability to learn and recognize collected data patterns. In this way, using this computer model we can obtain satisfactory results that allow us to predict breakage in similar scenarios, providing an alternative for evaluating the costs that this entails as a contribution to the work

Developing a long short-term memory-based model for forecasting the daily energy consumption of heating, ventilation, and air conditioning systems in buildings

Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.Se buscó pronosticar el consumo de energía de los sistemas de calefacción Heating, ventilating y aire acondicionado (HVAC) para la eficiencia energética de los edificios. En este estudio, se desarrolla un modelo de red neuronal artificial (RNA) recurrente del tipo Long short-term memory (LSTM) destinada a pronosticar el consumo de energía de un sistema HVAC en los edificios, en concreto una bomba de calor del Teatro Real de España. El trabajo comparó diferentes configuraciones del modelo con respecto a los datos reales proporcionados por el BMS del edificio y se identificó los hiperparámetros adecuados para el LSTM. El objetivo fue desarrollar y evaluar el modelo para pronosticar el consumo diario de energía de los sistemas HVAC, lográndose una predicción del uso de la energía según los criterios indicados por las directrices de American Society of Heating, Refrigerating and Air-Conditioning Engineers ASHRAE, The International Performance Measurement and Verification Protocol IPMVP y The Federal Energy Management Program organismos que validan un modelo HVAC. La contribución del solicitante se centró en el diseño del LSTM, y en la validación de las pruebas con los datos experimentales, así como en el análisis de los resultados obtenidos

Water treatment plant prototype with pH control modeled on fuzzy logic for removing arsenic using Fe(VI) and Fe(III)

This study proposes a fuzzy control strategy embedded in a Siemens IoT2040 gateway developed for removing inorganic arsenic from synthetic underground water in a treatment plant prototype. The prototype is used to dose a constant flow of Fe(VI) to maintain an oxide-reduction potential to guarantee the oxidation of arsenite into arsenate, while the fuzzy logic embedded in the IoT control manages the addition of Fe(III) to achieve a proper pH adjustment and efficient arsenate removal. The tests used synthetic Bangladesh groundwater enriched with 200 µg/L of arsenite and 200 µg/L of arsenate. The results revealed that the plant prototype yielded an effective treatment of the water. Arsenate was decreased to an average value of 6.66 µg/L and, the arsenite concentration decreased to 1.01 µg/L or less. These values were lower than the limit of 10 µg/L deemed by the World Health Organization as safe for human consumption.IDIC-Universidad de Lim

Development of a Fuzzy Logic-Based Solar Charge Controller for Charging Lead–Acid Batteries

The design and implementation of a solar charge controller for lead–acid batteries is intended to supplement a component of the water purification module of the water treatment unit for natural disaster relief. This unit contains a solar panel system that supplies power to the module by charging batteries through a controller comprising an Atmega 328 processor. The solar panel feeds voltage to the batteries through fuzzy logic-based software, which allows up to 6 A DC to pass through the controller’s power circuit. Consequently, the battery was charged in less time (an average of 7 h to reach maximum capacity), wherein battery lifespan is related to the charge wave frequency. Thus, our software may be adapted in different control algorithms without having to change hardware

Water treatment plant prototype with ph control modeled on fuzzy logic for removing arsenic using fe(VI) and fe(III)

This study proposes a fuzzy control strategy embedded in a Siemens IoT2040 gateway developed for removing inorganic arsenic from synthetic underground water in a treatment plant prototype. The prototype is used to dose a constant flow of Fe(VI) to maintain an oxide-reduction potential to guarantee the oxidation of arsenite into arsenate, while the fuzzy logic embedded in the IoT control manages the addition of Fe(III) to achieve a proper pH adjustment and efficient arsenate removal. The tests used synthetic Bangladesh groundwater enriched with 200 µg/L of arsenite and 200 µg/L of arsenate. The results revealed that the plant prototype yielded an effective treatment of the water. Arsenate was decreased to an average value of 6.66 µg/L and, the arsenite concentration decreased to 1.01 µg/L or less. These values were lower than the limit of 10 µg/L deemed by the World Health Organization as safe for human consumption

Electronic Health Record Interoperability System in Peru Using Blockchain

In Peru, there is currently no integrated electronic health record (EHR) system that can be automatically shared between healthcare facilities. This leads to increased service costs due to duplicated examinations and records, as well as additional time required to manage patients’ clinical information. One alternative for ensuring the secure interoperability of EHRs while preserving data privacy is the use of blockchain technology. However, existing works consider a pre-established format for exchanging EHRs, which is not applicable when systems have different formats, as is the case in Peru. This work proposes an architecture and a web application for exchanging EHRs in heterogeneous systems. The proposed system includes the homologation of an EHR with rapid interoperability resources for medical attention using FHIR HL7, and vice versa, to achieve interoperability. Additionally, it utilizes blockchain technology to ensure data security and privacy. The web application was tested using a case simulation to demonstrate EHR interoperability between clinics in a clear, secure, and efficient manner. In addition, a survey was conducted with 30 patients regarding adoption, and another survey was conducted with 10 doctors from a public hospital in Peru regarding usability. The results demonstrate a very high level of adoption and usability for them all. Unlike other studies, the proposal does not necessitate alterations to existing EHR systems for interoperability. In other words, the proposal presents a feasible and cost-effective alternative to addressing the EHR interoperability issue in clinics and hospitals in Peru