Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods.

Forecasting is of great importance in the field of renewable energies because it allows us to know the quantity of energy that can be produced, and thus, to have an efficient management of energy sources. However, determining which prediction system is more adequate is very complex, as each energy infrastructure is different. This work studies the influence of some variables when making predictions using ensemble methods for different locations. In particular, the proposal analyzes the influence of the aspects: the variation of the sampling frequency of solar panel systems, the influence of the type of neural network architecture and the number of ensemble method blocks for each model. Following comprehensive experimentation across multiple locations, our study has identified the most effective solar energy prediction model tailored to the specific conditions of each energy infrastructure. The results offer a decisive framework for selecting the optimal system for accurate and efficient energy forecasting. The key point is the use of short time intervals, which is independent of type of prediction model and of their ensemble method.

Reptile Identification for Endemic and Invasive Alien Species Using Transfer Learning Approaches.

The Canary Islands are considered a hotspot of biodiversity and have high levels of endemicity, including endemic reptile species. Nowadays, some invasive alien species of reptiles are proliferating with no control in different parts of the territory, creating a dangerous situation for the ecosystems of this archipelago. Despite the fact that the regional authorities have initiated actions to try to control the proliferation of invasive species, the problem has not been solved as it depends on sporadic sightings, and it is impossible to determine when these species appear. Since no studies for automatically identifying certain species of reptiles endemic to the Canary Islands have been found in the current state-of-the-art, from the Signals and Communications Department of the Las Palmas de Gran Canaria University (ULPGC), we consider the possibility of developing a detection system based on automatic species recognition using deep learning (DL) techniques. So this research conducts an initial identification study of some species of interest by implementing different neural network models based on transfer learning approaches. This study concludes with a comparison in which the best performance is achieved by integrating the EfficientNetV2B3 base model, which has a mean Accuracy of 98.75%.

Labeled dataset for training despeckling filters for SAR imagery.

When training Artificial Intelligence and Deep Learning models, especially by using Supervised Learning techniques, a labeled dataset is required to have an input with data and its corresponding labeled output data. In the case of images, for classification, segmentation, or other processing tasks, a pair of images is required in the same sense, one image as an input (the noisy image) and the desired (the denoised image) one as an output. For SAR despeckling applications, the common approach is to have a set of optical images that then are corrupted with synthetic noise, since there is no ground truth available. The corrupted image is considered the input and the optical one is the noiseless one (ground truth). In this paper, we provide a dataset based on actual SAR images. The ground truth was obtained from SAR images of Sentinel 1 of the same region in different instants of time and then they were processed and merged into one single image that serves as the output of the dataset. Every SAR image (noisy and ground truth) was split into 1600 images of 512 × 512 pixels, so a total of 3200 images were obtained. The dataset was also split into 3000 for training and 200 for validation, all of them available in four labeled folders.

Robust Identification System for Spanish Sign Language Based on Three-Dimensional Frame Information.

Nowadays, according to the World Health Organization (WHO), of the world's population suffers from a hearing disorder that makes oral communication with other people challenging. At the same time, in an era of technological evolution and digitization, designing tools that could help these people to communicate daily is the base of much scientific research such as that discussed herein. This article describes one of the techniques designed to transcribe Spanish Sign Language (SSL). A Leap Motion volumetric sensor has been used in this research due to its capacity to recognize hand movements in 3 dimensions. In order to carry out this research project, an impaired hearing subject has collaborated in the recording of 176 dynamic words. Finally, for the development of the research, Dynamic Time Warping (DTW) has been used to compare the samples and predict the input with an accuracy of 95.17%.

Analysis of the Nosema Cells Identification for Microscopic Images.

The use of image processing tools, machine learning, and deep learning approaches has become very useful and robust in recent years. This paper introduces the detection of the Nosema disease, which is considered to be one of the most economically significant diseases today. This work shows a solution for recognizing and identifying Nosema cells between the other existing objects in the microscopic image. Two main strategies are examined. The first strategy uses image processing tools to extract the most valuable information and features from the dataset of microscopic images. Then, machine learning methods are applied, such as a neural network (ANN) and support vector machine (SVM) for detecting and classifying the Nosema disease cells. The second strategy explores deep learning and transfers learning. Several approaches were examined, including a convolutional neural network (CNN) classifier and several methods of transfer learning (AlexNet, VGG-16 and VGG-19), which were fine-tuned and applied to the object sub-images in order to identify the Nosema images from the other object images. The best accuracy was reached by the VGG-16 pre-trained neural network with 96.25%.

A deep learning-based COVID-19 automatic diagnostic framework using chest X-ray images.

