Fabio Mendonça

University of Madeira | UMA · Faculdade de Ciências Exatas e da Engenharia

This work introduces a deep-learning architecture tailored for accurate wind speed and direction forecasting for airports using a grid-based input. Moving beyond the limitations of conventional forecasting methods, which struggle with rapid and localized atmospheric changes and demand substantial computational power, this study positions a machine-...

This study examines the application of machine learning to enhance wind nowcasting by using a Kolmogorov-Arnold Network model to improve predictions from the Global Forecast System at Madeira International Airport, a site affected by complex terrain. The research addresses the limitations of traditional numerical weather prediction models, which of...

The intersection of artificial intelligence and environmental sustainability has become a relevant exploration domain in the contemporary era of rapid technological advancements and complex global challenges. This work reviews the application of machine learning (ML) models to address the pressing issue of plastic waste (PW) management. By systemat...

Wind direction nowcasting is crucial in various sectors, particularly for ensuring aviation operations and safety. In this context, the TELMo (Time-series Embeddings from Language Models) model, a sophisticated deep learning architecture, has been introduced in this work for enhanced wind-direction nowcasting. Developed by using three years of data...

The conventional process of visual detection and manual harvesting of the banana bunch has been a known problem faced by the agricultural industry. It is a laborious activity associated with inconsistency in the inspection and grading process, leading to post-harvest losses. Automated fruit harvesting using computer vision empowered by deep learnin...

In aviation, accurate wind prediction is crucial, especially during takeoff and landing at complex sites like Gran Canaria Airport. This study evaluated five Deep Learning models: Long Short-Term Memory (LSTM), Vanilla Recurrent Neural Network (vRNN), One-Dimensional Convolutional Neural Network (1dCNN), Convolutional Neural Network Long Short-Term...

Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large system...

LoRa systems are emerging as a promising technology for wireless sensor networks due to their exceptional range and low power consumption. The successful deployment of LoRa networks relies on accurate propagation models to facilitate effective network planning. Therefore, this review explores the landscape of propagation models supporting LoRa netw...

Accurate wind speed and direction nowcasting in regions with complex terrains remains a challenge, and critical for applications like aviation. This study proposes a new methodology by harnessing Convolutional Neural Networks and Long Short-Term Memory models with satellite imagery to address wind predictions in a complex terrain, centered on Madei...

Traditional methods for water-level measurement usually employ permanent structures, such as a scale built into the water system, which is costly and laborious and can wash away with water. This research proposes a low-cost, automatic water-level estimator that can appraise the level without disturbing water flow or affecting the environment. The e...

This study focuses on improving sentiment analysis in restaurant reviews by leveraging transfer learning and transformer-based pre-trained models. This work evaluates the suitability of pre-trained deep learning models for analyzing Natural Language Processing tasks in Portuguese. It also explores the viability of utilizing edge devices for Natural...

Sleep is a complex process divided into different stages, and a decrease in sleep quality can lead to adverse health-related effects. Therefore, diagnosing and treating sleep-related conditions is crucial. The Cyclic Alternating Pattern (CAP) is an indicator of sleep instability and can assist in assessing sleep-related disorders such as sleep apne...

The unique geographical and topographical features of Madeira International Airport in Portugal significantly influence flight safety, primarily due to variable wind patterns. In this study, a machine learning approach is developed to predict runway operational statuses at Madeira International Airport, focusing on addressing wind-related challenge...

This study presents a novel approach for kernel selection based on Kullback–Leibler divergence in variational autoencoders using features generated by the convolutional encoder. The proposed methodology focuses on identifying the most relevant subset of latent variables to reduce the model’s parameters. Each latent variable is sampled from the dist...

Wind forecasting, which is essential for numerous services and safety, has significantly improved in accuracy due to machine learning advancements. This study reviews 23 articles from 1983 to 2023 on machine learning for wind speed and direction nowcasting. The wind prediction ranged from 1 min to 1 week, with more articles at lower temporal resolu...

Wind factors significantly influence air travel, and extreme conditions can cause operational disruptions. Machine learning approaches are emerging as a valuable tool for predicting wind patterns. This research, using Madeira International Airport as a case study, delves into the effectiveness of feature creation and selection for wind nowcasting,...

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical ap...

Cyclic Alternating Pattern (CAP) is a sleep instability marker defined based on the amplitude and frequency of the electroencephalogram signal. Because of the time and intensive process of labeling the data, different machine learning and automatic approaches are proposed. However, due to the low accuracy of the traditional approach and the black b...

