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Granular computing is a widely used computational paradigm nowadays. Particularly, within the rough set theory, granular computing plays a key role. In this paper, we propose a generic approach of rough sets, the granular extended multigranular sets (GEMS) for dealing with both mixed and incomplete information systems. Not only our proposal does use the traditional optimistic and pessimistic granulations with respect to single attributes, but also we introduce granulations with respect to attribute sets, as well as two new ways of granulating: the optimistic + pessimistic granulation and the pessimistic + optimistic granulation. In addition, we have developed a particular case of the proposed GEMS: the multigranular maximum similarity rough sets (MMSRS). We have proved some of the properties of the MMSRS, and we tested its effectiveness with respect to other existing granular rough sets models. The experimental results show the flexibility and the capabilities of the proposed model, while handling mixed and incomplete information systems. [ABSTRACT FROM AUTHOR]

In this study, a thorough analysis of the proposed approach in the context of emotion classification using both single-modal (A-13sbj) and multi-modal (B-12sbj) sets from the YAAD dataset was conducted. This dataset encompassed 25 subjects exposed to audiovisual stimuli designed to induce seven distinct emotional states. Electrocardiogram (ECG) and galvanic skin response (GSR) biosignals were collected and classified using two deep learning models, BEC-1D and ELINA, along with two different preprocessing techniques, a classical fourier-based filtering and an Empirical Mode Decomposition (EMD) approach. For the single-modal set, this proposal achieved an accuracy of 84.43±30.03, precision of 85.16±28.91, and F1-score of 84.06±29.97. Moreover, in the extended configuration the model maintained strong performance, yielding scores of 80.95±22.55, 82.44±24.34, and 79.91±24.55, respectively. Notably, for the multi-modal set (B-12sbj), the best results were obtained with EMD preprocessing and the ELINA model. This proposal achieved an improved accuracy, precision, and F1-score scores of 98.02±3.78, 98.31±3.31, and 97.98±3.83, respectively, demonstrating the effectiveness of this approach in discerning emotional states from biosignals. [ABSTRACT FROM AUTHOR]

This paper addresses social networking and collaborative learning in the medical domain by focusing on two main objectives: the first one concerns about social networking between computer science experts and postgraduate students, while the second concerns about collaborative learning between medical experts and less experienced physicians. The tasks of algorithms testing and performance evaluation were assigned to computer science postgraduate students. They made extensive use of social networking in order to implement associative models to perform pattern classification tasks in medical datasets and share performance results. Associative memories have a number of properties, including a rapid, compute efficient best-match and intrinsic noise tolerance that make them ideal for diagnostic hypothesis-generation processes in the medical domain. Using supervised machine learning algorithms allows less experienced physicians to compare their diagnostic results between workgroups and verify whether their knowledge is consistent with the results delivered by computational tools. Throughout the experimental phase the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of five different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of DAM against the performance achieved by other twenty well known algorithms. Experimental results have shown that DAM achieved the best performance in three of the five pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. Experimental results confirm that the proposed algorithm can be a valuable tool for promoting collaborative learning among less experienced physicians. [ABSTRACT FROM AUTHOR]

Nowadays, we are immersed in the social and mobile networks era. As a positive consequence of this, collaborative and mobile learning in educational environments have been encouraged thanks to the use of computing for human learning. By coupling the advantages of collaborative and mobile learning, the teaching-learning processes involved in postgraduate courses may be greatly enhanced. The pedagogical experiences in this regard lived by the authors in the Alpha–Beta Research Group when coupling collaborative and mobile learning in the context of postgraduate level courses, are presented in this paper. [ABSTRACT FROM AUTHOR]

Bidirectional Associative Memories (BAM) are systems that allow to associate pairs of patterns. Once a memory has learned, patterns can be recalled in two directions. BAMs have many applications in pattern recognition and image processing. The aim of this survey is to present several models of BAM throughout time, since Kosko [1988] proposed the first model; followed by those works based on or inspired by it, trying to improve recall capacity; to some recent one-shot models—such as Morphological BAM and Alpha-Beta BAM—which are of particular interest, given their superior performance. [ABSTRACT FROM AUTHOR]

