جست‌وجوی استاد و پژوهشگر

Francisco Charte Ojedaمشاهده پروفایل

دانشیار

• Machine Learning
• Multilabel Classification
• Autoencoders
• +۶ مورد دیگر

Francisco Charte Ojeda is a Titular de Universidad (Associate Professor) at the University of Jaén, Spain, working in the Department of Computer Science within the Faculty of Experimental Sciences. He is affiliated with the Andalusian Inter-University Institute in Data Science and Computational Intelligence and leads research in the Intelligent Systems and Data Mining group. His academic journey includes earning a doctorate from the University of Granada with a thesis on hybrid flexible computing methods for multilabel classification. His research interests span machine learning with particular emphasis on multilabel classification, autoencoders, feature learning, time series forecasting, and dimensionality reduction. Professor Charte Ojeda has developed several R packages including mldr, mldr.datasets, ruta, and predtoolsTS that have become valuable tools in the machine learning community. His work often focuses on solving practical challenges in data science through innovative algorithmic approaches and software implementations. Analysis of his recent publications reveals a strong focus on autoencoder architectures, multilabel learning techniques, and time series forecasting methods. His research demonstrates a consistent pattern of developing practical software tools alongside theoretical contributions, bridging the gap between academic research and real-world applications. The trend shows increasing emphasis on explainable AI, ensemble methods, and efficient implementations for big data environments. Professor Charte Ojeda has been involved in multiple research projects funded by Spanish national grants (PID2019-107793GB-I00/AEI, TIN2015-68854-R). His collaborative network includes prominent researchers in the field such as Antonio J. Rivera, Francisco Herrera, and María J. del Jesús. He maintains an active presence in academic communities through his personal website (fcharte.com), GitHub repositories, and contributions to open-source software. His educational contributions include textbooks on programming languages, operating systems, and computational tools, reflecting his commitment to both research and teaching excellence.

• Machine Learning
• Computational Intelligence
• Data Mining
• +۵ مورد دیگر

María Dolores Pérez Godoy is a full-time Professor in the Department of Computer Science at the University of Jaén. She contributes to the Andalusian Interuniversity Institute for Data Science and Computational Intelligence and leads the Intelligent Systems and Data Mining research group. PhD in Computer Science (2010) - University of Jaén Thesis: Hybrid cooperative-competitive evolutionary methods for radial basis function networks Research Focus: Computational Intelligence, Time Series Forecasting, Data Mining, Evolutionary Algorithms, Neural Networks, and Big Data Applications. Her work bridges theoretical advancements in RBFN design with practical implementations in agriculture (olive oil price forecasting) and resource-constrained systems. Recent Article Trends: 2025 work addresses multilabel imbalance with diffusion models. 2024 contributions include tools like Nets4Learning platform, DESReg library, and data governance frameworks. Earlier studies explore transformer models, clustering for crop mapping, and data stream classification. Technical Contributions: Developer of GRNN multi-series forecasting, data stream neural network implementations, and MEFASD-BD multi-objective evolutionary algorithms.

María José del Jesus Díazمشاهده پروفایل

پژوهشگر

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۳ مورد دیگر

Dr. María José del Jesús Díaz is a researcher affiliated with the University of Jaén (ujaen.es). Her work spans multiple domains including Machine Learning, Artificial Intelligence, and Data Mining, with a strong focus on Explainable AI, Fuzzy Systems, and Evolutionary Computation. Research Interests Time Series Forecasting & Transfer Learning Multilabel Classification & Imbalance Handling Autoencoders & Feature Fusion Evolutionary Algorithms for Pattern Mining Explainable AI in Healthcare & Environmental Applications Scientific Contributions Developed resampling techniques for multilabel datasets (e.g., REMEDIAL-HwR, MLSMOTE) Created tools like mldr.resampling and KEEL 3.0 for data mining Explored hybrid models for AI interpretability Published extensively on autoencoder applications in data streams (2018–2023)