Digital Library[ Search Result ]
ConvLSTM-Based COVID-19 Outbreak Prediction using Feature Combination of Multivariate Dataset
Yejin Kim, Seokyeon Kim, Yun Jang
http://doi.org/10.5626/JOK.2021.48.4.405
COVID-19 is transmitted through the droplets expelled by infected people. The propagation of splash is affected by space-time. The transmission of infectious diseases depends on the interaction of various factors such as the health status of the infected and the non-infected people and different environmental factors. However, it is difficult to include all information related to the epidemic in the predictive model and understand the relationship between the information. In this research, we propose a method to include the infectious features of COVID-19 in a learning dataset of the deep learning model and understand the effect of the combination of COVID-19 spreading data on the predictive performance of deep learning. Before predicting, the infectious features of COVID-19 are identified and considerations for including the COVID-19 spreading features are defined in the data preprocessing step. In deep learning modeling, a prediction model using ConvLSTM is designed for spatiotemporal prediction. In the process of testing the model, various features related to COVID-19 spread are combined and the effect of the combination on the performance of the model is analyzed. We tested 120 feature combinations with 47 features composed of personal information of confirmed patients and spatial characteristics of the places that they had visited. We used MAPE as an indicator to evaluate performance of the models. In the case of COVID-19 dataset, the MAPE value of the model with combined features was 1.234, and that of the model with not combined features was 2.217.
Improvement in Network Intrusion Detection based on LSTM and Feature Embedding
Hyeokmin Gwon, Chungjun Lee, Rakun Keum, Heeyoul Choi
http://doi.org/10.5626/JOK.2021.48.4.418
Network Intrusion Detection System (NIDS) is an essential tool for network perimeter security. NIDS inspects network traffic packets to detect network intrusions. Most of the existing works have used machine learning techniques for building the system. While the reported works demonstrated the effectiveness of various artificial intelligence algorithms, only a few of them have utilized the time-series information of network traffic data. Also, categorical information of network traffic data has not been included in neural network-based approaches. In this paper, we propose network intrusion detection models based on sequential information using the long short-term memory (LSTM) network and categorical information using the embedding technique. We have conducted experiments using models with UNSW-NB15, which is a comprehensive network traffic dataset. The experiment results confirm that the proposed method improves the performance, with a binary classification accuracy rate of 99.72%.
An Effective Detection Method of Anomalous Sequences Considering the Occurrence Order and Time Interval of the Elements
http://doi.org/10.5626/JOK.2021.48.4.469
Recently, a rapid generation of sequence data consisting of elements in various applications has been witnessed over time. Although various methods for detecting anomalous sequences among the given sequences have been actively studied, most of them mainly consider only the occurrence order of the elements. In this paper, we propose an effective anomalous sequence detection method considering not only the occurrence order of the elements but also the time interval between the elements. Apparently, the proposed method uses a model that combines two autoencoders. The first is an LSTM autoencoder, which learns the features of the occurrence order of elements, and the second is a graph autoencoder, which learns the features of the time interval between the elements. After completion of the training, each sequence is input to the trained model and reconstructed by the trained model. If the occurrence order and time interval of elements in the reconstructed sequence greatly differ from those in the original sequence, the corresponding sequence is determined as an anomalous sequence. Through various experiments using synthetic data, we confirmed that the proposed method can detect anomalous sequences more effectively than the method that uses an RNN autoencoder to learn the occurrence order of the elements, the methods that use a single LSTM autoencoder and the method that doesn’t use deep learning model.
An Embedding Technique for Weighted Graphs using LSTM Autoencoders
http://doi.org/10.5626/JOK.2021.48.1.13
Graph embedding is the representation of graphs as vectors in a low-dimensional space. Recently, research on graph embedding using deep learning technology have been conducted. However, most research to date has focused mainly on the topology of nodes, and there are few studies on graph embedding for weighted graphs, which has an arbitrary weight on the edges between the nodes. Therefore, in this paper, we proposed a new graph embedding technique for weighted graphs. Given weighted graphs to be embedded, the proposed technique first extracts node-weight sequences that exist inside the graphs, and then encodes each node-weight sequence into a fixed-length vector using an LSTM (Long Short-Term Memory) autoencoder. Finally, for each graph, the proposed technique combines the encoding vectors of node-weight sequences extracted from the graph to generate one final embedding vector. The embedding vectors of the weighted graphs obtained by the proposed technique can be used for measuring the similarity between weighted graphs or classifying weighted graphs. Experiments on synthetic and real datasets consisting of groups of similar weighted graphs showed that the proposed technique provided more than 94% accuracy in finding similar weighted graphs.
