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yongyun cho

Professor

Sunchon National University

South Korea

Research Interests

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About

Yongyun Cho is a Professor at Sunchon National University in South Korea. His research areas include phishing website detection, DDoS attack detection in Linux containers, and renewable energy forecasting. He has contributed to significant advancements in machine learning applications for agricultural data analysis, cybersecurity, and big data handling. His recent publications reflect a strong focus on hybrid models and innovative techniques in these domains.

Articles (14)

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2026

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2026

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2022

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2021

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2020

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2020

Collaborators (3)

Woosuk Choi

Associate Professor

Sejong University

SOUTH KOREA

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Ginu Rajan

Reader (Associate Professor)

Cardiff University

UNITED KINGDOM

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Changsun Shin

Sunchon National University

SOUTH KOREA

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yongyun cho

Research Interests

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About

Articles (14)

Novel VMD-Informer-LSTM architecture for solar radiation forecasting with signal decomposition and sparse attention for smart energy applications

Adaptive AI Hybrid Control Method for Large Unmanned Aerial Vehicle Systems in Dynamic Environments

BGL-PhishNet: Phishing Website Detection Using Hybrid Model-BERT, GNN, and LightGBM

eBPF based Runtime Detection of Semantic DDoS Attacks in Linux Containers

Efficient solar power generation forecasting for greenhouses: A hybrid deep learning approach

An Effective Detection Approach for Phishing URL Using ResMLP

Enhancing Security in LLNs Using a Hybrid Trust-Based Intrusion Detection System for RPL

Evasion Attacks and Defense Mechanisms for Machine Learning-Based Web Phishing Classifiers

Saltus–“A Sudden Transition” Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

The Power of Vision Transformers and Acoustic Sensors for Cotton Pest Detection

A Crop Growth Prediction Model Using Energy Data Based on Machine Learning in Smart Farms

Efficient energy consumption prediction model for a data analytic-enabled industry building in a smart city

Season wise bike sharing demand analysis using random forest algorithm

Using data mining techniques for bike sharing demand prediction in metropolitan city

Collaborators (3)

Woosuk Choi

Ginu Rajan

Changsun Shin

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