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A bidirectional LSTM deep learning approach for intrusion detection
Expert Systems with Applications, 2021Abstract The rise in computer networks and internet attacks has become alarming for most service providers. It has triggered the need for the development and implementation of intrusion detection systems (IDSs) to help prevent and or mitigate the challenges posed by network intruders.
Yakubu Imrana +3 more
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DECAB-LSTM: Deep Contextualized Attentional Bidirectional LSTM for cancer hallmark classification
Knowledge-Based Systems, 2020Abstract The great number of online scientific publications on cancer research makes large scale data mining possible. The hallmarks or characteristics of cancer can be used to distinguish cancerous cells from normal cells. Therefore, it is extremely necessary to organize and categorize a sea of scientific articles into the corresponding hallmarks by
Jiang L. +3 more
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Bidirectional LSTM for Automatic Punctuation Restoration
2016The output of generic automatic speech recognition systems consists of raw word sequences without any punctuation symbols. When sequences are longer, it is difficult for humans to read and understand them. Also, many natural language understanding and processing tools expect that input will contain punctuation.
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Sequence-Based Recommendation with Bidirectional LSTM Network
2018In modern recommendation systems, most methods often neglect the sequential relationship between items. So we propose a novel Sequence-based Recommendation model with Bidirectional Long Short-Term Memory neural network (BiLSTM4Rec) which can capture the sequential feature of items to predict what a user will choose next. By collecting consumed items of
Hailin Fu +4 more
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Modeling Genome Data Using Bidirectional LSTM
2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC), 2019Bidirectional Long Short-Term Memory (LSTM) is a special kind of Recurrent Neural Network (RNN) architecture which is designed to model sequences and their long-range dependencies more precisely than RNNs. This paper proposes to use deep bidirectional LSTM for sequence modeling as an approach to perform locality-sensitive hashing (LSH)-based sequence ...
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Face alignment with Cascaded Bidirectional LSTM Neural Networks
2016 23rd International Conference on Pattern Recognition (ICPR), 2016Face alignment is an important issue in many computer vision problems. The key problem is to find the nonlinear mapping from face image or feature to landmark locations. In this paper, we propose a novel cascaded approach with bidirectional Long Short Term Memory (LSTM) neural networks to approximate this nonlinear mapping.
Yu Chen 0037 +3 more
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Implementation of Bidirectional LSTM Accelerator Based on FPGA
2022 IEEE 22nd International Conference on Communication Technology (ICCT), 2022Hao Wang, Danfeng Qiu, Fen Ge, Ying Yang
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A Bidirectional LSTM Language Model for Code Evaluation and Repair
Symmetry, 2021Md Mostafizer Rahman +2 more
exaly
Car Tourist Trajectory Prediction Based on Bidirectional LSTM Neural Network
Electronics (Switzerland), 2021Alexey Kashevnik +2 more
exaly
Enhancing Electrical Load Prediction Using a Bidirectional LSTM Neural Network
Electronics (Switzerland), 2023Christos Pavlatos +2 more
exaly