scholarly journals A Hybrid Deep Learning Model to Forecast Particulate Matter Concentration Levels in Seoul, South Korea

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 348 ◽  
Author(s):  
Guang Yang ◽  
HwaMin Lee ◽  
Giyeol Lee
Keyword(s):  
Air Pollution ◽  
Deep Learning ◽  
Short Term Memory ◽  
Hybrid Models ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit ◽  
Concentration Levels

Both long- and short-term exposure to high concentrations of airborne particulate matter (PM) severely affect human health. Many countries now regulate PM concentrations. Early-warning systems based on PM concentration levels are urgently required to allow countermeasures to reduce harm and loss. Previous studies sought to establish accurate, efficient predictive models. Many machine-learning methods are used for air pollution forecasting. The long short-term memory and gated recurrent unit methods, typical deep-learning methods, reliably predict PM levels with some limitations. In this paper, the authors proposed novel hybrid models to combine the strength of two types of deep learning methods. Moreover, the authors compare hybrid deep-learning methods (convolutional neural network (CNN)—long short-term memory (LSTM) and CNN—gated recurrent unit (GRU)) with several stand-alone methods (LSTM, GRU) in terms of predicting PM concentrations in 39 stations in Seoul. Hourly air pollution data and meteorological data from January 2015 to December 2018 was used for these training models. The results of the experiment confirmed that the proposed prediction model could predict the PM concentrations for the next 7 days. Hybrid models outperformed single models in five areas selected randomly with the lowest root mean square error (RMSE) and mean absolute error (MAE) values for both PM10 and PM2.5. The error rate for PM10 prediction in Gangnam with RMSE is 1.688, and MAE is 1.161. For hybrid models, the CNN–GRU better-predicted PM10 for all stations selected, while the CNN–LSTM model performed better on predicting PM2.5.

scholarly journals Optimasi Deep Learning untuk Prediksi Saham di Masa Pandemi Covid-19

2021 ◽  
Vol 7 (2) ◽  
pp. 133
Author(s):  
Widi Hastomo ◽  
Adhitio Satyo Bayangkari Karno ◽  
Nawang Kalbuana ◽  
Ervina Nisfiani ◽  
Lussiana ETP
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Hidden Layer ◽  
Gated Recurrent Unit ◽  
Blue Chip

Penelitian ini bertujuan untuk meningkatkan akurasi dengan menurunkan tingkat kesalahan prediksi dari 5 data saham blue chip di Indonesia. Dengan cara mengkombinasikan desain 4 hidden layer neural nework menggunakan Long Short Term Memory (LSTM) dan Gated Recurrent Unit (GRU). Dari tiap data saham akan dihasilkan grafik rmse-epoch yang dapat menunjukan kombinasi layer dengan akurasi terbaik, sebagai berikut; (a) BBCA dengan layer LSTM-GRU-LSTM-GRU (RMSE=1120,651, e=15), (b) BBRI dengan layer LSTM-GRU-LSTM-GRU (RMSE =110,331, e=25), (c) INDF dengan layer GRU-GRU-GRU-GRU (RMSE =156,297, e=35 ), (d) ASII dengan layer GRU-GRU-GRU-GRU (RMSE =134,551, e=20 ), (e) TLKM dengan layer GRU-LSTM-GRU-LSTM (RMSE =71,658, e=35 ). Tantangan dalam mengolah data Deep Learning (DL) adalah menentukan nilai parameter epoch untuk menghasilkan prediksi akurasi yang tinggi.

scholarly journals Application of deep learning methods to predict ionosphere parameters in real time

2020 ◽  
Vol 196 ◽  
pp. 02007
Author(s):  
Vladimir Mochalov ◽  
Anastasia Mochalova
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Real Time ◽  
Network Architecture ◽  
Short Term Memory ◽  
Neural Network Architecture ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

