Electrocorticography based motor imagery movements classification using long short-term memory (LSTM) based on deep learning approach

The impact of this pandemic affects various sectors in Indonesia, especially in the economic sector, due to the large-scale social restrictions policy to suppress this case's growth. The details of the growth of Covid-19 in Indonesia are still fluctuating and cannot be fully understood. Recently it has been developed by researchers related to the prediction of Covid-19 cases in various countries. One of them is using a machine learning technique approach to predict cases of daily increase Covid-19. However, the use of machine learning techniques results in the MSE error value in the thousands. This high number indicates that the prediction data using the model is still a high error rate compared to the actual data. In this study, we propose a deep learning approach using the Long Short Term Memory (LSTM) method to build a prediction model for the daily increase cases of Covid-19. This study's LSTM model architecture uses the LSTM layer, Dropout layer, Dense, and Linear Activation Function. Based on various hyperparameter experiments, using the number of neurons 10, batch size 32, and epochs 50, the MSE values were 0.0308, RMSE 0.1758, and MAE 0.13. These results prove that the deep learning approach produces a smaller error value than machine learning techniques, even closer to zero.

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A Deep Learning Approach Based on Sparse Autoencoder with Long Short-Term Memory for Network Intrusion Detection

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.140.592 ◽

2020 ◽

Vol 140 (6) ◽

pp. 592-599

Author(s):

Zolzaya Kherlenchimeg ◽

Naoshi Nakaya

Keyword(s):

Deep Learning ◽

Intrusion Detection ◽

Short Term Memory ◽

Network Intrusion Detection ◽

Learning Approach ◽

Short Term ◽

Term Memory ◽

Network Intrusion ◽

Sparse Autoencoder ◽

Long Short Term Memory

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A novel solution of using deep learning for early prediction cardiac arrest in Sepsis patient: enhanced bidirectional long short-term memory (LSTM)

Multimedia Tools and Applications ◽

10.1007/s11042-021-11176-5 ◽

2021 ◽

Author(s):

Samit Baral ◽

Abeer Alsadoon ◽

P. W. C. Prasad ◽

Sarmad Al Aloussi ◽

Omar Hisham Alsadoon

Keyword(s):

Cardiac Arrest ◽

Deep Learning ◽

Sepsis Patient ◽

Short Term Memory ◽

Early Prediction ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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

Österreichische Wasser- und Abfallwirtschaft ◽

10.1007/s00506-021-00768-y ◽

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.

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Concrete compressive strength prediction modeling utilizing deep learning long short-term memory algorithm for a sustainable environment

Environmental Science and Pollution Research ◽

10.1007/s11356-021-12877-y ◽

2021 ◽

Author(s):

Sarmad Dashti Latif

Keyword(s):

Compressive Strength ◽

Deep Learning ◽

Short Term Memory ◽

Strength Prediction ◽

Concrete Compressive Strength ◽

Prediction Modeling ◽

Short Term ◽

Term Memory ◽

Sustainable Environment ◽

Long Short Term Memory

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

Remote Sensing ◽

10.3390/rs13101953 ◽

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.

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