S
Sung Wook Baik
Researcher at Sejong University
Publications - 234
Citations - 8776
Sung Wook Baik is an academic researcher from Sejong University. The author has contributed to research in topics: Convolutional neural network & Computer science. The author has an hindex of 38, co-authored 202 publications receiving 5183 citations. Previous affiliations of Sung Wook Baik include George Mason University & Universiti Teknologi Malaysia.
Papers
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A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction
TL;DR: A hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset is developed and the experimental results show that HAOC will give the best performance value for bankruptcy Prediction compared with the existing approaches.
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DB-Net: A novel dilated CNN based multi-step forecasting model for power consumption in integrated local energy systems
TL;DR: A novel hybrid network model ‘DB-Net’ is presented by incorporating a dilated convolutional neural network (DCNN) with bidirectional long short-term memory (BiLSTM) with better predictive performance than existing methods, thereby confirming its effectiveness.
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Erratum to: Video Summarization Based Tele-endoscopy: A Service to Efficiently Manage Visual Data Generated During Wireless Capsule Endoscopy Procedure
TL;DR: A video summarization-based tele-endoscopy service that estimates the semantically relevant video frames from the perspective of gastroenterologists that ensures the sending of diagnostically relevant frames to the gastroenterologist instead of sending all the data, thus saving transmission costs and bandwidth.
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Image steganography for authenticity of visual contents in social networks
TL;DR: This paper proposes a secure crystographic framework for authenticity of visual contents using image steganography, utilizing color model transformation, three-level encryption algorithm (TLEA), and Morton scanning least significant bit (LSB) substitution.
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An Efficient Deep Learning Framework for Intelligent Energy Management in IoT Networks
TL;DR: This article focuses on the requirements of todays' smart grids, homes, and industries to propose a deep-learning-based framework for intelligent energy management and applies several preprocessing techniques to deal with the diverse nature of electricity data, followed by an efficient decision-making algorithm for short-term forecasting.