scholarly journals A Multistep Prediction of Hydropower Station Inflow Based on Bagging-LSTM Model

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lulu Wang ◽  
Hanmei Peng ◽  
Mao Tan ◽  
Rui Pan
Keyword(s):  
Short Term Memory ◽  
Southern China ◽  
Time Lag ◽  
Weather Data ◽  
Hydropower Station ◽  
Related Factors ◽  
Joint Influence ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Deep Learning Model

The inflow forecasting is one of the most important technologies for modern hydropower station. Under the joint influence of soil, upstream inflow, and precipitation, the inflow is often characterized by time lag, nonlinearity, and uncertainty and then results in the difficulty of accurate multistep prediction of inflow. To address the coupling relationship between inflow and the related factors, this paper proposes a long short-term memory deep learning model based on the Bagging algorithm (Bagging-LSTM) to predict the inflows of future 3 h, 12 h, and 24 h, respectively. To validate the proposed model, the inflow and related weather data come from a hydropower station in southern China. Compared with the classical time series models, the results show that the proposed model outperforms them on different accuracy metrics, especially in the scenario of multistep prediction.

Regularized Deep LSTM Autoencoder for Phonological Deviation Assessment

2020 ◽  
pp. 2152002
Author(s):  
Sheena Christabel Pravin ◽  
M. Palanivelan
Keyword(s):  
Short Term Memory ◽  
Drop Out ◽  
Severity Assessment ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Phonological Features ◽  
Decoder Architecture ◽  
Long Short Term Memory ◽  
Deep Learning Model

In this paper, the Deep Long-short term memory Autoencoder (DLAE), a regularized deep learning model, is proposed for the automatic severity assessment of phonological deviations which are crucial stuttering markers in children. This automatic noninvasive severity assessment plays a paramount role in prevenient diagnosis, progress inference, and post-care for the patients with specific speech disorder. The proposed model is an implementation of a multi-layered Autoencoder in the Encoder–Decoder architecture of the Long-Short Term Memory (LSTM) model with hierarchically appended hidden layers and hidden units. The DLAE has definite advantage over the baseline Autoencoders. During the training phase, the proposed DLAE reconstructs the phonological features in an unsupervised fashion and the latent bottleneck features are extracted from the Encoder. The trained and regularized DLAE model with drop out is then used to predict the severity of the phonological deviation with high precision and classification accuracy compared to the baseline models.

scholarly journals Temperature Prediction Using the Missing Data Refinement Model Based on a Long Short-Term Memory Neural Network

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 718 ◽  
Author(s):  
Park ◽  
Kim ◽  
Lee ◽  
Kim ◽  
Song ◽  
...  
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Prediction Model ◽  
Short Term Memory ◽  
Weather Data ◽  
Short Term ◽  
Temperature Prediction ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

In this paper, we propose a new temperature prediction model based on deep learning by using real observed weather data. To this end, a huge amount of model training data is needed, but these data should not be defective. However, there is a limitation in collecting weather data since it is not possible to measure data that have been missed. Thus, the collected data are apt to be incomplete, with random or extended gaps. Therefore, the proposed temperature prediction model is used to refine missing data in order to restore missed weather data. In addition, since temperature is seasonal, the proposed model utilizes a long short-term memory (LSTM) neural network, which is a kind of recurrent neural network known to be suitable for time-series data modeling. Furthermore, different configurations of LSTMs are investigated so that the proposed LSTM-based model can reflect the time-series traits of the temperature data. In particular, when a part of the data is detected as missing, it is restored by using the proposed model’s refinement function. After all the missing data are refined, the LSTM-based model is retrained using the refined data. Finally, the proposed LSTM-based temperature prediction model can predict the temperature through three time steps: 6, 12, and 24 h. Furthermore, the model is extended to predict 7 and 14 day future temperatures. The performance of the proposed model is measured by its root-mean-squared error (RMSE) and compared with the RMSEs of a feedforward deep neural network, a conventional LSTM neural network without any refinement function, and a mathematical model currently used by the meteorological office in Korea. Consequently, it is shown that the proposed LSTM-based model employing LSTM-refinement achieves the lowest RMSEs for 6, 12, and 24 h temperature prediction as well as for 7 and 14 day temperature prediction, compared to other DNN-based and LSTM-based models with either no refinement or linear interpolation. Moreover, the prediction accuracy of the proposed model is higher than that of the Unified Model (UM) Local Data Assimilation and Prediction System (LDAPS) for 24 h temperature predictions.

scholarly journals A COMBINED DEEP LEARNING MODEL FOR PERSIAN SENTIMENT ANALYSIS

2019 ◽  
Vol 20 (1) ◽  
pp. 129-139 ◽  
Author(s):  
Zahra Bokaee Nezhad ◽  
Mohammad Ali Deihimi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Deep Learning Model