Abstract: Classification is one of the key issues in medical diagnosis. In this paper, a novel approach to perform pattern classification tasks is presented. This model is called Associative Memory based Classifier (AMBC). Throughout the experimental phase, the proposed algorithm is applied to help diagnose diseases; particularly, it is applied in the diagnosis of seven different problems in the medical field. The performance of the proposed model is validated by comparing classification accuracy of AMBC against the performance achieved by other twenty well known algorithms. Experimental results have shown that AMBC achieved the best performance in three of the seven pattern classification problems in the medical field. Similarly, it should be noted that our proposal achieved the best classification accuracy averaged over all datasets. [Copyright &y& Elsevier]

Alpha-beta bidirectional associative memories are implemented for storing concept lattices. We use Lindig's algorithm to construct a concept lattice of a particular context; this structure is stored into an associative memory just as a human being does, namely, associating patterns. Bidirectionality and perfect recall of Alpha-Beta associative model make it a great tool to store a concept lattice. In the learning phase, objects and attributes obtained from Lindig's algorithm are associated by Alpha-Beta bidirectional associative memory; in this phase the data is stored. In the recalling phase, the associative model allows to retrieve objects from attributes or vice versa. Our model assures the recalling of every learnt concept. [ABSTRACT FROM AUTHOR]

In the supervised classification area, the algorithm selection problem (ASP) refers to determining the a priori performance of a given classifier in some specific problem, as well as the finding of which is the most suitable classifier for some tasks. Recently, this topic has attracted the attention of international research groups because a very promising vein of research has emerged: the application of some measures of data complexity in the pattern classification algorithms. This paper aims to analyze the response of the Customized Naïve Associative Classifier (CNAC) in data taken from the business area when some measures of data complexity are introduced. To perform this analysis, we used classification datasets from real-world related to business, 22 in total; then, we computed the value of nine measures of data complexity to compare the performance of the CNAC against other algorithms of the state of the art. A very important aspect of performing this task is the creation of an artificial dataset for meta-learning purposes, in which we considered the performance of CNAC, and then we trained a decision tree as meta learner. As shown, the CNAC classifier obtained the best results for 10 out of 22 datasets of the experimental study. [ABSTRACT FROM AUTHOR]

The maintenance of software‐intensive systems (SISs) must be undertaken to correct faults, improve the design, implement enhancements, adapt programmes such that different hardware, software, system features, and telecommunications facilities can be used, as well as to migrate legacy software. A lack of planning has been identified as one explanation for late and over budget software projects. An activity of planning is effort prediction. The goal of this study is to propose the application of a stochastic gradient boosting (SGB) model for predicting the SIS maintenance effort. We compare the SGB prediction accuracy with those obtained with statistical regression, neural network, support vector regression, decision trees, and association rules. We trained and tested the models with five SIS data sets selected from the International Software Benchmarking Standards Group Release 11. The SGB prediction accuracy was statistically better than the mentioned five models in the two larger data sets. We can conclude that a SGB can be applied to predict the maintenance effort of SISs coded in languages of the third generation and developed on either mainframes or multi‐platform. The predicted effort corresponds to the aggregate of efforts obtained from the project team, project management, and project administration. [ABSTRACT FROM AUTHOR]

The rapid proliferation of connectivity, availability of ubiquitous computing, miniaturization of sensors and communication technology, have changed healthcare in all its areas, creating the well-known healthcare paradigm of e-Health. In this paper, an embedded system capable of monitoring, learning and classifying biometric signals is presented. The machine learning model is based on associative memories to predict the presence or absence of coronary artery disease in patients. Classification accuracy, sensitivity and specificity results show that the performance of our proposal exceeds the performance achieved by each of the fifty widely known algorithms against which it was compared. [ABSTRACT FROM AUTHOR]

