Emotion recognition from electroencephalogram (EEG) signals has garnered significant attention owing to its potential applications in affective computing, human-computer interaction, and mental health monitoring. This paper presents a comparative analysis of different machine learning methods...

This study presents a methodology to enhance energy management systems (EMS) in hybrid electric vehicles (HEVs) to reduce fuel consumption and greenhouse gas emissions. A novel surrogate-assisted optimization framework is employed, incorporating key performance metrics such as fuel efficiency...

This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets, focusing on Bitcoin (BTC) and Ethereum (ETH). Traditional data sampling methods, such as time bars, often fail to capture the nuances of...

Recently, internet users have significantly increased their use of search engines, and market investors are no exception. As a result, predictive models that incorporate scattered web-based information are developing as an area of forecasting. The objective of this research is to compare the...

The study conducts a bibliometric review of artificial intelligence applications in two areas: the entrepreneurial finance literature, and the corporate finance literature with implications for entrepreneurship. A rigorous search and screening of the web of science core collection identified...

The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can...