Carlos Fernandez-BassoUniversity of Granada | UGR · Department of Computer Science and Artificial Intelligence
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Citations since 2017
19 Research Items
M.Sc. degree in data science from the Universidad de Granada, in 2014 and 2015, respectively, where he is currently pursuing the Ph.D. degree in computer science and energy efficiency. He was a Lead Developer in the EU FP7 Project Energy IN TIME in the topics of building simulation and control, data analytics, and machine learning. He also collaborates with the Data Science Institute, Imperial College London, where he has carried out research stays, from 2016 to 2018. https://cjferba.github.io/
October 2016 - July 2020
Ciencias de la Computación e Inteligencia Artificial
- PhD Student
En este trabajo, proponemos un estudio del cyberbullying basado en técnicas de PLN (Procesamiento de Lenguaje Natural) y Minería de Datos, como el clustering difuso y los modelos de representación vectorial. La premisa inicial es que en un mismo mensaje pueden aparecer varios tipos de cyberbullying y su modelado con un grado de pertenencia puede of...
En nuestro trabajo abordamos la extracción de conocimiento a partir de historias clínicas utilizando técnicas de minería de datos junto con reglas de asociación en conjunción con la lógica difusa en un entorno distribuido. Además de la extracción del conocimiento nos centramos en la transformación y tratamiento de los datos para mejorar la modeliza...
In this paper we have addressed the extraction of hidden knowledge from medical records using data mining techniques such as association rules in conjunction with fuzzy logic in a distributed environment. A significant challenge in this domain is that although there are a lot of studies devoted to analysing health data, very few focus on the unders...
Social networks and new technologies are present today in almost all aspects of social relations. These technologies, which have so many advantages in the daily lives of young and older people, can become a double-edged sword. One of the most negative connotations created by the incursion of social networks and new technologies in the lives of chil...
Today’s information society has led to the emergence of a large number of applications that generate and consume digital data. Many of these applications are based on social networks, and therefore their information often comes in the form of unstructured text. This text from social media also tends to contain a high level of noise and untrustworth...
The enormous quantity of data handled by Building management systems are key to develop more efficient energy operational systems. However, the inability of current systems to take benefit from the generated data may waste good opportunities of improving building performance. Big Data appears as a suitable framework to sustain the management system...
The high computational impact when mining fuzzy association rules grows significantly when managing very large data sets, triggering in many cases a memory overflow error and leading to the experiment failure without its conclusion. It is in these cases when the application of Big Data techniques can help to achieve the experiment completion. There...
With the exponential growth of users and user-generated content present on online social networks, fake news and its detection have become a major problem. Through these, smear campaigns can be generated, aimed for example at trying to change the political orientation of some people. Twitter has become one of the main spreaders of fake news in the...
The discovery and exploitation of hidden information in collected data has always gain attention in many areas, and particularly in the energy field due to the economic and environmental impact. Data Mining techniques then emerge as a suitable toolbox for analysing the data collected in modern network management systems in order to obtain a meaning...
Despite the increasing capabilities of information technologies for data acquisition and processing, building energy management systems still require manual configuration and supervision to achieve optimal performance. Model predictive control (MPC) aims to leverage equipment control – particularly heating, ventilation and air conditioning (HVAC)–...
The amount of information generated in social media channels or economical/business transactions exceeds the usual bounds of static databases and is in continuous growing. In this work, we propose a frequent itemset mining method using sliding windows capable of extracting tendencies from continuous data flows. For that aim, we develop this method...
The large amount of information stored by companies and the rise of social networks and the Internet of Things are producing exponential growth in the amount of data being produced. Data analysis techniques must therefore be improved to enable all this information to be processed. One of the most commonly used techniques for extracting information...
The overall objective of the project is to develop a practical and useful system to conveniently collect, store, analyse and exploit the continuous flow of data from sensors and other sources of information to support the decision-making process. sensors and other sources of information to support the decision-making process. This can be broken down into several sub-objectives: 1. to create a system capable of collecting and storing (where possible) data from sensors, electronic documents, social networks... in general streaming data sources. 2. Develop a library that includes a wide range of data mining algorithms suitably adapted to handle streaming data. adapted to handle streaming data with a large volume and that can present some kind of and that may present kind of inaccuracy. 3. Implement a user-friendly interface that allows for (1) data manipulation for (1) data manipulation to select/add/remove appropriate information for further analysis, (2) acquisition of specialised knowledge (3) graphical representation of the data and results obtained. (3) graphical representation of the data and results obtained. 4. Validating the developed algorithms with test and real data from different sources. 4. Validate the developed algorithms with test and real data from different sources. In particular, a use case for the detection of cyberbullying will be developed. of cyberbullying.
The general objective of this project is to build a system for processing massive medical information (Big Medical Data) for secondary use: hospital management, clinical research, treatment comparison, etc.
Nowadays, the volume of data digitally stored and managed in building facilities has considerably increased due to the increasing use of easy-to-install IoT devices. The large number of measurements taken, enriched with external data, such as weather forecasts and occupation schedules, can provide meaningful insights when analyzing the energy efficiency of buildings, thus increasing the improvement opportunities to efficiently manage the energy footprint of buildings. This Special Issue is dedicated to high-quality research and solutions proposing original data science applications for building energy management that address theoretical and/or practical problems based on solid theory and/or empirical analysis. https://www.mdpi.com/journal/energies/special_issues/data_science_for_building_energy_management