Over time, human beings have built increasingly large astronomical observatories to increase the number of discoveries related to celestial objects. However, the amount of collected elements far exceeds the human capacity to analyze findings without help. For this reason, researchers must now turn to machine learning to analyze such data, identifying and classifying transient objects or events within extensive observations of the firmament. Algorithms from the family of random forests (an ensemble of decision trees) have become a powerful tool that can be used to classify astronomical events and objects. This work aims to illustrate the versatility of machine learning algorithms, such as decision trees, to facilitate the identification and classification of celestial bodies by manipulating hyperparameters and studying the attributes of celestial body datasets. By applying a random forest algorithm to a well-known dataset that includes three types of celestial bodies, its effectiveness was compared against some supervised classifiers of the most important approaches (Bayes, nearest neighbors, support vector machines, and neural networks). The results show that random forests are a good alternative for data analysis and classification in astronomical observations. [ABSTRACT FROM AUTHOR]

This paper introduces the Gamma Rough Sets for management information systems where the universe objects are represented by continuous attributes and are connected by similarity relations. Some properties of such sets are demonstrated in this paper. In addition, Gamma Rough Sets are used to improve the Gamma associative classifier, by selecting instances. The results indicate that the selection of instances significantly reduces the computational cost of the Gamma classifier without affecting its effectiveness. The results also suggest that the selection of instances using Gamma Rough Sets favors other lazy learners, such as Nearest Neighbor and ALVOT. [ABSTRACT FROM AUTHOR]

In this paper a new associative classification algorithm is presented. The proposed algorithm overcomes the limitations of the original Alpha-Beta associative memory, while maintaining the fundamental set recalling capacity. This algorithm has two phases. The first phase is based on an Alpha-Beta auto-associative memory, which works in the domain of real numbers, unlike the traditional Alpha-Beta associative memories. In the second phase, normalized difference between the results of first phase and every pattern of the fundamental set is calculated. In order to demonstrate the behaviour and accuracy of the algorithm, multiple well known datasets and classification algorithms have been used. Experimental results have shown that our proposal achieved the best performance in three of the eight pattern classification problems in the medical field, using Stratified 10 Fold cross-validation. Our proposal achieved the best classification accuracy averaged over the all datasets addressed in the present work. Experimental results and statistical significance tests, allow us to affirm that the proposed model is an efficient alternative to perform pattern classification tasks. [ABSTRACT FROM AUTHOR]

A qualitative common perception of the software industry is that it finishes its projects late and over budget, whereas from a quantitative point of view, only 39 % of software projects are finished on time compared to the schedule when the project started. This low percentage has been attributed to factors such as unrealistic time frames and lack of planning regarding poor prediction. The main techniques used for predicting project schedule have mainly been based on expert judgment and mathematical models. In this study, a new model, derived from the multivariate adaptive regression splines (MARS) model, is proposed. This new model, optimized MARS (OMARS), uses a simulated annealing process to find a transformation of the input data space prior to applying MARS in order to improve accuracy when predicting the schedule of software projects. The prediction accuracy of the OMARS model is compared to that of stand-alone MARS and a multiple linear regression (MLR) model with a logarithmic transformation. The two independent variables used for training and testing the models are functional size, which corresponds to a composite value of 19 independent variables, and the maximum size of the team of developers. The data set of projects was obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11. Results based on the absolute residuals and t paired and Wilcoxon statistical tests showed that prediction accuracy with OMARS is statistically better than that with the MARS and MLR models. [ABSTRACT FROM AUTHOR]

One of the four basic machine learning tasks is pattern classification. The selection of the proper learning algorithm for a given problem is a challenging task, formally known as the algorithm selection problem (ASP). In particular, we are interested in the behavior of the associative classifiers derived from Alpha-Beta models applied to the financial field. In this paper, the behavior of four associative classifiers was studied: the One-Hot version of the Hybrid Associative Classifier with Translation (CHAT-OHM), the Extended Gamma (EG), the Naïve Associative Classifier (NAC), and the Assisted Classification for Imbalanced Datasets (ACID). To establish the performance, we used the area under the curve (AUC), F-score, and geometric mean measures. The four classifiers were applied over 11 datasets from the financial area. Then, the performance of each one was analyzed, considering their correlation with the measures of data complexity, corresponding to six categories based on specific aspects of the datasets: feature, linearity, neighborhood, network, dimensionality, and class imbalance. The correlations that arise between the measures of complexity of the datasets and the measures of performance of the associative classifiers are established; these results are expressed with Spearman's Rho coefficient. The experimental results correctly indicated correlations between data complexity measures and the performance of the associative classifiers. [ABSTRACT FROM AUTHOR]

