Manuel Jesús Jiménez Navarro

• PhD
• University of Seville

Reference evapotranspiration is a crucial metric in agricultural contexts, characterizing the evapotranspiration rate from a well-hydrated surface and serving as a fundamental benchmark for water management and crop irrigation, especially in arid regions. This study applied neural networks that integrates a Temporal Selection Layer to enhance the p...

Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, dependin...

Time series forecasting is a well-known deep learning application field in which previous data are used to predict the future behavior of the series. Recently, several deep learning approaches have been proposed in which several nonlinear functions are applied to the input to obtain the output. In this paper, we introduce a novel method to improve...

Traditional time series forecasting models often use all available variables, including potentially irrelevant or noisy features, which can lead to overfitting and poor performance. Feature selection can help address this issue by selecting the most informative variables in the temporal and feature dimensions. However, selecting the right features...

This paper proposes a novel approach that combines an association rule algorithm with a deep learning model to enhance the interpretability of prediction outcomes. The study aims to gain insights into the patterns that were learned correctly or incorrectly by the model. To identify these scenarios, an association rule algorithm is applied to extrac...

In the workflow of machine/deep learning, a common question that data scientists ask before training a model is whether to feed the model with the entire set of input dataset features or just a subset of them. In this scenario, the precise selection of features from the input data has a significant impact on the efficiency of model training and the...

The quality of university teaching is essential for the success of students and the academic excellence of an educational institution. The purpose of this work is to provide a methodology based on the Association Rule technique using the Apriori algorithm to analyze the results obtained from the student evaluation process regarding their satisfacti...

The year 2022 was the driest year in Portugal since 1931 with 97% of territory in severe drought. Water is especially important for the agricultural sector in Portugal, as it represents 78% total consumption according to the Water Footprint report published in 2010. Reference evapotranspiration is essential due to its importance in optimal irrigati...

Ensuring the optimal performance of power transformers is a laborious task in which the insulation system plays a vital role in decreasing their deterioration. The insulation system uses insulating oil to control temperature, as high temperatures can reduce the lifetime of the transformers and lead to expensive maintenance. Deep learning architectu...

Time series is one of the most common data types in the industry nowadays. Forecasting the future of a time series behavior can be useful in planning ahead, saving time, resources, and helping avoid undesired scenarios. To make the forecasting, historical data is utilized due to the causal nature of the time series. Several deep learning algorithms...

Neural networks have proven to be a good alternative in application fields such as healthcare, time-series forecasting and artificial vision, among others, for tasks like regression or classification. Their potential has been particularly remarkable in unstructured data, but recently developed architectures or their ensemble with other classical me...

The agricultural sector has been, and still is, the most important economic sector in many countries. Due to advances in technology, the amount and variety of available data have been increasing over the years. However, compared to other economic sectors, there is not always enough quality data for one particular domain (crops, plantations, plots)...

Time series forecasting is a well-known application area for deep learning, in which the historical data are used to predict the future behavior of the series. Several deep learning methods have been proposed in this context, but they usually try to generate the output from the input, with no data transformation. In this paper, we introduce a novel...

A new transfer learning strategy is proposed for classification in this work, based on fully connected neural networks. The transfer learning process consists in a training phase of the neural network on a source dataset. Then, the last two layers are retrained using a different small target dataset. Clustering techniques are also applied in order...

Forecasting electricity demand is crucial for the management of smart grids to ensure a secure, reliable and sustainable supply. Recently, a variant of convolutional neural networks, called temporal convolutional networks, has emerged for data sequence, competing directly with deep recurrent neural networks in terms of execution time and memory req...