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Researchers often use machine learning and deep learning to predict price trends in the financial markets, aiming to achieve high returns. However, accurately predicting market prices is challenging due to their nonlinear and seemingly random nature. Improving the accuracy of the prediction model is the common focus of researchers, yet it is crucial to also consider the data used in training. Traditional labeling methods used in most price trend prediction studies are not robust as they are sensitive to small price changes, leading to inefficient model training. To address this issue, this study introduces a Dynamic Threshold Breakout (DTB) labeling system that labels data based on the price percentage change during a specific period. This proposed labeling system was then integrated into an automated trading system using LightGBM and evaluated using three different markets. The results showed that the DTB labeling method is effective for trading in financial markets in terms of winning ratio, payoff ratio, profit factor, accuracy and ROI in trading performance. [ABSTRACT FROM AUTHOR]

Microarray data provide lots of information regarding gene expression levels. Due to the large amount of such data, their analysis requires sufficient computational methods for identifying and analyzing gene regulation networks; however, researchers in this field are faced with numerous challenges such as consideration for too many genes and at the same time, the limited number of samples and their noisy nature of the data. In this paper, a hybrid method base on fuzzy cognitive map and compressed sensing is used to identify interactions between genes. For this purpose, in inference of the gene regulation network, the Ensemble Kalman filtered compressed sensing is used to learn the fuzzy cognitive map. Using the Ensemble Kalman filter and compressed sensing, the fuzzy cognitive map will be robust against noise. The proposed algorithm is evaluated using several metrics and compared with several well know methods such as LASSOFCM, KFRegular, CMI2NI. The experimental results show that the proposed method outperforms methods proposed in recent years in terms of SSmean, Data Error and accuracy.