Highlights: [•] We introduce a novel non-linear forecasting technique based on the Gamma classifier. [•] Its performance for long-term time horizons was tested on synthetic and real data. [•] Two benchmark time series were used for testing. [•] Six time series related to monthly oil production were also used. [•] The Gamma classifier model outperformed previous techniques in forecast accuracy. [Copyright &y& Elsevier]

Pattern recognition and classification are two of the key topics in computer science. In this paper a novel method for the task of pattern classification is presented. The proposed method combines a hybrid associative classifier (Clasificador Híbrido Asociativo con Traslación, CHAT, in Spanish), a coding technique for output patterns called one-hot vector and majority voting during the classification step. The method is termed as CHAT One-Hot Majority (CHAT-OHM). The performance of the method is validated by comparing the accuracy of CHAT-OHM with other well-known classification algorithms. During the experimental phase, the classifier was applied to four datasets related to the medical field. The results also show that the proposed method outperforms the original CHAT classification accuracy. [ABSTRACT FROM AUTHOR]

Age-Related Macular Degeneration (AMD) is a dangerous, chronic, and progressive illness that mostly affects people over 60 years old. This disease is related to the appearance of drusen: deposits of extracellular material located in the macular region. One way to effectively and non-invasively pre-diagnose AMD is by detecting the presence of drusen in fundus images. In this work we propose a new method that combines Digital Image Processing, Mathematical Morphology and a robust and powerful Machine Learning model: a Support Vector Machine (SVM). The enclosed macular region is subjected to a contrast enhancement method, followed by the application of basic morphological operations. We use invariant moments as the features of the processed image. The resulting vector is classified by an SVM as positive or negative for drusen. The proposed method is able to discriminate between healthy and afflicted cases with a classification accuracy that outperforms many well-regarded state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Feature selection is a common solution to microarray analysis. Previous approaches either select features based on classical statistical tests that can be tuned up with a classifier, or using regularization penalties incorporated in the cost function. Here we propose to use a feature ranking and weighting scheme instead, which combines statistical techniques with a weighted k -NN classifier using a modified forward selection procedure. We demonstrate that classification accuracy of our proposal outperforms existing methods on a range of public microarray gene expression datasets. The proposed method is also compared to state-of-the-art feature selection algorithms by means of the Friedman test. Although a bunch of feature selection techniques has been used for genomic data, the experimental results show the classification superiority of our method on most of the present gene expression datasets. [ABSTRACT FROM AUTHOR]

Problem: Online higher education (OHE) failure rates reach 40% worldwide. Prediction of student performance at early stages of the course calendar has been proposed as strategy to prevent student failure. Objective: To investigate the application of genetic programming (GP) to predict the final grades (FGs) of online students using grades from an early stage of the course as the independent variable Method: Data were obtained from the learning management system; we performed statistical analyses over FGs as dependent variable and 11 independent variables; two statistical and one GP models were generated; the prediction accuracies of the models were compared by means of a statistical test. Results: GP model was better than statistical models with confidence levels of 90% and 99% for the training testing data sets respectively. These results suggest that GP could be implemented for supporting decision making process in OHE for early student failure prediction. [ABSTRACT FROM AUTHOR]

Background and Objective: The treatment and care of patients with chronic diseases depends directly on the evolution of biomedical parameters. It is important to have a monitoring health care system that provides biomedical data at any time and place. Here, a multi-sensing health care monitoring system with a built-in non-invasive blood glucose level estimation method is presented.Methods: Six biomedical parameters were obtained from 15 participants. Glucose levels were obtained using a computer vision approach. A standard glucose laboratory test was taken as a baseline, and a commercial glucometer as a secondary reference. The remaining parameters were also contrasted with a commercial vital signs monitor.Results: In comparison to standard test, our proposal reported a better performance (RMSE of 9.811) than obtained with the commercial glucometer; the Mann-Whitney test found no significant differences. The remaining biomedical parameters exhibit similar results to the commercial vital signs monitor as validated by a cardiologist.Conclusion: The results suggest the proposed approach could be considered highly competitive regarding standard tests and validated with commercial health care monitoring systems. [ABSTRACT FROM AUTHOR]

