Repositorio Universidad Europea del Atlántico
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Twitting Against the Enemy: Populist Radical Right Parties Discourse Against the (Political) “Other”
Get PDFThere is a common agreement in considering populism as a Manichean worldview that oversimplifies and polarizes political options reducing them to a symbolical struggle between an “us” and a “them.” “Us” is embodied by “the people,” equated with “good,” and “them” is identified by political “Others,” often embodied by “the elites” who are depicted as inherently “evil.” Naturally, the nature and composition of the people and the elite vary according to both ideology and political opportunities. This article examines the discursive construction of political opponents in two populist radical right parties: Lega in Italy and Vox in Spain. Based on the analysis of a selection of tweets by the two party leaders, Santiago Abascal and Matteo Salvini, this study applies clause-based semantic text analysis to detect the main discursive representations of political opponents. The article concludes that Salvini focuses all the attention on the left, while Abascal, although predominantly identifying the left as the main enemy, also targets pro-independence parties. The discursive construction of the “enemy” is based on two main strategies: demonization, the framing of opponents as “enemies of the people” who, along with dangerous “Others” such as immigrants, conspire against the “people” and are blamed for everything that is “wrong” in society; secondly, character assassination of individual politicians through personal attacks, which aim to undermine their reputation and deflect attention from the real issues towards their personal traits and actions
Enhancing Cricket Performance Analysis with Human Pose Estimation and Machine Learning
No full textCricket has a massive global following and is ranked as the second most popular sport globally, with an estimated 2.5 billion fans. Batting requires quick decisions based on ball speed, trajectory, fielder positions, etc. Recently, computer vision and machine learning techniques have gained attention as potential tools to predict cricket strokes played by batters. This study presents a cutting-edge approach to predicting batsman strokes using computer vision and machine learning. The study analyzes eight strokes: pull, cut, cover drive, straight drive, backfoot punch, on drive, flick, and sweep. The study uses the MediaPipe library to extract features from videos and several machine learning and deep learning algorithms, including random forest (RF), support vector machine, k-nearest neighbors, decision tree, linear regression, and long short-term memory to predict the strokes. The study achieves an outstanding accuracy of 99.77% using the RF algorithm, outperforming the other algorithms used in the study. The k-fold validation of the RF model is 95.0% with a standard deviation of 0.07, highlighting the potential of computer vision and machine learning techniques for predicting batsman strokes in cricket. The study’s results could help improve coaching techniques and enhance batsmen’s performance in cricket, ultimately improving the game’s overall quality
Analyzing Sentiments Regarding ChatGPT Using Novel BERT: A Machine Learning Approach
No full textChatbots are AI-powered programs designed to replicate human conversation. They are capable of performing a wide range of tasks, including answering questions, offering directions, controlling smart home thermostats, and playing music, among other functions. ChatGPT is a popular AI-based chatbot that generates meaningful responses to queries, aiding people in learning. While some individuals support ChatGPT, others view it as a disruptive tool in the field of education. Discussions about this tool can be found across different social media platforms. Analyzing the sentiment of such social media data, which comprises people’s opinions, is crucial for assessing public sentiment regarding the success and shortcomings of such tools. This study performs a sentiment analysis and topic modeling on ChatGPT-based tweets. ChatGPT-based tweets are the author’s extracted tweets from Twitter using ChatGPT hashtags, where users share their reviews and opinions about ChatGPT, providing a reference to the thoughts expressed by users in their tweets. The Latent Dirichlet Allocation (LDA) approach is employed to identify the most frequently discussed topics in relation to ChatGPT tweets. For the sentiment analysis, a deep transformer-based Bidirectional Encoder Representations from Transformers (BERT) model with three dense layers of neural networks is proposed. Additionally, machine and deep learning models with fine-tuned parameters are utilized for a comparative analysis. Experimental results demonstrate the superior performance of the proposed BERT model, achieving an accuracy of 96.49%
A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation
No full textObjective
This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy.
Method
The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture.
Results
The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture.
