The use of photovoltaic systems for clean electrical energy has increased. However, due to their low efficiency, researchers have looked for ways to increase their effectiveness and improve their efficiency. The Maximum Power Point Tracking (MPPT) inverters allow us to maximize the extraction of as much energy as possible from PV panels, and they require algorithms to extract the Maximum Power Point (MPP). Several intelligent algorithms show acceptable performance; however, few consider using Artificial Neural Networks (ANN). These have the advantage of giving a fast and accurate tracking of the MPP. The controller effectiveness depends on the algorithm used in the hidden layer and how well the neural network has been trained. Articles over the last six years were studied. A review of different papers, reports, and other documents using ANN for MPPT control is presented. The algorithms are based on ANN or in a hybrid combination with FL or a metaheuristic algorithm. ANN MPPT algorithms deliver an average performance of 98% in uniform conditions, exhibit a faster convergence speed, and have fewer oscillations around the MPP, according to this research.

Automatic sign language recognition is a challenging task in machine learning and computer vision. Most works have focused on recognizing sign language using hand gestures only. However, body motion and facial gestures play an essential role in sign language interaction. Taking this into account, we introduce an automatic sign language recognition system based on multiple gestures, including hands, body, and face. We used a depth camera (OAK-D) to obtain the 3D coordinates of the motions and recurrent neural networks for classification. We compare multiple model architectures based on recurrent networks such as Long Short-Term Memories (LSTM) and Gated Recurrent Units (GRU) and develop a noise-robust approach. For this work, we collected a dataset of 3000 samples from 30 different signs of the Mexican Sign Language (MSL) containing features coordinates from the face, body, and hands in 3D spatial coordinates. After extensive evaluation and ablation studies, our best model obtained an accuracy of 97% on clean test data and 90% on highly noisy data.

This paper proposes a deep learning model based on an artificial neural network with a single hidden layer for predicting the diagnosis of multiple sclerosis. The hidden layer includes a regularization term that prevents overfitting and reduces the model complexity. The purposed learning model achieved higher prediction accuracy and lower loss than four conventional machine learning techniques. A dimensionality reduction method was used to select the most relevant features from 74 gene expression profiles for training the learning models. The analysis of variance test was performed to identify the statistical difference between the mean of the proposed model and the compared classifiers. The experimental results show the effectiveness of the proposed artificial neural network.

A New Measure of Circularity Based on Distribution of the Radius

The impact of the air pollution phenomenon has been long studied, but most often with a fragmented approach, without closely looking at the relationship between different components that characterize it, such as sensor-based data, health data from institutional databases, and data on how it is perceived by human beings in social media. The research developed in this study introduces an integrated methodological framework that analyses sensor data on air pollution distributed in space and time, combined with health data and social data narratives that reflect how different communities perceive this phenomenon in space and time; exploring how these different heterogeneous sources can be combined to better understand the impact of air pollution phenomena at the large-city level in the Valley of Mexico. We introduce a Spatio-temporal data integration and mining framework that aims to discover trends and insights regarding the distribution of the impact of an air pollution phenomenon in terms of human health and perception. The main peculiarity of our methodological framework is the integration of different large data sources by combining a series of methods: NLP (topic modeling), data mining (data cubes, unsupervised learning, and clustering), and GIS capabilities (spatial interpolation, choropleth maps) that together provide a better understanding of the quantitative and qualitative patterns emerging at a different spatial scale and temporal granularity. Overall, this shows how social data, when combined with quantitative data, can provide a better understanding of the impact of a given phenomenon, such as air pollution.

The symptoms of multiple sclerosis (MS) are determined by the location of demyelinating lesions in the white matter of the brain and spinal cord. Currently, magnetic resonance imaging (MRI) is the most common tool used for diagnosing MS, understanding the course of the disease, and analyzing the effects of treatments. However, undesirable components may appear during the generation of MRI scans, such as noise or intensity variations. Mathematical morphology (MM) is a powerful image analysis technique that helps to filter the image and extract relevant structures. Granulometry is an image measurement tool for measuring MM that determines the size distribution of objects in an image without explicitly segmenting each object. While several methods have been proposed for the automatic segmentation of MS lesions in MRI scans, in some cases, only simple data preprocessing, such as image resizing to standardize the input dimensions, has been performed before the algorithm training. Therefore, this paper proposes an MRI preprocessing algorithm capable of performing elementary morphological transformations in brain images of MS patients and healthy individuals in order to delete undesirable components and extract the relevant structures such as MS lesions. Also, the algorithm computes the granulometry in MRI scans to describe the size qualities of lesions. Using this algorithm, we trained two artificial neural networks (ANNs) to predict MS diagnoses. By computing the differences in granulometry measurements between an image with MS lesions and a reference image (without lesions), we determined the size characterization of the lesions. Then, the ANNs were evaluated with the validation set, and the performance results (test accuracy = 0.9753; cross-entropy loss = 0.0247) show that the proposed algorithm can support specialists in making decisions to diagnose MS and estimating the disease progress based on granulometry values.