World's Best Scientists 2026 revealed!
Sung-Bae Cho

Sung-Bae Cho

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Computer Science
Korea
2025

D-Index & Metrics

Computer Science

D-Index
63
Citations
17152
World Ranking
2741
National Ranking
12

Research.com Recognitions

  • 2025 - Research.com Computer Science in Korea Leader Award
  • 2023 - Research.com Computer Science in Korea Leader Award
  • 2022 - Research.com Computer Science in Korea Leader Award

Overview

Sung-Bae Cho is affiliated with Yonsei University in South Korea. Their research spans several interdisciplinary fields predominantly within computer science and engineering. The main fields of study include Computer Science, with 131 publications, and Engineering, with 55 publications.

The subfields of Sung-Bae Cho's work reflect a focus on advanced computational methods and systems, including:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering
  • Computer Networks and Communications
  • Information Systems

Their research covers various key topics, emphasizing practical and theoretical aspects of computing technology. These topics are:

  • Network Security and Intrusion Detection
  • Energy Load and Power Forecasting
  • Anomaly Detection Techniques and Applications
  • Neural Networks and Applications
  • Advanced Malware Detection Techniques
  • Stock Market Forecasting Methods
  • Recommender Systems and Techniques

Sung-Bae Cho has contributed to numerous publications frequently appearing in specific venues, including:

  • IEEE Access
  • Neurocomputing
  • Journal of KIISE
  • arXiv (Cornell University)
  • Expert Systems with Applications

A selection of recent papers illustrates the focus and diversity of their work:

  • "Obfuscated Malware Detection Using Deep Generative Model based on Global/Local Features" (2021), Computers & Security
  • "Optimizing CNN-LSTM neural networks with PSO for anomalous query access control" (2021), Neurocomputing
  • "3D-Convolutional Neural Network with Generative Adversarial Network and Autoencoder for Robust Anomaly Detection in Video Surveillance" (2020), International Journal of Neural Systems
  • "Towards effective detection of elderly falls with CNN-LSTM neural networks" (2022), Neurocomputing
  • "Explainable prediction of electric energy demand using a deep autoencoder with interpretable latent space" (2021), Expert Systems with Applications

The scientist's collaborations include frequent coauthors:

  • Seok-Jun Bu (21 publications)
  • Satchidananda Dehuri (15 publications)
  • Hyung-Jun Moon (13 publications)
  • Sandeep Kumar Satapathy (8 publications)
  • Sarat Chandra Nayak (7 publications)

In addition to journal articles, Sung-Bae Cho has authored books published by renowned academic publishers. These book publications include:

  • Intelligent Data Engineering and Automated Learning - IDEAL 2021 (2021), Springer Science+Business Media
  • Pattern Recognition (2023), Springer Science+Business Media
  • Biologically Inspired Techniques in Many-Criteria Decision Making (2020), Springer International Publishing

Best Publications

  • Predicting residential energy consumption using CNN-LSTM neural networks

    Tae Young Kim;Sung Bae Cho

  • Human activity recognition with smartphone sensors using deep learning neural networks

    Charissa Ann Ronao;Sung-Bae Cho

  • Location-based recommendation system using Bayesian user's preference model in mobile devices

    Moon-Hee Park;Jin-Hyuk Hong;Sung-Bae Cho

  • Combining multiple neural networks by fuzzy integral for robust classification

    Sung-Bae Cho;J.H. Kim

  • Application of interactive genetic algorithm to fashion design

    Hee Su Kim;Sung Bae Cho

  • Machine learning in DNA microarray analysis for cancer classification

    Sung-Bae Cho;Hong-Hee Won

  • Web traffic anomaly detection using C-LSTM neural networks

    Tae Young Kim;Sung Bae Cho

  • Multiple network fusion using fuzzy logic

    Sung-Bae Cho;J.H. Kim

  • Activity recognition using hierarchical hidden markov models on a smartphone with 3D accelerometer

    Young-Seol Lee;Sung-Bae Cho

  • Neural-network classifiers for recognizing totally unconstrained handwritten numerals

    Sung-Bae Cho

  • Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders

    Jin Young Kim;Seok Jun Bu;Sung Bae Cho

  • Efficient anomaly detection by modeling privilege flows using hidden Markov model

    Sung-Bae Cho;Hyuk-Jang Park

  • A context-aware music recommendation system using fuzzy bayesian networks with utility theory

    Han-Saem Park;Ji-Oh Yoo;Sung-Bae Cho

  • Evolutionary neural networks for anomaly detection based on the behavior of a program

    Sang-Jun Han;Sung-Bae Cho

  • A comprehensive survey on functional link neural networks and an adaptive PSO–BP learning for CFLNN

    Satchidananda Dehuri;Sung-Bae Cho

  • Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors

    Charissa Ann Ronao;Sung Bae Cho

  • An efficient genetic algorithm with less fitness evaluation by clustering

    Hee-Su Kim;Sung-Bae Cho

  • Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve Bayes classifiers

    Jin-Hyuk Hong;Jun-Ki Min;Ung-Keun Cho;Sung-Bae Cho

  • Hybrid Artificial Intelligent Systems

    Emilio Corchado;Vaclav Snasel;Ajith Abraham;Michał Woźniak

  • Estimating the efficiency of recognizing gender and affect from biological motion.

    Frank E. Pollick;Vaia Lestou;Jungwon Ryu;Sung Bae Cho

Frequent Co-Authors

Ashish Ghosh
Ashish Ghosh Indian Statistical Institute
Frank E. Pollick
Frank E. Pollick University of Glasgow
Ajith Abraham
Ajith Abraham Sai University
Emilio Corchado
Emilio Corchado University of Salamanca
Hideyuki Takagi
Hideyuki Takagi Kyushu University
Xin Yao
Xin Yao Lingnan University
Vaclav Snasel
Vaclav Snasel VSB – Technical University of Ostrava
Bernard J. Baars
Bernard J. Baars Florida Atlantic University
Manuel Graña
Manuel Graña University of the Basque Country
Jong-Hwan Kim
Jong-Hwan Kim Korea Advanced Institute of Science and Technology

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