Gender Classification Based on Electrocardiogram Signals Using Long Short Term Memory and Bidirectional Long Short Term Memory

Data driven models of heating ventilation and air conditioning (HVAC) such as Back Propagation (BP) neural network, Support Vector Machine (SVM), Long Short Term Memory (LSTM) and bidirectional Long Short Term Memory (BiLSTM) offer an excellent opportunity for the prediction of energy consumption. In contrast, different kinds of input characteristics and complex actual operating conditions reduce the accuracy of the prediction. In this paper, a large scale of operation data was collected from the EnergyPlus simulation, which was previously developed based on the characteristics of a real case study house. The paper discusses the influence of outdoor environment, previous output, temperature schedule on the prediction accuracy. The results indicate that BiLSTM method could lead to more stable energy consumption prediction and outdoor relative humidity has significantly improved the accuracy of prediction.

The case of inflation can influence monetary policy. Therefore, in assisting policy decision-making, inflation forecasts can be made. Inflation forecasting is a connecting bridge to determine the value of inflation for the coming period. Running inflation allows it to change from time to time, resulting in a nonlinear model that will provide a more accurate forecast of inflation. The neural network is a general function approach capable of mapping any nonlinear function. One part of the neural network method used in forecasting is the Long Short Term Memory (LSTM) method. This method has the advantage of storing information for a more extended period. However, the efficiency of the neural network method depends on the network structure of the number of hidden neurons and epochs in converging conditions. This study aims to obtain the best inflation forecasting model in Indonesia using the LSTM method. This method is a development network of the Recurrent Neural Network, which is composed of forget gate, input gate, cell state, and output gate. Based on the research results, the best LSTM model in predicting inflation in Indonesia has more than one hidden neuron with the optimum number of epochs. However, too many hidden neurons are used, and the use of epochs that are not optimized will make the root mean square error value and mean absolute error based on the sample out worse. This indicates that too many hidden neurons and epochs will lead to overfitting in Indonesia's inflation forecasting.

News text is text that contains information about an incident that is submitted in writing or not. In the news text there is a lot of unstructured information and makes it difficult for people to know what entities are contained in the news text. To overcome these problems, information extraction is carried out which is useful for adapting information entities in the form of structured text. In this study, information extraction was carried out using the Long Short-Term Memory (LSTM) method in a news data related to Telkom University. Therefore, in this study, the development of an information extraction system was carried out by focusing on the Person entity using the LSTM method. The test results obtained an F1 Score of 81% with a tuning parameter 256 word embedding size, 32 batch, 150 epochs, 0.1 dropout, 150 units, and 1 layer on LSTM.

This study aims to predict the Dollar-Rupiah exchange rate based on Indonesia's economic growth using the Long Short-Term Memory (LSTM) method. Exchange rate fluctuations that occurred from 1997 to 2024 have had a significant impact on national economic stability, so a predictive model capable of accurately interpreting historical patterns is needed. Research data was obtained from the Central Bureau of Statistics (BPS), Bank Indonesia (BI), and global economic sources with variables used including the Dollar-Rupiah exchange rate, Gross Domestic Product (GDP), inflation, and interest rates. The research stages include data preprocessing, LSTM architecture development, training, and testing using Google Colab. The results show that the LSTM model is able to produce exchange rate predictions that are close to actual data with a relatively low error rate. Thus, the LSTM method can be used as an effective approach to assisting in the analysis of exchange rate movements and supporting economic policymaking in Indonesia.

Analysis and prediction of fire water pressure in buildings based on IoT data

Dengue Hemorrhagic Fever (DHF) an infectious disease with fluctuating case numbers that can suddenly increase, posing significant public health challenges. In Pontianak City, 106 cases with 1 death were recorded in 2019, decreasing from 195 cases with 3 deaths in 2018. In 2020, the number dropped further to 27 cases with no fatalities. This condition indicates the need for a prediction system capable of accurately estimating the number of cases to support decision-making processes. This study aims to develop a model for predicting daily DHF cases in Pontianak City using the Long Short-Term Memory (LSTM) method. The data used includes daily DHF cases, average temperature, average humidity, and rainfall from 2020 to 2025. The research stages included data cleaning, normalization using Min-Max Scaling, historical data formation, model training, and evaluation using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). The best model employed a single LSTM layer with 64 neurons, 50 epochs, and a batch size of 32, yielding an RMSE of 0.87 and MAE of 0.63. These results indicate that the LSTM method is capable of generating predictions close to actual values and is reliable for estimating daily DHF cases in Pontianak City. The developed Streamlit-based application provides interactive visualization and accurate predictions, making it a valuable tool for health authorities in DHF prevention and control efforts.

