Correction To: Modelling the seismic activity of Kahramanmaraş, Türkiye with recurrent neural network (RNN) and long short-term memory (LSTM) methods

In the news portal text, there is a lot of important information such as the name of the person, the name of the organization, or the name of the place. To obtain information in text documents manually, humans must read the contents of the entire news text. To overcome this issue, information extraction, commonly called Named Entity Recognition (NER) was used. The extraction of information expressly for the NER field is used to make it easier to process word or sentence data. It helps search engines and also helps to classify places, times, and organizations. There is a limited number of NER in Indonesian texts using only the Recurrent Neural Network (RNN) method. Similar previous studies only employed other versions of RNN such as LSTM (Long Short Term Memory), BiLSTM (Bidirectional Long Short Term Memory), and CNN (Convolutional Neural Network). NER is one of the answers to the problems that exist in a large number of news portal texts to obtain information effectively and efficiently. The results of this study indicate that the NER system using the RNN method in Indonesian news texts has an F1 -Score of 80%

The movement of the share price of PT.Telkom (Tbk) fluctuates so it is necessary to do forecasting analysis. Forecasting the share price of PT.Telkom (Tbk) can be done using the Long Short Term Memory (LSTM) method. LSTM is a development of the Recurrent Neural Network (RNN)method. In this study using PT.Telkom Stock price data for 2018-2023 and PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023) this is done to see if a significant price decline has an impact on forecasting the stock price for the next period. Both data have a very small difference in MAPE value. The data that has the smallest MAPE value is PT.Telkom (Tbk) stock price data after Covid-19 (20121-2023) with a MAPE of 0,93% with an optimal parameter combination of neurons, batch size 64, and epoch 80.

Network traffic prediction aims at predicting the subsequent network traffic by using the previous network traffic data. This can serve as a proactive approach for network management and planning tasks. The family of recurrent neural network (RNN) approaches is known for time series data modeling which aims to predict the future time series based on the past information with long time lags of unrevealed size. RNN contains different network architectures like simple RNN, long short term memory (LSTM), gated recurrent unit (GRU), identity recurrent unit (IRNN) which is capable to learn the temporal patterns and long range dependencies in large sequences of arbitrary length. To leverage the efficacy of RNN approaches towards traffic matrix estimation in large networks, we use various RNN networks. The performance of various RNN networks is evaluated on the real data from GEANT backbone networks. To identify the optimal network parameters and network structure of RNN, various experiments are done. All experiments are run up to 200 epochs with learning rate in the range [0.01-0.5]. LSTM has performed well in comparison to the other RNN and classical methods. Moreover, the performance of various RNN methods is comparable to LSTM.

This study aims to analyze public sentiment toward President Joko Widodo’s performance over his two terms in office by utilizing data from the social media platform Twitter. Twitter was chosen due to its real-time nature and popularity among users for expressing opinions openly. The method employed is a Recurrent Neural Network (RNN) with a Long Short-Term Memory (LSTM) architecture, which is well-suited for processing sequential text data. The research process involves several preprocessing stages, including text cleaning, normalization, tokenization, stopword removal, and stemming, to prepare the textual data for model input. The dataset consists of 1,006 Indonesian-language tweets categorized into three sentiment classes: positive, negative, and neutral. After simplifying the classification into two classes, the model achieved an Accuracy of 63.33%. Evaluation using a Confusion matrix and classification report indicates that the model performs better in identifying positive sentiment, though it still struggles with misclassifying negative sentiment. The results demonstrate that the RNN method is fairly effective for social media-based sentiment analysis. It is expected that this study can serve as a reference for government institutions or policy researchers to understand public perceptions and support more data-driven and responsive policymaking processes.

This article explores the suitability of a long short-term memory recurrent neural network (LSTM-RNN) and artificial intelligence (AI) method for low-flow time series forecasting. The long short-term memory works on the sequential framework which considers all of the predecessor data. This forecasting method used daily discharged data collected from the Basantapur gauging station located on the Mahanadi River basin, India. Different metrics [root-mean-square error (RMSE), Nash–Sutcliffe efficiency (ENS), correlation coefficient (R) and mean absolute error] were selected to assess the performance of the model. Additionally, recurrent neural network (RNN) model is also used to compare the adaptability of LSTM-RNN over RNN and naive method. The results conclude that the LSTM-RNN model (R = 0.943, ENS = 0.878, RMSE = 0.487) outperformed RNN model (R = 0.935, ENS = 0.843, RMSE = 0.516) and naive method (R = 0.866, ENS = 0.704, RMSE = 0.793). The finding of this research concludes that LSTM-RNN can be used as new reliable AI technique for low-flow forecasting.

