Road pollution estimation using static cameras and neural networks

Este artículo presenta una metodología para estimar la contaminación en carreteras mediante el análisis de secuencias de video de tráfico. El objetivo es aprovechar la gran red de cámaras IP existente en el sistema de carreteras de cualquier estado o país para estimar la contaminación en cada área. Esta propuesta utiliza redes neuronales de aprendizaje profundo para la detección de objetos, y un modelo de estimación de contaminación basado en la frecuencia de vehículos y su velocidad. Los experimentos muestran prometedores resultados que sugieren que el sistema se puede usar en solitario o combinado con los sistemas existentes para medir la contaminación en carreteras.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Pneumonia Detection in Chest X-ray Images using Convolutional Neural Networks

Pneumonia is an infectious and deadly disease which strikes over millions of people. Usually, chest X-rays are used by radiotherapist to diagnose pneumonia. In this paper, a Computer- Aided Diagnosis (CAD) system for pneumonia detection in chest X-ray images is proposed. This system is based on Convolutional Neural Networks (CNNs) which are able to classify the image into two classes (pneumonia or normal). Experimental results show that the proposed system obtained an accuracy rate of 98.59%.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Stenosis detection in coronary angiography images using deep learning models

The emergence of deep learning has caused its massive application to different fields in industry and research, among which is the clinical field, especially in those where the data is structured in the form of images or video. The present proposal intends to develop a coronary angiography image analysis system based on artificial intelligence. These images are radiocontrast X-ray images of the coronary arteries. The proposed system will be able to analyze these coronary angiography images of patients with no obstructive coronary lesions to detect and characterize smooth and irregular coronary arteries and predict the presence of cardiovascular events during follow-up. Deep learning convolutional artificial neural networks will support the algorithmic basis of the proposed system.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Vehicle overtaking hazard detection over onboard cameras using deep convolutional networks

The development of artificial vision systems to support driving has been of great interest in recent years, especially after new learning models based on deep learning. In this work, a framework is proposed for detecting road speed anomalies, taking as reference the driving vehicle. The objective is to warn the driver in realtime that a vehicle is overtaking dangerously to prevent a possible accident. Thus, taking the information captured by the rear camera integrated into the vehicle, the system will automatically determine if the overtaking that other vehicles make is considered abnormal or dangerous or is considered normal. Deep learning-based object detection techniques will be used to detect the vehicles in the road image. Each detected vehicle will be tracked over time, and its trajectory will be analyzed to determine the approach speed. Finally, statistical regression techniques will estimate the degree of anomaly or hazard of said overtaking as a preventive measure. This proposal has been tested with a significant set of actual road sequences in different lighting conditions with very satisfactory results.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Fuzzy techniques for IPO underpricing prediction

Initial public offerings often show abnormal fist-day returns. These, usually referred to as underpricing, are difficult to predict. Among the main obstacles, we could mention challenges like the fact that not all relevant variables have been identified yet; the mix of weak and strong indicators or the prevalence of outliers. In this context, we suggest that adaptive neuro-fuzzy inference systems and fuzzy rule-based system with genetic optimization have a lot to bring to the table. We test the predictive performance of these on a sample of 866 US IPOs and we benchmark them against six fuzzy algorithms and a set of eight classic machine learning alternatives. We conclude that both fuzzy systems, especially the former should be seriously considered in this domain.D. Quintana acknowledges financial support by the Spanish Ministry of Science under grant ENE2014-56126-C2-2-R. F. Chávez acknowledges financial support granted by Seventh Framework Programme of the European Union through the Marie Curie Inter-national Research Staff Scheme, FP7-PEOPLE-2013-IRSES, Grant612689 ACoBSEC, Spanish Ministry of Science and Innovation under project TIN2014-56494-C4-2-P (EPHEMECH), regional government Gobierno de Extremadura and FEDER, project GRU10029. Rafael M. Luque and Francisco Luna acknowledge support by Ministry of Economy, Industry and Competitiveness under contract TIN2016-75097P, and Universidad de M´alaga under contract PPIT.UMA.B12017/15

Dietary α-linolenic acid, marine ω-3 fatty acids, and mortality in a population with high fish consumption: Findings from the PREvención con DIeta MEDiterránea (PREDIMED) Study

12 Páginas.-- 6 Tablas.-- 1 FiguraBackground-Epidemiological evidence suggests a cardioprotective role of α-linolenic acid (ALA), a plant-derived ω-3 fatty acid. It is unclear whether ALA is beneficial in a background of high marine ω-3 fatty acids (long-chain n-3 polyunsaturated fatty acids) intake. In persons at high cardiovascular risk from Spain, a country in which fish consumption is customarily high, we investigated whether meeting the International Society for the Study of Fatty Acids and Lipids recommendation for dietary ALA (0.7% of total energy) at baseline was related to all-cause and cardiovascular disease mortality. We also examined the effect of meeting the society's recommendation for long-chain n-3 polyunsaturated fatty acids (≥500 mg/day). Methods and Results-We longitudinally evaluated 7202 participants in the PREvención con DIeta MEDiterránea (PREDIMED) trial. Multivariable-adjusted Cox regressionmodels were fitted to estimate hazard ratios. ALA intake correlated towalnut consumption (r=0.94). During a 5.9-y follow-up, 431 deaths occurred (104 cardiovascular disease, 55 coronary heart disease, 32 sudden cardiac death, 25 stroke). The hazard ratios formeeting ALArecommendation (n=1615, 22.4%) were 0.72 (95% CI 0.56-0.92) for all-causemortality and 0.95 (95% CI 0.58-1.57) for fatal cardiovascular disease. The hazard ratios formeeting the recommendation for long-chain n-3 polyunsaturated fatty acids (n=5452, 75.7%) were 0.84 (95% CI 0.67-1.05) for all-causemortality, 0.61 (95% CI 0.39-0.96) for fatal cardiovascular disease, 0.54 (95% CI 0.29-0.99) for fatal coronary heart disease, and 0.49 (95% CI 0.22-1.01) for sudden cardiac death. The highest reduction in all-cause mortality occurred in participants meeting both recommendations (hazard ratio 0.63 [95% CI 0.45-0.87]). Conclusions-In participants without prior cardiovascular disease and high fish consumption, dietary ALA, supplied mainly by walnuts and olive oil, relates inversely to all-cause mortality, whereas protection from cardiac mortality is limited to fish-derived long-chain n-3 polyunsaturated fatty acids.This study was funded in part by Instituto de Salud Carlos III (ISCIII) (Spanish Ministry of Economy) through grants RTIC G03/140, RTIC RD 06/0045, Centro Nacional de Investigaciones Cardiovasculares CNIC 06/2007, ISCIII FIS PS09/01292, the Spanish Ministry of Science and Innovation (MICINN) AGL2010‐22319‐C03‐02 and AGL2009‐13906‐C02‐02, and an unrestricted grant from the California Walnut Commission. Sala‐Vila holds a Miguel Servet I fellowship from the Ministry of Economy and Competitiveness through the ISCIII