With increasing members in social media sites today, people tend to share their views about everything online. It is a convenient way to convey their messages to end users on a specific subject. Sentiment Analysis is a subfield of Natural Language Processing (NLP) that refers to the identification of users’ opinions toward specific topics. It is used in several fields such as marketing, customer services, etc. However, limited works have been done on Persian Sentiment Analysis. On the other hand, deep learning has recently become popular because of its successful role in several Natural Language Processing tasks. The objective of this paper is to propose a novel hybrid deep learning architecture for Persian Sentiment Analysis. According to the proposed model, local features are extracted by Convolutional Neural Networks (CNN) and long-term dependencies are learned by Long Short Term Memory (LSTM). Therefore, the model can harness both CNN's and LSTM's abilities. Furthermore, Word2vec is used for word representation as an unsupervised learning step. To the best of our knowledge, this is the first attempt where a hybrid deep learning model is used for Persian Sentiment Analysis. We evaluate the model on a Persian dataset that is introduced in this study. The experimental results show the effectiveness of the proposed model with an accuracy of 85%. ABSTRAK: Hari ini dengan ahli yang semakin meningkat di laman media sosial, orang cenderung untuk berkongsi pandangan mereka tentang segala-galanya dalam talian. Ini adalah cara mudah untuk menyampaikan mesej mereka kepada pengguna akhir mengenai subjek tertentu. Analisis Sentimen adalah subfield Pemprosesan Bahasa Semula Jadi yang merujuk kepada pengenalan pendapat pengguna ke arah topik tertentu. Ia digunakan dalam beberapa bidang seperti pemasaran, perkhidmatan pelanggan, dan sebagainya. Walau bagaimanapun, kerja-kerja terhad telah dilakukan ke atas Analisis Sentimen Parsi. Sebaliknya, pembelajaran mendalam baru menjadi popular kerana peranannya yang berjaya dalam beberapa tugas Pemprosesan Bahasa Asli (NLP). Objektif makalah ini adalah mencadangkan senibina pembelajaran hibrid yang baru dalam Analisis Sentimen Parsi. Menurut model yang dicadangkan, ciri-ciri tempatan ditangkap oleh Rangkaian Neural Convolutional (CNN) dan ketergantungan jangka panjang dipelajari oleh Long Short Term Memory (LSTM). Oleh itu, model boleh memanfaatkan kebolehan CNN dan LSTM. Selain itu, Word2vec digunakan untuk perwakilan perkataan sebagai langkah pembelajaran tanpa pengawasan. Untuk pengetahuan yang terbaik, ini adalah percubaan pertama di mana model pembelajaran mendalam hibrid digunakan untuk Analisis Sentimen Persia. Kami menilai model pada dataset Persia yang memperkenalkan dalam kajian ini. Keputusan eksperimen menunjukkan keberkesanan model yang dicadangkan dengan ketepatan 85%.

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

Atmosphere ◽  
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.

scholarly journals Feasibility of Using Electrical Impedance Spectroscopy for Assessing Biological Cell Damage during Freezing and Thawing

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4129
Author(s):  
Sisay Mebre Abie ◽  
Ørjan Grøttem Martinsen ◽  
Bjørg Egelandsdal ◽  
Jie Hou ◽  
Frøydis Bjerke ◽  
...  
Keyword(s):  
Phase Angle ◽  
Short Term Memory ◽  
Cell Damage ◽  
Electrical Impedance Spectroscopy ◽  
Electric Impedance ◽  
Freezing And Thawing ◽  
Short Term ◽  
Long Short Term Memory ◽  
Impedance Parameters ◽  
Deep Learning Model

This study was performed to test bioimpedance as a tool to detect the effect of different thawing methods on meat quality to aid in the eventual creation of an electric impedance-based food quality monitoring system. The electric impedance was measured for fresh pork, thawed pork, and during quick and slow thawing. A clear difference was observed between fresh and thawed samples for both impedance parameters. Impedance was different between the fresh and the frozen-thawed samples, but there were no impedance differences between frozen-thawed samples and the ones that were frozen-thawed and then stored at +3 °C for an additional 16 h after thawing. The phase angle was also different between fresh and the frozen-thawed samples. At high frequency, there were small, but clear phase angle differences between frozen-thawed samples and the samples that were frozen-thawed and subsequently stored for more than 16 h at +3 °C. Furthermore, the deep learning model LSTM-RNN (long short-term memory recurrent neural network) was found to be a promising way to classify the different methods of thawing.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

scholarly journals Air pollution forecasting application based on deep learning model and optimization algorithm

2021 ◽  
Author(s):  
Azim Heydari ◽  
Meysam Majidi Nezhad ◽  
Davide Astiaso Garcia ◽  
Farshid Keynia ◽  
Livio De Santoli
Keyword(s):  
Air Pollution ◽  
Wind Speed ◽  
Power Plant ◽  
Air Temperature ◽  
Short Term Memory ◽  
Combined Cycle ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory

AbstractAir pollution monitoring is constantly increasing, giving more and more attention to its consequences on human health. Since Nitrogen dioxide (NO2) and sulfur dioxide (SO2) are the major pollutants, various models have been developed on predicting their potential damages. Nevertheless, providing precise predictions is almost impossible. In this study, a new hybrid intelligent model based on long short-term memory (LSTM) and multi-verse optimization algorithm (MVO) has been developed to predict and analysis the air pollution obtained from Combined Cycle Power Plants. In the proposed model, long short-term memory model is a forecaster engine to predict the amount of produced NO2 and SO2 by the Combined Cycle Power Plant, where the MVO algorithm is used to optimize the LSTM parameters in order to achieve a lower forecasting error. In addition, in order to evaluate the proposed model performance, the model has been applied using real data from a Combined Cycle Power Plant in Kerman, Iran. The datasets include wind speed, air temperature, NO2, and SO2 for five months (May–September 2019) with a time step of 3-h. In addition, the model has been tested based on two different types of input parameters: type (1) includes wind speed, air temperature, and different lagged values of the output variables (NO2 and SO2); type (2) includes just lagged values of the output variables (NO2 and SO2). The obtained results show that the proposed model has higher accuracy than other combined forecasting benchmark models (ENN-PSO, ENN-MVO, and LSTM-PSO) considering different network input variables. Graphic abstract

Sentence similarity evaluation using Sent2Vec and siamese neural network with parallel structure

2021 ◽  
pp. 1-10
Author(s):  
Hye-Jeong Song ◽  
Tak-Sung Heo ◽  
Jong-Dae Kim ◽  
Chan-Young Park ◽  
Yu-Seop Kim
Keyword(s):  
Neural Network ◽  
Language Processing ◽  
Short Term Memory ◽  
Parallel Structure ◽  
Short Term ◽  
Similarity Estimation ◽  
Accurate Judgment ◽  
Proposed Model ◽  
Sentence Similarity ◽  
Long Short Term Memory

Sentence similarity evaluation is a significant task used in machine translation, classification, and information extraction in the field of natural language processing. When two sentences are given, an accurate judgment should be made whether the meaning of the sentences is equivalent even if the words and contexts of the sentences are different. To this end, existing studies have measured the similarity of sentences by focusing on the analysis of words, morphemes, and letters. To measure sentence similarity, this study uses Sent2Vec, a sentence embedding, as well as morpheme word embedding. Vectors representing words are input to the 1-dimension convolutional neural network (1D-CNN) with various sizes of kernels and bidirectional long short-term memory (Bi-LSTM). Self-attention is applied to the features transformed through Bi-LSTM. Subsequently, vectors undergoing 1D-CNN and self-attention are converted through global max pooling and global average pooling to extract specific values, respectively. The vectors generated through the above process are concatenated to the vector generated through Sent2Vec and are represented as a single vector. The vector is input to softmax layer, and finally, the similarity between the two sentences is determined. The proposed model can improve the accuracy by up to 5.42% point compared with the conventional sentence similarity estimation models.

An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

2021 ◽  
Vol 13 (2) ◽  
pp. 1-12
Author(s):  
Sumit Das ◽  
Manas Kumar Sanyal ◽  
Sarbajyoti Mallik
Keyword(s):  
Artificial Intelligence ◽  
Web Application ◽  
Short Term Memory ◽  
Real Data ◽  
Fake News ◽  
Artificial Intelligence Technique ◽  
News Reports ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

Production Forecasting with the Interwell Interference by Integrating Graph Convolutional and Long Short-Term Memory Neural Network

2021 ◽  
pp. 1-17
Author(s):  
Enda Du ◽  
Yuetian Liu ◽  
Ziyan Cheng ◽  
Liang Xue ◽  
Jing Ma ◽  
...  
Keyword(s):  
Neural Network ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Production Forecasting ◽  
Temporal Correlations ◽  
Proposed Model ◽  
The Mean ◽  
Long Short Term Memory ◽  
The Impact

Summary Accurate production forecasting is an essential task and accompanies the entire process of reservoir development. With the limitation of prediction principles and processes, the traditional approaches are difficult to make rapid predictions. With the development of artificial intelligence, the data-driven model provides an alternative approach for production forecasting. To fully take the impact of interwell interference on production into account, this paper proposes a deep learning-based hybrid model (GCN-LSTM), where graph convolutional network (GCN) is used to capture complicated spatial patterns between each well, and long short-term memory (LSTM) neural network is adopted to extract intricate temporal correlations from historical production data. To implement the proposed model more efficiently, two data preprocessing procedures are performed: Outliers in the data set are removed by using a box plot visualization, and measurement noise is reduced by a wavelet transform. The robustness and applicability of the proposed model are evaluated in two scenarios of different data types with the root mean square error (RMSE), the mean absolute error (MAE), and the mean absolute percentage error (MAPE). The results show that the proposed model can effectively capture spatial and temporal correlations to make a rapid and accurate oil production forecast.

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