Abstract
The rapid developments in Internet-of-Things (IoT), cloud computing, and big data technologies have increased the popularity of machine learning (ML) techniques. As a result, of all ML techniques, deep learning (DL) is at the forefront of innovation, outperforming all other techniques in many application domains. DL has made breakthroughs in speech recognition, image processing, forecasting, natural language processing, fault detection, power disturbance classification, energy trading, and much more. DL is a complex ML approach composed of multiple processing layers, which allows pattern and structure recognition on huge datasets. This chapter takes an in-depth look at the most recent and promising DL works in the literature for intelligent power and energy systems (PES). Several types of problems are explored, including regression, classification, and decision-making problems. The presented works show an increasing trend of new DL techniques that outperform traditional approaches, either through novel architectures or hybrid systems. © 2023 The Institute of Electrical and Electronics Engineers, Inc.

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In the areas of machine-learning/big data, feature selection is normally regarded as a very important problem to be solved, as it directly impacts both data analysis and model creation. The problem of optimizing the selected features of a given dataset is not always trivial, however, throughout the years various ways to counter this optimization problem have been presented. This work presents how feature-selection fits in the larger context of multi-objective problems as well as a review of how both multi-objective evolutionary algorithms and metaheuristics are being used in order to solve feature selection problems.

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For some years now, master data has become extremely relevant to business success and continuity in an increasingly competitive and global business environment. The banking sector is one example of how the implementation of well-structured and designed master data management policies and initiatives is crucial for reaching positive results. One of the areas in which banks need to ensure extremely fruitful master data management approaches and data governance procedures is when dealing with risk-related data, as it not only ensures accurate and well-supported management and decision-making, but also because banks are required to do so by imposed regulations, such as the BCBS 239. Drawing on a DSR methodology supported research project, where banking and IS-related expertise was continuously merged with existing theoretical knowledge on MDM and BCBS 239 related topics, and a permanent focus on the technical and functional complexity associated with implementing master data management and well-established data governance procedures that ensure regulatory compliance, we propose a novel, six-phase action plan that will allow banks to ensure compliance with BCBS 239 and, consequently, ensure efficient and effective risk data management and reporting.

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(1) Background: Initiatives aimed at assessing and intervening in health literacy have the potential to promote adherence to self-care behaviours, which is the main focus of intervention by rehabilitation nurses. Thus, the objectives were to analyse the level of health literacy of working-age citizens and identify priority areas for intervention by rehabilitation nurses. (2) Methods: Quantitative, correlational and cross-sectional study, conducted in a multinational company, with the participation of 161 workers. The data were collected between 14 April and 7 May 2021, using a self-completion questionnaire composed of sociodemographic and clinical characterization and the European Health Literacy Survey, following a favourable opinion from the Ethics Committee and the company's management. (3) Results: Overall, low to moderate literacy scores were predominant. Age and education were significantly associated with literacy scores. Workers with higher levels of health literacy had no diagnosed illnesses, took less medication, reported less sadness, fewer memory changes and less muscle and joint pain. (4) Conclusions: The fact that higher levels of health literacy trigger self-care behaviours and, consequently, fewer health problems reinforces the need for rehabilitation nurses to invest in this area.