Search Results (10)

Background: COPD is a chronic disease characterized by frequent exacerbations that require hospitalization, significantly increasing the care burden. In recent years, the use of artificial intelligence-based tools to improve the management of patients with COPD has progressed, but the prediction of readmission has been less explored. In fact, in the state of the art, no models specifically designed to make medium-term readmission predictions (2–3 months after admission) have been found. This work presents a new intelligent clinical decision support system to predict the risk of hospital readmission in 90 days in patients with COPD after an episode of acute exacerbation. Methods: The system is structured in two levels: the first one consists of three machine learning algorithms —Random Forest, Naïve Bayes, and Multilayer Perceptron—that operate concurrently to predict the risk of readmission; the second level, an expert system based on a fuzzy inference engine that combines the generated risks, determining the final prediction. The employed database includes more than five hundred patients with demographic, clinical, and social variables. Prior to building the model, the initial dataset was divided into training and test subsets. In order to reduce the high dimensionality of the problem, filter-based feature selection techniques were employed, followed by recursive feature selection supported by the use of the Random Forest algorithm, guaranteeing the usability of the system and its potential integration into the clinical environment. After training the models in the first level, the knowledge base of the expert system was determined on the training data subset using the Wang–Mendel automatic rule generation algorithm. Results: Preliminary results obtained on the test set are promising, with an AUC of approximately 0.8. At the selected cutoff point, a sensitivity of 0.67 and a specificity of 0.75 were achieved. Conclusions: This highlights the system’s future potential for the early identification of patients at risk of readmission. For future implementation in clinical practice, an extensive clinical validation process will be required, along with the expansion of the database, which will likely contribute to improving the system’s robustness and generalization capacity. Full article

Long COVID is a condition that affects a significant proportion of patients who have had COVID-19. It is characterised by the persistence of associated symptoms after the acute phase of the illness has subsided. Although several studies have investigated the risk factors associated with long COVID, identifying which patients will experience long-term symptoms remains a complex task. Among the various symptoms, dyspnea is one of the most prominent due to its close association with the respiratory nature of COVID-19 and its disabling consequences. This work proposes a new intelligent clinical decision support system to predict dyspnea 12 months after a severe episode of COVID-19 based on the SeguiCovid database from the Álvaro Cunqueiro Hospital in Vigo (Galicia, Spain). The database is initially processed using a CART-type decision tree to identify the variables with the highest predictive power. Based on these variables, a cascade of expert systems has been defined with Mamdani-type fuzzy-inference engines. The rules for each system were generated using the Wang-Mendel automatic rule generation algorithm. At the output of the cascade, a risk indicator is obtained, which allows for the categorisation of patients into two groups: those with dyspnea and those without dyspnea at 12 months. This simplifies follow-up and the performance of studies aimed at those patients at risk. The system has produced satisfactory results in initial tests, supported by an AUC of 0.75, demonstrating the potential and usefulness of this tool in clinical practice. Full article

From the point of view of the synthesis of control influences, arc steelmaking furnaces are complex nonlinear objects with strongly expressed mutual influences. It has been demonstrated that at a given supply voltage, the distribution of the current values of the phase currents in the quasi-steady-state mode makes it possible to estimate the situation in the arc space of an arc steelmaking furnace and identify the value of arc lengths. This dependence is preserved in transient modes. In order to identify arc lengths from the phase currents, it is proposed to use an approach based on the theory of fuzzy sets. The construction of the fuzzy arc length identifier rule base was carried out in two stages: the first stage used data from quasi-steady-state modes and the Wang–Mendel algorithm; the second stage involved adding a new rule to the database if the activity level of the formed rules was lower than the established level α for the data obtained in the dynamic mode. Further optimization of the parameters of the fuzzy identifier for operation in dynamic modes was carried out using the “back-propagation” algorithm. Based on the identified values of arc lengths, a control system for the movement of electrodes in an arc steelmaking furnace was synthesised. The proposed control system makes it possible to eliminate unproductive electrode movements due to changes in the situation in other phases of the arc steelmaking furnace and simplifies the application of modern methods of synthesising a control system for such complex objects. The results obtained in the mathematical model have confirmed the effectiveness of the proposed control system for the movement of electrodes in an arc steelmaking furnace. Full article