In this paper, the previously obtained results on recognition of ionograms using deep learning are expanded to predict the parameters of the ionosphere. After the ionospheric parameters have been identified on the ionogram using deep learning in real time, we can predict the parameters for some time ahead on the basis of the new data obtained Examples of predicting the ionosphere parameters using an artificial recurrent neural network architecture long short-term memory are given. The place of the block for predicting the parameters of the ionosphere in the system for analyzing ionospheric data using deep learning methods is shown.

scholarly journals A Spectral-Spatial Cascaded 3D Convolutional Neural Network with a Convolutional Long Short-Term Memory Network for Hyperspectral Image Classification

2019 ◽  
Vol 11 (20) ◽  
pp. 2363 ◽  
Author(s):  
Wenchao Qi ◽  
Xia Zhang ◽  
Nan Wang ◽  
Mao Zhang ◽  
Yi Cen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Hyperspectral Image ◽  
Short Term ◽  
Dynamic Learning ◽  
Learning Methods ◽  
Term Memory ◽  
Long Short Term Memory

Deep learning methods used for hyperspectral image (HSI) classification often achieve greater accuracy than traditional algorithms but require large numbers of training epochs. To simplify model structures and reduce their training epochs, an end-to-end deep learning framework incorporating a spectral-spatial cascaded 3D convolutional neural network (CNN) with a convolutional long short-term memory (CLSTM) network, called SSCC, is proposed herein for HSI classification. The SSCC framework employs cascaded 3D CNN to learn the spectral-spatial features of HSIs and uses the CLSTM network to extract sequence features. Residual connections are used in SSCC to accelerate model convergence, with the outputs of previous convolutional layers concatenated as inputs for subsequent layers. Moreover, the data augmentation, parametric rectified linear unit, dynamic learning rate, batch normalization, and regularization (including dropout and L2) methods are used to increase classification accuracy and prevent overfitting. These attributes allow the SSCC framework to achieve good performance for HSI classification within 20 epochs. Three well-known datasets including Indiana Pines, University of Pavia, and Pavia Center were employed to evaluate the classification performance of the proposed algorithm. The GF-5 dataset of Anxin County, obtained from China’s recently launched spaceborne Advanced Hyperspectral Imager, was also used for classification experiments. The experimental results demonstrate that the proposed SSCC framework achieves state-of-the-art performance with better training efficiency than other deep learning methods.

Investigating the Feasibility of Deep Learning Methods for Urdu Word Sense Disambiguation

2022 ◽  
Vol 21 (2) ◽  
pp. 1-16
Author(s):  
Ali Saeed ◽  
Rao Muhammad Adeel Nawab ◽  
Mark Stevenson
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Short Term Memory ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Sense Disambiguation ◽  
Long Short Term Memory

Word Sense Disambiguation (WSD), the process of automatically identifying the correct meaning of a word used in a given context, is a significant challenge in Natural Language Processing. A range of approaches to the problem has been explored by the research community. The majority of these efforts has focused on a relatively small set of languages, particularly English. Research on WSD for South Asian languages, particularly Urdu, is still in its infancy. In recent years, deep learning methods have proved to be extremely successful for a range of Natural Language Processing tasks. The main aim of this study is to apply, evaluate, and compare a range of deep learning methods approaches to Urdu WSD (both Lexical Sample and All-Words) including Simple Recurrent Neural Networks, Long-Short Term Memory, Gated Recurrent Units, Bidirectional Long-Short Term Memory, and Ensemble Learning. The evaluation was carried out on two benchmark corpora: (1) the ULS-WSD-18 corpus and (2) the UAW-WSD-18 corpus. Results (Accuracy = 63.25% and F1-Measure = 0.49) show that a deep learning approach outperforms previously reported results for the Urdu All-Words WSD task, whereas performance using deep learning approaches (Accuracy = 72.63% and F1-Measure = 0.60) are low in comparison to previously reported for the Urdu Lexical Sample task.

scholarly journals Silent Speech Decoding Using Spectrogram Features Based on Neuromuscular Activities

