A data-mining based process to early identify breast cancer from metabolomic data

Abstract of our work presented at EURO 2018, the largest and most important conference for Operational Research, co-authored by Víctor M. Rivas Santos, jointly with researchers of Complejo Hospitalario de Jaén and Fundación Medina.

This paper was presented last 9-July-2018 at Valencia, as part of the stream Data Mining and Statistics.

A data-mining based process to early identify breast cancer from metabolomic data

Abstract

We present the results yielded by our multidisciplinary group in the task of discriminating blood samples coming from breast cancer patients and healthy people. Models used to classify samples have been built using data mining techniques; data have been collected by means of liquid chromatography-mass spectrometry, a technique that detects and quantifies the metabolites present in blood samples.

Different algorithms have been tested under 10-CV and 75/25 scenarios. Our experiments showed that IBk, and J48 and Logistic Model Trees yielded rates greater than 90% only for healthy people. Naive Bayes and Random Forest enhanced the previous results in the 10-CV approach, but they did not yield more than 85% of true positives for patients in the 75/25 one. Finally, Bayesian network resulted to be the best algorithm as rates greater than 90% were yielded for both patients and rest of the people.

Many statistics have been computed as well as confusion matrices, showing that the model built by Bayesian network can effectively be used to solve this problem. Currently, the metabolites used to do built the model are being identified by biochemists. This last step will be definitive in order to consider them as a valid biomarker for breast cancer.

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Detección y predicción de flujos de personas y vehículos

En el marco del congreso CIMAS 21, que se celebrará en Granada, haré una presentación sobre las posibilidades de nuestro sistema de detección de tramas WiFi y Bluetooth, del que ya hemos hablado varias veces.

La presentación se centrará en los aspectos más analíticos de la plataforma, viendo las posibilidades que puede tener para un destino turístico con énfasis deportivo.

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Our TORCS driving controller presented at EvoGAMES 2017

Last week, @jjmerelo presented at EvoGAMES 2017 (inside Evo* 2017) our work titled “Driving in TORCS using modular fuzzy controllers”.

This paper presents a novel car racing controller for TORCS (The Open Racing Car Simulator), which is based in the combination of two fuzzy subcontrollers, one for setting the speed, and one to control the steer angle. The obtained results are quite promissing, as the controller is quite competitive even against very tough TORCS teams.

The abstract of the paper is:

When driving a car it is essential to take into account all possible factors; even more so when, like in the TORCS simulated race game, the objective is not only to avoid collisions, but also to win the race within a limited budget. In this paper, we present the design of an autonomous driver for racing car in a simulated race. Unlike previous controllers, that only used fuzzy logic approaches for either acceleration or steering, the proposed driver uses simultaneously two fuzzy controllers for steering and computing the target speed of the car at every moment of the race. They use the track border sensors as inputs and besides, for enhanced safety, it has also taken into account the relative position of the other competitors. The proposed fuzzy driver is evaluated in practise and timed races giving good results across a wide variety of racing tracks, mainly those that have many turning points.

There was an interactive presentation at the conference, together with a poster:

The paper is available online from: https://link.springer.com/chapter/10.1007/978-3-319-55849-3_24

Enjoy (and cite) it! :D

 

How to improve the Systems Security using Data Mining

During this week, Geneura Team has welcomed Professor Ja’far Alqatawna from the University of Jordan. Ja’far Alqatawna is an Associate Professor at King Abdullah II School for Information Technology, University of Jordan. He received his B.Eng degree in Computer Engineering from Mu’tah University, Jordan, followed by MSc. in Information and Communication Systems Security from The Royal Institute of Technology (KTH), Sweden. In 2010 He has been awarded his Ph.D. Degree in Computer Information Systems with specialisation in Information Security and e-Business from Sheffield Hallam University, UK. He was part of researching projects for investigating XACML as a policy language for distributed networks at Security, Policy and Trust Lab (SPOT) of the Swedish Institute of Computer Science (SICS), Sweden. His current research interests are in the field of Cybersecurity in which he tries to look for multi-dimensional approaches that go beyond the technical dimension in order to develop trustworthy Cyberspace. Yesterday he presented a talk about the use of Data Mining for improving the security of the software systems which you can see in slide share, http://es.slideshare.net/MaribelGarcaArenas/data-mining-in-security-jafar-alqatawna. For this time, he presented and discussed several security areas in which data mining has the possibility of enhancing the existing security methods.

 

Jornada sobre Smart Cities y Movilidad

El jueves 26 de noviembre de 2015 celebramos en la Sala de Usos Múltiples del CITIC-UGR (C/ Periodista Rafael Gómez, nº 2) la Jornada sobre Smart Cities y Movilidad, enmarcada en el Programa De Ayudas Genil Para Realización De Actividades Por Grupos De Investigación Interdiciplinares (RAGII-2015).

El objeto de esta Jornada fue la investigación en el área de la gestión de la movilidad, internet de las cosas y smart cities.

A lo largo de la mañana asistimos a varias conferencias, impartidas por responsables del Area de Movilidad del Ayuntamiento de Granada, de varias empresas, así como por parte de investigadores de la Universidad de Granada en este ámbito.

Los objetivos finales fueron crear sinergia entre los diversos grupos de investigación y empresas de este área, así como facilitar el contacto de cara a promover colaboraciones, tales como solicitar proyectos, o realizar transferencia de conocimiento a partir de los resultados de investigación.

El desarrollo de la Jornada se basó en presentaciones de unos 40 minutos, en las que el ponente, por parte del Área de Movilidad del Ayuntamiento de Granada, Nazaríes, UXMobile, Geokeda, e investigadores de los grupos de investigación, comentaron los proyectos en los que trabajan actualmente en el área de las smart cities, así como las problemáticas, y los retos a los que se enfrentan.

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GeNeura at European Project MUSES Final Review

GeNeura’s members have been working in the three-year long, FP7 European project MUSES, which faced its last review last week at the European Commision Beaulieu Quarter Buildings in Brussels.

UGR was one of the partners participating in this project. More concretely, GeNeura’s members have contributed by leading WP2 – MUSES framework definition and integration during the completion of tasks to define the MUSES System Architecture. In addition, GeNeura’s research has been applied to the project in WP5 – Self-adaptive event correlation, lead by a Spanish security company S2 Grupo. The main purpose of this WP was to develop a system which, on the one side, uses event correlation to detect Security Policy violations and, on the other side, performs an analysis of all the data in the system and creates new Security Policies or enhances the existing ones. Different types of classification, rule association, and clustering algorithms, as well as Data Mining techniques, have been applied with satisfactory results. These results were specially welcomed by the comission, ponting that such a system will be very helpful to enhance security. Also, MUSES is an Open Software project, and you can contribute at https://github.com/MusesProject

The results were presented by S2 Grupo and GeNeura together. The slides are now published on Slideshare:

It has been a pleasure for GeNeura to work in MUSES

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