Handover management in 5G/6G networks presents significant challenges mainly due to ultradense deployments, high mobility and diversity of scenarios. This work presents a digital twin-based Deep Q-Learning (DQL) strategy to optimize handover processes and resource management in these networks. It is proposed to consider digital twins to support the implementation of real-time network conditions where DQL agents are trained to improve quality of service (QoS) parameters related to throughput. The suggested approach, deployed with the ns-3 mmWave module and ns3-ai framework, shows relevant benefits, including reduced handovers. Moreover, the consideration of digital twins allows adaptive learning and scalability, allowing networks to respond more effectively to dynamic user behavior and environmental modifications. These results underline the potential of combining DQL and digital twins as an efficient solution for smart handover management, and thus, for sustainable and efficient communication in 5G/6G networks.

There is an overwhelming number of medications that can affect the morphology of the beating heart by delaying the ventricular depolarization and repolarization processes, resulting in an elongated QT interval. This circumstance, in turn, can lead to ventricular arrhythmias, which can result in the patient’s death, as was overwhelmingly the case at the beginning of the coronavirus pandemic. In this regard, this document presents an innovative expert system-based application devoted to estimating the level of risk of fatal ventricular arrhythmia as a consequence of QT prolonging treatments. For this purpose, the use of a fuzzy rule-based system has been considered for the decision-making process, establishing three different risk levels regarding the QT interval obtained from an electrocardiogram, its evolution from a basal electrocardiogram, and other significant clinical information of the patient such as age, sex, among others. This novel expert system-based application has been designed by considering the criteria of several specialist physicians from four different Spanish private and public hospitals. The proposed application can be especially important in primary health care centers, where the absence of specialists is remarkable because it provides clinical staff with a powerful tool for estimating the level of risk of fatal ventricular arrhythmia with 96.5% accuracy when comparing its decisions to those of specialist physicians, which could imply a significant reduction in the risk of death due to fatal ventricular arrhythmias in patients with QT prolonging treatment. In this respect, the developed application is currently used in some primary health care centers in Spain.

This COVID-19 pandemic is impacting the world in health and economic terms since 2020 with more than 200 million confirmed infected people and more than 4 million deaths across 190 countries. Treatment used against COVID-19 disease has initially been based on the combination of several medicaments, such as hydroxychloroquine/chloroquine, azithromycin and kaletra, each of which can individually delay the ventricular depolarization and repolarization processes through morphological changes in the patient’s electrocardiogram. These changes can produce serious arrhythmias that lead to the sudden death of the patient.This paper presents an interpretable fuzzy rule-based system for fatal ventricular arrhythmia risk level estimation due to COVID-19 treatment, whose decisions are made on the basis of the evolution of electrocardiogram morphology and certain patient’s clinical information. For the risk level estimation, the proposed fuzzy rule-based system considers three different risk levels (High, Moderate and Low) which are indicated by means of three different colors (Red, Orange and Green). Decisions made by the fuzzy rule-based system present a reliable behavior in comparison with cardiologist’s decision. To be precise, the obtained accuracy, when comparing both decisions, reaches the 96.43%, which, joint to the high measured interpretability of the decision making system, result in a powerful tool in order to avoid death in patients, even in health centers without specialized clinical staff, and to reduce the stress in medical centers by reducing reaction times in critical patient situations.

At the dawn of the fourth industrial revolution, artificial intelligence is impregnating our society by an overwhelming number of applications with rising complexity. This circumstance has triggered a new debate on explainable artificial intelligence in terms of transparency and confidence. Therefore, the relevance of interpretability is remarkable since this concept provides us with transparence and understandability in avoidance of black-box systems. On the other hand, cloud computing is a new paradigm of distributed computation based on the externalization of computing needs offered as services, whose performance has a significant economic and environmental impact and is strongly influenced by the scheduler, which is, likely, the most critical part in charge of allocation computational resources. In this regard, fuzzy rule-based systems are knowledge-based systems that are increasingly arising as an alternative for the development of cloud scheduling systems, mainly due to their intrinsic features such as adaptability to environments, dynamism and capability to cope with uncertainty. Bearing in mind the above-mentioned ideas, this paper presents a study-case in which the interpretability in fuzzy rule-based schedulers for cloud computing, obtained through automatic learning processed, has been analyzed. Results show how an inherent feature, like interpretability, vanishes due to the use of learning processes for both total execution time and power consumption optimization, which put the focus on the relevance of facing interpretability in this kind of systems.

