Candidemia is a severe fungal bloodstream infection with high mortality rates, especially in immunocompromised patients. Traditional diagnostic methods, such as blood cultures, suffer from low sensitivity and long turnaround times, delaying targeted antifungal therapy. In this study, we explore the potential of electronic noses (e-noses) combined with artificial intelligence (AI) models to provide a rapid and accurate diagnosis of Candida infections directly from blood culture broth samples. Samples were analyzed using an e-nose, generating datasets that underwent preprocessing steps including outlier removal, cycle equalization, feature transformation, and optional oversampling to address species imbalances. To ensure clinical relevance, we employed a dual-scenario validation strategy, assessing both intra-sample sensor stability and inter-sample biological generalization. We tested both traditional machine learning models and time series classifiers, selecting models based on prior research and performance in similar tasks. The best-performing models were evaluated based on accuracy, precision, recall, F1-score, specificity, and computational efficiency. Results demonstrated that while the framework achieved > 98% consistency in sensor stability tests, the Support Vector Classifier (SVC) emerged as the most robust model for generalization, achieving statistical parity with complex Time Series models like InceptionTime, but with significantly higher computational efficiency. This study highlights the feasibility of AI-enhanced e-noses for rapid Candida detection, offering a promising alternative to conventional diagnostics in clinical settings.

The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis and C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose’s low production costs. Our proof-of-concept experimental results, carried out with culture samples, demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.

Abstract The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused by Candida spp., remains challenging in clinical practice. Blood culture, the current gold standard technique, suffers from lengthy turnaround times and limited sensitivity. To address these limitations, we propose a novel approach utilizing an Electronic Nose (E-nose) combined with Time Series-based classification techniques to analyze and identify Candida spp. rapidly from blood, using culture species of C. albicans, C.kodamaea ohmeri, C. glabrara, C. haemulonii, C. parapsilosis e C. krusei as control samples. This innovative method not only enhances diagnostic accuracy and reduces decision time for healthcare professionals in selecting appropriate treatments but also offers the potential for expanded usage and cost reduction due to the E-nose’s low production costs. Our experimental results demonstrate promising outcomes, with the Inception Time classifier achieving an impressive average accuracy of 97.46% during the test phase. This paper presents a groundbreaking advancement in the field, empowering medical practitioners with an efficient and reliable tool for early and precise identification of candidemia, ultimately leading to improved patient outcomes.

PurposeTo evaluate the classification performance of structured report features, radiomics, and machine learning (ML) models to differentiate between Coronavirus Disease 2019 (COVID-19) and other types of pneumonia using chest computed tomography (CT) scans.MethodsSixty-four COVID-19 subjects and 64 subjects with non-COVID-19 pneumonia were selected. The data was split into two independent cohorts: one for the structured report, radiomic feature selection and model building (n = 73), and another for model validation (n = 55). Physicians performed readings with and without machine learning support. The model's sensitivity and specificity were calculated, and inter-rater reliability was assessed using Cohen's Kappa agreement coefficient.ResultsPhysicians performed with mean sensitivity and specificity of 83.4 and 64.3%, respectively. When assisted with machine learning, the mean sensitivity and specificity increased to 87.1 and 91.1%, respectively. In addition, machine learning improved the inter-rater reliability from moderate to substantial.ConclusionIntegrating structured reports and radiomics promises assisted classification of COVID-19 in CT chest scans.

Infections triggered by fungi of the genus Candida are widely known, although the high incidence and mortality factors are still unclear. The classic methods of identifying Candida species are subject to errors, requiring new techniques with faster and more accurate performance. We present a study for identifying fungi species by analyzing volatile organic compounds of cultures acquired and interpreted using Electronic Nose and Artificial Intelligence methods. The proposed approach contributes to establishing an agile and appropriate treatment, reducing the complications of the disease and the number of deaths. We perform experiments with three species of Candida obtaining accuracy above 90% in the fungi identification. Therefore, future works are encouraged to deal with more types of fungi to help create a new identification methodology faster and more reliable using artificial intelligence methods.

