A study of forecasting stocks price by using deep Reinforcement Learning

Khan, Razib Hayat; Miah, Jonayet; Rahman, Md Minhazur; Hasan, Md. Maruf; Mamun, Muntasir

doi:10.1109/aiiot58121.2023.10174358

Cited by 8 publications

(1 citation statement)

References 12 publications

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“…A recent significant advancement comes in the form of Deep Convolutional Neural Networks. These networks have made it possible to extract meaningful semantic information from real images [13,14,15]. These networks are trained for specific objectives such as recognizing objects, and they possess the ability to generalize high-level content across various datasets and even different visual tasks, encompassing aspects like recognizing textures and categorizing artistic styles.…”

Section: Methodsmentioning

confidence: 99%

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Miah,

Cao,

Sayed

et al. 2023

JCSTS

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Artistic style transfer, a captivating application of generative artificial intelligence, involves fusing the content of one image with the artistic style of another to create unique visual compositions. This paper presents a comprehensive overview of a novel technique for style transfer using Convolutional Neural Networks (CNNs). By leveraging deep image representations learned by CNNs, we demonstrate how to separate and manipulate image content and style, enabling the synthesis of high-quality images that combine content and style in a harmonious manner. We describe the methodology, including content and style representations, loss computation, and optimization, and showcase experimental results highlighting the effectiveness and versatility of the approach across different styles and content.

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Section: Methodsmentioning

confidence: 99%

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Miah,

Cao,

Sayed

et al. 2023

JCSTS

View full text Add to dashboard Cite

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Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates

Park,

Kim,

Huh

2024

IEEE Access

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With the development of artificial intelligence, there have been many attempts to incorporate artificial intelligence into algorithmic trading. In particular, reinforcement learning, which aims to solve dynamic decision-making problems, is attracting attention because of its high utilization in algorithmic trading. In this paper, we will implement a simple Deep Reinforcement Learning (DRL) trading robot to check the performance of DRL. In addition, we tried to find out how much performance improvement can be achieved by comparing a robot that learned a single stock data with a robot that learned stock data, market index, and interest rate data. This paper aims to develop a stock investment robot using a Proximal Policy Optimization (PPO) reinforcement learning algorithm and analyze the performance of the robot. The first robot used only the stock data of APPL INC, a single stock, as input, and the second robot used stock data of APPL INC and the S&P 500 index together with US interest rate data as input data. Afterward, the stock investment performance of the two robots for APPL INC was comparatively analyzed using the test data.

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Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study

Haque,

Amin,

Ahmad

et al. 2023

2023 10th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)

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A study of forecasting stocks price by using deep Reinforcement Learning

Cited by 8 publications

References 12 publications

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Generative AI Model for Artistic Style Transfer Using Convolutional Neural Networks

Deep Reinforcement Learning Robots for Algorithmic Trading: Considering Stock Market Conditions and U.S. Interest Rates

Predicting Kidney Failure using an Ensemble Machine Learning Model: A Comparative Study

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