scholarly journals Hierarchical Gated Recurrent Unit with Semantic Attention for Event Prediction

2020 ◽  
Vol 12 (2) ◽  
pp. 39
Author(s):  
Zichun Su ◽  
Jialin Jiang
Keyword(s):  
Time Series ◽  
Prior Knowledge ◽  
Financial Risk ◽  
State Of The Art ◽  
Language Model ◽  
Statistical Language Model ◽  
Event Prediction ◽  
Event Information ◽  
The Impact ◽  
Gated Recurrent Unit

Event prediction plays an important role in financial risk assessment and disaster warning, which can help government decision-making and economic investment. Previous works are mainly based on time series for event prediction such as statistical language model and recurrent neural network, while ignoring the impact of prior knowledge on event prediction. This makes the direction of event prediction often biased or wrong. In this paper, we propose a hierarchical event prediction model based on time series and prior knowledge. To ensure the accuracy of the event prediction, the model obtains the time-based event information and prior knowledge of events by Gated Recurrent Unit and Associated Link Network respectively. The semantic selective attention mechanism is used to fuse the time-based event information and prior knowledge, and finally generate predicted events. Experimental results on Chinese News datasets demonstrate that our model significantly outperforms the state-of-the-art methods, and increases the accuracy by 2.8%.

scholarly journals Evaluation and Comparison of Random Forest and A-LSTM Networks for Large-scale Winter Wheat Identification

2019 ◽  
Vol 11 (14) ◽  
pp. 1665 ◽  
Author(s):  
Tianle He ◽  
Chuanjie Xie ◽  
Qingsheng Liu ◽  
Shiying Guan ◽  
Gaohuan Liu
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Winter Wheat ◽  
Large Scale ◽  
Short Term Memory ◽  
State Of The Art ◽  
Training Sample ◽  
Central China ◽  
Surface Reflectance ◽  
The Impact

Machine learning comprises a group of powerful state-of-the-art techniques for land cover classification and cropland identification. In this paper, we proposed and evaluated two models based on random forest (RF) and attention-based long short-term memory (A-LSTM) networks that can learn directly from the raw surface reflectance of remote sensing (RS) images for large-scale winter wheat identification in Huanghuaihai Region (North-Central China). We used a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) images over one growing season and the corresponding winter wheat distribution map for the experiments. Each training sample was derived from the raw surface reflectance of MODIS time-series images. Both models achieved state-of-the-art performance in identifying winter wheat, and the F1 scores of RF and A-LSTM were 0.72 and 0.71, respectively. We also analyzed the impact of the pixel-mixing effect. Training with pure-mixed-pixel samples (the training set consists of pure and mixed cells and thus retains the original distribution of data) was more precise than training with only pure-pixel samples (the entire pixel area belongs to one class). We also analyzed the variable importance along the temporal series, and the data acquired in March or April contributed more than the data acquired at other times. Both models could predict winter wheat coverage in past years or in other regions with similar winter wheat growing seasons. The experiments in this paper showed the effectiveness and significance of our methods.

scholarly journals Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2228
Author(s):  
Mostafa Farrokhabadi
Keyword(s):  
Time Series ◽  
Energy Storage ◽  
Energy Cost ◽  
Prince Edward Island ◽  
State Of The Art ◽  
Negative Impact ◽  
Probabilistic Method ◽  
Data Driven ◽  
Renewable Energy Resources ◽  
The Impact

This paper presents findings on mitigating the negative impact of renewable energy resources variability on the energy scheduling problem, in particular for island grids and microgrids. The methods and findings presented in this paper are twofold. First, data obtained from the City of Summerside in the province of Prince Edward Island, Canada, is leveraged to demonstrate the effectiveness of state-of-the-art time series predictors in mitigating energy scheduling inaccuracy. Second, the outcome of the time series prediction analysis is used to propose a novel data-driven battery energy storage system (BESS) sizing study for energy scheduling purposes. The proposed probabilistic method accounts for intra-interval variations of generation and demand, thus mitigating the trade-off between time resolution of the problem formulation and the solution accuracy. In addition, as part of the sizing study, a BESS management strategy is proposed to minimize energy scheduling inaccuracies, and is then used to obtain the optimal BESS size. Finally, the paper presents quantitative analyses of the impact of both the energy predictors and the BESS on the supplied energy cost using the actual data of the Summerside Electric grid. The paper reveals the significant potential for reducing energy cost in renewable-penetrated grids and microgrids through state-of-the-art predictors combined with applications of properly-sized energy storage systems.

