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Amin Ullah
Researcher at Sejong University
Publications - 45
Citations - 3042
Amin Ullah is an academic researcher from Sejong University. The author has contributed to research in topics: Convolutional neural network & Feature extraction. The author has an hindex of 17, co-authored 43 publications receiving 1210 citations. Previous affiliations of Amin Ullah include Islamia College University & Oregon State University.
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Action Recognition in Video Sequences using Deep Bi-Directional LSTM With CNN Features
TL;DR: A novel action recognition method by processing the video data using convolutional neural network (CNN) and deep bidirectional LSTM (DB-LSTM) network that is capable of learning long term sequences and can process lengthy videos by analyzing features for a certain time interval.
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Multi-grade brain tumor classification using deep CNN with extensive data augmentation
TL;DR: A novel convolutional neural network (CNN) based multi-grade brain tumor classification system that is experimentally evaluated on both augmented and original data and results show its convincing performance compared to existing methods.
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A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting
Muhammad Sajjad,Zulfiqar Ahmad Khan,Amin Ullah,Tanveer Hussain,Waseem Ullah,Mi Young Lee,Sung Wook Baik +6 more
TL;DR: The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features’ extraction potentials of CNNs and effectual gated structure of multi-layered GRU.
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Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
TL;DR: This survey highlights the power of DL architectures in terms of reliability and efficient real-time performance and overviews state-of-the-art strategies for safe AD, with their major achievements and limitations.
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CNN features with bi-directional LSTM for real-time anomaly detection in surveillance networks
TL;DR: This paper presents an efficient deep features-based intelligent anomaly detection framework that can operate in surveillance networks with reduced time complexity and reports a 3.41% and 8.09% increase in accuracy on UCF-Crime and UCFCrime2Local datasets compared to state-of-the-art methods.