Conclusion
Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance
Competitive Coevolution-Based Improved Phasor Particle Swarm Optimization Algorithm for Solving Continuous Problems
No full textVariables related to Physical Exercise in Cancer Patients and Survivors
Get PDFCancer constitutes a significant global contributor to morbidity and mortality, inducing adverse effects that impact individuals both during and after treatment. Noteworthy among these effects are depression, anxiety, fatigue, and diminished quality of life. This study aims to ascertain the association between quality of life, fatigue, depression, and anxiety variables and engagement in physical exercise within a cohort of cancer patients and survivors affiliated with the Spanish Association Against Cancer of Cantabria. Additionally, the investigation seeks to identify barriers contributing to physical inactivity in this demographic. Employing a descriptive research design, this study endeavours to illuminate the interplay between these factors in the specified population. A survey was conducted to assess variables such as physical exercise levels, quality of life, fatigue, depression, anxiety, and barriers to physical activity. The findings indicated correlations between physical exercise and depression (p=0.002), anxiety (p< 0.001), fatigue (p< 0.001), and quality of life (p< 0.001) in both cancer patients and survivors. Similarly, survivors exhibited associations between physical exercise and depression (p<0.001), anxiety (p<0.001), fatigue (p<0.001), and quality of life (p<0.001). Conversely, patients and survivors demonstrated significant differences in individual (p<0.001), interpersonal (p=0.002), community-institutional (p=0.001), and time-obligations (p=0.002) barriers. The outcomes affirm the impact of physical exercise on depression, anxiety, fatigue, and quality of life among both cancer patients and survivors, while also elucidating the barriers that rationalize physical inactivity within this demographic
Digital Simulator for Entrepreneurial Finance (FINANCEn_LAB)
No full textA partir de los datos introducidos y de diferentes escenarios, la herramienta del simulador digital genera distintos retos a los estudiantes-emprendedores para poner a prueba y evaluar la parte financiera de una propuesta de emprendimiento y también ofrece recomendaciones en función de la aportación real de diferentes agentes financieros como bancos, inversores privados, business angels o plataformas de financiación colaborativa
Evaluación de los efectos del ejercicio físico en pacientes con cáncer de mama: una revisión sistemática
No full textEl objetivo principal de esta revisión fue evaluar la eficacia de un programa de ejercicio físico (EF) en pacientes con cáncer de mama (CM) y sus efectos sobre la calidad de vida, la fatiga percibida, la depresión y la condición física. Se realizó una búsqueda sistemática, basada en las directrices PRISMA, utilizando tres bases de datos diferentes: Medline, Pubmed y Google Académico. Los criterios de inclusión fueron; adultos (>18 años), pacientes con CM durante la terapia adyuvante, intervenciones de EF con el efecto de influir en la calidad de vida, la fatiga y la condición física. Así mismo, los criterios de exclusión fueron; realizar la intervención de EF después de la enfermedad, artículos publicados antes del 2010 o en idiomas que no fueran inglés, castellano y/o francés. Los resultados incluyeron cinco artículos para la revisión y todos los estudios mostraron mejoras en la calidad de vida, la condición física y/o en la composición corporal, además de en la percepción de fatiga percibida y de la depresión. Se puede llegar a la conclusión de que las incorporaciones complementarias de programas de EF sistematizado durante la terapia adyuvante a mujeres con CM ofrece tanto mejoras en la calidad de vida, como en la condición física y una disminución de la fatiga y la depresión, sea cual sea el tipo de programa de entrenamiento (resistencia, fuerza o combinación de ambas)
Unleashing the Potential of Blockchain and Machine Learning: Insights and Emerging Trends From Bibliometric Analysis
Get PDFBlockchain and machine learning (ML) has garnered growing interest as cutting-edge technologies that have witnessed tremendous strides in their respective domains. Blockchain technology provides a decentralized and immutable ledger, enabling secure and transparent transactions without intermediaries. Alternatively, ML is a sub-field of artificial intelligence (AI) that empowers systems to enhance their performance by learning from data. The integration of these data-driven paradigms holds the potential to reinforce data privacy and security, improve data analysis accuracy, and automate complex processes. The confluence of blockchain and ML has sparked increasing interest among scholars and researchers. Therefore, a bibliometric analysis is carried out to investigate the key focus areas, hotspots, potential prospects, and dynamical aspects of the field. This paper evaluates 700 manuscripts drawn from the Web of Science (WoS) core collection database, spanning from 2017 to 2022. The analysis is conducted using advanced bibliometric tools (e.g., Bibliometrix R, VOSviewer, and CiteSpace) to assess various aspects of the research area regarding publication productivity, influential articles, prolific authors, the productivity of academic countries and institutions, as well as the intellectual structure in terms of hot topics and emerging trends. The findings suggest that upcoming research should focus on blockchain technology, AI-powered 5G networks, industrial cyber-physical systems, IoT environments, and autonomous vehicles. This paper provides a valuable foundation for both academic scholars and practitioners as they contemplate future projects on the integration of blockchain and ML
A 50 Hz magnetic field influences the viability of breast cancer cells 96 h after exposure
Get PDFBackground
The exposure of breast cancer to extremely low frequency magnetic fields (ELF-MFs) results in various biological responses. Some studies have suggested a possible cancer-enhancing effect, while others showed a possible therapeutic role. This study investigated the effects of in vitro exposure to 50 Hz ELF-MF for up to 24 h on the viability and cellular response of MDA-MB-231 and MCF-7 breast cancer cell lines and MCF-10A breast cell line.
Methods and results
The breast cell lines were exposed to 50 Hz ELF-MF at flux densities of 0.1 mT and 1.0 mT and were examined 96 h after the beginning of ELF-MF exposure. The duration of 50 Hz ELF-MF exposure influenced the cell viability and proliferation of both the tumor and nontumorigenic breast cell lines. In particular, short-term exposure (4–8 h, 0.1 mT and 1.0 mT) led to an increase in viability in breast cancer cells, while long and high exposure (24 h, 1.0 mT) led to a decrease in viability and proliferation in all cell lines. Cancer and normal breast cells exhibited different responses to ELF-MF. Mitochondrial membrane potential and reactive oxygen species (ROS) production were altered after ELF-MF exposure, suggesting that the mitochondria are a probable target of ELF-MF in breast cells.
Conclusions
The viability of breast cells in vitro is influenced by ELF-MF exposure at magnetic flux densities compatible with the limits for the general population and for workplace exposures. The effects are apparent after 96 h and are related to the ELF-MF exposure time