scholarly journals A Bidirectional Long Short-Term Memory Model Algorithm for Predicting COVID-19 in Gulf Countries

Life ◽  
2021 ◽  
Vol 11 (11) ◽  
pp. 1118
Author(s):  
Theyazn H. H. Aldhyani ◽  
Hasan Alkahtani
Keyword(s):  
Saudi Arabia ◽  
Deep Learning ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Accurate Prediction ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Gulf Countries ◽  
Long Short Term Memory

Accurate prediction models have become the first goal for aiding pandemic-related decisions. Modeling and predicting the number of new active cases and deaths are important steps for anticipating and controlling COVID-19 outbreaks. The aim of this research was to develop an accurate prediction system for the COVID-19 pandemic that can predict the numbers of active cases and deaths in the Gulf countries of Saudi Arabia, Oman, the United Arab Emirates (UAE), Kuwait, Bahrain, and Qatar. The novelty of the proposed approach is that it uses an advanced prediction model—the bidirectional long short-term memory (Bi-LSTM) network deep learning model. The datasets were collected from an available repository containing updated registered cases of COVID-19 and showing the global numbers of active COVID-19 cases and deaths. Statistical analyses (e.g., mean square error, root mean square error, mean absolute error, and Spearman’s correlation coefficient) were employed to evaluate the results of the adopted Bi-LSTM model. The Bi-LSTM results based on the correlation metric gave predicted confirmed COVID-19 cases of 99.67%, 99.34%, 99.94%, 99.64%, 98.95%, and 99.91% for Saudi Arabia, Oman, the UAE, Kuwait, Bahrain, and Qatar, respectively, while testing the Bi-LSTM model for predicting COVID-19 mortality gave accuracies of 99.87%, 97.09%, 99.53%, 98.71%, 95.62%, and 99%, respectively. The Bi-LSTM model showed significant results using the correlation metric. Overall, the Bi-LSTM model demonstrated significant success in predicting COVID-19. The Bi-LSTM-based deep learning network achieves optimal prediction results and is effective and robust for predicting the numbers of active cases and deaths from COVID-19 in the studied Gulf countries.

scholarly journals Prediksi Harga Beras Menggunakan Metode Multilayer Perceptron (MLP) dan Long Short Term Memory (LSTM)

2020 ◽  
Vol 12 (1) ◽  
pp. 35-41
Author(s):  
Steven Sen ◽  
Dedy Sugiarto ◽  
Abdul Rochman
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Multilayer Perceptron ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer

Beras adalah salah satu komoditas utama di masyarakat Indonesia. Masalah utama dengan beras secara nasional adalah inflasi harga beras. Oleh karena itu, penelitian ini memprediksi harga beras menggunakan arsitektur jaringan saraf tiruan Multilayer Perceptron (MLP) dan deep learning : Long Short Term Memory (LSTM) untuk mengantisipasi masalah ini. Data yang digunakan dalam penelitian ini adalah data riil harga beras selama 2016 - 2019 yang diperoleh dari PT. Food Station. Total dataset adalah 1307 dengan distribusi 1123 sebagai data train dan 184 sebagai data uji. Hasil akhir yang diperoleh dalam penelitian ini adalah LSTM lebih unggul dari MLP, dengan nilai Root Mean Square Error (RMSE) data train : 0,49, dan nilai loss RMSE dari data tes adalah 0,27. Model LSTM paling optimal dari 3 tes dilakukan, yaitu jumlah hidden layer = 16 dan epochs = 150 kali.

