Prediksi Pencapaian Target Kerja Menggunakan Metode Deep Learning dan Data Envelopment Analysis

Along with the rapid development of technology, especially in the computer field, several methods have been developed for target setting. Data Envelopment Analysis (DEA) is commonly employed to analyze efficiency levels based on historical data with static targets. Data Envelopment Analysis results in a low level of efficiency against the use of static targets. A new target setting solution is needed to handle dynamic targets. Based on the need, we propose a method to predict more realistic dynamic targets using Deep Learning Long Short Term Memory (LSTM) approach from the results of the Data Envelopment Analysis (DEA). This study leads to a prediction model with 71.2% average accuracy.

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Psychological Stress Detection According to ECG Using a Deep Learning Model with Attention Mechanism

Applied Sciences ◽

10.3390/app11062848 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2848

Author(s):

Pengfei Zhang ◽

Fenghua Li ◽

Lidong Du ◽

Rongjian Zhao ◽

Xianxiang Chen ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Learning Model ◽

Attention Mechanism ◽

Stress Detection ◽

Short Term ◽

Term Memory ◽

Average Accuracy ◽

Long Short Term Memory ◽

Deep Learning Model

To satisfy the need to accurately monitor emotional stress, this paper explores the effectiveness of the attention mechanism based on the deep learning model CNN (Convolutional Neural Networks)-BiLSTM (Bi-directional Long Short-Term Memory) As different attention mechanisms can cause the framework to focus on different positions of the feature map, this discussion adds attention mechanisms to the CNN layer and the BiLSTM layer separately, and to both the CNN layer and BiLSTM layer simultaneously to generate different CNN–BiLSTM networks with attention mechanisms. ECG (electrocardiogram) data from 34 subjects were collected on the server platform created by the Institute of Psychology of the Chinese Academy of Science and the researches. It verifies that the average accuracy of CNN–BiLSTM is up to 0.865 without any attention mechanism, while the highest average accuracy of 0.868 is achieved using the CNN–attention–based BiLSTM.

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Non-Intrusive Load Identification Method Based on Improved Long Short Term Memory Network

Energies ◽

10.3390/en14030684 ◽

2021 ◽

Vol 14 (3) ◽

pp. 684

Author(s):

Jiateng Song ◽

Hongbin Wang ◽

Mingxing Du ◽

Lei Peng ◽

Shuai Zhang ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Rapid Development ◽

Series Data ◽

Load Identification ◽

Short Term ◽

Term Memory ◽

Identification Method ◽

Proposed Model ◽

Long Short Term Memory

Non-intrusive load monitoring (NILM) is an important research direction and development goal on the distribution side of smart grid, which can significantly improve the timeliness of demand side response and users’ awareness of load. Due to rapid development, deep learning becomes an effective way to optimize NILM. In this paper, we propose a novel load identification method based on long short term memory (LSTM) on deep learning. Sequence-to-point (seq2point) learning is introduced into LSTM. The innovative combination of the LSTM and the seq2point brings their respective advantages together, so that the proposed model can accurately identify the load in process of time series data. In this paper, we proved the feature of reducing identification error in the experimental data, from three datasets, UK-DALE dataset, REDD dataset, and REFIT dataset. In terms of mean absolute error (MAE), the three datasets have increased by 15%, 14%, and 18% respectively; in terms of normalized signal aggregate error (SAE), the three datasets have increased by 21%, 24%, and 30% respectively. Compared with the existing models, the proposed model has better accuracy and generalization in identifying three open source datasets.

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Application of Rough and Fuzzy Set Theory for Prediction of Stochastic Wind Speed Data Using Long Short-Term Memory

Atmosphere ◽

10.3390/atmos12070924 ◽

2021 ◽

Vol 12 (7) ◽

pp. 924

Author(s):

Moslem Imani ◽

Hoda Fakour ◽

Wen-Hau Lan ◽

Huan-Chin Kao ◽

Chi Ming Lee ◽

...

Keyword(s):

Deep Learning ◽

Wind Speed ◽

Set Theory ◽

Rough Set ◽

Fuzzy Set Theory ◽

Short Term Memory ◽

Learning Model ◽

Short Term ◽

Long Short Term Memory ◽

Deep Learning Model

Despite the great significance of precisely forecasting the wind speed for development of the new and clean energy technology and stable grid operators, the stochasticity of wind speed makes the prediction a complex and challenging task. For improving the security and economic performance of power grids, accurate short-term wind power forecasting is crucial. In this paper, a deep learning model (Long Short-term Memory (LSTM)) has been proposed for wind speed prediction. Knowing that wind speed time series is nonlinear stochastic, the mutual information (MI) approach was used to find the best subset from the data by maximizing the joint MI between subset and target output. To enhance the accuracy and reduce input characteristics and data uncertainties, rough set and interval type-2 fuzzy set theory are combined in the proposed deep learning model. Wind speed data from an international airport station in the southern coast of Iran Bandar-Abbas City was used as the original input dataset for the optimized deep learning model. Based on the statistical results, the rough set LSTM (RST-LSTM) model showed better prediction accuracy than fuzzy and original LSTM, as well as traditional neural networks, with the lowest error for training and testing datasets in different time horizons. The suggested model can support the optimization of the control approach and the smooth procedure of power system. The results confirm the superior capabilities of deep learning techniques for wind speed forecasting, which could also inspire new applications in meteorology assessment.

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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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