The main objective of this work is to reduce power consumption and energy of a datacenter. There are various power saving techniques. A fuzzy system-based scheduler has been used, comparing its results with other well-known algorithms. The methods used in this paper are based on a combination of the DVFS algorithm and the development of a rule-based expert system to provide power-based planners for task planning domains. The parameters considered in the system are explained in detail and the results obtained are analyzed.

This work proposes the use of bio-inspired knowledge acquisition for Fuzzy Systems founded on Swarm Intelligence-Particle Swarm Optimization (SI-PSO). Swarm-based models consider knowledge entities as particles that move in the space to reach the higher quality. Fuzzy Systems following SI-PSO for knowledge acquisition are categorized in this work as Swarm Fuzzy Systems (SFSs). Specifically, two learning methodologies, KASIA (using rule bases as particles in PSO) and KARP (using rules as particles in PSO) are introduced. SFSs performance is studied in a problem of practical importance nowadays with data sets, the learning of fuzzy meta-schedulers in computational grids. Fuzzy meta-schedulers are Fuzzy Systems doing intelligent allocation of jobs to improve the performance of the grid, such as the reduction of the execution time of workload. The scheduling decisions are taken based on the knowledge of the Fuzzy System and in this way, the relevance of their learning process are critical. In this work, compared results of the performance of the different SFSs and a comparison between SFSs and Genetic Fuzzy Systems are presented. Simulations results show that SFSs can achieve a faster convergence and higher quality with a reduced number of control parameters what makes them a good alternative to Genetic Fuzzy Systems.

While the capability for growing on demand is the design foundation of the new generation of software platforms, physical systems always have limited resources. Therefore, there is a need to optimise the existing infrastructures and evolving them building on interoperability. The optimisation of the infrastructure entails not only addressing the computational and storage capabilities but also the network, its associated intelligence and the exchanged information. Therefore, the interoperability requirement in a large scale cyber-system spans from the lowest layers, interfacing with the physical resources, to the software building blocks, client devices and handled data. In this paper, several initiatives addressing interoperability among cloud IaaS layers, IaaS-PaaS, PaaS-SaaS, Cloud-Network and data are presented.

Knowledge acquisition is a long-standing problem in fuzzy-rule-based systems. In spite of the existence of several approaches, much effort is still required to increase the efficiency of the learning process. This study introduces a new method for the fuzzy-rule evolution that forms an expert system knowledge: the knowledge acquisition with a swarm-intelligence approach (KASIA). Specifically, this strategy is based on the use of particle-swarm optimization (PSO) to obtain the antecedents, consequences, and connectives of the rules. To test the feasibility of the suggested method, the inverted-pendulum problem is studied, and results are compared for two of the most extensively used methodologies in machine learning: the genetic-based Pittsburgh approach and the Q-learning-based strategy, i.e., state-action-reward-state-action (SARSA). Moreover, KASIA is analyzed as a learning strategy in fuzzy-rule-based metascheduler design for grid computing, and performance is compared with other scheduling strategies based on genetic learning and existing scheduling approaches, i.e., EASY-backfilling and ESG+local periodical search. To be more precise, simulation results prove the fact that the proposed strategy outperforms classical learning approaches in terms of final results and computational effort. Furthermore, the main advantage is the capability to control convergence and its simplicity.

Automatic speech/music discrimination is an important tool used in many multimedia applications, becoming a research topic of interest in the last years. This paper presents our last works in the speech/music discrimination field, aiming to improve the coding efficiency of standard audio coders (i.e. MP3, AAC) when speech and music signals are involved. In order to discriminate between speech and music, a fuzzy rules-based expert system is incorporated into the decision-taking stage of traditional speech/music discrimination systems. The knowledge base of the fuzzy expert system has been obtained by means of a typical genetic learning algorithm (the Pittsburgh algorithm). The proposed speech/music discrimination scheme manages the operation of an intelligent audio coder, which selects a GSM coder for speech frames and an AAC coder for music ones, resulting in a lower bit rate regarding the case of using a standardized audio coder (AAC in this work). Further, the intelligent audio coder has been designed aiming to obtain a similar subjective audio quality than AAC. GSM operates at 13 kbits/s, while in the experiments the bit rate specification for AAC has been 32 kbits/s for one-channel audio signals.