The wide proliferation of renewable energy and deregulation of power grid systems require small power utilization systems to deploy intelligent methods of adjustment to the user power demand. Small power utilization systems can benefit from the techniques developed for the smart grid in general. The present paper is devoted to the development of the forecasting model based on the Long Short-Term Memory (LSTM) method. The paper describes the small smart grid architecture and role of the LSTM in this architecture. The LSTM method is implemented with a number of interruptible appliances to predict power demand over 24 hour segments. The experiments demonstrate that the developed algorithm generates a stable pattern of daily power demand; therefore, it has a high predictive power.

This research aims to implement deep learning techniques to determine fact and fake news in Indonesian language. The methods used are Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). The stages of the research consisted of collecting data, labeling data, preprocessing data, word embedding, splitting data, forming CNN and LSTM models, evaluating, testing new input data and comparing evaluations of the established CNN and LSTM models. The Data are collected from a fact and fake news provider site that is valid, namely TurnbackHoax.id. There are 1786 news used in this study, with 802 fact and 984 fake news. The results indicate that the CNN and LSTM methods were successfully applied to determine fact and fake news in Indonesian language properly. CNN has an accuracy test, precision and recall value of 0.88, while the LSTM model has an accuracy test and precision value of 0.84 and a recall of 0.83. In testing the new data input, all of the predictions obtained by CNN are correct, while the prediction results obtained by LSTM have 1 wrong prediction. Based on the evaluation results and the results of testing the new data input, the model produced by the CNN method is better than the model produced by the LSTM method.

Mining activities may trigger hazards such as mountain subsidence. To predict the extent of mountain subsidence and analyze the evolutionary characteristics of mountain subsidence, the optical images and Small Baseline Subset InSAR (SBAS-InSAR) method were used, and the mountain subsidence was circled and interpreted. The cumulative subsidence in the area from 2024 to 2026 was predicted by combining the long short-term memory (LSTM) method, and the mountain surface subsidence slip was derived using MatDEM. The results of the study show that mountain surface subsidence begins with the formation of a primary subsidence zone, which slowly leads to the formation of primary and secondary subsidence zones. Under the influence of the penetrating channel, the primary and secondary subsidence areas merge to form a larger subsidence area. The subsidence area gradually disintegrates into several small areas during the sliding process, and the small areas underneath contribute to the main force of the subsidence movement, with a substantial slip displacement. Based on this study, it is concluded that the accuracy of the results obtained from the LSTM method is higher than that of the numerical simulation results, and the maximum cumulative subsidence is expected to reach 2,180 mm in 2026.

The consumption of electrical power continues to increase every day, along with technological advances, the increase in the use of machines that depend on electricity and the growth of the human population. Prediction of electric power consumption is difficult due to various things such as weather conditions and dynamic behavior of residents. So to overcome this, deep learning methods are used, namely CNN (convolutional neural network), LSTM (long-short term memory), BDLSTM (bi-directional long short term memory), CNN-LSTM and CNN-M-BDLSTM with three stages. The first stage is fixing the missing value. The second stage performs data processing and building model from deep learning methods. The third stage evaluates the prediction results with error metrics. From the results of the analysis, the CNN method produces the smallest loss and training time of 0.0010 9/step ms, the LSTM and CNN-LSTM methods produce the largest loss of 0.0019 while the longest training time is 139/step ms in the BDLSTM method. Method The largest MSE, MAE, and MAPE values are 0.150, 0.258 and 0.176 respectively the results of the CNN method. The smallest MSE and MAE values are 0.082 and 0.174 the results of the BDLSTM method. While the smallest MAPE value obtained by the CNN-M-BDLSTM method is 0.148. This proves that the ability to predict short-term electric power consumption using the deep learning method has a good ability in terms of the error metric results.

