The evaluation of white blood cells is essential to assess the quality human immune system; however, assessment smear depends on pathologist's expertise. Most machine learning tools make a one-level classification for cell classification. This work presents two-stage hybrid multi-level scheme that efficiently classifies four groups: lymphocytes and monocytes (mononuclear) segmented neutrophils eosinophils (polymorphonuclear). At first level, Faster R-CNN network applied identification region...

Forecasting the industry’s electricity consumption is essential for energy planning in a given country or region. Thus, this study aims to apply time-series forecasting models (statistical approach and artificial neural network approach) industrial Brazilian system. For statistical approach, Holt–Winters, SARIMA, Dynamic Linear Model, TBATS (Trigonometric Box–Cox transform, ARMA errors, Trend, Seasonal components) were considered. of networks, NNAR (neural autoregression) MLP (multilayer...

Abstract The prediction of air pollution is great importance in highly populated areas because it directly impacts both the management city’s economic activity and health its inhabitants. This work evaluates predicts Spatio-temporal behavior quality Metropolitan Lima, Peru, using artificial neural networks. conventional feedforward backpropagation known as Multilayer Perceptron (MLP) Recurrent Artificial Neural network Long Short-Term Memory networks (LSTM) were implemented for hourly...

Long-term dependence is an essential feature for the predictability of time series. Estimating parameter that describes long memory to describing behavior series models. However, most estimation methods assume this has a constant value throughout series, and do not consider may change over time. In work, we propose automated methodology combines methodologies fractional differentiation (and/or Hurst parameter) with its application Recurrent Neural Networks (RNNs) in order said networks learn...

Experts and international organizations hypothesize that the number of cases fatal intimate partner violence against women increased during COVID-19 pandemic, primarily due to social distancing strategies implementation lockdowns reduce spread virus. We described attempted femicide in Chile before (January 2014 February 2020) (March 2020 June 2021) pandemic. The attempted-femicide rate pandemic (incidence ratio: 1.22 [95% confidence interval: 1.04 1.43], p value: 0.016), while remained...

Renewable energy forecasting is crucial for pollution prevention, management, and long‐term sustainability. In response to the challenges associated with forecasting, simultaneous deployment of several data‐processing approaches has been used in a variety studies order improve energy–time‐series analysis, finding that, when combined wavelet deep learning techniques can achieve high accuracy applications. Consequently, we investigate implementation various wavelets within structure long...

Brain tumors are a leading cause of mortality worldwide, with early and accurate diagnosis being essential for effective treatment. Although Deep Learning (DL) models offer strong performance in tumor detection segmentation using MRI, their black-box nature hinders clinical adoption due to lack interpretability. We present hybrid AI framework that integrates 3D U-Net Convolutional Neural Network MRI-based radiomic feature extraction. Dimensionality reduction is performed machine learning, an...

Cerebral aneurysm is a cerebrovascular disorder characterized by bulging in weak area the wall of an artery that supplies blood to brain. It relevant understand mechanisms leading apparition aneurysms, their growth and, more important, rupture. The purpose this study impact on rupture combination different parameters, instead focusing only one factor at time as frequently found literature, using machine learning and feature extraction techniques. This discussion takes relevance context...

The academic success of university students is a problem that depends in multi-factorial way on the aspects related to student and career itself. A with this level complexity needs be faced integral approaches, which involves complement numerical quantitative analysis other types analysis. This study uses novel visual-predictive data approach obtain relevant information regarding performance from Peruvian university. joins together domain understanding data-visualization analysis,...

Singular spectrum analysis is a powerful nonparametric technique used to decompose the original time series into set of components that can be interpreted as trend, seasonal, and noise. For their part, neural networks are family information-processing techniques capable approximating highly nonlinear functions. This study proposes improve precision in prediction air quality. this purpose, hybrid adaptation considered. It based on an integration singular recurrent network long short-term...

Due to the high proliferation of web services, selecting best services from functional equivalent service providers have become a real challenge, where quality plays crucial role. But is uncertain, therefore, several researchers applied Fuzzy logic address imprecision (QoS) constraints. Furthermore, market highly dynamic and competitive, are constantly entering exiting this market, they continually improving themselves due competition. Current fuzzy-based techniques expert and/or...

Intravoxel incoherent motion (IVIM) analysis has attracted the interest of clinical community due to its close relationship with microperfusion. Nevertheless, there is no clear reference protocol for implementation; one questions being which b-value distribution use. This study aimed stress importance sampling scheme and show that an optimized decreases variance associated IVIM parameters in brain respect a regular healthy volunteers.Ten volunteers were included this study; images acquired...

Lima is considered one of the cities with highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are charge permanently monitoring quality; therefore, quality visualization system must manage large amounts data different concentrations. In this study, a spatio-temporal approach was developed for exploration PM10 concentration Metropolitan Lima, where spatial behavior, at time scales, hourly concentrations analyzed using basic specialized charts. The...

Within the e-commerce sphere, optimizing product classification process assumes pivotal importance, owing to its direct influence on operational efficiency and profitability. In this context, employing machine learning algorithms stands out as a premier solution for effectively automating process. The design of these models commonly adopts either flat or local (hierarchical) approach. However, each them exhibits significant limitations. approach introduces taxonomic inconsistencies in...