Systolic genetic search, a parallel metaheuristic for GPUs

La utilización de unidades de procesamiento gráfico (GPUs) para la resolución de problemas de propósito general ha experimentado un crecimiento vertiginoso en los últimos años, sustentado en su amplia disponibilidad, su bajo costo económico y en contar con una arquitectura inherentemente paralela, así como en la aparición de lenguajes de programación de propósito general que han facilitado el desarrollo de aplicaciones en estas plataformas. En este contexto, el diseño de nuevos algoritmos paralelos que puedan beneficiarse del uso de GPUs es una línea de investigación prometedora e interesante. Las metaheurísticas son algoritmos estocásticos capaces de encontrar soluciones muy precisas (muchas veces óptimas) a problemas de optimización en un tiempo razonable. Sin embargo, como muchos problemas de optimización involucran tareas que exigen grandes recursos computacionales y/o el tamaño de las instancias que se están abordando actualmente se están volviendo muy grandes, incluso las metaheurísticas pueden ser computacionalmente muy costosas. En este escenario, el paralelismo surge como una alternativa exitosa con el fin de acelerar la búsqueda de este tipo de algoritmos. Además de permitir reducir el tiempo de ejecución de los algoritmos, las metaheurísticas paralelas a menudo son capaces de mejorar la calidad de los resultados obtenidos por los algoritmos secuenciales tradicionales.Si bien el uso de GPUs ha representado un dominio inspirador también para la investigación en metaheurísticas paralelas, la mayoría de los trabajos previos tenían como objetivo portar una familia existente de algoritmos a este nuevo tipo de hardware. Como consecuencia, muchas publicaciones están dirigidas a mostrar el ahorro en tiempo de ejecución que se puede lograr al ejecutar los diferentes tipos paralelos de metaheurísticas existentes en GPU. En otras palabras, a pesar de que existe un volumen considerable de trabajo sobre este tópico, se han propuesto pocas ideas novedosas que busquen diseñar nuevos algoritmos y/o modelos de paralelismo que exploten explícitamente el alto grado de paralelismo disponible en las arquitecturas de las GPUs. Esta tesis aborda el diseño de una propuesta innovadora de algoritmo de optimización paralelo denominada Búsqueda Genética Sistólica (SGS), que combina ideas de los campos de metaheurísticas y computación sistólica. SGS, así como la computación sistólica, se inspiran en el mismo fenómeno biológico: la contracción sistólica del corazón que hace posible la circulación de la sangre. En SGS, las soluciones circulan de forma síncrona a través de una grilla (rejilla) de celdas. Cuando dos soluciones se encuentran en una celda se aplican operadores evolutivos adaptados para generar nuevas soluciones que continúan moviéndose a través de la grilla (rejilla). La implementación de esta nueva propuesta saca partido especialmente de las características específicas de las GPUs. Un extenso análisis experimental que considera varios problemas de benchmark clásicos y dos problemas del mundo real del área de Ingeniería de Software, muestra que el nuevo algoritmo propuesto es muy efectivo, encontrando soluciones óptimas o casi óptimas en tiempos de ejecución cortos. Además, los resultados numéricos obtenidos por SGS son competitivos con los resultados del estado del arte para los dos problemas del mundo real en cuestión. Por otro lado, la implementación paralela en GPU de SGS ha logrado un alto rendimiento, obteniendo grandes reducciones de tiempo de ejecución con respecto a la implementación secuencial y mostrando que escala adecuadamente cuando se consideran instancias de tamaño creciente. También se ha realizado un análisis teórico de las capacidades de búsqueda de SGS para comprender cómo algunos aspectos del diseño del algoritmo afectan a sus resultados numéricos. Este análisis arroja luz sobre algunos aspectos del funcionamiento de SGS que pueden utilizarse para mejorar el diseño del algoritmo en futuras variantes