Cloud computing has become a de facto choice of many individuals and enterprises for computing solutions. In the last few years, many cloud providers appear in the market that offers the same services. It is a trivial job to choose an optimal service best suited for organisations in such a massive arms race of service providers. Existing consumer experience could help significantly build a holistic perception of their experiences that ultimately influence service adoption decisions. Sentiment analysis is an effective tool to understand consumer experience about the product or service. The sophisticated sentiment analysis could help businesses to gain a better insight and respond proactively to consumer issues. There are various methods for sentiment analysis that produces ideal results under different conditions. Therefore, it is very important to choose the right method to predict consumer's sentiment for a greatest result. In this paper we analyse the sentiment prediction accuracy of widely used neural network methods - recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent network (GRU). We use software as a service (SaaS) dataset having 6258 reviews. From analysis results we find that GRU outperforms the LSTM and RNN methods.

Artificial Neural Networks is a computing system that is inspired by how the nervous system works in humans and continues to grow rapidly until now. Just like the nervous system in humans, artificial neural networks work through the process of studying existing data to formulate new data outputs. An artificial neural network using the Recurrent Neural Network (RNN) method is one of the popular models used today, especially in forecasting cases. In simple terms, the forecasting flow using the RNN method begins by dividing the test data and training data, the forward calculation process, the backward calculation process, the optimization calculation, and the evaluation calculation of the forecasting model. The main obstacle of the RNN method is the presence of a vanishing gradient which can cause poor forecasting results. In this study, the authors propose a Principal Component Analysis (PCA) dimension reduction method to obtain the most influential variables and become inputs for the prediction model that is built to minimize existing errors. The author also uses the K-means clustering method to divide the data with similar trend variations. To increase the clustering effect, the researcher used similarity calculation based on Euclidean distance. So that in an effort to build optimal prediction results, first time series data with the most influential variables will be selected using the PCA method. Furthermore, the data are grouped using the K-means method and will be included in the prediction model that is built. In the RNN prediction model, the data will be trained using the Backpropagation Through Time (BPTT) method and the optimization method used is Stochastic Gradient Descent (SGD). Forecasting with the RNN method with PCA produces an accuracy of 93%, while forecasting using the RNN method without PCA produces an accuracy of 82%. The experimental results show that the RNN method with PCA achieves higher predictive accuracy and flexibility than RNN without PCA.

Modelling the seismic activity of Kahramanmaraş, Türkiye with recurrent neural network (RNN) and long short-term memory (LSTM) methods

Solar power forecasting is substantial for the utilization, planning, and designing of solar power plants. Global solar irradiation (GSI) and meteorological variables have a crucial role in solar power generation. The ever-changing meteorological variables and imprecise measurement of GSI raise difficulties for forecasting photovoltaic (PV) output power. In this context, a major motivation appears for the accurate forecast of GSI to perform effective forecasting of the short-term output power of a PV plant. The presented study comprises of four artificial neural network (ANN) methods; recurrent neural network (RNN) method, feedforward backpropagation neural network (FFBPNN) method, support vector regression (SVR) method, and long short-term memory (LSTM) for daily total GSI prediction of Tarsus by using meteorological data. Moreover, this study proposes a model that utilizes the predicted daily GSI for output power forecasting of a grid-connected PV plant. The obtained results are compared with the output power generation data of a 350 kW solar power plant. The results are evaluated with the performance indices as mean absolute percentage error (MAPE), normalized root mean squared error (NRMSE), weighted mean absolute error (WMAE), and normalized mean absolute error (NMAE). FFBPNN method is chosen with the best results of MAPE 7.066%, NMAE 3.629%, NRMSE 4.673%, and WMAE 5.256%.

It is a great significance for the accurate and real-time prediction of passenger flow in rail transit operation. In the process of passenger flow prediction, the new method of recurrent neural networks (RNNs) can well solve the problems of randomness and nonlinearity which can not be solved by the existed linear models. In this paper, the long short-term memory (LSTM) and the gated recurrent unit (GRU) networks, which are methods of RNNs, are employed to predict the dayparting passenger flow and the raw passenger flow data is denoised by the wavelet transform. Experimental results show that LSTM and GRU networks can well predict the passenger flow. And compared to LSTM, GRU is better for passenger flow prediction.

