What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Machine learning
- Operating system
Sung Wook Baik mainly investigates Artificial intelligence, Convolutional neural network, Encryption, Computer vision and Fire detection.
His study in Artificial intelligence is interdisciplinary in nature, drawing from both Machine learning and Pattern recognition.
His study on Deep cnn is often connected to Multi-factor authentication as part of broader study in Pattern recognition.
The various areas that Sung Wook Baik examines in his Convolutional neural network study include Feature extraction, Deep learning, Speech recognition and Benchmark.
His Encryption study combines topics from a wide range of disciplines, such as RGB color model, Key and Steganography.
Sung Wook Baik interconnects Automatic summarization and Key frame in the investigation of issues within Computer vision.
His most cited work include:
- Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features (215 citations)
- Multi-grade brain tumor classification using deep CNN with extensive data augmentation (126 citations)
- Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption (125 citations)
What are the main themes of his work throughout his whole career to date?
Sung Wook Baik focuses on Artificial intelligence, Computer vision, Pattern recognition, Convolutional neural network and Data mining.
His research on Artificial intelligence often connects related areas such as Machine learning.
In Computer vision, Sung Wook Baik works on issues like Automatic summarization, which are connected to Frame, Key and Video tracking.
His Pattern recognition study combines topics in areas such as Hash function and Feature.
Sung Wook Baik has researched Convolutional neural network in several fields, including Discriminative model, Search engine indexing and Benchmark.
Sung Wook Baik combines subjects such as Contextual image classification, Visualization and Speech recognition with his study of Feature extraction.
He most often published in these fields:
- Artificial intelligence (62.12%)
- Computer vision (29.29%)
- Pattern recognition (21.72%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (62.12%)
- Convolutional neural network (17.17%)
- Deep learning (7.58%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Artificial intelligence, Convolutional neural network, Deep learning, Data mining and Pattern recognition.
His Artificial intelligence research is multidisciplinary, relying on both Machine learning and Computer vision.
His studies deal with areas such as Autoencoder, Frame, Real-time computing and Benchmark as well as Convolutional neural network.
His Deep learning research integrates issues from Ensemble learning, Civil engineering, Energy management and Violence detection.
The study incorporates disciplines such as Function, Power management, Energy and Automatic summarization in addition to Data mining.
The Pattern recognition study combines topics in areas such as RGB color model and Hash function.
Between 2018 and 2021, his most popular works were:
- Multi-grade brain tumor classification using deep CNN with extensive data augmentation (126 citations)
- Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications (100 citations)
- Raspberry Pi assisted face recognition framework for enhanced law-enforcement services in smart cities (68 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Operating system
- Machine learning
His scientific interests lie mostly in Convolutional neural network, Artificial intelligence, Real-time computing, Feature extraction and Benchmark.
His Convolutional neural network research includes themes of Frame, Speech recognition, Discriminative model and Automatic summarization.
His Automatic summarization research is multidisciplinary, incorporating perspectives in Data mining and Search engine indexing.
His research in Artificial intelligence intersects with topics in Machine learning and Pattern recognition.
Sung Wook Baik combines subjects such as Hash function and Medical imaging with his study of Pattern recognition.
As part of the same scientific family, Sung Wook Baik usually focuses on Real-time computing, concentrating on Mobile edge computing and intersecting with Jitter, Server-side, Encoder, Quality of service and Smoothing.
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