Extreme learning machine adapted to noise based on optimization algorithms

The extreme learning machine for neural networks of feedforward of a single hidden layer randomly assigns the weights of entry and analytically determines the weights the output by means the Moore-Penrose inverse, this algorithm tends to provide an extremely fast learning speed preserving the adjustment levels achieved by classifiers such as multilayer perception and support vector machine. However, the Moore-Penrose inverse loses precision when using data with additive noise in training. That is why in this paper a method to robustness of extreme learning machine to additive noise proposed. The method consists in computing the weights of the output layer using non-linear optimization algorithms without restrictions. Tests are performed with the gradient descent optimization algorithm and with the Levenberg-Marquardt algorithm. From the implementation it is observed that through the use of these algorithms, smaller errors are achieved than those obtained with the Moore-Penrose inverse

Conditioning of extreme learning machine for noisy data using heuristic optimization

This article provides a tool that can be used in the exact sciences to obtain good approximations to reality when noisy data is inevitable. Two heuristic optimization algorithms are implemented: Simulated Annealing and Particle Swarming for the determination of the extreme learning machine output weights. The first operates in a large search space and at each iteration it probabilistically decides between staying at its current state or moving to another. The swarm of particles, it optimizes a problem from a population of candidate solutions, moving them throughout the search space according to position and speed. The methodology consists of building data sets around a polynomial function, implementing the heuristic algorithms and comparing the errors with the traditional computation method using the Moore–Penrose inverse. The results show that the heuristic optimization algorithms implemented improve the estimation of the output weights when the input have highly noisy data

New anisotropic diffusion operator in images filtering

The anisotropic di usion lters have become in the fundamental bases to address the medical images noise problem. The main attributes of these lters are: the noise removal e ectiveness and the preservation of the information belonging to the edges that delimit the objects of an image. Due to these excellent attributes, through this article, a comparative study is proposed between a new di usion operator and the Lorentz operator, proposed by the pioneers of anisotropic di usion. For this, a strategy consisting of two phases is designed. In the rst, called operator construction, the composition of functions is used to generate a new di usion operator that meets with the conditions reported for this kind of the mathematical object. In the second phase, denominated ltering, a synthetic cardiac images database, based on computed tomography, is ltered using the aforementioned operators. According with the value obtained for the peak of the signal-to-noise ratio, the new operator shows similar performance to the Lorentz operator. The implementation of this new operator contributes to the generation of new knowledge in digital image processing context

Problem solving strategy in the teaching and learning processes of quantitative reasoning

The study presents an analysis of Polya's problem-solving strategy used in the training processes of quantitative reasoning competence in students of the Universidad Simón Bolívar, San José de Cúcuta, Colombia. The research was based on a descriptive design and had an intentional sample of 58 students who were studying the sciences and general competencies elective. For the collection of information, a diagnostic test (pre-test) and a final test (post-test) were applied, in order to check the incidence of the applied strategy. The results showed a significant improvement in the final results obtained by the students in each of the processes formed: interpretation, representation and modeling, and argumentation

Pulmonary adenocarcinoma characterization using computed tomography images

Lung cancer is one of the pathologies that sensitively affects the health of human beings. Particularly, the pathology called pulmonary adenocarcinoma represents 25% of all lung cancers. In this research, we propose a semiautomatic technique for the characterization of a tumor (adenocarcinoma type), present in a three-dimensional pulmonary computed tomography dataset. Following the basic scheme of digital image processing, first, a bank of smoothing filters and edge detectors is applied allowing the adequate preprocessing over the dataset images. Then, clustering methods are used for obtaining the tumor morphology. The relative percentage error and the accuracy rate were the metrics considered to determine the performance of the proposed technique. The values obtained from the metrics used reflect an excellent correlation between the morphology of the tumor, generated manually by a pneumologist and the values obtained by the proposed technique. In the clinical and surgical contexts, the characterization of the detected lung tumor is made in terms of volume occupied by the tumor and it allows the monitoring of this disease as well as the activation of the respective protocols for its approach

The rubric as an assessment strategy in the mathematical argumentation process

The article shares the proposal of an analytical rubric as a strategy for the assessment and monitoring of learning outcomes in students who develop an argumentative plot from the mathematics field, to solve any problem situation in daily life. The study was based on the theory of mathematical argumentation proposed by Duval and the contributions of León and Calderón, as well as the dimensions presented to us by the logical frameworks in the design of analytical rubrics. The research was developed under the social critical paradigm through the design of pedagogical action research, and the focus group technique was used for the collection of information composed by five professors from the department of basic sciences. As a result, a collective rubric that, in addition to generating processes of self-assessment and self-training in teachers, evidences a decrease in the existent subjectivity of the evaluation processes, thus strengthening its objectivity