In this paper the Naïve Associative Classifier (NAC), a novel supervised learning model, is presented. Its strengths lie in its simplicity, transparency, transportability and accuracy. The creation, design, implementation and application of the NAC are sustained by an original similarity operator of our own design, the Mixed and Incomplete Data Similarity Operator (MIDSO). One of the key features of MIDSO is its ability to handle missing values as well as mixed numerical and categorical data types. The proposed model was tested by performing numerical experiments using finance-related datasets including credit assignment, bank telemarketing, bankruptcy, and banknote authentication. The experimental results show the adequacy of the model for decision support in those environments, outperforming several state-of-the-art pattern classifiers. Additionally, the advantages and limitations of the NAC, as well as possible improvements, are discussed. [ABSTRACT FROM AUTHOR]

The present work describes a new model of pattern classification and its application to align instances from different ontologies, which are in turn related to e-learning educative content in a Knowledge Society context. In general, ontologies are the fundamental tool inherent to Semantic Web. In particular, the problem of ontology matching is modeled in this paper as a binary pattern classification problem. The original model presented here was validated through experiments, which were done on data taken from the OAEI (Ontology Alignment Evaluation Initiative) 2014 campaign, presented in the OWL (Web Ontology Language) format, as well as on data taken from two international repositories, ADRIADNE and MERLOT, in LOM (Learning Objects Metadata) format. The results obtained show a high precision measurement when compared against some of the best methods present in the state of the art. [ABSTRACT FROM AUTHOR]

In models and standard international existence spite that allow to evaluate the quality that it possess the developed software products, this it constitutes an aspect that continues being a problem for many organizations that develop software. The software tests possess great importance inside the product development process, due to the time and effort that it bears to carry out them. To contribute the time and effort that it is used for the tests realization to diminish it is had tools that automate in some measure the evaluation process. However, a big number of the tools are de-signed to evaluate traditional software systems, obviating those smaller components that conform these systems. This work is centered in the creation of a plugin to carry out performance tests to software components using aspect-oriented programming in Java, given the necessity to automate this system test type that it lose protagonism before other tests types in many occasions like they could be the functional tests. [ABSTRACT FROM AUTHOR]

The test phase is a difficult process that consumes a large percentage of the cost in terms of the time of the process of software development. The purpose of the tests is to determine if the developed products meet the requirements accord with the users and customers on specifications. Therefore, the processes, methods and tools that allow obtain good sets of test of a system are needed. This article introduces a component that automatically combines values to perform unit tests and for that were applied metaheuristics algorithms. The proposed solution has been tested in a case study, and was compared with values obtained through other algorithms proposed by authors who work the issue in the scientific community. The component allows to obtain a reduced set of test values, with less time of execution and a coverage of 100%. [ABSTRACT FROM AUTHOR]

The study of the influence of the Green Information Technology (GIT), using their potential to reduce the negative impact of the exhaust emission produced by light-duty vehicles, attending verification compliance to circulate in urban areas, is the main topic of the present contribution. Data collected through surveys answered by car owners in Mexico City, place the attention over the knowledge of the official exhaust emission test, their general acceptance, and the understanding of the exhaust test context, once they have received information from the proposed GIT (pGIT). The surveys consider the behavioral intention, attitude toward behavior, perceived usefulness, social influence and volitional control constructs to estimate response of car owners to the concerns about the pollutant emission and their willingness to change their position toward the pollutant emission reduction. The results show the interest of car owners to participate in a collaborative process, and develop positive attitude to cooperate in a social dilemma by considering the use of social networking. [ABSTRACT FROM AUTHOR]