Deteção de patologia cardíaca usando machine learning

Segundo a Organização Mundial da Saúde, as doenças cardiovasculares (DCV) representam 32% do número de mortes no mundo. A redução deste valor pode ser atingida através da deteção precoce que pode levar a um tratamento mais preciso, melhorando a expectativa de vida do paciente. A ausculta cardíaca é a principal técnica utilizada pelos profissionais de saúde para identificar muitas DCV. No entanto, a auscultação dos sons cardíacos é um procedimento difícil, já que muitos sons são fracos e difíceis de detetar, sendo necessário um processo de treino contínuo. Os estetoscópios modernos podem amplificar os sons cardíacos, reduzir o ruído de ambiente, melhorar a percepção do usuário e, mais importante, converter um sinal acústico em digital. Isto permitiu o desenvolvimento de sistemas de decisão assistidos por computador baseados na auscultação. Este documento apresenta uma metodologia que pode detectar automaticamente a existência de DCV através de sons cardíacos obtidos de diferentes partes do coração. Diversas tecnologias foram analisadas, assim como projetos que tentam resolver parte do problema em questão e a partir deles, três alternativas diferentes foram elaboradas e documentadas, assim como a divisão do dataset e métricas a serem usadas nos testes. Essas alternativas visam classificar anomalias na auscultação cardíaca dos pacientes. Vários modelos das duas primeiras alternativas foram implementados e seus resultados apresentados. Também é feita uma comparação entre as experiências desenvolvidas entre si, também com experiências básicas que não utilizam mecanismos inteligentes e com outros trabalhos que tenham o mesmo objetivo. O melhor resultado obtido foi pela primeira abordagem com uma exatidão de 94%, precisão de 81% e recall de 67%.According to World Health Organization, the cardiovascular diseases (CVD) represent 32% of the number of deaths worldwide. Early detection leads to a more accurate treatment plan and improves the patient’s life expectancy. Cardiac auscultation is the main technique used by health professionals to identify many CVD. Nevertheless, heart sound auscultation is a difficult procedure, since it requires continuous training and many heart sounds are faint and hard to detect. However, modern stethoscopes can amplify heart sounds, reduce the environment noise, improve the user’s perception and, more importantly, convert an acoustic signal to a digital one. This allowed, the development of computer assisted decision systems based on auscultation. This document presents a methodology that can automatically detect the existence of CVD through cardiac sounds obtained from different parts of the heart. Several technologies were analysed, as well as projects that try to solve part of the problem in question and from them, three different alternatives were elaborated and documented, as well as the division of test data and the metrics for their evaluation. These alternatives are intended to classify anomalies in patients' cardiac auscultation. Several models of the first two alternatives were implemented and their results presented. A comparison is also made between the experiences developed among themselves, also with basic experiments that do not use intelligent mechanisms and with other works that have the same objective. The best result obtained was by the first approach with an accuracy of 94%, precision of 81% and recall of 67%

Improving Knowledge of Parents in Dietary Management for Children with Diabetes and Hyperlipidemia: A Quality Improvement Project

Abstract: There is evidence that the number of cases of hyperlipidemia and diabetes in children is increasing along with the prevalence of childhood obesity in low- and high-income countries. Several factors may influence the dietary management of chronic conditions in children and adolescents, including the perceptions and beliefs of parents and caregivers about the conditions and their role in treating them. Shifting the focus of obesity, hyperlipidemia, and diabetes prevention interventions to the early education of parents has the potential to change children’s dietary habits significantly and decrease their risk of suffering from these chronic conditions. Ab important goal of this Quality Improvement project was to initiate an educational program that addresses community dietary management and the prevention of chronic conditions such as hyperlipidemia and diabetes. METHODS: The parents of pediatric patients diagnosed with hyperlipidemia and diabetes were administered as a pre- and post-test the Revised General Nutrition Knowledge Questionnaire (GNKQ), which consists of four domains of nutrition knowledge: dietary recommendations (DR), sources of nutrients in food (SON), knowledge of healthy food choices (HFC), and diet, disease, and weight management (DDWM). The scores were tabulated for each section to obtain a total score (T0 and T1). RESULTS: The total and individual section scores for the pre-test (T0 = 60%) and post-test (T1= 69.55%) for the GNKQ. The mean overall GNKQ score including the pre- and post-test values for the parent participants was 57.00 (± 8.85), representing 64.77%. CONCLUSION: The results indicated improvement in the participating parents’ overall nutritional knowledge after the implementation of nutritional education in this Quality Improvement project. These sections measured knowledge of the groups of food selection and the suggested serving sizes, sources of nutrients in food, and the correlation between diet and disease

Computational intelligence contributions to readmisision risk prediction in Healthcare systems

136 p.The Thesis tackles the problem of readmission risk prediction in healthcare systems from a machine learning and computational intelligence point of view. Readmission has been recognized as an indicator of healthcare quality with primary economic importance. We examine two specific instances of the problem, the emergency department (ED) admission and heart failure (HF) patient care using anonymized datasets from three institutions to carry real-life computational experiments validating the proposed approaches. The main difficulties posed by this kind of datasets is their high class imbalance ratio, and the lack of informative value of the recorded variables. This thesis reports the results of innovative class balancing approaches and new classification architectures