2020 ◽  
Vol 10 (7) ◽  
pp. 442 ◽  
Author(s):  
You Wang ◽  
Ming Zhang ◽  
RuMeng Wu ◽  
Han Gao ◽  
Meng Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Transfer Learning ◽  
Short Term Memory ◽  
Human Subjects ◽  
Short Term ◽  
Learning Methods ◽  
Term Memory ◽  
Spatial Features ◽  
Long Short Term Memory ◽  
Silent Speech

Silent speech decoding is a novel application of the Brain–Computer Interface (BCI) based on articulatory neuromuscular activities, reducing difficulties in data acquirement and processing. In this paper, spatial features and decoders that can be used to recognize the neuromuscular signals are investigated. Surface electromyography (sEMG) data are recorded from human subjects in mimed speech situations. Specifically, we propose to utilize transfer learning and deep learning methods by transforming the sEMG data into spectrograms that contain abundant information in time and frequency domains and are regarded as channel-interactive. For transfer learning, a pre-trained model of Xception on the large image dataset is used for feature generation. Three deep learning methods, Multi-Layer Perception, Convolutional Neural Network and bidirectional Long Short-Term Memory, are then trained using the extracted features and evaluated for recognizing the articulatory muscles’ movements in our word set. The proposed decoders successfully recognized the silent speech and bidirectional Long Short-Term Memory achieved the best accuracy of 90%, outperforming the other two algorithms. Experimental results demonstrate the validity of spectrogram features and deep learning algorithms.

scholarly journals The use of long short-term memory and gated recurrent unit for predicting the values of geomagnetic indices

2020 ◽  
pp. 110-121
Author(s):  
В.А. Мочалов ◽  
А.В. Мочалова
Keyword(s):  
Deep Learning ◽  
Initial Data ◽  
Short Term Memory ◽  
Short Term ◽  
Forecasting Accuracy ◽  
Term Memory ◽  
Geomagnetic Indices ◽  
Source Data ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

В работе с помощью глубокого обучения рассматривается прогнозирование значений следующих геомагнитных индексов (ГИ): Dst, Kp, AE и AP. Для прогнозирования используются архитектуры долгой краткосрочной памяти (LSTM) и управляемых рекуррентных блоков (GRU). Для различных ГИ индексов анализируется функция потерь в за-висимости от периодичности исходных данных. Установлено, что чем меньше периодичность исходных данных ГИ тем точнее осуществляется прогноз следующего значения ГИ. Для анализа использовались следую-щие периоды исходных данных ГИ: час, 3 часа, сутки. In this work, with the help of deep learning, predicting the values of the following geomagnetic indices (GI) is considered: Dst, Kp, AE and Ap. For forecasting we use the architectures are long short-term memory (LSTM) and gated recurrent unit (GRU). For various GI indices, the loss function is analyzed depending on the periodicity of the source data. It has been established that forecasting accuracy increases with decreasing periodicity of the initial data of geomagnetic indices. For the analysis, the following periods of the initial GI data were used: hour, 3 hours, day. For the analysis we used hour, 3 hours and day periods of the initial GI source data.

Multilabel Text Classification in News Articles Using Long-Term Memory with Word2Vec

2020 ◽  
Vol 4 (2) ◽  
pp. 276-285
Author(s):  
Winda Kurnia Sari ◽  
Dian Palupi Rini ◽  
Reza Firsandaya Malik ◽  
Iman Saladin B. Azhar
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Text Classification ◽  
Large Scale ◽  
Short Term Memory ◽  
Short Term ◽  
Learning Methods ◽  
Processing Variable ◽  
Term Memory ◽  
Long Short Term Memory

