Breaking barriers in Candida spp. detection with Electronic Noses and artificial intelligence Michael L. Bastos, Clayton A. Benevides, Cleber Zanchettin, Frederico D. Menezes, Cícero P. Inácio, et al. Scientific Reports, 2024 The timely and accurate diagnosis of candidemia, a severe bloodstream infection caused byCandidaspp., 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 identifyCandidaspp. rapidly, using culture species of C.albicans, C.kodamaea ohmeri, C.glabrara, C.haemulonii, C.parapsilosisand C.kruseias 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.
Breakthrough of Clinical Candida Cultures Identification Using the Analysis of Volatile Organic Compounds and Artificial Intelligence Methods Maria C. A. Castro, Leandro M. Almeida, Renan Williams M. Ferreira, Clayton A. Benevides, Cleber Zanchettin, et al. IEEE Sensors Journal, 2022 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.
Computer vision and machine learning for tuna and salmon meat classification Erika Carlos Medeiros, Leandro Maciel Almeida, José Gilson de Almeida Teixeira Filho Informatics, 2021 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.
Identification of Microorganism Colony Odor Signature using InceptionTime Paulo M. Vasconcelos, David Macedo, Leandro M. Almeida, Reginaldo G. L. Neto, Clayton A. Benevides, et al. Conference Proceedings IEEE International Conference on Systems Man and Cybernetics, 2021 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.
Ensembles with Clustering-and-Selection Model Using Evolutionary Algorithms Leandro Maciel Almeida, Pedro Sereno Galvao Proceedings 2016 5th Brazilian Conference on Intelligent Systems Bracis 2016, 2017 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.
Building Ensembles with Classifier Selection Using Self-Organizing Maps Leandro Maciel Almeida, Cleber Zanchettin, Hilton Pintor Bezerra Leite Proceedings 2016 5th Brazilian Conference on Intelligent Systems Bracis 2016, 2017 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.
An intelligent monitoring system for natural gas odorization Cleber Zanchettin, Leandro Maciel Almeida, Frederico Duarte de Menezes IEEE Sensors Journal, 2015 In this paper, we present the design of an intelligent monitoring system consisting of physical sensors and intelligent software for the automatic identification of the concentration of natural gas odorants in the environment. An optical-based sensor array was proposed comprising the hardware module. The software module employs wavelets filters and artificial neural networks to recognize the concentration of odorant in a natural gas sample. The objective is to help the natural gas odorization process by means of end point monitoring through the recognizing of the odorant concentration. The recognizing process uses a benchmark index, which measures the degrees of human perception of gas in the environment. In this way, the proposed system tries to mimic the human perception of a natural gas leak and helps one to indicate if more or less amount of odorant should be added into the gas pipeline. 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 and improvements are presented.
Clustering and selection using grouping genetic algorithms for blockmodeling to construct neural network ensembles Evandro Jose Da Rocha E Silva, Teresa Bernarda Ludermir, Leandro Maciel Almeida Proceedings International Conference on Tools with Artificial Intelligence Ictai, 2013 The choice of a Committee of Classifiers is based on the idea that two or more classifiers can make a better decision than a single one. In the literature there are several methodologies for construction of committees and among them Classifier Selection that determines the best or a subset with the most efficient classifiers in each region of the feature space. Blockmodeling is a useful tool for describing the fundamental structure of social networks, but it is used in this work in a non-social data. As shown in the literature, clustering data and then choosing a classifier for each cluster can increase the committee performance and Evolutionary Algorithms can increase even more the performance. Thus this paper proposes BMGGAVS using a combination of Blockmodeling and Genetic Algorithms in order to cluster data and through a simple vote system assign a Neural Network for each cluster. Results from experiments in 9 databases indicate that BMGGAVS is able to obtain a good performance.