The lethal novel coronavirus disease 2019 (COVID-19) pandemic is affecting the health of the global population severely, and a huge number of people may have to be screened in the future. There is a need for effective and reliable systems that perform automatic detection and mass screening of COVID-19 as a quick alternative diagnostic option to control its spread. A robust deep learning-based system is proposed to detect the COVID-19 using chest X-ray images. Infected patient's chest X-ray images reveal numerous opacities (denser, confluent, and more profuse) in comparison to healthy lungs images which are used by a deep learning algorithm to generate a model to facilitate an accurate diagnostics for multi-class classification (COVID vs. normal vs. bacterial pneumonia vs. viral pneumonia) and binary classification (COVID-19 vs. non-COVID). COVID-19 positive images have been used for training and model performance assessment from several hospitals of India and also from countries like Australia, Belgium, Canada, China, Egypt, Germany, Iran, Israel, Italy, Korea, Spain, Taiwan, USA, and Vietnam. The data were divided into training, validation and test sets. The average test accuracy of 97.11 ± 2.71% was achieved for multi-class (COVID vs. normal vs. pneumonia) and 99.81% for binary classification (COVID-19 vs. non-COVID). The proposed model performs rapid disease detection in 0.137 s per image in a system equipped with a GPU and can reduce the workload of radiologists by classifying thousands of images on a single click to generate a probabilistic report in real-time.

Efficient Multi-Object Detection and Smart Navigation Using Artificial Intelligence for Visually Impaired People.

Visually impaired people face numerous difficulties in their daily life, and technological interventions may assist them to meet these challenges. This paper proposes an artificial intelligence-based fully automatic assistive technology to recognize different objects, and auditory inputs are provided to the user in real time, which gives better understanding to the visually impaired person about their surroundings. A deep-learning model is trained with multiple images of objects that are highly relevant to the visually impaired person. Training images are augmented and manually annotated to bring more robustness to the trained model. In addition to computer vision-based techniques for object recognition, a distance-measuring sensor is integrated to make the device more comprehensive by recognizing obstacles while navigating from one place to another. The auditory information that is conveyed to the user after scene segmentation and obstacle identification is optimized to obtain more information in less time for faster processing of video frames. The average accuracy of this proposed method is 95.19% and 99.69% for object detection and recognition, respectively. The time complexity is low, allowing a user to perceive the surrounding scene in real time.

Perspective and Evolution of Gesture Recognition for Sign Language: A Review.

This review analyses the different gesture recognition systems through a timeline, showing the different types of technology, and specifying which are the most important features and their achieved recognition rates. At the end of the review, Leap Motion sensor possibilities are described in detail, in order to consider its application on the field of sign language. This device has many positive characteristics that make it a good option for sign language. One of the most important conclusions is the ability of the Leap Motion sensor to provide 3D information from the hands for due identification.

Segmentation of elastic fibres in images of vessel wall sections stained with Weigert's resorcin-fuchsin.

BACKGROUND AND OBJECTIVE: The elastic fibres are an essential component of the extracellular matrix in blood vessel walls that allows a long-range of deformability and passive recoil without energy input. The quantitative determination of elastic fibres will provide information on the state of the vascular wall and to determine the role and behaviour of this key structural element in different physiological and pathological vascular processes. METHODS: We present a segmentation method to identify and quantify elastic fibres based on a local threshold technique and some morphological characteristics measured on the segmented objects that facilitate the discrimination between elastic fibres and other image components. The morphological characteristics analysed are the thickness and the length of an object. RESULTS: The segmentation method was evaluated using an image database of vein sections stained with Weigert's resorcin-fuchsin. The performance results are based on a ground truth generated manually resulting in values of sensitivity greater than 80% with the exception in two samples, and specificity values above 90% for all samples. Medical specialists carried out a visual evaluation where the observations indicate a general agreement on the segmentation results' visual quality, and the consistency between the methodology proposed and the subjective observation of the doctors for the evaluation of pathological changes in vessel wall. CONCLUSIONS: The proposed methodology provides more objective measurements than the qualitative methods traditionally used in the histological analysis, with a significant potential for this method to be used as a diagnostic aid for many other vascular pathological conditions and in similar tissues such as skin and mucous membranes.

Quantification and Statistical Analysis Methods for Vessel Wall Components from Stained Images with Masson's Trichrome.

PURPOSE: To develop a digital image processing method to quantify structural components (smooth muscle fibers and extracellular matrix) in the vessel wall stained with Masson's trichrome, and a statistical method suitable for small sample sizes to analyze the results previously obtained. METHODS: The quantification method comprises two stages. The pre-processing stage improves tissue image appearance and the vessel wall area is delimited. In the feature extraction stage, the vessel wall components are segmented by grouping pixels with a similar color. The area of each component is calculated by normalizing the number of pixels of each group by the vessel wall area. Statistical analyses are implemented by permutation tests, based on resampling without replacement from the set of the observed data to obtain a sampling distribution of an estimator. The implementation can be parallelized on a multicore machine to reduce execution time. RESULTS: The methods have been tested on 48 vessel wall samples of the internal saphenous vein stained with Masson's trichrome. The results show that the segmented areas are consistent with the perception of a team of doctors and demonstrate good correlation between the expert judgments and the measured parameters for evaluating vessel wall changes. CONCLUSION: The proposed methodology offers a powerful tool to quantify some components of the vessel wall. It is more objective, sensitive and accurate than the biochemical and qualitative methods traditionally used. The permutation tests are suitable statistical techniques to analyze the numerical measurements obtained when the underlying assumptions of the other statistical techniques are not met.