A Knowledge Graph Embedding-based Ensemble Model for Link Prediction
http://doi.org/10.5626/JOK.2020.47.5.473
Knowledge bases often suffer from their limited applicability due to missing information in their entities and relations. Link prediction has been investigated to complete the missing information and makes a knowledge base more useful. The existing studies on link prediction often rely on knowledge graph embedding and have shown trade-off in their performance. In this paper, we propose an ensemble model for knowledge graph embedding to improve quality of link prediction. The proposed model combines multiple knowledge graph embeddings that have unique characteristics. In this way, the ensemble model is able to consider various aspects of the entries within a knowledge base and reduce the variation of accuracy depending on hyper-parameters. Our experiment shows that the proposed model outperforms other knowledge graph embedding methods by 13.5% on WN18 and FB15K dataset.
Evaluation of Korean Reviews Automatically Generated using Long Short-Term Memory Unit
http://doi.org/10.5626/JOK.2019.46.6.515
Opinion Spam is the act of misleading the public to comply with the intent of a particular group. This is a serious problem at this point in time, when online services are widely deployed and the reputation of a specific target seller relative to the offline market can be easily affected. In this context, detecting and preventing opinion spam is an important research topic. This study investigates the current status of the domestic online market and poses possible damages caused by opinion spam through the automatic generation of Korean reviews using a recurrent neural network (RNN). In particular, we applied a learning model based on an LSTM (long short-term memory) unit for the Korean language in order to improve the long-term dependency problem of the typical RNN. Then, the Word2Vec is exploited to improve the relevance by replacing keywords with a set of candidates from the target market. We show the translation of the learning model based on a foreign language to Korean, then evaluate its appropriateness with regard to the development of countermeasures with which to effectively prevent automatically generated opinion spam in the near future.
Malware Detection Model with Skip-Connected LSTM RNN
Jangseong Bae, Changki Lee, Suno Choi, Jonghyun Kim
http://doi.org/10.5626/JOK.2018.45.12.1233
A program can be viewed as a sequence of consecutive Opcodes in which malware is a malicious program. In this paper, we assume that the program is a sequence of Opcodes with semantic information and detect the malware using the Long Short-Term Memory Recurrent Neural Network (LSTM RNN), which is a deep learning model suitable for sequence data modeling. For various experiments, the Opcode sequence is divided into a uni-gram sequence and a tri-gram sequence and used as the input features of the various deep learning models. Several deep learning models use the input Opcodes sequence to determine whether the program is a normal file or malware. We also show that the proposed Skip-Connected LSTM RNN model is superior to the LSTM encoder and the Convolutional Neural Network(CNN) model for malware detection. Experimental results show that the Skip-Connected LSTM RNN model has better performance than the LSTM encoder and CNN model in the Opcode sequence tri-gram data.
Expansion of Word Representation for Named Entity Recognition Based on Bidirectional LSTM CRFs
Named entity recognition (NER) seeks to locate and classify named entities in text into pre-defined categories such as names of persons, organizations, locations, expressions of times, etc. Recently, many state-of-the-art NER systems have been implemented with bidirectional LSTM CRFs. Deep learning models based on long short-term memory (LSTM) generally depend on word representations as input. In this paper, we propose an approach to expand word representation by using pre-trained word embedding, part of speech (POS) tag embedding, syllable embedding and named entity dictionary feature vectors. Our experiments show that the proposed approach creates useful word representations as an input of bidirectional LSTM CRFs. Our final presentation shows its efficacy to be 8.05%p higher than baseline NERs with only the pre-trained word embedding vector.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr