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All Journal Indonesian Journal of Data and Science Makara Journal of Technology
Yousefi, Neda
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Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mathematics Mechanical Engineering

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[WITHDRAWN] Deep Reinforcement Learning for Tehran Stock Trading Yousefi, Neda
Indonesian Journal of Data and Science Vol. 3 No. 3 (2022): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v3i3.53

Abstract

One of the most interesting topics for research and also for making a profit is stock trading. Artificial intelligence has had a great impact on this path. A lot of research has been done to investigate the application of machine learning, and deep learning methods in stock trading. Despite the large amount of research done in the field of prediction and automation trading, stock trading as a deep reinforcement-learning problem remains an open research area. The progress of reinforcement learning as well as the intrinsic properties of reinforcement learning make it a suitable method for market trading in theory. In this paper, single stock trading models are presented based on the fine-tuned state-of-the-art deep reinforcement learning algorithms (Deep Deterministic Policy Gradient (DDPG) and Advantage Actor Critic (A2C)). These algorithms are able to interact with the trading market and capture the financial market dynamics. The proposed models are compared, evaluated, and verified on historical stock trading data. Annualized return and Sharpe ratio have been used to evaluate the performance of proposed models. The results show that the agent designed based on both algorithms is able to make intelligent decisions on historical data. The DDPG strategy performs better than the A2C and achieves better results in terms of convergence, stability, and evaluation criteria.
Semantic-Guided Diffusion Autoencoder for Medical Image Synthesis and Representation Learning Yousefi, Neda; Koochari, Abbas; Zamanifar, Azadeh
Makara Journal of Technology Vol. 30, No. 2
Publisher : UI Scholars Hub

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Abstract

The development of robust machine learning models for medical diagnostics is constrained by the scarcity of large, annotated imaging datasets, a challenge rooted in high acquisition costs and stringent privacy regulations. To address this, generative approaches for data synthesis have emerged as an essential tool, and this study explores the use of diffusion models to generate medical images. This study introduces a novel Diffusion Autoencoder Model (DAM) integrated with a semantic encoder framework to generate interpretable and decodable image representations. Our DAM learns disentangled latent features via its encoder, which then conditions a diffusion probabilistic model acting as the decoder, enabling the synthesis of highly informative medical images. The model’s performance is assessed by using quantitative metrics demonstrating superior results compared to state-of-the-art generative models. In addition to its generative precision, the proposed model has significant potential for image classification. Notably, the semantic encoder learned by the model generates representations that lead to a significant increase in classification accuracy: for PneumoniaMNIST, the binary classification accuracy improved from 0.854 to 0.95; for RetinaMNIST, the multiclass accuracy increased from 0.528 to 0.94. These findings underscore the potential of DAMs to tackle data scarcity issues, enhance classification performance, and ultimately improve diagnostic methodologies.
Co-Authors Koochari, Abbas Zamanifar, Azadeh