Multiclass Mask Classification with a New Convolutional Neural Model and Its Real-Time Implementation.

The world has been greatly affected by the COVID-19 pandemic, causing people to remain isolated and decreasing the interaction between people. Accordingly, various measures have been taken to continue with a new normal way of life, which is why there is a need to implement the use of technologies and systems to decrease the spread of the virus. This research proposes a real-time system to identify the region of the face using preprocessing techniques and then classify the people who are using the mask, through a new convolutional neural network (CNN) model.

Architecture Optimization of a Non-Linear Autoregressive Neural Networks for Mackey-Glass Time Series Prediction Using Discrete Mycorrhiza Optimization Algorithm.

Recurrent Neural Networks (RNN) are basically used for applications with time series and sequential data and are currently being used in embedded devices. However, one of their drawbacks is that RNNs have a high computational cost and require the use of a significant amount of memory space. Therefore, computer equipment with a large processing capacity and memory is required.

Interval Type-3 Fuzzy Adaptation of the Bee Colony Optimization Algorithm for Optimal Fuzzy Control of an Autonomous Mobile Robot.

In this study, the first goal is achieving a hybrid approach composed by an Interval Type-3 Fuzzy Logic System (IT3FLS) for the dynamic adaptation of α and β parameters of Bee Colony Optimization (BCO) algorithm. The second goal is, based on BCO, to find the best partition of the membership functions (MFs) of a Fuzzy Controller (FC) for trajectory tracking in an Autonomous Mobile Robot (AMR). A comparative with different types of Fuzzy Systems, such as Fuzzy BCO with Type-1 Fuzzy Logic System (FBCO-T1FLS), Fuzzy BCO with Interval Type-2 Fuzzy Logic System (FBCO-IT2FLS) and Fuzzy BCO with Generalized Type-2 Fuzzy Logic System (FBCO-GT2FLS) is analyzed.

Interval type-3 fuzzy aggregators for ensembles of neural networks in COVID-19 time series prediction.

In this work we are presenting an approach for fuzzy aggregation in ensembles of neural networks for forecasting. The aggregator is used in an ensemble to combine the outputs of the networks forming the ensemble. This is done in such a way that the total output of the ensemble is better than the outputs of the individual modules.

A review on quantum computing and deep learning algorithms and their applications.

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms.

Fuzzy dynamic parameter adaptation in the bird swarm algorithm for neural network optimization.

Fuzzy dynamic parameter adaptation has proven to be of great help when it is implemented in bio-inspired algorithms for optimization in different application areas, such as control, mathematical functions, classification, among others. One of the main contributions of this work is the proposed improvement of the Bird Swarm algorithm using a Fuzzy System approach, and we called this improvement the Fuzzy Bird Swarm Algorithm. Furthermore, we use a set of complex Benchmark Functions of the Congress on Evolutionary Computation Competition 2017 to compare the results between the original algorithm and the proposed improvement of the algorithm.

A new fuzzy fractal control approach of non-linear dynamic systems: The case of controlling the COVID-19 pandemics.

This article is presenting a first attempt on a proposed fuzzy fractal control method for efficiently controlling nonlinear dynamic systems. The main goal is to combine the main advantages of fractal theoretical concepts and fuzzy logic theory for achieving efficient control of nonlinear dynamic systems. The concept coming from Fractal theory, known as the fractal dimension, can be utilized to measure the complexity of the dynamic behavior of a non-linear plant.

A Novel Method for a COVID-19 Classification of Countries Based on an Intelligent Fuzzy Fractal Approach.

We outline in this article a hybrid intelligent fuzzy fractal approach for classification of countries based on a mixture of fractal theoretical concepts and fuzzy logic mathematical constructs. The mathematical definition of the fractal dimension provides a way to estimate the complexity of the non-linear dynamic behavior exhibited by the time series of the countries. Fuzzy logic offers a way to represent and handle the inherent uncertainty of the classification problem.

Optimization using the firefly algorithm of ensemble neural networks with type-2 fuzzy integration for COVID-19 time series prediction.

In this paper, the latest global COVID-19 pandemic prediction is addressed. Each country worldwide has faced this pandemic differently, reflected in its statistical number of confirmed and death cases. Predicting the number of confirmed and death cases could allow us to know the future number of cases and provide each country with the necessary information to make decisions based on the predictions.

A new prediction approach of the COVID-19 virus pandemic behavior with a hybrid ensemble modular nonlinear autoregressive neural network.

We describe in this paper an approach for predicting the COVID-19 time series in the world using a hybrid ensemble modular neural network, which combines nonlinear autoregressive neural networks. At the level of the modular neural network, which is formed with several modules (ensembles in this case), the modules are designed to be efficient predictors for each country. In this case, an integrator is used to combine the outputs of the modules, in this way achieving the goal of predicting a set of countries.

A new approach for classifying coronavirus COVID-19 based on its manifestation on chest X-rays using texture features and neural networks.

Since the recent challenge that humanity is facing against COVID-19, several initiatives have been put forward with the goal of creating measures to help control the spread of the pandemic. In this paper we present a series of experiments using supervised learning models in order to perform an accurate classification on datasets consisting of medical images from COVID-19 patients and medical images of several other related diseases affecting the lungs. This work represents an initial experimentation using image texture feature descriptors, feed-forward and convolutional neural networks on newly created databases with COVID-19 images.

Forecasting of COVID-19 time series for countries in the world based on a hybrid approach combining the fractal dimension and fuzzy logic.

We describe in this paper a hybrid intelligent approach for forecasting COVID-19 time series combining fractal theory and fuzzy logic. The mathematical concept of the fractal dimension is used to measure the complexity of the dynamics in the time series of the countries in the world. Fuzzy Logic is used to represent the uncertainty in the process of making a forecast.