The purpose of this study is to predict the decade rainfall in Semarang City using two main methods, namely Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX) and Long Short Term Memory (LSTM). The methodology of this study begins with data pre-processing, which includes data deletion analysis using dropna and data normalization using Min-Max Scaling to reduce scale the to between 0 and 1. The dataset is then divided into 80% training data and 20% test data. The validity of the data (X_test, Y_test) using the best 56-epoch data validation (val_loss) is better than the validity of the training data (loss). On the other hand, SARIMAX uses the (2,1,2), (2,1,2,36) model, and its validation techniques include Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Coefficient of Determination (R2). Specifically, the RMSE of the LSTM model is 19.6, and its RMSE of the SARIMAX is 31.05. The MAE of LSTM is 15.0, SARIMAX is 24.5, and the R2 of LSTM is 0.814, and SARIMAX is 0.52. Lower RMSE and MAE values indicate lower prediction errors, but a higher R2 value of 1 indicates that LSTM can explain 81% of the actual data variation, which is better than SARIMAX, which is only about 52%. The main finding of this study is that the LSTM model performs better when recommending rainfall datasets.

Research on sentiment analysis in recent years has increased. However, in sentiment analysis research there are still few ideas about the handling of negation, one of which is in the Indonesian sentence. This results in sentences that contain elements of the word negation have not found the exact polarity.The purpose of this research is to analyze the effect of the negation word in Indonesian. Based on positive, neutral and negative classes, using attention-based Long Short Term Memory and word2vec feature extraction method with continuous bag-of-word (CBOW) architecture. The dataset used is data from Twitter. Model performance is seen in the accuracy value.The use of word2vec with CBOW architecture and the addition of layer attention to the Long Short Term Memory (LSTM) and Bidirectional Long Short Term Memory (BiLSTM) methods obtained an accuracy of 78.16% and for BiLSTM resulted in an accuracy of 79.68%. whereas in the FSW algorithm is 73.50% and FWL 73.79%. It can be concluded that attention based BiLSTM has the highest accuracy, but the addition of layer attention in the Long Short Term Memory method is not too significant for negation handling. because the addition of the attention layer cannot determine the words that you want to pay attention to.

In the process of air combat intention identification, expert experience and traditional algorithm are relied on to analyze enemy aircraft combat intention in a single moment, but the identification time and accuracy are not excellent. In this paper, from the dynamic attributes of an airspace fighter air combat target and the dynamic and time series changing characteristics of the battlefield environment, we introduce the bidirectional long short-term memory neural network (BiLSTM + Attention) intention identification method based on the attention mechanism for air combat intention identification. In this method, five kinds of state parameters, including target maneuver type, distance, flight velocity, altitude and heading angle, were taken as datasets. The BiLSTM + Attention was used to extract enemy aircraft intention features. By introducing attention mechanism, the weight coefficients of characteristic states corresponding to air combat victories were corrected. Finally, it was input into the SoftMax function to obtain the category of the enemy’s intention. Experimental results showed that the proposed method can effectively identify enemy aircraft in the case of high complexity, multidimensional and large amount of data. Compared with bidirectional long short-term memory (BiLSTM), long short-term memory (LSTM), long short-term memory based on attention mechanisms (LSTM + Attention) and support vector machine (SVM) classification, the proposed method had higher accuracy and lower loss value.

Efficient irrigation scheduling is crucial for optimizing crop yields and conserving water resources. In this paper, we present robust models for predicting soil moisture trends in cotton based on the integration of IoT, machine learning (ML), and deep learning (DL) frameworks. We access soil moisture and weather forecast data from IoT field sensors using web APIs, then process and incorporate them into ML and DL models to predict soil moisture trends. Specifically, in DL we used the Long Short-Term Memory (LSTM) with two LSTM architectures (Single Stream LSTM and Dual LSTM), while in ML we used the Gradient Boosting (GB), XGBoost, Random Forest (RF), and AdaBoost methods. Experiment results show that, Single Stream LSTM achieved MSE (0.0710) and (0.9684) R 2 outperforming its Dual counterpart which recorded MSE (0.1200) and (0.9466) R 2 , while under ML, Gradient Boosting (MSE = 0.2822, R² = 0.9909) outperforms other ML models. In general, LSTM methods achieved the lowest error while ML methods recorded the highest R 2 -score. Predictive methods presented in this paper, have shown great potential in estimating soil moisture patterns for optimal irrigation schedules in cotton, thereby enhancing water use efficiency and increasing crops yield.

Improving PV power plant forecast accuracy: A hybrid deep learning approach compared across short, medium, and long-term horizons