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Comparative Analysis of Accuracy and Stability of Software Reliability Estimation Models based on Recurrent Neural Networks
Taehyoun Kim, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2023.50.8.688
Existing studies on software reliability estimation based on recurrent neural networks have used networks to create one model under the same conditions and evaluated the accuracy of the model. However, due to the randomness of artificial neural networks, such recurrent neural networks can generate different training results of models even under the same conditions, which can lead to inaccurate software reliability estimation. Therefore, this paper compares and analyzes which recurrent neural networks could estimate software reliability more stably and accurately. We estimated software reliability in eight real projects using three representative recurrent neural networks and compared and analyzed the performances of these models in terms of accuracy and stability. As a result, Long Short-Term Memory showed the most stable and accurate software reliability estimation performance. A more accurate and stable software reliability estimation model is expected to be selected based on the results of this study.
Improving Performance of Recurrent Neural Network based Recommendations by Utilizing Personal Preferences
Dong Shin Lim, Yong Jun Yang, Shin Cho
http://doi.org/10.5626/JOK.2021.48.11.1211
As the amount of content provided on the platform surged, a recommendation system became an essential element of the platform. The collaborative filtering technique is a widely used recommendation system in academia and industry, but it also has a limitation because it relies on quantitative information from consumers such as ratings and purchase history. To overcome this shortcoming, various studies have been done in a bid to improve its performance by collecting qualitative information such as review data in a model. Recently, some studies that applied recurrent neural networks showed better performance than the existing recommendation system by using time-series behavioral data only, but studies that reflect each customer"s preference in the recommendation model have not yet been made. In this paper, an improved recommendation model was presented by calculating a preference matrix based on customer log data and learning it in a recurrent neural network through an embedding vector. It was confirmed that the prediction performance was improved compared to the existing recurrent neural network recommendation model.
Variational Recurrent Neural Networks with Relational Memory Core Architectures
Geon-Hyeong Kim, Seokin Seo, Shinhyung Kim, Kee-Eung Kim
http://doi.org/10.5626/JOK.2020.47.2.189
Recurrent neural networks are designed to model sequential data and learn generative models for sequential data. Therefore, VRNNs (variational recurrent neural networks), which incorporate the elements of VAE (variational autoencoder) into RNN (recurrent neural network), represent complex data distribution. Meanwhile, the relationship between inputs in each sequence has been attributed to RMC (relational memory core), which introduces self-attention-based memory architecture into RNN memory cell. In this paper, we propose a VRMC (variational relation memory core) model to introduce a relational memory core architecture into VRNN. Further, by investigating the music data generated, we showed that VRMC was better than in previous studies and more effective for modeling sequential data.
Improving Recurrent Neural Network based Recommendations by Utilizing Embedding Matrix
Myung Ha Kwon, Sung Eon Kong, Yong Suk Choi
http://doi.org/10.5626/JOK.2018.45.7.659
Recurrent neural networks(RNNs) have recently been successfully applied to recommendation tasks. RNNs were adopted by session-based recommendation, which recommends items by the records only within a session, and a movie recommendation that recommends movies to the users by analyzing the consumption records collected through multiple accesses to the websites. The new approaches showed improvements over traditional approaches for both tasks where only implicit feedback such as clicks or purchase records are available. In this work, we propose the application of weight-tying to improve the existing movie recommendation model based on RNNs. We also perform experiments with an incremental recommendation method to more precisely evaluate the performance of recommendation models.
Water Level Forecasting based on Deep Learning : A Use Case of Trinity River-Texas-The United States
Quang-Khai Tran, Sa-kwang Song
http://doi.org/10.5626/JOK.2017.44.6.607
This paper presents an attempt to apply Deep Learning technology to solve the problem of forecasting floods in urban areas. We employ Recurrent Neural Networks (RNNs), which are suitable for analyzing time series data, to learn observed data of river water and to predict the water level. To test the model, we use water observation data of a station in the Trinity River, Texas, the U.S., with data from 2013 to 2015 for training and data in 2016 for testing. Input of the neural networks is a 16-record-length sequence of 15-minute-interval time-series data, and output is the predicted value of the water level at the next 30 minutes and 60 minutes. In the experiment, we compare three Deep Learning models including standard RNN, RNN trained with Back Propagation Through Time(RNN-BPTT), and Long Short-Term Memory (LSTM). The prediction quality of LSTM can obtain Nash Efficiency exceeding 0.98, while the standard RNN and RNN-BPTT also provide very high accuracy.
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