Over time, a myriad of applications have been generated for pattern classification algorithms. Several case studies include parametric classifiers such as the Multi-Layer Perceptron (MLP) classifier, which is one of the most widely used today. Others use non-parametric classifiers, Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), Naïve Bayes (NB), Adaboost, and Random Forest (RF). However, there is still little work directed toward a new trend in Artificial Intelligence (AI), which is known as eXplainable Artificial Intelligence (X-AI). This new trend seeks to make Machine Learning (ML) algorithms increasingly simple and easy to understand for users. Therefore, following this new wave of knowledge, in this work, the authors develop a new pattern classification methodology, based on the implementation of the novel Minimalist Machine Learning (MML) paradigm and a higher relevance attribute selection algorithm, which we call dMeans. We examine and compare the performance of this methodology with MLP, NB, KNN, SVM, Adaboost, and RF classifiers to perform the task of classification of Computed Tomography (CT) brain images. These grayscale images have an area of 128 × 128 pixels, and there are two classes available in the dataset: CT without Hemorrhage and CT with Intra-Ventricular Hemorrhage (IVH), which were classified using the Leave-One-Out Cross-Validation method. Most of the models tested by Leave-One-Out Cross-Validation performed between 50% and 75% accuracy, while sensitivity and sensitivity ranged between 58% and 86%. The experiments performed using our methodology matched the best classifier observed with 86.50% accuracy, and they outperformed all state-of-the-art algorithms in specificity with 91.60%. This performance is achieved hand in hand with simple and practical methods, which go hand in hand with this trend of generating easily explainable algorithms. [ABSTRACT FROM AUTHOR]

Machine learning in the medical area has become a very important requirement. The healthcare professional needs useful tools to diagnose medical illnesses. Classifiers are important to provide tools that can be useful to the health professional for this purpose. However, questions arise: which classifier to use? What metrics are appropriate to measure the performance of the classifier? How to determine a good distribution of the data so that the classifier does not bias the medical patterns to be classified in a particular class? Then most important question: does a classifier perform well for a particular disease? This paper will present some answers to the questions mentioned above, making use of classification algorithms widely used in machine learning research with datasets relating to medical illnesses under the supervised learning scheme. In addition to state-of-the-art algorithms in pattern classification, we introduce a novelty: the use of meta-learning to determine, a priori, which classifier would be the ideal for a specific dataset. The results obtained show numerically and statistically that there are reliable classifiers to suggest medical diagnoses. In addition, we provide some insights about the expected performance of classifiers for such a task. [ABSTRACT FROM AUTHOR]

The linear associator is a classic associative memory model. However, due to its low performance, it is pertinent to note that very few linear associator applications have been published. The reason for this is that this model requires the vectors representing the patterns to be orthonormal, which is a big restriction. Some researchers have tried to create orthogonal projections to the vectors to feed the linear associator. However, this solution has serious drawbacks. This paper presents a proposal that effectively improves the performance of the linear associator when acting as a pattern classifier. For this, the proposal involves transforming the dataset using a powerful mathematical tool: the singular value decomposition. To perform the experiments, we selected fourteen medical datasets of two classes. All datasets exhibit balance, so it is possible to use accuracy as a performance measure. The effectiveness of our proposal was compared against nine supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, support vector machines, and neural networks), including three classifier ensembles. The Friedman and Holm tests show that our proposal had a significantly better performance than four of the nine classifiers. Furthermore, there are no significant differences against the other five, although three of them are ensembles. [ABSTRACT FROM AUTHOR]

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations. [ABSTRACT FROM AUTHOR]

Swarm intelligence has appeared as an active field for solving numerous machine-learning tasks. In this paper, we address the problem of clustering data with missing values, where the patterns are described by mixed (or hybrid) features. We introduce a generic modification to three swarm intelligence algorithms (Artificial Bee Colony, Firefly Algorithm, and Novel Bat Algorithm). We experimentally obtain the adequate values of the parameters for these three modified algorithms, with the purpose of applying them in the clustering task. We also provide an unbiased comparison among several metaheuristics based clustering algorithms, concluding that the clusters obtained by our proposals are highly representative of the "natural structure" of data. [ABSTRACT FROM AUTHOR]

