@article{CSUR2025,

title = {A Survey of Quantum Machine Learning: Foundations, Algorithms, Frameworks, Data and Applications},

author = {F. Rodríguez-Díaz and D. Gutiérrez-Avilés and A. Troncoso and F. Martínez-Álvarez},

url = {https://dl.acm.org/doi/10.1145/3764582},

doi = {10.1145/3764582},

year  = {2025},

date = {2025-10-01},

urldate = {2025-10-01},

journal = {ACM Computing Surveys},

volume = {58},

issue = {4},

pages = {1-35},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Vellinger2025Livestock,

title = {Forecasting Livestock Activity through Interpretable Neuroevolutionary Transfer Learning},

author = {A. Vellinger and F. Rodriguez-Diaz and F. Divina and J. F. Torres},

url = {https://doi.org/10.1093/jigpal/jzaf034},

doi = {10.1093/jigpal/jzaf034},

year  = {2025},

date = {2025-01-01},

journal = {Logic Journal of the IGPL},

volume = {to appear},

number = {to appear},

pages = {to appear},

abstract = {In this paper, we describe a neuroevolutionary approach to livestock activity forecasting, specifically targeting the prediction of Iberian pigs movements. We successfully integrated Transfer Learning to save computational time and used an Explainable Artificial Intelligence technique to provide valuable insights from the model predictions. Inspired by previous work, we employ Deep Evolutionary Network Structured Representation to optimize both Long Short-Term Memory networks and Convolutional Neural Networks using genetic algorithms and dynamic structured grammatical evolution, and we compare the results with other commonly used approaches for time series forecasting. Experimental results demonstrate the superior performance of the proposed Long Short-Term Memory models over more traditional methods, highlighting their precision and consistency in predicting livestock activities. Furthermore, the application of Explainable Artificial Intelligence techniques enable to gain a deeper understanding and trust in AI-driven decisions within precision livestock farming.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{RODRIGUEZ24,

title = {Explainable Olive grove and Grapevine pest forecasting through machine learning-based classification and regression},

author = {F. Rodríguez-Díaz and A. M. Chacón-Maldonado and A. R. Troncoso-García and G. Asencio-Cortés},

url = {https://www.sciencedirect.com/science/article/pii/S2590123024013136},

doi = {https://doi.org/10.1016/j.rineng.2024.103058},

year  = {2024},

date = {2024-09-09},

urldate = {2024-09-09},

journal = {Results in Engineering},

volume = {24},

pages = {103058},

abstract = {Pests significantly impact agricultural productivity, making early detection crucial for maximizing yields. This paper explores the use of machine learning models to predict olive fly and red spider mite infestations in Andalusia. Four datasets on crop phenology, pest populations, and damage levels were used, with models developed using the Python package H20, which focuses on interpretability through SHAP values and ICE plots. The results showed high precision in predicting pest outbreaks, particularly for the olive fly, with minimal differences between models using feature selection. In the vineyard dataset, the selection of characteristics improved the performance of the model by reducing the MAE and increasing R2. Explainability techniques identified solar radiation and wind direction as key factors in olive fly predictions, while past pest occurrences and wind velocity were influential for red spider mites, providing farmers with actionable insights for timely pest control.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{10.1093/bfgp/elae006,

title = {ASACO: Automatic and Serial Analysis of CO-expression to discover gene modifiers with potential use in drug repurposing},

author = {C. Moral-Turón an G. Asencio-Cortés and F. Rodriguez-Diaz and A. Rubio and A. G. Navarro and A. M. Brokate-Llanos and A. Garzón and M. J. Muñoz and A. J. Pérez-Pulido},

url = {https://doi.org/10.1093/bfgp/elae006},

doi = {10.1093/bfgp/elae006},

issn = {2041-2657},

year  = {2024},

date = {2024-01-01},

journal = {Briefings in Functional Genomics},

volume = {23},

number = {4},

pages = {484-494},

abstract = {Massive gene expression analyses are widely used to find differentially expressed genes under specific conditions. The results of these experiments are often available in public databases that are undergoing a growth similar to that of molecular sequence databases in the past. This now allows novel secondary computational tools to emerge that use such information to gain new knowledge. If several genes have a similar expression profile across heterogeneous transcriptomics experiments, they could be functionally related. These associations are usually useful for the annotation of uncharacterized genes. In addition, the search for genes with opposite expression profiles is useful for finding negative regulators and proposing inhibitory compounds in drug repurposing projects. Here we present a new web application, Automatic and Serial Analysis of CO-expression (ASACO), which has the potential to discover positive and negative correlator genes to a given query gene, based on thousands of public transcriptomics experiments. In addition, examples of use are presented, comparing with previous contrasted knowledge. The results obtained propose ASACO as a useful tool to improve knowledge about genes associated with human diseases and noncoding genes. ASACO is available at http://www.bioinfocabd.upo.es/asaco/.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}