The use of intelligent systems in clinical diagnostics has evolved, integrating statistical learning and knowledge-based representation models. Two recent works propose the identification of risk factors for the diagnosis of obstructive sleep apnea (OSA). The first uses statistical learning to identify indicators associated with different levels of the apnea-hypopnea index (AHI). The second paper combines statistical and symbolic inference approaches to obtain risk indicators (Statistical Risk and Symbolic Risk) for a given AHI level. Based on this, in this paper we propose a new intelligent system that considers different AHI levels and generates risk pairs for each level. A learning-based model generates Statistical Risks based on objective patient data, while a cascade of fuzzy expert systems determines a Symbolic Risk using symptom data from patient interviews. The aggregation of risk pairs at each level involves a fuzzy expert system with automatically generated fuzzy rules using the Wang-Mendel algorithm. This aggregation produces an Apnea Risk indicator for each AHI level, allowing discrimination between OSA and non-OSA cases, along with appropriate recommendations. This approach improves variability, usefulness, and interpretability, increasing the reliability of the system. Initial tests on data from 4400 patients yielded AUC values of 0.74–0.88, demonstrating the potential benefits of the proposed intelligent system architecture. Full article

This paper focuses on the application of fuzzy modeling methods in the field of environmental engineering. Since predicting meteorological data is considered to be a challenging task, the current work aimed to assess the performance of various fuzzy models on temperature, solar radiation, and wind speed forecasting. The models studied were taken from the fuzzy systems literature, varying from well-established to the most recent methods. Four cases were considered: a Wang–Mendel (WM)-based fuzzy predictive model, an adaptive network fuzzy inference system (ANFIS), a fuzzy system ensemble, and patch learning (PL). The prediction systems were built from input/output data without any prior information, in a model-free approach. The ability of the models to display high performance on complex real datasets, provided by the National Observatory of Athens, was demonstrated through numerical studies. Patch learning managed to not only display a similar approximation ability to that of strong machine learning models, such as support vector machines and Gaussian processes, but also outperform them on the highly demanding problem of wind speed prediction. More accurately, as far as wind speed prediction is concerned, patch learning produced a $0.9211$ root mean squared error for the training data and a value of $0.9841$ for the testing data. The support vector machine provided a $0.9306$ training root mean squared error and a $0.9891$ testing value. The Gaussian process model resulted in a $0.9343$ root mean squared error for the training data and a value of $0.9861$ for the testing data. Finally, as shown by the numerical experiments, the fuzzy system ensemble exhibited the highest generalisation performance among all the intelligent models. Full article

Traffic flow prediction is a critical component of intelligent transportation systems, especially in the prevention of traffic congestion in urban areas. While significant efforts have been devoted to enhancing the accuracy of traffic prediction, the interpretability of traffic prediction also needs to be considered to enhance persuasiveness, particularly in the era of deep-learning-based traffic cognition. Although some studies have explored interpretable neural networks from the feature and result levels, model-level explanation, which explains the reasoning process of traffic prediction through transparent models, remains underexplored and requires more attention. In this paper, we propose a novel self-constructed deep fuzzy neural network, SCDFNN, for traffic flow prediction with model interpretability. By leveraging recent advances in neuro-symbolic computation for automatic rule learning, SCDFNN learns interpretable human traffic cognitive rules based on deep learning, incorporating two innovations: (1) a new fuzzy neural network hierarchical architecture constructed for spatial-temporal dependences in the traffic feature domain; (2) a modified Wang–Mendel method used to fuse regional differences in traffic data, resulting in adaptive fuzzy-rule weights without sacrificing interpretability. Comprehensive experiments on well-known traffic datasets demonstrate that the proposed approach is comparable to state-of-the-art deep models, and the SCDFNN’s unique hierarchical architecture allows for transparency. Full article