When facing certain problems in science, engineering or technology, it is not enough to find a solution, but it is essential to seek and find the best possible solution through optimization. In many cases the exact optimization procedures are not applicable due to the great computational complexity of the problems. As an alternative to exact optimization, there are approximate optimization algorithms, whose purpose is to reduce computational complexity by pruning some areas of the problem search space. To achieve this, researchers have been inspired by nature, because animals and plants tend to optimize many of their life processes. The purpose of this research is to design a novel bioinspired algorithm for numeric optimization: the Mexican Axolotl Optimization algorithm. The effectiveness of our proposal was compared against nine optimization algorithms (artificial bee colony, cuckoo search, dragonfly algorithm, differential evolution, firefly algorithm, fitness dependent optimizer, whale optimization algorithm, monarch butterfly optimization, and slime mould algorithm) when applied over four sets of benchmark functions (unimodal, multimodal, composite and competition functions). The statistical analysis shows the ability of Mexican Axolotl Optimization algorithm of obtained very good optimization results in all experiments, except for composite functions, where the Mexican Axolotl Optimization algorithm exhibits an average performance. [ABSTRACT FROM AUTHOR]

A fundamental issue of the software engineering economics is productivity. In this regard, one measure of software productivity is delivery speed. Software productivity prediction is useful to determine corrective activities, as well as to identify improvement alternatives. A type of software maintenance is enhancement. In this paper, we propose a data analytics-based software engineering algorithm called search method based on feature construction (SMFC) for predicting the delivery speed of software enhancement projects. The SMFC belongs to the minimalist machine learning paradigm, and as such it always generates a two-dimensional model. Unlike the usual data analytics methods, SMFC includes an original algorithmic training procedure, in which both the independent and dependent variables are considered for transformation. SMFC prediction performance is compared to those of statistical regression, neural networks, support vector regression, and fuzzy regression. To do this, seven datasets of software enhancement projects obtained from the International Software Benchmarking Standards Group (ISBSG) Release 2017 were used. The validation method is leave-one-out cross validation, whereas absolute residuals have been chosen as the performance measure. The results indicate that the SMFC is statistically better than statistical regression. This fact represents an obvious advantage in favor of SMFC, because the other two methods are not statistically better than SMFC. [ABSTRACT FROM AUTHOR]

As of the end of 2019, the world suffered from a disease caused by the SARS-CoV-2 virus, which has become the pandemic COVID-19. This aggressive disease deteriorates the human respiratory system. Patients with COVID-19 can develop symptoms that belong to the common flu, pneumonia, and other respiratory diseases in the first four to ten days after they have been infected. As a result, it can cause misdiagnosis between patients with COVID-19 and typical pneumonia. Some deep-learning techniques can help physicians to obtain an effective pre-diagnosis. The content of this article consists of a deep-learning model, specifically a convolutional neural network with pre-trained weights, which allows us to use transfer learning to obtain new retrained models to classify COVID-19, pneumonia, and healthy patients. One of the main findings of this article is that the following relevant result was obtained in the dataset that we used for the experiments: all the patients infected with SARS-CoV-2 and all the patients infected with pneumonia were correctly classified. These results allow us to conclude that the proposed method in this article may be useful to help physicians decide the diagnoses related to COVID-19 and typical pneumonia. [ABSTRACT FROM AUTHOR]

The Lernmatrix is a classic associative memory model. The Lernmatrix is capable of executing the pattern classification task, but its performance is not competitive when compared to state-of-the-art classifiers. The main contribution of this paper consists of the proposal of a simple mathematical transform, whose application eliminates the subtractive alterations between patterns. As a consequence, the Lernmatrix performance is significantly improved. To perform the experiments, we selected 20 datasets that are challenging for any classifier, as they exhibit class imbalance. The effectiveness of our proposal was compared against seven supervised classifiers of the most important approaches (Bayes, nearest neighbors, decision trees, logistic function, support vector machines, and neural networks). By choosing balanced accuracy as a performance measure, our proposal obtained the best results in 10 datasets. The elimination of subtractive alterations makes the new model competitive against the best classifiers, and sometimes beats them. After applying the Friedman test and the Holm post hoc test, we can conclude that within a 95% confidence, our proposal competes successfully with the most effective classifiers of the state of the art. [ABSTRACT FROM AUTHOR]