This paper proposes a new distributed architecture for supervised classification of large volumes of earth observation data on a cloud computing environment. The architecture supports distributed execution, network communication, and fault tolerance in a transparent way to the user. The architecture is composed of three abstraction layers, which support the definition and implementation of applications by researchers from different scientific investigation fields. The implementation of architecture is also discussed. A software prototype (available online), which runs machine learning routines implemented on the cloud using the Waikato Environment for Knowledge Analysis (WEKA), a popular free software licensed under the GNU General Public License, is used for validation. Performance issues are addressed through an experimental analysis in which two supervised classifiers available in WEKA were used: random forest and support vector machines. This paper further describes how to include other classification methods in the available software prototype.

Keywords: Big Data, MapReduce Framework, Hadoop, Classification Algorithms, Cloud Computing Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA’s machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

High-resolution satellite images have always been in high demand due to the greater detail and precision they offer, as well as the wide scope of the fields in which they could be applied; however, satellites in operation offering very high-resolution (VHR) images has experienced an important increase, but they remain as a smaller proportion against existing lower resolution (HR) satellites. Recent models of convolutional neural networks (CNN) are very suitable for applications with image processing, like resolution enhancement of images; but in order to obtain an acceptable result, it is important, not only to define the kind of CNN architecture but the reference set of images to train the model. Our work proposes an alternative to improve the spatial resolution of HR images obtained by Sentinel-2 satellite by using the VHR images from PeruSat1, a peruvian satellite, which serve as the reference for the super-resolution approach implementation based on a Generative Adversarial Network (GAN) model, as an alternative for obtaining VHR images. The VHR PeruSat-1 image dataset is used for the training process of the network. The results obtained were analyzed considering the Peak Signal to Noise Ratios (PSNR) and the Structural Similarity (SSIM). Finally, some visual outcomes, over a given testing dataset, are presented so the performance of the model could be analyzed as well.

Tuberculosis is a disease with one of the most leading causes of deaths in the world, however, its fatality index could be reduced if it is diagnosed and treated on time. The Ziehl-Neelsen stained sputum smear method is the most used for bacilli detection and for developing a proper diagnosis by the specialist. Nevertheless, these stained images do not always present an adequate contrast, then, the elaboration of a reliable diagnosis is a complex, time consuming and a difficult process. This research proposes an alternative method to perform automatic bacilli segmentation in Ziehl-Neelsen images using Adaptive Signal Processing techniques, like the Least Mean Squares and Reduced Rank with Eigendecomposition algorithms. The quantitative results achieved, in correlation and true positives detection, are encouraging and suggest the use of this approach as a feasible alternative, when compared with the classical segmentation techniques, for automatic bacilli segmentation in the Ziehl-Neelsen images.

In this work, we introduce a novel, distributed version of the N-FINDR endmember extraction algorithm, which is able to exploit computer cluster resources in order to efficiently process large volumes of hyperspectral data. The implementation of the distributed algorithm was done by extending the InterCloud Data Mining Package, originally adopted for land cover classification, through the HyperCloud-RS framework, here adapted for endmember extraction, which can be executed on cloud computing environments, allowing users to elastically administer processing power and storage space for adequately handling very large datasets. The framework supports distributed execution, network communication, and fault tolerance, transparently and efficiently to the user. The experimental analysis addresses the performance issues, evaluating both accuracy and execution time, over the processing of different synthetic versions of the AVIRIS Cuprite hyperspectral dataset, with 3.1 Gb, 6.2 Gb, and 15.1Gb respectively, thus addressing the issue of dealing with large-scale hyperspectral data. As a further contribution of this work, we describe in detail how to extend the HyperCloud-RS framework by integrating other endmember extraction algorithms, thus enabling researchers to implement algorithms specifically designed for their own assessment.

Over the years, technology has rapidly evolved, with social networks and media emerging as prominent examples. Social media facilitates the creation and exchange of content, while social networks connect individuals and organizations based on shared interests or values. Among these platforms, Twitter (now X) has gained significant attention and has been a valuable tool for monitoring reputation and brands, including political discourse. With the widespread use of social networks, the demand for advanced textual data analysis techniques, particularly sentiment analysis, has grown. Sentiment analysis, or polarity classification, aims to gauge the positivity or negativity of textual content, providing information on public opinion dynamics. In the context of the Peruvian presidential elections of 2021, characterized by political uncertainty exacerbated by the COVID-19 pandemic, social media platforms, especially Twitter/X, emerged as vital arenas for political discourse and participation. The election featured various candidates representing a wide spectrum of ideologies, reflecting the complexities of Peruvian society. Against this backdrop, this research uses Twitter/X data to analyze public sentiment toward presidential candidates and election-related topics. Through state-of-the-art sentiment analysis algorithms, this study categorizes tweets into positive, negative, or neutral sentiments, revealing key trends and themes driving public opinion on Twitter/X during the election period. By examining sentiment dynamics in response to major events and comparing Twitter/X sentiment trends with official voting data, the research aims to assess the predictive power of social media sentiment analysis in forecasting electoral outcomes. Therefore, this research contributes to understanding the role of social media in shaping political discourse and public opinion during elections. By conducting sentiment analysis on Twitter data, valuable insights are offered to policymakers, political analysts, and researchers who want to leverage social media to understand and forecast electoral trends.