Abstract Background Effective management of patients with infected wounds is a crucial concern. A delay in prescribing the appropriate antibacterial agent can lead to life-threatening clinical complications. Thus, the electronic nose technique (eNose) can provide a diagnostic aid tool that allows rapid and accurate identification of pathogens. Results This study examines the effectiveness of using eNoses to aid in the diagnosis of bacterially infected wounds. The systematic search in the literature retrieved 3,326 publications, of which 97 were for a complete review, and of these, 09 comprised the sample of this study. These studies involved the analysis of seven types of wounds, the most common being the infected skin wound. The most frequent bacteria were P. aeruginosa, E. coli and methicillin-susceptible Staphylococcus aureus (MSSA). The average accuracy of the eNoses in identifying these microorganisms was 95.13% for the training set and 91.5% for the test set, including the ability to differentiate between bacteria of the same genus but sensitive or resistant to antibiotics. Among the Artificial Intelligence techniques used to classify the models, the Support Vector Machine (SVM) was the most commonly used in the experiments. Conclusion The eNoses devices observed may have broad applicability in aiding diagnosis of wound infection through their high efficacy values. However, further research needs to explore the reduction of interferences in the accuracy of the application of Machine Learning algorithms.

Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy.

Microorganisms that cause infectious diseases are defined as pathogens, as they multiply and cause tissue damage. All microorganisms isolated in culture from a location on the body should be considered potential pathogens. The infectious processes demonstrate physiological responses to the multiplication invasion of the aggressor microorganism. The disease’s development is influenced by the patient’s general health, defense mechanisms, and previous contact with the offending agent. When an infectious disease is suspected, cultures should be performed. This article uses an electronic nose to collect and analyze volatile organic compounds VOCs expelled by colonies of microorganisms. We propose signature identification of these colony odors from microorganisms using InceptionTime. The InceptionTime model is a set of models of the deep convolutional neural network, inspired by the Inception-v4 architecture. The results were excellent, with an average accuracy in the test set above 98%. The aim of our research is to propose a faster, cheaper and more accurate method of detecting these pathogens and the encouraging results of this stage encourage further research.

This paper describes a method that automatically searches Artificial Neural Networks using Cellular Genetic Algorithms. The main difference of this method for a common genetic algorithm is the use of a cellular automaton capable of providing the location for individuals, reducing the possibility of local minima in search space. This method employs an evolutionary search for simultaneous choices of initial weights, transfer functions, architectures and learning rules. Experimental results have shown that the developed method can find compact, efficient networks with a satisfactory generalization power and with shorter training times when compared to other methods found in the literature.

Este artigo apresenta uma metodologia para análise de sentimentos aplicada em tweets realizados pelos candidatos com maior intenção de voto no primeiro turno das eleições presidenciais brasileiras de 2018. A ideia do projeto nasceu a partir da dificuldade em avaliar o conteúdo das postagens dos candidatos, devido a escala considerável de dados gerados durante a campanha, e a possibilidade de criar uma análise das similaridades entre os comportamentos dos candidatos. Os tweets foram submetidos a técnicas de pré-processamento, uso de dicionários léxicos e algoritmos para agrupamento de dados. Os resultados obtidos permitiram a identificação de comportamentos como o grau de positividade ou negatividade dos candidatos, considerando fatores como a divulgação de pesquisas de intenção de votos realizadas, grau de similaridade e a frequência de termos utilizados nas postagens.

