Prof.Dr.Jesús Gil Ruiz

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected. At present, the closure of production areas is based on expert knowledge and the existence of a predictive model would help when… Show more

Harmful algal blooms (HABs) are episodes of high concentrations of algae that are potentially toxic for human consumption. Mollusc farming can be affected by HABs because, as filter feeders, they can accumulate high concentrations of marine biotoxins in their tissues. To avoid the risk to human consumption, harvesting is prohibited when toxicity is detected. At present, the closure of production areas is based on expert knowledge and the existence of a predictive model would help when conditions are complex and sampling is not possible. Although the concentration of toxin in meat is the method most commonly used by experts in the control of shellfish production areas, it is rarely used as a target by automatic prediction models. This is largely due to the irregularity of the data due to the established sampling programs. As an alternative, the activity status of production areas has been proposed as a target variable based on whether mollusc meat has a toxicity level below or above the legal limit. This new option is the most similar to the actual functioning of the control of shellfish production areas. For this purpose, we have made a comparison between hybrid machine learning models like Neural-Network-Adding Bootstrap (BAGNET) and Discriminative Nearest Neighbor Classification (SVM-KNN) when estimating the state of production areas. The study has been carried out in several estuaries with different levels of complexity in the episodes of algal blooms to demonstrate the generalization capacity of the models in bloom detection. As a result, we could observe that, with an average recall value of 93.41% and without dropping below 90% in any of the estuaries, BAGNET outperforms the other models both in terms of results and robustness. Show less

Background:
ecommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes… Show more

Background:
ecommender systems are one of the most widely used technologies by electronic businesses and internet applications as part of their strategies to improve customer experiences and boost sales. Recommender systems aim to suggest content based on its characteristics and on user preferences. The best recommender systems are able to deliver recommendations in the shortest possible time and with the least possible number of errors, which is challenging when working with large volumes of data.
Method:
This article presents a novel technique to optimize recommender systems using particle swarm algorithms. The objective of the selected genetic algorithm is to find the best hyperparameters that minimize the difference between the expected values and those obtained by the recommender system.
Results:
The algorithm demonstrates viability given the results obtained, highlighting its simple implementation and the minimal and easily attainable computational resources necessary for its execution.
Conclusions:
It was possible to develop an algorithm using the most convenient properties of particle swarms in order to optimize recommender systems, thus achieving the ideal behavior for its implementation in the proposed scenario. Show less

Las energías renovables han tenido un crecimiento exponencial en los últimos años como queda reflejado por la agencia internacional de energía (AIE), según sus previsiones, la participación de las renovables en el suministro eléctrico pasará del 26% de 2018 al 44% en 2040. Actualmente la toma de decisiones en dirección y gestión de proyectos sigue siendo por juicio de expertos ( en base a la experiencia), y esto genera mucha incertidumbre en el desarrollo de los proyectos, derivando muchos de… Show more

Las energías renovables han tenido un crecimiento exponencial en los últimos años como queda reflejado por la agencia internacional de energía (AIE), según sus previsiones, la participación de las renovables en el suministro eléctrico pasará del 26% de 2018 al 44% en 2040. Actualmente la toma de decisiones en dirección y gestión de proyectos sigue siendo por juicio de expertos ( en base a la experiencia), y esto genera mucha incertidumbre en el desarrollo de los proyectos, derivando muchos de ellos en un fracaso o en un éxito parcial. La inteligencia Artificial es el futuro de la dirección y gestión de proyectos, esta investigación abre el camino hacia la automatización de dirección de proyectos por IA, y en concreto, en proyectos fotovoltaicos. Se presenta una Red neuronal híbrida difusa evolutiva (EFHNN), que tendrá la capacidad de realizar planificaciones de proyectos de plantas fotovoltaicas, dicha red se habrá entrenado con un total de 15 planificaciones. Show less

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and… Show more

The field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management.

DOI: 10.9781/ijimai.2020.12.003 Show less

This project deals with the calculation and design of the different high-power mechanical transmissions for the carriage of a port gantry crane, intended for the transshipment of containers from ship to dock and vice versa.