Multiple Ensemble Neural Network Models with Fuzzy Response Aggregation for Predicting COVID-19 Time Series: The Case of Mexico.

In this paper, a multiple ensemble neural network model with fuzzy response aggregation for the COVID-19 time series is presented. Ensemble neural networks are composed of a set of modules, which are used to produce several predictions under different conditions. The modules are simple neural networks.

Analysis of Spatial Spread Relationships of Coronavirus (COVID-19) Pandemic in the World using Self Organizing Maps.

We describe in this paper an analysis of the spatial evolution of coronavirus pandemic around the world by using a particular type of unsupervised neural network, which is called self-organizing maps. Based on the clustering abilities of self-organizing maps we are able to spatially group together countries that are similar according to their coronavirus cases, in this way being able to analyze which countries are behaving similarly and thus can benefit by using similar strategies in dealing with the spread of the virus. Publicly available datasets of coronavirus cases around the globe from the last months have been used in the analysis.

Blood Pressure Classification Using the Method of the Modular Neural Networks.

In this paper, we present a new model based on modular neural networks (MNN) to classify a patient's blood pressure level (systolic and diastolic pressure and pulse). Tests are performed with the Levenberg-Marquardt (trainlm) and scaled conjugate gradient backpropagation (traincsg) training methods. The modular neural network architecture is formed by three modules.

A Grey Wolf Optimizer for Modular Granular Neural Networks for Human Recognition.

A grey wolf optimizer for modular neural network (MNN) with a granular approach is proposed. The proposed method performs optimal granulation of data and design of modular neural networks architectures to perform human recognition, and to prove its effectiveness benchmark databases of ear, iris, and face biometric measures are used to perform tests and comparisons against other works. The design of a modular granular neural network (MGNN) consists in finding optimal parameters of its architecture; these parameters are the number of subgranules, percentage of data for the training phase, learning algorithm, goal error, number of hidden layers, and their number of neurons.

Fuzzy Sets in Dynamic Adaptation of Parameters of a Bee Colony Optimization for Controlling the Trajectory of an Autonomous Mobile Robot.

A hybrid approach composed by different types of fuzzy systems, such as the Type-1 Fuzzy Logic System (T1FLS), Interval Type-2 Fuzzy Logic System (IT2FLS) and Generalized Type-2 Fuzzy Logic System (GT2FLS) for the dynamic adaptation of the alpha and beta parameters of a Bee Colony Optimization (BCO) algorithm is presented. The objective of the work is to focus on the BCO technique to find the optimal distribution of the membership functions in the design of fuzzy controllers. We use BCO specifically for tuning membership functions of the fuzzy controller for trajectory stability in an autonomous mobile robot.

Fuzzy Index to Evaluate Edge Detection in Digital Images.

In literature, we can find different metrics to evaluate the detected edges in digital images, like Pratt's figure of merit (FOM), Jaccard's index (JI) and Dice's coefficient (DC). These metrics compare two images, the first one is the reference edges image, and the second one is the detected edges image. It is important to mention that all existing metrics must binarize images before their evaluation.

C terminus of nucleotide binding domain 1 contains critical features for cystic fibrosis transmembrane conductance regulator trafficking and activation.

The cystic fibrosis transmembrane conductance regulator (CFTR) is a Cl(-) channel physiologically important in fluid-transporting epithelia and pathologically relevant in several human diseases. Here, we show that mutations in the C terminus of the first nucleotide binding domain comprising the latest beta strands (beta(c)5 and beta(c)6) influence the trafficking, channel activity, and pharmacology of CFTR. We mutated CFTR amino acids located in the beta(c)5-beta(c)6 hairpin, within the beta(c)5 strand (H620Q), within the beta-turn linking the two beta strands (E621G, G622D), as well as within (S623A, S624A) and at the extremity (G628R) of the beta(c)6 strand.

Adenovirus 5-fiber 35 chimeric vector mediates efficient apical correction of the cystic fibrosis transmembrane conductance regulator defect in cystic fibrosis primary airway epithelia.

In vivo gene transfer to the human respiratory tract by adenovirus serotype 5 (Ad5) vectors has revealed their limitations related to inefficient gene transfer, host antiviral response, and innate adenoviral toxicity. In the present work, we compared the cytotoxicity and efficiency of Ad5 and a chimeric Ad5F35 vector with respect to CFTR gene transfer to cystic fibrosis (CF) and non-CF human airway epithelial cells. We found that high doses of Ad5 vector had an adverse effect on the function of exogenous and endogenous CFTR.

Endosomal SNARE proteins regulate CFTR activity and trafficking in epithelial cells.

The Cystic Fibrosis Transmembrane conductance Regulator (CFTR) protein is a chloride channel localized at the apical plasma membrane of epithelial cells. We previously described that syntaxin 8, an endosomal SNARE (Soluble N-ethylmaleimide-sensitive factor Attachment protein REceptor) protein, interacts with CFTR and regulates its trafficking to the plasma membrane and hence its channel activity. Syntaxin 8 belongs to the endosomal SNARE complex which also contains syntaxin 7, vti1b and VAMP8.

CFTR inhibition by glibenclamide requires a positive charge in cytoplasmic loop three.

The sulfonylurea glibenclamide is widely used as an open-channel blocker of the CFTR chloride channel. Here, we used site-directed mutagenesis to identify glibenclamide site of interaction: a positively charged residue K978, located in the cytoplasmic loop 3. Charge-neutralizing mutations K978A, K978Q, K978S abolished the inhibition of forskolin-activated CFTR chloride current by glibenclamide but not by CFTR(inh)-172.