Este trabalho integra-se num trabalho de investigacao mais vasto sobre a revisao da legislacao nos ultimos 100 anos em Portugal e nas suas regioes Autonomas, relativamente as medidas educativas destinadas aos alunos sobredotados e talentosos. Neste trabalho, em particular, interessou-nos comparar os diplomas legais publicadas nas duas ultimas decadas do seculo XXI, em Portugal Continental e nas suas Regioes Autonomas dos Acores e Madeira. A partir dos resultados foi possivel verificar que a palavra “sobredotado” emerge apenas na legislacao das duas Regioes Autonomas. O conceito de sobredotacao referente a legislacao da Regiao Autonoma da Madeira e mais abrangente do que o que emerge a partir da legislacao da Regiao dos Acores, que e restrito as capacidades excecionais de aprendizagem. Especificamente, na legislacao sobre a educacao especial, so nas regioes Autonomas e contemplado o apoio aos alunos sobredotados, aparecendo o apoio aos alunos com capacidades excecionais de aprendizagem, disperso por varios diplomas em Portugal Continental, sucessivamente revogados e retomados. A partir desta investigacao emerge a necessidade de se simplificar e condensar nos mesmos diplomas a legislacao disponivel, nomeadamente se o objetivo e implementar efetivamente medidas concretas de sinalizacao e apoio a estes alunos em Portugal. Palavras-chave: Educacao inclusiva, Sobredotacao, Apoio educativo, Alunos com altas capacidades, Legislacao. Abstract: This work is part of a larger research work on the revision of the legislation in the last 100 years in Portugal and its Autonomous Regions, regarding educational measures for and talented students. In this work, in particular, we were interested in comparing the legal diplomas published in the last two decades of the 21st century, in mainland Portugal and its Autonomous Regions of Azores and Madeira. From the results it was possible to verify that the word gifted emerges only in the legislation of the two Autonomous Regions. The concept of giftedness regarding the legislation of the Autonomous Region of Madeira is broader than what emerges from the legislation of the Azores Region, which is restricted to exceptional learning abilities. Specifically, in the legislation on special education, only in the Autonomous Regions the support for students is contemplated, appearing support for students with exceptional learning abilities, spread over several diplomas in mainland Portugal, successively repealed and retaken. From this research emerges the need to simplify and summarize in the same diplomas the available legislation, namely if the objective is to effectively implement concrete signaling and support measures for these students in Portugal. Key-words: Inclusive education, Giftedness, Educational support, High-ability students, Legislation

Ensembles of classifiers is a way to improve the performance of the approach with single classifiers. The idea is to find and combine a set of classifiers that are responsible for smaller and theoretically easier parts of a problem to solve, in other words, divide to conquer. Between the ensembles models, there is the clustering and selection in which the training data are clustering, and a classifier is built for each cluster found. An answer for an input data is given based on a distance to the available clusters that has an associated classifier. In this paper, the clustering and selection model is explored with the use of Evolutionary Algorithms to search clusters that optimize the ensemble's performance. Experiments are conducted with ten datasets and using recent advances in classification methods. The results achieved good and promising performances compared to classical clustering-and-selection model and other methods to build ensembles.

Improving the performance of supervised classification methods is a subject of many literature works. An efficient strategy is the adoption of an ensemble of classifiers to divide the classification problem. In ensembles with classifier selection, there is no fusion of the classifiers decisions. A particular classifier is selected according to the input data instead. In this paper, well-known clustering methods based on self-organizing structures are used to implement ensembles with classifier selection. The self-organizing structures are used to detect the topological structure of data and help to divide the problem into smaller and easier sub-problems to solve. Experiments with different datasets show that the use of clustering methods to perform the classifier selection can contribute to split the problem and improve the classification accuracy compared to some traditional strategies. Additionally, the results encourage the development of more research to find out other ways to split problems using data clustering techniques.

This paper presents a system consisting of physical sensors and intelligent software for the automatic identification of the concentration of natural gas odorants and details the development of the sensor and pattern recognition systems. The sensor system uses spectroscopic technology and the pattern recognition system uses wavelet and artificial neural network technology. The aim is to determine the concentration of a natural gas odorant in the environment and associate this concentration with the benchmark index, which measures the degree of human perception to the presence of gas in the environment. Experiments were conducted comparing the performance of the system with human performance, which is normally used to deal with this problem. The proposed system demonstrated promising results.