PERAMALAN BEBAN JANGKA PENDEK SISTEM KELISTRIKAN KOTA BATU MENGGUNAKAN DEEP LEARNING LONG SHORT-TERM MEMORY

Transmisi ◽  
2021 ◽  
Vol 23 (3) ◽  
pp. 97-102
Author(s):  
Heru Purnomo ◽  
Hadi Suyono ◽  
Rini Nur Hasanah
Keyword(s):  
Deep Learning ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Dalam rangka proyeksi kebutuhan listrik dimasa mendatang, maka penyedia listrik dapat melakukan peramalan terkait besarnya kebutuhan dan permintaan energi listrik. Apabila besarnya permintaan listrik tidak dilakukan peramalan, maka akan terjadi kelebihan kapasitas yang menyebabkan tidak terserapnya sumber energi yang tersedia. Berdasarkan hasil penelitian diperoleh kesimpulan bahwa model terbaik dari metode Deep Learning LSTM  yang digunakan untuk melakukan prakiraan beban konsumsi listrik jangka pendek memiliki nilai RMSE (Root Mean Square Error) yang kecil Artinya tingkat akurasi dari metode Deep Learning LSTM tersebut lebih baik daripada ARIMA, hasil tersebut menunjukkan bahwa metode Deep Learning LSTM layak digunakan untuk memprakirakan beban konsumsi listrik jangka pendek di Kota Batu.

Prediksi Belanja Pemerintah Indonesia Menggunakan Long Short-Term Memory (LSTM)

2021 ◽  
Vol 5 (1) ◽  
pp. 99-106
Author(s):  
Sabar Sautomo ◽  
Hilman Ferdinandus Pardede
Keyword(s):  
Mean Square Error ◽  
Short Term Memory ◽  
Moving Average ◽  
Government Expenditure ◽  
Average Error ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
The Government

Abstract Estimates of government expenditure for the next period are very important in the government, for instance for the Ministry of Finance of the Republic of Indonesia, because this can be taken into consideration in making policies regarding how much money the government should bear and whether there is sufficient availability of funds to finance it. As is the case in the health, education and social fields, modeling technology in machine learning is expected to be applied in the financial sector in government, namely in making modeling for spending predictions. In this study, it is proposed the application of Long Short-Term Memory (LSTM) Model for expenditure predictions. Experiments show that LSTM model using three hidden layers and the appropriate hyperparameters can produce Mean Square Error (MSE) performance of 0.2325, Root Mean Square Error (RMSE) of 0.4820, Mean Average Error (MAE) of 0.3292 and Mean Everage Presentage Error (MAPE) of 0.4214. This is better than conventional modeling using the Auto Regressive Integrated Moving Average (ARIMA) as a comparison model.

scholarly journals Prediksi Data Time Series Saham Bank BRI Dengan Mesin Belajar LSTM (Long ShortTerm Memory)

2020 ◽  
Vol 1 (1) ◽  
pp. 1-8
Author(s):  
Adhitio Satyo Bayangkari Karno
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Short Term Memory ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Abstract   This study aims to measure the accuracy in predicting time series data using the LSTM (Long Short-Term Memory) machine learning method, and determine the number of epochs needed to produce a small RMSE (Root Mean Square Error) value. The result of this research is a high level of variation in RMSE value to the number of epochs needed in the data processing. This variation is quite difficult to obtain the right epoch value. By doing an iteration of the LSTM process on the number of different epochs (visualized in the graph), then the number of epochs with a minimum RMSE value will be easier to obtain. From the research of BBRI's stock data prediction, a good RMSE value was obtained (RMSE = 227.470333244533).   Keywords: long short-term memory, machine learning, epoch, root mean square error, mean square error.   Abstrak   Penelitian ini bertujuan untuk mengukur ketelitian dalam memprediksi data time series menggunakan metode mesin belajar LSTM (Long Short-Term Memory), serta menentukan banyaknya epoch yang diperlukan untuk menghasilkan nilai RMSE (Root Mean Square Error) yang kecil. Hasil dari penelitian ini adalah tingkat variasi yang tinggi nilai rmse terhdap jumlah epoch yang diperlukan dalam proses pengolahan data. Variasi ini cukup menyulitkan untuk memperoleh nilai epoch yang tepat. Dengan melakukan iterasi dari proses LSTM terhadap jumlah epoch yang berbeda (di visualisasikan dalam grafik), maka jumlah epoch dengan nilai RMSE minimal akan lebih mudah diperoleh. Dari penelitan prediksi data saham  BBRI diperoleh nilai RMSE yang cukup baik yaitu 227,470333244533. Kata kunci: long short-term memory, machine learning, epoch, root mean square error, mean square error.