Time series analysis model for forecasting unsteady electric load in buildings

Hoax Identification On Social Media Using Recurrent Neural Network (RNN) And Long Short-term Memory (LSTM) Methods

In the undertaken study, we have used a customized dataset termed ``Cardiac-200'' and the benchmark dataset ``PhysioNet.'' which contains 1500 heartbeat acoustic event samples (without augmentation) and 1950 samples (with augmentation) heartbeat acoustic events such as normal, murmur, extrasystole, artifact, and other unlabeled heartbeat acoustic events. The primary reason for designing a customized dataset, ``cardiac-200,'' is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events is 10-12 s. In the undertaken study, we have analyzed and evaluated various heartbeat acoustic events using audio processing libraries such as Chromagram, Chroma-cq, Chroma-short-time Fourier transform (STFT), Chroma-cqt, and Chroma-cens to extract more information from the recorded heartbeat sound signals. The noise removal process has been carried out using local binary pattern (LBP) methodology. The noise-robust heartbeat acoustic images are classified using long short-term memory (LSTM)-convolutional neural network (CNN), recurrent neural network (RNN), LSTM, Bi-LSTM, CNN, K-means Clustering, and support vector machine (SVM) methods. The obtained results have shown that the proposed InfusedHeart Framework had outclassed all the other customized machine learning and deep learning approaches such as RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM-based classification methodologies. The proposed Knowledge-infused Learning Framework has achieved an accuracy of 89.36% (without augmentation), 93.38% (with augmentation), and a standard deviation of 10.64 (without augmentation), and 6.62 (with augmentation). Furthermore, the proposed framework has been tested for various signal-to-noise ratio conditions such as SignaltoNoiseRatio0, SignaltoNoiseRatio3, SignaltoNoiseRatio6, SignaltoNoiseRatio9, SignaltoNoiseRatio12, SignaltoNoiseRatio15, and SignaltoNoiseRatio18. In the end, we have shown a detailed comparison of texture and without texture approaches and have discussed future enhancements and prospective ways for future directions.

Rainfall forecast is necessary for many aspects of regional management. Prediction of rainfall is useful for reducing negative impacts caused by the intensity of rainfall, such as landslides, floods, and storms. Hence, a rainfall forecast with good accuracy is needed. Many rainfall forecasting models have been developed, including the adaptive Holt-Winters exponential smoothing method and the Recurrent Neural Network (RNN) method. The research aimed to compare the result of forecasting between the Holt-Winters adaptive exponential smoothing method and the Recurrent Neural Network (RNN) method. The data were monthly rainfall data in Malang City from January 1983 to December 2019 obtained from a website. Then, the data were divided into training data and testing data. Training data consisted of rainfall data in Malang City from January 1983 to December 2017. Meanwhile, the testing data were rainfall data in Malang City from January 2018 to December 2019. The comparison result was assessed based on the values of Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE). The result reveals that the RNN method has better RMSE and MAPE values, namely RMSE values of 0,377 and MAPE values of 1,596, than the Holt-Winter Adaptive Exponential Smoothing method with RMSE values of 0,500 and MAPE values of 0,620. It can be concluded that the non-linear model has better forecasting than the linear model. Therefore, the RNN model can be used in modeling and forecasting trend and seasonal time series.

In an extremely broad range of industrial applications, especially in electric vehicles, permanent magnet synchronous motors (PMSMs) play a vital role. Any failure in PMSMs may cause possible safety hazards, a drop in productivity, and expensive downtime. Therefore, their reliable operation is essential. Accurate failure identification and classification allow for addressing problems before they escalate, which helps ensure the seamless operation of PMSMs and reduces the likelihood of equipment failure. Therefore, in this paper, novel failure identification methods based on gated recurrent unit (GRU) and long short-term memory (LSTM) from recurrent neural network (RNN) methods are proposed for early identification of stator inter-turn short circuit failure (ISCF) and demagnetization failure (DF) occurring in PMSMs under multiple operating conditions. The proposed methods use three-phase current signals recorded from the experimental study under multiple operating conditions of the motor as input data. In the proposed methods, both feature extraction and classification are executed within a unified framework. The experimental outcomes obtained demonstrate that the proposed methods can identify a total of six unique motor conditions, including three ISCF variations and two DF variations, with high accuracy. The LSTM and GRU approaches predicted the identification of failures with 98.23% and 98.72% accuracy, respectively. Compared to existing methods, the success of the proposed approaches is satisfactory. In addition, LSTM and GRU-based failure identification methods are also compared in detail for accuracy, precision, sensitivity, specificity, and training time in this study.