Workplace screening programs for chronic disease prevention: a rapid review

This review examined the effectiveness of workplace screening programs for chronic disease prevention based on evidence retrieved from the main databases of biomedical and health economic literature published to March 2012, supplemented with relevant reports. The review found: 1. Strong evidence of effectiveness of HRAs (when used in combination with other interventions) in relation to tobacco use, alcohol use, dietary fat intake, blood pressure and cholesterol 2. Sufficient evidence for effectiveness of worksite programs to control overweight and obesity 3. Sufficient evidence of effectiveness for workplace HRAs in combination with additional interventions to have favourable impact on the use of healthcare services (such as reductions in emergency department visits, outpatient visits, and inpatient hospital days over the longer term) 4. Sufficient evidence for effectiveness of benefits-linked financial incentives in increasing HRA and program participation 5. Sufficient evidence that for every dollar invested in these programs an annual gain of $3.20 (range$1.40 to $4.60) can be achieved 6. Promising evidence that even higher returns on investment can be achieved in programs incorporating newer technologies such as telephone coaching of high risk individuals and benefits-linked financial incentive

Variable selection in high-dimensional data: application in a SARS-CoV-2 pneumonia clinical data-set

As a result of the COVID-19 pandemic that collapsed hospitals in some countries, numerous studies have been carried out to understand the development of the disease and how it affects patients with different characteristics, in order to make optimal use of the available resources. This project is part of a multicentre study that aims to predict the severity of patients with SARS-CoV-2 pneumonia, for which different variables related to health, demographic and socio-economic factors and exposure to pollutants of patients have been collected. Given the number of variables contained in the data-set, it is necessary to reduce the number of variables in order to create a practical model for interpretation, as well as to reduce the amount of information that doctors have to collect on each patient. In this project, an exhaustive analysis of variable or feature selection techniques has been carried out in order to determine their performance and relevance in terms of stability, similarity and computation time. Based on the techniques that have shown the best characteristics, the most meaningful factors in preventing the severity of pneumonia have been identified, in accordance with what has been proposed by other studies

AI/ML Algorithms and Applications in VLSI Design and Technology

An evident challenge ahead for the integrated circuit (IC) industry in the nanometer regime is the investigation and development of methods that can reduce the design complexity ensuing from growing process variations and curtail the turnaround time of chip manufacturing. Conventional methodologies employed for such tasks are largely manual; thus, time-consuming and resource-intensive. In contrast, the unique learning strategies of artificial intelligence (AI) provide numerous exciting automated approaches for handling complex and data-intensive tasks in very-large-scale integration (VLSI) design and testing. Employing AI and machine learning (ML) algorithms in VLSI design and manufacturing reduces the time and effort for understanding and processing the data within and across different abstraction levels via automated learning algorithms. It, in turn, improves the IC yield and reduces the manufacturing turnaround time. This paper thoroughly reviews the AI/ML automated approaches introduced in the past towards VLSI design and manufacturing. Moreover, we discuss the scope of AI/ML applications in the future at various abstraction levels to revolutionize the field of VLSI design, aiming for high-speed, highly intelligent, and efficient implementations

Cardiovascular Disease Risk Factor Assessment and Lifestyle Adjustments in African Americans

Multiple studies have indicated a higher burden of overweight/obesity and exposure to environmental toxins, such as alcohol and tobacco smoke, in association with higher prevalence of cardiovascular disease (CVD) in the African American population. Thus, the purpose of this research was to determine if there was a significant difference in the use of lifestyle adjustments such as moderating alcohol consumption, weight loss, and smoking cessation on the prevalence of CVD in the African American population. The theoretical foundation was social cognitive theory and the social ecological model that posits the interplay of individual, social, and environmental factors. This cross-sectional quantitative study was designed to assess the effects of lifestyle adjustments of weight loss, moderating alcohol consumption, and smoking cessation in the prevalence of CVD in African Americans between 40 and 60 years of age. Analysis of secondary data from the National Health and Nutrition Examination survey for the years 2013-2014 was conducted using binary logistic regression. The findings showed no significant difference in the use of weight loss, moderating alcohol consumption, and smoking cessation in the prevalence of CVD in African Americans between 40 and 60 years of age. However, the odds of moderate alcohol consumption and weight loss were greater than 1. Thus, this study may have a small potential impact on CVD in African Americans by encouraging lifestyle adjustments, and may contribute to positive social change by increasing life expectancy, improving quality of life, and reducing the burden of certain chronic diseases and reduction of healthcare cost