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Seungmin Rho

Professor

Chung-Ang University

South Korea

Research Interests

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Professors in South Korea Professors in Reinforcement Learning Reinforcement Learning professors in South Korea

About

Professor Seungmin Rho is a faculty member at Chung-Ang University, South Korea. His research areas encompass advanced methodologies in financial market forecasting, portfolio management using deep reinforcement learning, and privacy-conscious object detection. Additionally, he explores machine learning applications in biomedical segmentation, gait recognition systems, and energy forecasting for building management systems.

Articles (22)

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Collaborators (6)

Jihoon Moon

Soonchunhyang University

SOUTH KOREA

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Yasmeen Ansari

Associate Professor

Saudi Electronic University

SAUDI ARABIA

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Yongsung Kim

Chungbuk National University

SOUTH KOREA

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Chifu Yang

Associate Professor

Harbin Institute of Technology

CHINA

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Mi Young Lee

Sejong University

SOUTH KOREA

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Dr. Saira Gillani

Professor

Central University of Punjab

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Seungmin Rho

Research Interests

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About

Articles (22)

Driving Investor Success: Novel Macro Momentum Accelerated Reinforcement Learning Framework for Stock Trading

An improved deep learning-based MSA-Net model for small liver tumor segmentation

Macroeconomic context-aware graph topology learning for stock price forecasting using graph neural network

A Novel Glow Adversarial Attack for Proactive Deepfake Defense

CMGM: A novel cross-market assets and multi-market modeling graph neural networks for financial market forecasting leveraging market states dependencies

A Novel RMS-Driven Deep Reinforcement Learning for Optimized Portfolio Management in Stock Trading

Predictive modeling for ubiquitin proteins through advanced machine learning technique

Machine Learning Based Missing Data Imputation in Categorical Datasets

A Federated Learning Framework for Brain Tumor Segmentation Without Sharing Patient Data

Object detection under the lens of privacy: A critical survey of methods, challenges, and future directions

An end-to-end gait recognition system for covariate conditions using custom kernel CNN

A Multifaceted Approach to Stock Market Trading Using Reinforcement Learning

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

Mordo: Silent Command Recognition Through Lightweight Around-Ear Biosensors

An autonomous decision-making framework for gait recognition systems against adversarial attack using reinforcement learning

An Interpretable Multivariate Time-series Anomaly Detection Method in Cyber-Physical Systems Based on Adaptive Mask

Learning Spatial Graph Structure for Multivariate KPI Anomaly Detection in Large-Scale Cyber-Physical Systems

Toward explainable electrical load forecasting of buildings: A comparative study of tree-based ensemble methods with Shapley values

Tuberculosis Detection in Chest Radiographs Using Spotted Hyena Algorithm Optimized Deep and Handcrafted Features

A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting

An Empirical Study of Macroeconomic Factors and Stock Returns in the Context of Economic Uncertainty News Sentiment Using Machine Learning

Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU

Collaborators (6)

Jihoon Moon

Yasmeen Ansari

Yongsung Kim

Chifu Yang

Mi Young Lee

Dr. Saira Gillani

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