Acrylamide is a potentially toxic compound present in many plant-based foods, such as coffee, breads, and potato fries, which is reported to have carcinogenic, neurotoxic, and antifertility properties in vivo, suggesting the need to keep the acrylamide content of widely consumed food as low as possible. As pigmented rice contains bioactive phenolic and flavonoid compounds, the objective of this study was to potentially enhance the beneficial properties of flatbreads by evaluating the acrylamide content and proximate composition of 12 novel flatbreads prepared from the following commercial pigmented rice seeds: Black Japonica, Chinese Black, French Camargue, Himalayan Red, Long Grain Brown, Purple Sticky, Short Grain Brown, Wehani, Wild, Indian Brown Basmati, Organic Brown Jasmine, and Organic Jade Pearl. Although acrylamide levels ranged from 4.9 µg/kg in Long Grain Brown to 50.8 µg/kg in Chinese Black, the absolute values were all low (though statistically significantly differences existed among varieties). Acrylamide content did not correlate with its precursor asparagine. The variations in protein, carbohydrate, fat, ash, dry matter, and water content determined by proximate analysis, and the reported health benefits of colored rice cultivars used to prepare the flatbreads, might also be useful for relating composition to nutritional qualities and health properties, facilitating their use as nutritional and health-promoting functional foods. Full article

Dietary acrylamide formed during baking and frying of plant-based foods such as bread and other cereal products, coffee, fried potatoes, and olives is reported to induce genotoxic, carcinogenic, neurotoxic, and antifertility properties in vivo, suggesting the need to keep the acrylamide content low with respect to widely consumed heat-processed food including flatbreads. Due to the fact that pigmented corn flours contain biologically active and health-promoting phenolic and anthocyanin compounds, the objective of this study was to potentially define beneficial properties of flatbread by evaluating the acrylamide content determined by high-performance liquid chromatography/mass spectrometry (HPLC/MS) with a detection limit of 1.8 µg/kg and proximate composition by standard methods of six experimental flatbreads made from two white, two blue, one red, and one yellow corn flours obtained by milling commercial seeds. Acrylamide content was also determined in experimental flatbreads made from combinations in quinoa flour, wheat flour, and peanut meal with added broccoli or beet vegetables and of commercial flatbreads including tortillas and wraps. Proximate analysis of flatbreads showed significant differences in protein and fat but not in carbohydrate, mineral, and water content. The acrylamide content of 16 evaluated flatbreads ranged from 0 to 49.1 µg/kg, suggesting that these flatbreads have the potential to serve as low-acrylamide functional foods. The dietary significance of the results is discussed. Full article

The selection of a third-party logistics (3PL) provider is an important and demanding task for many companies and organizations dealing with distribution activities. To assist in decision making, this paper proposes the implementation of fuzzy logic. To design a fuzzy inference system (FIS), the first prerequisite is to determine a set of evaluation criteria and sub-criteria and to find the relationship between them. This task was solved by an extensive review of the literature and expert opinions on implementing the Fuzzy Analytic Hierarchy Process (AHP) approach. The results obtained in the first part of the research, together with data collected from 20 3PL providers, were further used in the second part, which was related to the implementation of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. Finally, a decision-making tool for 3PL provider selection was designed as an FIS structure, where the inputs were the previously defined criteria and the output was a preference for 3PL selection. The fuzzy rules were generated on the basis of the collected empirical data, the preferences obtained by the TOPSIS method, and expert opinion using the Wang–Mendel method. The proposed fuzzy model is particularly suitable when input data are not crisp values but are provided descriptively through linguistic statements. Full article

Flatbreads are a major food consumed worldwide. To mitigate an undesirable safety aspect of flatbreads that might be associated with the potentially-toxic compound acrylamide, we recently developed recipes using a variety of grains that resulted in the production of low-acrylamide flatbreads. To further enhance the functionality of flatbreads, we have developed, in this work, new experimental flatbreads using potato, quinoa, and wheat flours supplemented with peel powders prepared from commercial nonorganic and organic fruits and vegetables (apples, cherry tomatoes, melons, oranges, pepino melons, sweet potato yams), potato peels, and mushroom powders (Lion’s Mane, Hericium erinaceus; Reishi, Ganoderma lucidum; and Turkey Tail, Trametes versicolor). These additives have all been reported to contain beneficial compositional and health properties. The results of fortification of the baked flatbreads showed either no effect or increases in acrylamide content by unknown mechanisms. Since the additives did not increase the acrylamide content of the quinoa flour flatbreads for the most part, such supplemented quinoa flatbreads have the potential to serve as a nutritional, gluten-free, low-acrylamide, health-promoting functional food. Mushroom powder-fortified wheat flatbreads with relatively low acrylamide content may also have health benefits. Full article