Business and Social Behaviour Intelligence Analysis Using PSO

The goal of this paper is to elaborate swarm intelligence for business intelligence decision making and the business rules management improvement. The paper introduces the decision making model which is based on the application of Artificial Neural Networks (ANNs) and Particle Swarm Optimization (PSO) algorithm. Essentially the business spatial data illustrate the group behaviors. The swarm optimization, which is highly influenced by the behavior of creature, performs in group. The Spatial data is defined as data that is represented by 2D or 3D images. SQL Server supports only 2D images till now. As we know that location is an essential part of any organizational data as well as business data: enterprises maintain customer address lists, own property, ship goods from and to warehouses, manage transport flows among their workforce, and perform many other activities. By means to say a lot of spatial data is used and processed by enterprises, organizations and other bodies in order to make the things more visible and self-descriptive. From the experiments, we found that PSO is can facilitate the intelligence in social and business behaviour

Perspective Chapter: The Metaverse for Education

Due to ever-evolving teaching methods and technologies, the education sector has undergone and still undergoes several advances. The issue of knowledge creation and application through tutoring has evolved into the primary source of competitive advantage among colleges and a crucial element for success. In a turbulent world full of competitiveness, educational institutions and establishments strive to continuously provide excellence through top-notch education, international partnerships, collaborations, acknowledgments, and accreditations as well as updated technological tools such as e-learning platforms and online labs/workshops, constant investment in each person’s human capital. It is hoped that this would improve people’s knowledge, abilities, experiences, and cultural capital. Our digital lives have advanced quickly over the past two years to the point where living online and offline are now equally important. The purpose of this chapter is to discuss the significance of sustaining education while taking into consideration the undeniable technological advances. This chapter adopts a descriptive/narrative methodology approach. As a result, it can be concluded that, if implemented properly, virtual collaboration and success will become essential skills that professionals across almost all sectors would master. Hence, upcoming research could include the role of the metaverse in education on another aspect of the SGDs like “no poverty.

Is Metaverse in education a blessing or a curse: a combined content and bibliometric analysis

The Metaverse has been the centre of attraction for educationists for quite some time. This field got renewed interest with the announcement of social media giant Facebook as it rebranding and positioning it as Meta. While several studies conducted literature reviews to summarize the findings related to the Metaverse in general, no study to the best of our knowledge focused on systematically summarizing the finding related to the Metaverse in education. To cover this gap, this study conducts a systematic literature review of the Metaverse in education. It then applies both content and bibliometric analysis to reveal the research trends, focus, and limitations of this research topic. The obtained findings reveal the research gap in lifelogging applications in educational Metaverse. The findings also show that the design of Metaverse in education has evolved over generations, where generation Z is more targeted with artificial intelligence technologies compared to generation X or Y. In terms of learning scenarios, there have been very few studies focusing on mobile learning, hybrid learning, and micro learning. Additionally, no study focused on using the Metaverse in education for students with disabilities. The findings of this study provide a roadmap of future research directions to be taken into consideration and investigated to enhance the adoption of the Metaverse in education worldwide, as well as to enhance the learning and teaching experiences in the Metaverse

Método híbrido interativo sarima support vector regression wavelet de múltiplos núcleos na previsão de séries temporais de instrumentos de barragens

Orientador : Prof. Dr. Arinei Carlos Lindbeck da SilvaCo-orientador : Prof. Dr. Luiz Albino Teixeira JúniorTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 22/12/2015Inclui referências : f.82-93Resumo: Nesta tese de Doutorado é apresentado um novo método preditivo híbrido, formado basicamente pela combinação dos métodos SARIMA, Support Vector Regression e Wavelet, denominado como SARIMA Support Vector Regression Wavelet de Múltiplos Núcleos (SSVRWMN), para a predição de valores de leitura de instrumentos de barragens de concreto de usinas hidroelétricas. Tendo as previsões pontuais, estimase o intervalo de confiança por meio da técnica Bootstrap. O método SSVRWMN Bootstrap contempla as seguintes abordagens: os modelos SARIMA (para mapear estruturas de autodependência lineares sazonais e simples); a decomposição Wavelet integrada com modelos Support Vector Regression (SVRs) (que mapeiam estruturas de autodependência não lineares e da frequência espectral inerente aos dados); a programação não linear (utilizada no ajuste numérico dos parâmetros associados às combinações de previsões) e a técnica Bootstrap aplicada aos resíduos do modelo SSVRWMN com a finalidade de se estimar o intervalo de confiança Bootstrap. O objetivo é produzir previsões para as séries temporais provenientes de instrumentos de barragens, agregadoras de informações estocásticas distintas capturadas por diferentes métodos. A fim de avaliar a eficiência do método preditivo SSVRWMN, este foi aplicado a algumas séries temporais provenientes da aferição de instrumentos situados no bloco-chave I10 da barragem de Itaipu (as quais são utilizadas na análise probabilística de risco de tombamento dos blocos no sentido montante-jusante). O desempenho preditivo alcançado pelo método SSVRWMN, em relação aos métodos preditivos SARIMA, SVR e composto SARIMA-SVR, foi notadamente superior, na presente tese. Palavras-chave: Séries temporais, Instrumentação de barragens, SARIMA, Wavelet, Support Vector Regression, Programação matemática, Técnica Bootstrap.Abstract: In this doctoral thesis is presented a new hybrid predictive method, formed by the combination of the methods SARIMA, Support Vector Regression and Wavelet referred as: SARIMA Support Vector Regression Wavelet of multiple kernels (SSVRWMN), for the prediction of reading values of concrete dams of hydroelectric plants. With the forecasts, it is estimated the confidence interval by Bootstrap technique. The method SSVRWMN Bootstrap includes the following approaches: SARIMA models (to map linear auto-dependence structures simple and seasonal); Wavelet decomposition integrated with Support Vector Regression models (SVR) (which map non-linear auto-dependence structures and spectral frequency inherent to data); nonlinear programming (used in the numerical adjustment of the parameters associated with combinations of forecasts) and the Bootstrap residual technique applied to residue the model SSVRWMN in order to estimate the Bootstrap confidence interval. The goal is to produce forecasts for the time series from instruments of dams that are aggregators of distinctive stochastic information captured by different methods. In order to evaluate the efficiency of method SSVRWMN predictive , this was applied to some time series from instruments located in block-key I10, of Itaipu Dam (which are used in probabilistic analysis tipover risk of blocks in the downstreamupstream direction). The predictive performance achieved by SSVRWMN concerning the traditional approaches SARIMA, SVR and composed SARIMA-SVR, have been remarkable superior. Keywords: Time series, dam Instrumentation, SARIMA, Wavelet, Support Vector Regression, Mathematical programming, Bootstrap Technique