AI in finance refers to the applications of AI techniques in financial businesses. This area has
attracted attention for decades, with both classic and modern AI techniques applied to …

This paper introduces a novel application of Kolmogorov-Arnold Networks (KANs) to time
series forecasting, leveraging their adaptive activation functions for enhanced predictive …

The paradigm of machine learning and artificial intelligence has pervaded our everyday life
in such a way that it is no longer an area for esoteric academics and scientists putting their …

Determining the price movement of stocks is a challenging problem to solve because of
factors such as industry performance, economic variables, investor sentiment, company …

Particulate matter forecasting is fundamental for early warning and controlling air pollution,
especially PM2. 5. The increase in this level of concentration will lead to a negative impact …

Forecasting of future stock prices is a complex and challenging research problem due to the
random variations that the time series of these variables exhibit. In this work, we study the …

Wheat is one of the most produced and consumed grain products worldwide. Wheat is the
main grain product in developed and underdeveloped countries. Flour obtained from wheat …

Designing an optimum portfolio for allocating suitable weights to its constituent assets so
that the return and risk associated with the portfolio are optimised is a computationally hard …

Building predictive models for robust and accurate prediction of stock prices and stock price
movement is a challenging research problem to solve. The well-known efficient market …

Stock market prediction (SMP) is a challenging task due to its uncertainty, nonlinearity, and
volatility. Machine learning models, such as artificial neural networks (ANNs) and support …