A5032952434- Gene expression and cancer classification
- Bioinformatics and Genomic Networks
- Data Stream Mining Techniques
- Time Series Analysis and Forecasting
- Gene Regulatory Network Analysis
- Energy Load and Power Forecasting
- Smart Grid Energy Management
- Neural Networks and Applications
- IoT and Edge/Fog Computing
- Electricity Theft Detection Techniques
- Stock Market Forecasting Methods
- Remote Sensing in Agriculture
- Evolutionary Algorithms and Applications
- Geological and Geophysical Studies Worldwide
- Air Quality Monitoring and Forecasting
- Smart Agriculture and AI
- earthquake and tectonic studies
- Anomaly Detection Techniques and Applications
- Seismic Performance and Analysis
- IoT Networks and Protocols
- Computational Physics and Python Applications
- Innovation Diffusion and Forecasting
- Human Mobility and Location-Based Analysis
- Quantum-Dot Cellular Automata
- Hydraulic flow and structures
Universidad de Sevilla
2011-2024
Universidad Pablo de Olavide
2018-2022
A novel bioinspired metaheuristic is proposed in this work, simulating how the coronavirus spreads and infects healthy people. From an initial individual (the patient zero), new patients at known rates, creating populations of infected Every can either die or infect and, afterwards, be sent to recovered population. Relevant terms such as re-infection probability, super-spreading rate traveling are introduced model order simulate accurately possible activity. The Coronavirus Optimization...
Abstract The safety operation and management of hydropower dam play a critical role in social‐economic development ensure people's many countries; therefore, modeling forecasting the dam's deformations with high accuracy is crucial. This research aims to propose validate new model based on deep learning long short‐term memory (LSTM) coronavirus optimization algorithm (CVOA), named CVOA‐LSTM, for dam. second‐largest Vietnam, located Hoa Binh province, focused. Herein, we used LSTM establish...
Real-time algorithms have to adapt and adjust new incoming patterns provide timely accurate responses. This paper presents a distributed forecasting algorithm for streaming time series called StreamWNN. StreamWNN starts with an offline stage in which model based on tuples of information fusion is created historical data. In particular, this consists the composed past values future their k-nearest neighbors. Afterwards, data arrive. The incrementally updated online using buffer that more...
Precision agriculture focuses on the development of site-specific harvest considering variability each crop area. Vegetation indices allow study and delineation different characteristics field zone, generally invisible to naked-eye. This paper introduces a new big data triclustering approach based evolutionary algorithms. The algorithm shows its capability discover three-dimensional patterns basis vegetation from vine crops. Different have been tested find in results reported using vineyard...
Time series data can be defined as a chronological sequence of observations on variable interest. A streaming time is that arrives continuously at high speed and has distribution may change over time. Streaming usually comes from electronic devices such sensors many the applications dealing with in Industry 4.0 require real-time responses. Performing forecasting offers possibility to consider new types patterns incoming data, which not possible when working batch models. This paper presents...
Microarray technology is highly used in biological research environments due to its ability monitor the RNA concentration levels. The analysis of data generated represents a computational challenge characteristics these data. Clustering techniques are widely applied create groups genes that exhibit similar behavior. Biclustering relaxes constraints for grouping, allowing be evaluated only under subset conditions. Triclustering appears longitudinal experiments which certain conditions at...
A previous definition of seismogenic zones is required to do a probabilistic seismic hazard analysis for areas spread and low activity. Traditional zoning methods are based on the available catalog geological structures. It admitted that thermal resistant parameters crust provide better criteria zoning. Nonetheless, working out rheological profiles causes great uncertainty. This has generated inconsistencies, as different have been proposed same area. new method by means triclustering in...
Microarrays have revolutionized biotechnological research. The analysis of new data generated represents a computational challenge due to the characteristics these data. Clustering techniques are applied create groups genes that exhibit similar behavior. Biclustering emerges as valuable tool for microarray since it relaxes constraints grouping, allowing be evaluated only under subset conditions. However, if third dimension appears in data, triclustering is appropriate analysis. This occurs...
Microarray technology has led to a great advance in biological studies due its ability monitorize the RNA levels of vast amount genes under certain experimental conditions. The use computational techniques mine hidden knowledge from these data is interest research fields such as Data Mining and Bioinformatics. Finding patterns genetic behavior not only taking into account conditions but also time condition very challenging task nowadays. Clustering, biclustering novel triclustering offer...
Triclustering has shown to be a valuable tool for the analysis of microarray data since its appearance as an improvement classical clustering and biclustering techniques. The standard validation triclustering is based on three different measures: correlation, graphic similarity patterns functional annotations genes extracted from Gene Ontology project (GO).We propose TRIQ, single evaluation measure that combines measures previously described: annotation, providing value result tricluster...
One of the techniques that provides systematic insights into biological processes is High-Content Screening (HCS). It measures cells phenotypes simultaneously. When analysing these images, features like fluorescent colour, shape, spatial distribution and interaction between components can be found. STriGen, which works in real-time environment, leads to possibility studying time evolution real-time. In addition, data streaming algorithms are able process flows a fast way. this article,...
Abstract Processing data streams arriving at high speed requires the development of models that can provide fast and accurate predictions. Although deep neural networks are state-of-the-art for many machine learning tasks, their performance in real-time streaming scenarios is a research area has not yet been fully addressed. Nevertheless, much effort put into adaption complex (DL) to tasks by reducing processing time. The design asynchronous dual-pipeline DL framework allows making...