A way to predict the stock price of the Tehran Stock Exchange in relation to knowledge
Journal Title: Electronic and Cyber Defense - Year 2023, Vol 10, Issue 4
Abstract
In recent years, due to the profitability of the stock market in Iran, small and large investments were attracted to this market, but unfortunately, due to their lack of knowledge of the stock market and price forecasting, a large number of Iranians suffered great losses. In this study, we decided to use our previous research, which used a two-layer LSTM neural network, to strengthen its work and use a combination of convolution and lstm neural networks to predict stock prices on the Web Nation data set from the stock market. Use Tehran and its three databases, including ASP, car and construction. Finally, in order to evaluate the proposed method and the other two methods, three error functions, mean square error function (MSE), mean absolute error function (MAE) and root mean square function (RMSE) were evaluated. The results showed that it works much better in large datasets with high stock data and leads to fewer errors.
Authors and Affiliations
tuba toraby pour,safieh siadat,
Speeding up the execution-time of Crystals-Kyber PQC Algorithm on FPGA
Quantum computers have much more computing power than classical computers and this has created a challenge in the field of public-key cryptography algorithms, which is predicted quantum computers will reach the computati...
Identify malicious traffic on IoT infrastructure using neural networks and deep learning
The Internet of Things is a network of physical devices and equipment that includes sensors, software, and other technologies for exchanging data with other devices and systems over the Internet. The spread of the Intern...
Identify the Factors Affecting the Culture and Awareness of Cyber Security Using Theme Analysis
Cybercriminals are targeting more humans than machines these days because they try to exploit users' vulnerabilities to achieve their destructive goals. The main purpose of this study is to identify the factors affecting...
A Malware Classification Method Using visualization and Word Embedding Features
With the explosive growth of threats to Internet security, malware visualization in malware classification has become a promising study area in security and machine learning. This paper proposes a visualization method fo...
Developing a Threat-Tolerability Bilateral Concept within a Differential Game for the Analysis of the Insider/Adversary Behavior in Operational environment
Threat-tolerability as an innovative bilateral concept that focuses on the analysis of insider/adversary behavior is proposed. A zero sum differential game is designed to model the interaction between the two introduced...