scholarly journals PERBANDINGAN ALGORITMA LINEAR REGRESSION, LSTM, DAN GRU DALAM MEMPREDIKSI HARGA SAHAM DENGAN MODEL TIME SERIES

SEMINASTIKA ◽  
2021 ◽  
Vol 3 (1) ◽  
pp. 39-46
Author(s):  
Khalis Sofi ◽  
Aswan Supriyadi Sunge ◽  
Sasmitoh Rahmad Riady ◽  
Antika Zahrotul Kamalia
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Absolute Error ◽  
Mean Square ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Penelitian ini bertujuan untuk memprediksi harga saham dengan membandingkan algoritma Linear Regression, Long Short-Term Memory (LSTM), dan Gated Recurrent Unit (GRU) dengan dataset publik kemudian menentukan performa terbaik dari ketiga algoritma tersebut. Dataset yang diuji bersumber dari Indonesia Stock Exchange (IDX), yaitu dataset harga saham KEJU berbentuk time series dari tanggal 15 November 2019 sampai dengan 08 Juni 2021. Parameter yang digunakan untuk pengukuran perbandingan adalah RMSE (Root Mean Square Error), MSE (Mean Square Error), dan MAE (Mean Absolute Error). Setelah dilakukan proses training dan testing, dihasilkan sebuah analisis bahwa dari hasil perbandingan algoritma yang digunakan, algoritma Gated Recurrent Unit (GRU) memiliki performance paling baik dibandingkan Linear Regression dan Long-Short Term Memory (LSTM) dalam hal memprediksi harga saham, dibuktikan dengan nilai RMSE, MSE, dan MAE dari uji coba GRU paling rendah, yaitu nilai RMSE 0.034, MSE 0.001, dan nilai MAE 0.024.

scholarly journals Application of a long short-term memory neural network: a burgeoning method of deep learning in forecasting HIV incidence in Guangxi, China

2019 ◽  
Vol 147 ◽  
Author(s):  
G. Wang ◽  
W. Wei ◽  
J. Jiang ◽  
C. Ning ◽  
H. Chen ◽  
...  
Keyword(s):  
Neural Network ◽  
Mean Square Error ◽  
Short Term Memory ◽  
Mean Square ◽  
Hiv Incidence ◽  
Arima Models ◽  
Short Term ◽  
Term Memory ◽  
Incidence Data ◽  
Long Short Term Memory

AbstractGuangxi, a province in southwestern China, has the second highest reported number of HIV/AIDS cases in China. This study aimed to develop an accurate and effective model to describe the tendency of HIV and to predict its incidence in Guangxi. HIV incidence data of Guangxi from 2005 to 2016 were obtained from the database of the Chinese Center for Disease Control and Prevention. Long short-term memory (LSTM) neural network models, autoregressive integrated moving average (ARIMA) models, generalised regression neural network (GRNN) models and exponential smoothing (ES) were used to fit the incidence data. Data from 2015 and 2016 were used to validate the most suitable models. The model performances were evaluated by evaluating metrics, including mean square error (MSE), root mean square error, mean absolute error and mean absolute percentage error. The LSTM model had the lowest MSE when the N value (time step) was 12. The most appropriate ARIMA models for incidence in 2015 and 2016 were ARIMA (1, 1, 2) (0, 1, 2)12and ARIMA (2, 1, 0) (1, 1, 2)12, respectively. The accuracy of GRNN and ES models in forecasting HIV incidence in Guangxi was relatively poor. Four performance metrics of the LSTM model were all lower than the ARIMA, GRNN and ES models. The LSTM model was more effective than other time-series models and is important for the monitoring and control of local HIV epidemics.

scholarly journals Schätzung der Verdunstung mithilfe von Machine- und Deep Learning-Methoden

2021 ◽  
Author(s):  
Claire Brenner ◽  
Jonathan Frame ◽  
Grey Nearing ◽  
Karsten Schulz
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Sich Eine ◽  
Short Term ◽  
Term Memory ◽  
Neuronales Netzwerk ◽  
Long Short Term Memory

