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.
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.
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.
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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Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
Amin Ullah;Jamil Ahmad;Khan Muhammad;Muhammad Sajjad.
IEEE Access (2018)
Multi-grade brain tumor classification using deep CNN with extensive data augmentation
Muhammad Sajjad;Salman Khan;Khan Muhammad;Wanqing Wu.
Journal of Computational Science (2019)
Convolutional Neural Networks Based Fire Detection in Surveillance Videos
Khan Muhammad;Jamil Ahmad;Irfan Mehmood;Seungmin Rho.
IEEE Access (2018)
Speech Emotion Recognition from Spectrograms with Deep Convolutional Neural Network
Abdul Malik Badshah;Jamil Ahmad;Nasir Rahim;Sung Wook Baik.
international conference on platform technology and service (2017)
Early fire detection using convolutional neural networks during surveillance for effective disaster management
Khan Muhammad;Jamil Ahmad;Sung Wook Baik.
Efficient visual attention based framework for extracting key frames from videos
Naveed Ejaz;Irfan Mehmood;Sung Wook Baik.
Signal Processing-image Communication (2013)
Adaptive key frame extraction for video summarization using an aggregation mechanism
Naveed Ejaz;Tayyab Bin Tariq;Sung Wook Baik.
Journal of Visual Communication and Image Representation (2012)
A novel magic LSB substitution method (M-LSB-SM) using multi-level encryption and achromatic component of an image
Khan Muhammad;Muhammad Sajjad;Irfan Mehmood;Seungmin Rho.
Multimedia Tools and Applications (2016)
Secure Surveillance Framework for IoT Systems Using Probabilistic Image Encryption
Khan Muhammad;Rafik Hamza;Jamil Ahmad;Jaime Lloret.
IEEE Transactions on Industrial Informatics (2018)
Efficient Deep CNN-Based Fire Detection and Localization in Video Surveillance Applications
Khan Muhammad;Jamil Ahmad;Zhihan Lv;Paolo Bellavista.
IEEE Transactions on Systems, Man, and Cybernetics (2019)
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