Parallel methods for linear systems solution in extreme learning machines: an overview

This paper aims to present an updated review of parallel algorithms for solving square and rectangular single and double precision matrix linear systems using multi-core central processing units and graphic processing units. A brief description of the methods for the solution of linear systems based on operations, factorization and iterations was made. The methodology implemented, in this article, is a documentary and it was based on the review of about 17 papers reported in the literature during the last five years (2016-2020). The disclosed findings demonstrate the potential of parallelism to significantly decrease extreme learning machines training times for problems with large amounts of data given the calculation of the Moore Penrose pseudo inverse. The implementation of parallel algorithms in the calculation of the pseudo-inverse will allow to contribute significantly in the applications of diversifying areas, since it can accelerate the training time of the extreme learning machines with optimal results

Semi-automatic detection of the evolutionary forms of visceral leishmaniasis in microscopic blood smears

Leishmaniasis is a complex group of diseases caused by obligate unicellular and intracellular eukaryotic protozoa of the leishmania genus. Leishmania species generate diverse syndromes ranging from skin ulcers of spontaneous resolution to fatal visceral disease. These syndromes belong to three categories: visceral leishmaniasis, cutaneous leishmaniasis and mucosal leishmaniasis. The visceral leishmaniasis is based on the reticuloendothelial system producing hepatomegaly, splenomegaly and lymphadenopathy. In the present article, a semiautomatic segmentation strategy is proposed to obtain the segmentations of the evolutionary shapes of visceral leishmaniasis called parasites, specifically of the type amastigote and promastigote. For this purpose, the optical microscopy images containing said evolutionary shapes, which are generated from a blood smear, are subjected to a process of transformation of the color intensity space into a space of intensity in gray levels that facilitate their subsequent preprocessing and adaptation. In the preprocessing stage, smoothing filters and edge detectors are used to enhance the optical microscopy images. In a complementary way, a segmentation technique that groups the pixels corresponding to each one of the parasites, presents in the considered images, is applied. The results reveal a high correspondence between the available manual segmentations and the semi-automatic segmentations which are useful for the characterization of the parasites. The obtained segmentations let us to calculate areas and perimeters associated with the parasites segmented. These results are very important in clinical context where both the area and perimeter calculated are vital for monitoring the development of visceral leishmaniasis

Use of computational realistic models for the cardiac ejection fraction calculation

Ejection fraction is one of the most useful clinical descriptors to determine the cardiac function of a subject. For this reason, obtaining the value of this descriptor is of vital importance and requires high precision. However, in the clinical routine, to generate the mentioned descriptor value, a geometric hypothesis is assumed, obtaining an approximate value for this fraction, usually by excess, and which is a dependent-operator. The aim of the present work is to propose the accurate calculation of the ejection fraction from realistic models, obtained computationally, of the cardiac chamber called right ventricle. Normally, the geometric hypothesis that makes this ventricle coincide with a pyramidal type geometric shape, is not usually, fulfilled in subjects affected by several cardiac pathologies, so as an alternative to this problem, the computational segmentation process is used to generate the morphology of the right ventricle and from it proceeds to obtain, accurately, the ejection fraction value. In this sense, an automatic strategy based on no-lineal filters, smart operator and region growing technique is propose in order to generate the right ventricle ejection fraction. The results are promising due we obtained an excellent correspondence between the manual segmentation and the automatic one generated by the realistic models

Large cells cancer volumetry in chest computed tomography pulmonary images

Lung cancer is the leading oncological cause of death in the world. As for carcinomas, they represent between 90% and 95% of lung cancers; among them, non-small cell lung cancer is the most common type and the large cell carcinoma, the pathology on which this research focuses, is usually detected with the computed tomography images of the thorax. These images have three big problems: noise, artifacts and low contrast. The volume of the large cell carcinoma is obtained from the segmentations of the cancerous tumor generated, in a semi-automatic way, by a computational strategy based on a combination of algorithms that, in order to address the aforementioned problems, considers median and gradient magnitude filters and an unsupervised grouping technique for generating the large cell carcinoma morphology. The results of high correlation between the semi-automatic segmentations and the manual ones, drawn up by a pulmonologist, allow us to infer the excellent performance of the proposed technique. This technique can be useful in the detection and monitoring of large cell carcinoma and if it is considering this kind of computational strategy, medical specialists can establish the clinic or surgical actions oriented to address this pulmonary pathology