The current and unprecedented ability to digitise and store massive amounts of information has fostered key scientific breakthroughs in the medical sector, a key pillar of the welfare system. In this context, several works have been published in recent years focusing on extracting suitable information from medical images to improve pathological examinations and clinical diagnosis. A particularly insightful contribution to this field provides a comprehensive review of the published investigations in computed tomography (CT) lung cancer images and includes an overview of the commonly used feature selection methods and predictive models [9]. After comparing the impacts and limitations of each method in a clinical environment, the authors emphasise that assessment and validation of feature selection methods and predictive models remain essential to enhancing feature stability and reproducibility. In the same line, the authors of [10] describe a novel technique for detecting bone fractures in X-ray images. The authors use the well-known grey level co-occurrence matrix (GLCM) technique to identify the most representative features from each X-ray image. Selected features were applied to a fuzzy network classification model, achieving a performance accuracy of 0.8849. Finally, the work presented the authors of [11] introduces a novel methodology responsible for combining both feature extraction and selection techniques to detect and classify brain tumours in medical images. The first stage (feature extraction) obtains a set of features by blending GLCM and LBP techniques with four well-known convolutional neural network models (AlexNet, VGG16, EfficientNetB0 and ResNet50). Conversely, the second stage uses three optimisation algorithms (genetic algorithms, particle swarm optimisation, and artificial bee colony) over the previously obtained feature set to select the adequate ones. The remaining features were classified using a support vector machine model, achieving an accuracy rate of 98.22%. The current model of free access to online content is closely linked to the commodification of user data. Users navigate the Internet unaware that they are sharing their personal information as part of a transaction to gain access to unlimited services and content. This personal information is essential for performing accurate marketing strategies. Analysing user preferences, search patterns, and engagement metrics allows marketers to refine their targeting efforts, ensuring the delivery of relevant content and customised advertisements to maximise the efficiency and impact of digital marketing campaigns. One of the most interesting works related to feature extraction introduces a novel location-based advertising (LBA) scheme using a deep learning-based bidirectional hybrid model [12]. To this end, authors propose a methodology based on a geographical information system (GIS) to accurately obtain and interpret spatial user location, utilising a deep sparse autoencoder (DSA) to perform feature extraction and a bidirectional optimised hybrid model (BLSTM-DNN-ASOA) to conduct the classification task. This proposed model achieves better performance compared to existing approaches. Next, the authors of [13] discuss the role and relevance of using feature selection in neuromarketing to improve the accuracy of user preference detection, based on the brain's response to incoming marketing stimuli. To this end, an experimental study combining the most widely used feature selection methods with well-known machine learning models was conducted to identify the feature selection approach that achieves the best global performance. In the same line, the work presented by the authors of [14] uses a wrapped feature selection method (SVM-RFE) to predict future consumer choices. Stock market prediction is another interesting area that benefits greatly from the information age.