Cloud computing has become a cornerstone of modern technology, yet optimizing resource allocation while ensuring sustainability remains a significant challenge. This research explores the application of fuzzy game theory to optimize the allocation of renewable energy in cloud data centers (CDCs) based in virtual machine (VM) migration, aiming to improve energy efficiency and system performance. The purpose of the study is to enhance decision-making in cloud environments by integrating fuzzy logic into game-theoretic optimization. The approach is based on the Game Theory-enhanced Gray Wolf Optimizer (GTGWO) algorithm, which incorporates fuzzy rules to model uncertainties in cloud system dynamics. The proposed model was implemented and evaluated using a set of cloud computing scenarios, including varying workloads and renewable energy availability across multiple data centers. The results demonstrate that the GTGWO algorithm significantly outperforms traditional optimization algorithms, achieving improvements of up to 2.17% in renewable energy usage compared to existing methods. This work concludes that the integration of fuzzy game theory provides a more efficient and interpretable solution for optimizing energy consumption in cloud computing systems, offering valuable insights for sustainable cloud infrastructure management. • Integration of fuzzy logic into game theory, using fuzzy payoffs and strategies. • Novel fuzzy game-theoretic optimization, modeling strategic players with fuzzy rules. • Intelligent knowledge acquisition mechanism in game theory for cloud optimization.

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.

In this study, the authors present a novel voicing detection algorithm which employs the well‐known aperiodicity measure to detect voiced speech in signals contaminated with non‐stationary noise. The method computes a signal‐adaptive decision threshold which takes into account the current noise level, enabling voicing detection by direct comparison with the extracted aperiodicity. This adaptive threshold is updated at each frame by making a simple estimate of the current noise power, and thus is adapted to fluctuating noise conditions. Once the aperiodicity is computed, the method only requires a small number of operations, and enables its implementation in challenging devices (such as hearing aids) if an efficient approximation of the difference function is employed to extract the aperiodicity. Evaluation over a database of speech sentences degraded by several types of noise reveals that the proposed voicing classifier is robust against different noises and signal‐to‐noise ratios. In addition, to evaluate the applicability of the method for speech enhancement, a simple F 0 ‐based speech enhancement algorithm integrating the proposed classifier is implemented. The system is shown to achieve competitive results, in terms of objective measures, when compared with other well‐known speech enhancement approaches.

In a monaural polyphonic context, music transcription and specifically, multiple-F0 estimation systems have achieved promising results in the last decade. However, most of these systems present intermittent misses of pitch within a note or inaccurate definitions about onsets and offsets due to frame-by-frame analysis. In this paper, we propose a multiple-F0 estimation system which extracts a set of active pitches at each frame (analysis frame) but note tracking is performed defining temporal intervals by an accurate onset detector. Our system shows promising results, in terms of onset and multiple-F0 estimation, to be evaluated using real-world and synthesized polyphonic music recordings taken from MAPS music database.

Harmonic decompositions are a powerful tool dealing with polyphonic music signals in some potential applications such as music visualization, music transcription and instrument recognition. The usefulness of a harmonic decomposition relies on the design of a proper harmonic dictionary. Music scene-adaptive harmonic atoms have been used with this purpose. These atoms are adapted to the musical instruments and to the music scene, including aspects related with the venue, musician, and other relevant acoustic properties. In this paper, an unsupervised process to obtain music scene-adaptive spectral patterns for each MIDI-note is proposed. Furthermore, the obtained harmonic dictionary is applied to note-event detection with matching pursuits. In the case of a music database that only consists of one-instrument signals, promising results (high accuracy and low error rate) have been achieved for note-event detection.

This paper deals with the application of adaptive signal models for parametric audio coding. A fully parametric audio coder, which decomposes the audio signal into sinusoids, transients and noise, is here proposed. Adaptive signal models for sinusoidal, transient, and noise modeling are therefore included in the parametric scheme in order to achieve high-quality and low bit-rate audio coding. In this paper, a new sinusoidal modeling method based on a perceptual distortion measure is proposed. For transient modeling, a fast and effective method based on matching pursuit with a mixed dictionary is chosen. The residue of the previous models is analyzed as a noise-like signal. The proposed parametric audio coder allows high quality audio coding for one-channel audio signals at 16 kbits/s (average bit rate). A bit-rate scalable version of the parametric audio coder is also proposed in this work. Bit-rate scalability is intended for audio streaming applications, which are highly demanded nowadays. The performance of the proposed parametric audio coders (nonscalable and scalable coders) is assessed in comparison to widely used audio coders operating at similar bit rates.

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.

In this letter, we propose a novel matching pursuit-based method for transient modeling with application to parametric audio coding. The overcomplete dictionary for the matching pursuit is composed of wavelet functions that implement a wavelet-packet filter bank. The proposed transient modeling method is suitable to be integrated into a parametric audio coder based on the three-part model of sines, transients, and noise (STN model). Comparative analysis between wavelet and exponentially damped sinusoidal functions are shown in experimental results. The mean-squared-error performance of the proposed approach is better than that obtained with damped sinusoids.