ZusammenfassungDie Verdunstung ist ein entscheidender Prozess im globalen Wasser‑, Energie- sowie Kohlenstoffkreislauf. Daten zur räumlich-zeitlichen Dynamik der Verdunstung sind daher von großer Bedeutung für Klimamodellierungen, zur Abschätzung der Auswirkungen der Klimakrise sowie nicht zuletzt für die Landwirtschaft.In dieser Arbeit wenden wir zwei Machine- und Deep Learning-Methoden für die Vorhersage der Verdunstung mit täglicher und halbstündlicher Auflösung für Standorte des FLUXNET-Datensatzes an. Das Long Short-Term Memory Netzwerk ist ein rekurrentes neuronales Netzwerk, welchen explizit Speichereffekte berücksichtigt und Zeitreihen der Eingangsgrößen analysiert (entsprechend physikalisch-basierten Wasserbilanzmodellen). Dem gegenüber gestellt werden Modellierungen mit XGBoost, einer Entscheidungsbaum-Methode, die in diesem Fall nur Informationen für den zu bestimmenden Zeitschritt erhält (entsprechend physikalisch-basierten Energiebilanzmodellen). Durch diesen Vergleich der beiden Modellansätze soll untersucht werden, inwieweit sich durch die Berücksichtigung von Speichereffekten Vorteile für die Modellierung ergeben.Die Analysen zeigen, dass beide Modellansätze gute Ergebnisse erzielen und im Vergleich zu einem ausgewerteten Referenzdatensatz eine höhere Modellgüte aufweisen. Vergleicht man beide Modelle, weist das LSTM im Mittel über alle 153 untersuchten Standorte eine bessere Übereinstimmung mit den Beobachtungen auf. Allerdings zeigt sich eine Abhängigkeit der Güte der Verdunstungsvorhersage von der Vegetationsklasse des Standorts; vor allem wärmere, trockene Standorte mit kurzer Vegetation werden durch das LSTM besser repräsentiert, wohingegen beispielsweise in Feuchtgebieten XGBoost eine bessere Übereinstimmung mit den Beobachtung liefert. Die Relevanz von Speichereffekten scheint daher zwischen Ökosystemen und Standorten zu variieren.Die präsentierten Ergebnisse unterstreichen das Potenzial von Methoden der künstlichen Intelligenz für die Beschreibung der Verdunstung.

scholarly journals Multiple Pedestrians and Vehicles Tracking in Aerial Imagery Using a Convolutional Neural Network

2021 ◽  
Vol 13 (10) ◽  
pp. 1953
Author(s):  
Seyed Majid Azimi ◽  
Maximilian Kraus ◽  
Reza Bahmanyar ◽  
Peter Reinartz
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Object Tracking ◽  
Short Term Memory ◽  
Aerial Imagery ◽  
Future Research ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, we address various challenges in multi-pedestrian and vehicle tracking in high-resolution aerial imagery by intensive evaluation of a number of traditional and Deep Learning based Single- and Multi-Object Tracking methods. We also describe our proposed Deep Learning based Multi-Object Tracking method AerialMPTNet that fuses appearance, temporal, and graphical information using a Siamese Neural Network, a Long Short-Term Memory, and a Graph Convolutional Neural Network module for more accurate and stable tracking. Moreover, we investigate the influence of the Squeeze-and-Excitation layers and Online Hard Example Mining on the performance of AerialMPTNet. To the best of our knowledge, we are the first to use these two for regression-based Multi-Object Tracking. Additionally, we studied and compared the L1 and Huber loss functions. In our experiments, we extensively evaluate AerialMPTNet on three aerial Multi-Object Tracking datasets, namely AerialMPT and KIT AIS pedestrian and vehicle datasets. Qualitative and quantitative results show that AerialMPTNet outperforms all previous methods for the pedestrian datasets and achieves competitive results for the vehicle dataset. In addition, Long Short-Term Memory and Graph Convolutional Neural Network modules enhance the tracking performance. Moreover, using Squeeze-and-Excitation and Online Hard Example Mining significantly helps for some cases while degrades the results for other cases. In addition, according to the results, L1 yields better results with respect to Huber loss for most of the scenarios. The presented results provide a deep insight into challenges and opportunities of the aerial Multi-Object Tracking domain, paving the way for future research.

Sign in / Sign up

Export Citation Format

Share Document