In the financial industry, financial fraud is an ever-evolving risk with extreme consequences.
Data mining has been instrumental in the recognition of credit card fraud (CCF) during …

Forecasting financial markets is a complex task that requires addressing various challenges,
such as market complexity, data heterogeneity, the need for rapid response and constant …

In the period of such fluctuations, the financial market serves as a significant identifier of a
country's economy status with which the financial specialists/economists can compare the …

Stock market prediction by using Machine Learning (ML) models has been a hot topic of
research for more than a decade. Combined with the power of sentiment analysis and ML …

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic
approach; their task is often to predict the optimal control values working with a process …

Globally, one of the most fundamental applications of time-series data is stock market
forecasting, where each and every second is crucial and analysis is unpredictable, posing a …

The stock market is a complex and dynamic industry that has garnered the attention of
experts who seek to understand its various trends. Accurately predicting stock prices is …

Using metaheuristics such as the Evolutionary Algorithm (EA) within control structures is a
realistic approach for certain optimal control problems. They often predict the optimal control …

The use of algorithms in finance and trading has become an increasingly thriving research
area, with researchers creating automated and pre programmed trading instructions utilising …

When assessing an institution's performance, the level of academic achievement by its
students is a crucial factor. Educational Data Mining (EDM) uses ensemble models of Data …