Introduction: Evaluating sleep stability can provide valuable insights into understanding sleep disorders and their underlying causes. The present study introduces an approach to assessing sleep stability, by developing Near-Real-Time (NRT) Cyclic Alternating Pattern (CAP) A-phase Index (API). Materials & methods: The study evaluates 15 healthy and...

Chatbots are becoming increasingly popular and require the ability to interpret natural language to provide clear communication with humans. To achieve this, intent detection is crucial. However, current applications typically need a significant amount of annotated data, which is time-consuming and expensive to acquire. This article assesses the ef...

Through the development of artificial intelligence, some capabilities of human beings have been replicated in computers. Among the developed models, convolutional neural networks stand out considerably because they make it possible for systems to have the inherent capabilities of humans, such as pattern recognition in images and signals. However, c...

The number of coronavirus disease (COVID-19) cases is constantly rising as the pandemic continues, with new variants constantly emerging. Therefore, to prevent the virus from spreading, coronavirus cases must be diagnosed as soon as possible. The COVID-19 pandemic has had a devastating impact on people’s health and the economy worldwide. For COVID-...

The ripeness of bananas is the most significant factor affecting nutrient composition and demand. Conventionally, cutting and ripeness analysis requires expert knowledge and substantial human intervention, and different studies have been conducted to automate and substantially reduce human effort. Using the Preferred Reporting Items for the Systema...

The agrotech revolution is emerging and aims to use advanced precision technology, such as real-time analysis of soil nutrients and weather conditions using sensors to meet the future demands for food in a more sustainable, efficient, and eco-friendly way. IoT is remodeling agriculture, enabling farmers with a wide range of techniques, namely preci...

Study Objectives Sleep stability can be studied by evaluating the Cyclic Alternating Pattern (CAP) in electroencephalogram signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night’s sleep. Methods Two ensem...

This work proposed kernel selection approaches for probabilistic classifiers based on features produced by the convolutional encoder of a variational autoencoder. Particularly, the developed methodologies allow the selection of the most relevant subset of latent variables. In the proposed implementation, each latent variable was sampled from the di...

ProBoost, a new boosting algorithm for probabilistic classifiers, is proposed in this work. This algorithm uses the epistemic uncertainty of each training sample to determine the most challenging/uncertain ones; the relevance of these samples is then increased for the next weak learner, producing a sequence that progressively focuses on the samples...

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a...

The cyclic alternating pattern is a microstructure phasic event, present in the non-rapid eye movement sleep, which has been associated with multiple pathologies, and is a marker of sleep instability that is detected using the electroencephalogram. However, this technique produces a large quantity of information during a full night test, making the...

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and su...

Biomedical decision making involves multiple signal processing, either from different sensors or from different channels. In both cases, information fusion plays a significant role. A deep learning based electroencephalogram channels' feature level fusion is carried out in this work for the electroencephalogram cyclic alternating pattern A phase cl...

Resumo A qualidade do sono pode ser afetada pela ocorrência de um distúrbio relacionado ao sono e, entre esses distúrbios, a apneia obstrutiva do sono é um dos mais prevalentes. Apesar de a polissonografia ser o teste padrão para a análise do sono, é um exame caro e complexo que não está disponível para a maioria da população mundial. Desta forma,...

Sleep is part of the circadian rhythm and is characterized by sequences of stages with autonomous nervous system functions that are related to it. It is a complex physiological process inherent to each individual and commonly covers nearly one-third of the lifespan. Sleep quality is one of the most relevant factors that affects physical and mental...

The relevance of sleep quality examination for clinical diagnosis is increasing with the discovery of new relationships with several diseases and the overall wellness. This assessment is commonly performed by conducting interviews with the subjects, evaluating the self-report and psychological variables. However, this approach has a major constrain...

The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern eve...

Obstructive sleep apnea is considered to be one of the most prevalent sleep-related disorders that can affect the general population. However, the gold standard for the diagnosis, polysomnography, is an expensive and complicated process that is commonly unavailable to a large group of the population. Alternatively, automatic approaches have been de...

A probabilistic model for sleep analysis is proposed in this work, modeling the temporal relation between the sleep structure and the presence of the electroencephalogram (EEG) Cyclic Alternating Pattern (CAP) with a Hidden Markov Model (HMM). Sleep scoring is frequently performed by assigning a state to each thirty second epoch. However, this appr...

The quality of sleep can be affected by the occurrence of a sleep related disorder and, among these disorders, obstructive sleep apnea is commonly undiagnosed. Polysomnography is considered to be the gold standard for sleep analysis. However, it is an expensive and labor-intensive exam that is unavailable to a large group of the world population. T...

