… In recent years, with the rapid development of artificial intelligence, deep learning has been
… has outstanding performance in electricity price forecasting. This study listed four cases to …

… on expert analysis and to extract multiple fea-tures is also time-consuming. This study uses
… In addition, in the face of the small training sample problems common to deep learning, this …

… machine learning applications expected to run the machine … [13] Stock market trading
furthermore uses machine learning … [14] It has applications in about each other field of study and is …

… This study first attempted the use of modern machine learning models to predict semiconductor
supply chain shortages. It proposed the utilization of Days of Inventory and textual data …

… A system is developed by sentiment analysis for examining … In this study, sentiment analysis
will be applied to analyze … prone areas using Text Analysis and Machine Learning. The main …

… (transformer) in the deep learning technology as the basic net-… price, high price, low price,
closing price, and trading volume), the multi-scale stock price prediction model of this study …

… the results of a study in a metallurgy company where data analysis and resource selection
methods were employed from continuous equipment monitoring. Machine learning and data …

… in describing financial market risks. The emergence of new intelligent machine learning
algorithms such as … Study of the Stock Market Risk Warning Based on GWO-SVM[J]. Operations …

… model and extracted the stock price characteristics with the … stock price prediction. [Results]
Compared with the experimental results of three models LSTM, GRU and TCN on six stock …

… , this study proposes a qutrit-based ternary encoding that … in a short-selling portfolio, and
considering the stock markets of … extending stock solution space by ternary encoding, this study …