Background: The analysis of health and medical data is crucial for improving the diagnosis precision, treatments and prevention. In this field, machine learning techniques play a key role. However, the amount of health data acquired from digital machines has high dimensionality and not all data acquired from digital machines are relevant for a particular disease. Primary Progressive Aphasia (PPA) is a neurodegenerative syndrome including several specific diseases, and it is a good model to implement machine learning analyses. In this work, we applied five feature selection algorithms to identify the set of relevant features from 18F-fluorodeoxyglucose positron emission tomography images of the main areas affected by PPA from patient records. On the other hand, we carried out classification and clustering algorithms before and after the feature selection process to contrast both results with those obtained in a previous work. We aimed to find the best classifier and the more relevant features from the WEKA tool to propose further a framework for automatic help on diagnosis. Dataset contains data from 150 FDG-PET imaging studies of 91 patients with a clinic prognosis of PPA, which were examined twice, and 28 controls. Our method comprises six different stages: (i) feature extraction, (ii) expertise knowledge supervision (iii) classification process, (iv) comparing classification results for feature selection, (v) clustering process after feature selection, and (vi) comparing clustering results with those obtained in a previous work. Results: Experimental tests confirmed clustering results from a previous work. Although classification results for some algorithms are not decisive for reducing features precisely, Principal Components Analisys (PCA) results exhibited similar or even better performances when compared to those obtained with all features. Conclusions: Although reducing the dimensionality does not means a general improvement, the set of features is almost halved and results are better or quite similar. Finally, it is interesting how these results expose a finer grain classification of patients according to the neuroanatomy of their disease.

The limits of user visibility have been exceeded by the internet. The “dark web” or “dark net” refers to certain unknown portions of the web that cannot be found using standard search methods. A number of automated techniques are being explored to extract for crawling the concealed data. All users can freely interact on the surface web. Account credentials may be found on the dark web (DW), a hub for anonymized data, is a haven for terrorists and cybercriminals to promote their message and engage in illegal commerce. Officials in covert security and cyberpolicing are always trying to track down delinquent chains or hints. To find DW delinquents, the exercise may take 6–12 years. The suggested effort gives specialists an outline for building the automated engine for crawling all hazardous information from associated sources as well as data from DW crawling and shadow economy area scenarios.

Artificial Intelligence aids early diagnosis and development of new treatments, which is key to slow down the progress of the diseases, which to date have no cure. The patients’ evaluation is carried out through diagnostic techniques such as clinical assessments neuroimaging techniques, which provide high-dimensionality data. In this work, a computational tool is presented that deals with the data provided by the clinical diagnostic techniques. This is a Python-based framework implemented with a modular design and fully extendable. It integrates (i) data processing and management of missing values and outliers; (ii) implementation of an evolutionary feature engineering approach, developed as a Python package, called PyWinEA using Mono-objective and Multi-objetive Genetic Algorithms (NSGAII); (iii) a module for designing predictive models based on a wide range of machine learning algorithms; (iv) a multiclass decision stage based on evolutionary grammars and Bayesian networks. Developed under the eXplainable Artificial Intelligence and open science perspective, this framework provides promising advances and opens the door to the understanding of neurodegenerative diseases from a data-centric point of view. In this work, we have successfully evaluated the potential of the framework for early and automated diagnosis with neuroimages and neurocognitive assessments from patients with Alzheimer’s disease (AD) and frontotemporal dementia (FTD). Graphical abstract

Digitization and technological transformation in agriculture is no longer something of the future, but of the present. Many crops are being managed by using sophisticated sensors that allow farmers to know the status of their crops at all times. This modernization of crops also allows for better quality harvests as well as significant cost savings. In this study, we present a tool based on Deep Learning that allows us to analyse different varieties of plums using image analysis to identify the variety and its ripeness status. The novelty of the system is the conditions in which the designed algorithm can work. An uncontrolled photographic acquisition method has been implemented. The user can take a photograph with any device, smartphone, camera, etc., directly in the field, regardless of light conditions, focus, etc. The robustness of the system presented allows us to differentiate, with 92.83% effectiveness, three varieties of plums through images taken directly in the field and values above 94% when the ripening stage of each variety is analyzed independently. We have worked with three varieties of plums, Red Beaut, Black Diamond and Angeleno, with different ripening cycles. This has allowed us to obtain a robust classification system that will allow users to differentiate between these varieties and subsequently determine the ripening stage of the particular variety.

Evolutionary Algorithms (EAs) are routinely applied to solve a large set of optimization problems. Traditionally, their performance in solving those problems is analyzed using the fitness quality and computing time, and the effect of evolutionary operators on both metrics is routinely used to compare different versions of EAs. Nevertheless, scientists face nowadays the challenge of considering the energy efficiency in addition to computational time, which requires studying the energy consumption of algorithms. This paper discusses the interest of introducing power consumption as a new metric to analyze the performance of standard genetic programming (GP). Two well-studied benchmark problems are addressed on three different computing platforms, and two different approaches to measure the power consumption have been tested. Analyzing the population size, the results demonstrates its influence on the energy consumed: a non-linear relationship was found between size and energy required to complete an experiment. This analysis was extended to the cache memory and results show an exponential growth in the number of cache misses as the population size increases, which affects the energy consumed. This study shows that not only computing time or solution quality must be analyzed, but also the energy required to find a solution. Summarizing, this paper shows that when GP is applied, specific considerations on how to select parameter values must be taken into account if the goal is to obtain solutions while searching for energy efficiency. Although the study has been performed using GP, we foresee that it could be similarly extended to EAs.

Plant diseases cause economic losses and health risks, such as aflatoxins linked to liver cancer. These toxins, produced by fungi like Aspergillus flavus in figs, are often detected late through invasive methods or visual inspection. Since Spain, particularly Extremadura, is a key fig producer, alternative detection methods are essential to preventing aflatoxins in the food chain. The aim of this research is the early detection of Aspergillus flavus fungus using non-invasive techniques with hyperspectral imaging and applying artificial intelligence techniques, in particular deep learning. The images were taken after inoculation of the microtoxin using 3 different concentrations, related to three different classes and healthy figs (healthy controls). The analysis of the hyperspectral images was performed at the pixel level. Firstly, a fully connected neural network was used to analyze the spectral signature associated with each pixel; secondly, the wavelet transform was applied to each spectral signature. The resulting images were fed to a convolutional neural network. The hyperparameters of the proposed models were adjusted based on the parameter tuning process that was performed. The results are promising, with 83% accuracy, 82.75% recall, and 83.25% F1-measure for the fully connected neural network. The high F1-measure demonstrates that the model’s performance is good. The model has a low incidence of false positives for samples that contain aflatoxin, while a higher number of false positives appears in healthy controls. Due to the presence of false negatives, this class also has a high recall. The convolutional neural network results, accuracy, recall, and F1 are 77.25%, indicating moderate model performance. Only class 3, with higher aflatoxin concentration, achieves high precision and low false positive incidence. Healthy controls exhibit a high presence of false negatives. In conclusion, we demonstrate the effectiveness of pixel-level analysis in identifying the presence of the fungus and the viability of the non-invasive techniques applied in improving food safety. Although further research is needed, in this study, the fully connected neural network model shows good performance with lower energy consumption.