Multilabel text classification is a task of categorizing text into one or more categories. Like other machine learning, multilabel classification performance is limited to the small labeled data and leads to the difficulty of capturing semantic relationships. It requires a multilabel text classification technique that can group four labels from news articles. Deep Learning is a proposed method for solving problems in multilabel text classification techniques. Some of the deep learning methods used for text classification include Convolutional Neural Networks, Autoencoders, Deep Belief Networks, and Recurrent Neural Networks (RNN). RNN is one of the most popular architectures used in natural language processing (NLP) because the recurrent structure is appropriate for processing variable-length text. One of the deep learning methods proposed in this study is RNN with the application of the Long Short-Term Memory (LSTM) architecture. The models are trained based on trial and error experiments using LSTM and 300-dimensional words embedding features with Word2Vec. By tuning the parameters and comparing the eight proposed Long Short-Term Memory (LSTM) models with a large-scale dataset, to show that LSTM with features Word2Vec can achieve good performance in text classification. The results show that text classification using LSTM with Word2Vec obtain the highest accuracy is in the fifth model with 95.38, the average of precision, recall, and F1-score is 95. Also, LSTM with the Word2Vec feature gets graphic results that are close to good-fit on seventh and eighth models.

scholarly journals An LSTM and GRU based Trading Strategy Adapted to the Moroccan Market

2021 ◽  
Author(s):  
Yassine Touzani ◽  
Khadija Douzi
Keyword(s):  
Deep Learning ◽  
Stock Market ◽  
Short Term Memory ◽  
Decision Rules ◽  
Trading Strategy ◽  
Local Market ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

Abstract Forecasting stock prices is an extremely challenging job considering the high volatility and the number of variables that influence it (political, economical, social, etc.). Predicting the closing price provides useful information and helps the investor to make the right decision. The use of deep learning and more precisely the recurrent neural networks RNNs in stock market forecasting is an increasingly common practice in the literature. The Long Short Term Memory LSTM and Gated Recurrent Unit GRU architectures are among the most widely used types of RNN networks, given their suitability for sequential data. In this paper, we propose a trading strategy designed for the Moroccan stock market, based on two deep learning model: Long Short Term Memory LSTM and Gated Recurrent Unit GRU to predict respectively close price for short and mid term horizon. Decision rules for buying and selling stocks are implemented based on the forecasting given by the two models, then over four three-years periods, we simulate transactions using these decision rules with different parameters for each stock. We only hold stocks that ensure a return greater than a benchmark return over the four periods. Random search is then used to choose one of the available parameters and the performance of the portfolio built from the selected stocks will be tested over a further period. The repetition of this process with a variation of benchmark return makes it possible to select the best possible combination of stock each with the parameters optimized for the decision rules. The proposed strategy produces very promising results and outperform the performance of indices used as benchmarks in the local market.

scholarly journals A Hybrid Deep Learning-Driven SDN Enabled Mechanism for Secure Communication in Internet of Things (IoT)

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4884
Author(s):  
Danish Javeed ◽  
Tianhan Gao ◽  
Muhammad Taimoor Khan ◽  
Ijaz Ahmad
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Internet Of Things ◽  
Hybrid Model ◽  
Deep Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Gated Recurrent Unit

The Internet of Things (IoT) has emerged as a new technological world connecting billions of devices. Despite providing several benefits, the heterogeneous nature and the extensive connectivity of the devices make it a target of different cyberattacks that result in data breach and financial loss. There is a severe need to secure the IoT environment from such attacks. In this paper, an SDN-enabled deep-learning-driven framework is proposed for threats detection in an IoT environment. The state-of-the-art Cuda-deep neural network, gated recurrent unit (Cu- DNNGRU), and Cuda-bidirectional long short-term memory (Cu-BLSTM) classifiers are adopted for effective threat detection. We have performed 10 folds cross-validation to show the unbiasedness of results. The up-to-date publicly available CICIDS2018 data set is introduced to train our hybrid model. The achieved accuracy of the proposed scheme is 99.87%, with a recall of 99.96%. Furthermore, we compare the proposed hybrid model with Cuda-Gated Recurrent Unit, Long short term memory (Cu-GRULSTM) and Cuda-Deep Neural Network, Long short term memory (Cu- DNNLSTM), as well as with existing benchmark classifiers. Our proposed mechanism achieves impressive results in terms of accuracy, F1-score, precision, speed efficiency, and other evaluation metrics.

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