Obstructive sleep apnea (OSA) is a common sleep disorder characterized by interrupted breathing during sleep. Because of the cost, complexity, and accessibility issue related to polysomnography, the gold standard test for apnea detection, automation of the diagnostic test based on a simpler method is desired. Several signals can be used for apnea d...

Sleep related disorders can severely disturb the quality of sleep. Among these disorders, obstructive sleep apnea (OSA) is highly prevalent and commonly undiagnosed. Polysomnography is considered to be the gold standard exam for OSA diagnosis. Even though this multi-parametric test provides highly accurate results, it is time consuming, labor-inten...

Background: Multiple methods have been developed to assess what happens between and within time series. In a particular type of these series, the previous values of the currently observed series are contingent on the lagged values of another series. These cases can commonly be addressed by regression. However, a model selection criteria should be e...

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram s...

Sleep apnea is a sleep related disorder that significantly affects the population. Polysomnography, the gold standard, is expensive, inaccessible, uncomfortable and an expert technician is needed to score. Numerous researchers have proposed and implemented automatic scoring processes to address these issues, based on fewer sensors and automatic cla...

Objective: The term sleep quality is widely used by researchers and clinicians despite the lack of a definitional consensus, due to different assumptions on quality quantification. It is usually assessed using subject self-reporting, a method that has a major limitation since the subject is a poor self-observer of their sleep behaviors. A more pre...

The cyclic alternating pattern is a characteristic phasic event present in the electroencephalogram signals and is commonly scored by experts through a visual examination. This pattern is considered to be a marker of sleep instability and can be used for the assessment of sleep quality. However, in manual scoring, each one second epoch of the signa...

Sleep quality is directly related to overall wellness and can reveal symptoms of several diseases. However, the term “sleep quality” still lacks a definitional consensus and is commonly assessed in sleep labs with polysomnography, comprising high costs, or through sleep questionnaires, a highly subjective technique. Multiple methods have been propo...

The gold standard for assessment of sleep quality is the polysomnography where physiological signals are used to generate both quantitative and qualitative measurements. Despite the production of highly accurate results, polysomnography is a complex, uncomfortable and expensive process, inaccessible to a large group of the population. Home monitori...

The cyclic alternating pattern can be seen as an electroencephalogram marker of sleep instability. This pattern consists of alternations between activation and quiescent phases. An automatic cyclic alternating pattern detection method is proposed, having the advantage, over other previously proposed methods, of being featureless. Therefore, there i...

Obstructive sleep apnea is a highly prevalent sleep related breathing disorder and polysomnography is the gold standard exam for diagnosis. Despite providing results with high accuracy this multi-parametric test is expensive, time consuming and does not fit with the new tendency in health care that is changing the focus to prevention and wellness....

Sleep quality is commonly assessed with subject self-reporting, interviews and psychological variables. However, more precise methods comprise estimation of physiological signals where polysomnography is considered to be the gold standard and can be performed to produce qualitative or quantitative measurements regarding the subjects sleep. However,...

Sleep disorders are a common health condition that can affect numerous aspects of life. Obstructive sleep apnea is one of the most common disorders and is characterized by a reduction or cessation of airflow during sleep. In many countries this disorder is usually diagnosed in sleep laboratories, by a polysomnography, which is an expensive procedur...

One of the most common sleep-related disorders is obstructive sleep apnea, characterized by a reduction of airflow while breathing during sleep and cause significant health problems. This disorder is mainly diagnosed in sleep labs with polysomnography, involving high costs and stress for the patient. To address this situation multiple systems have...

The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then applying a finite state machine to implement the final classification. A public database was used to test the algorithms and a tota...

In a classical classification process, automatic sleep apnea detection involves creating and selecting the features, using prior knowledge, and apply them to a classifier. A different approach is applied in this paper, where a Deep Belief Network is used for feature extraction, without using domain-specific knowledge, and then the same network is u...

Health care is changing the focus from primary and specialty care to prevention and wellness. Therefore, home health care is seen as one of the most relevant wellness services due to high accessibility and low cost of diagnosis. The growth relevance given to the sleep related disorders, due to the high importance of sleep in our lives, is specifica...

It is well-known that energy harvesting from wind can be used to power remote monitoring systems. There are several studies that use wind energy in small-scale systems, mainly with wind turbine vertical axis. However, there are very few studies with actual implementations of small wind turbines. This paper compares the performance of horizontal and...