scholarly journals Enhancing clinical concept extraction with contextual embeddings

2019 ◽  
Vol 26 (11) ◽  
pp. 1297-1304 ◽  
Author(s):  
Yuqi Si ◽  
Jingqi Wang ◽  
Hua Xu ◽  
Kirk Roberts
Keyword(s):  
Language Processing ◽  
Semantic Information ◽  
Medical Information ◽  
State Of The Art ◽  
Language Model ◽  
The State ◽  
Concept Extraction ◽  
The Impact ◽  
Clinical Concept ◽  
Embedding Methods

Abstract Objective Neural network–based representations (“embeddings”) have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction. Recently, however, more advanced embedding methods and representations (eg, ELMo, BERT) have further pushed the state of the art in NLP, yet there are no common best practices for how to integrate these representations into clinical tasks. The purpose of this study, then, is to explore the space of possible options in utilizing these new models for clinical concept extraction, including comparing these to traditional word embedding methods (word2vec, GloVe, fastText). Materials and Methods Both off-the-shelf, open-domain embeddings and pretrained clinical embeddings from MIMIC-III (Medical Information Mart for Intensive Care III) are evaluated. We explore a battery of embedding methods consisting of traditional word embeddings and contextual embeddings and compare these on 4 concept extraction corpora: i2b2 2010, i2b2 2012, SemEval 2014, and SemEval 2015. We also analyze the impact of the pretraining time of a large language model like ELMo or BERT on the extraction performance. Last, we present an intuitive way to understand the semantic information encoded by contextual embeddings. Results Contextual embeddings pretrained on a large clinical corpus achieves new state-of-the-art performances across all concept extraction tasks. The best-performing model outperforms all state-of-the-art methods with respective F1-measures of 90.25, 93.18 (partial), 80.74, and 81.65. Conclusions We demonstrate the potential of contextual embeddings through the state-of-the-art performance these methods achieve on clinical concept extraction. Additionally, we demonstrate that contextual embeddings encode valuable semantic information not accounted for in traditional word representations.

scholarly journals Impact of Using Bidirectional Encoder Representations from Transformers (BERT) Models for Arabic Dialogue Acts Identification

2021 ◽  
Vol 26 (5) ◽  
pp. 469-475
Author(s):  
Alaa Joukhadar ◽  
Nada Ghneim ◽  
Ghaida Rebdawi
Keyword(s):  
Language Processing ◽  
State Of The Art ◽  
Language Model ◽  
Correct Identification ◽  
Dialogue Systems ◽  
Morphological Segmentation ◽  
Representation Model ◽  
Dialogue Acts ◽  
Language Representation ◽  
The Impact

In Human-Computer dialogue systems, the correct identification of the intent underlying a speaker's utterance is crucial to the success of a dialogue. Several researches have studied the Dialogue Act Classification (DAC) task to identify Dialogue Acts (DA) for different languages. Recently, the emergence of Bidirectional Encoder Representations from Transformers (BERT) models, enabled establishing state-of-the-art results for a variety of natural language processing tasks in different languages. Very few researches have been done in the Arabic Dialogue acts identification task. The BERT representation model has not been studied yet in Arabic Dialogue acts detection task. In this paper, we propose a model using BERT language representation to identify Arabic Dialogue Acts. We explore the impact of using different BERT models: AraBERT Original (v0.1, v1), AraBERT Base (v0.2, and v2) and AraBERT Large (v0.2, and v2), which are pretrained on different Arabic corpora (different in size, morphological segmentation, language model window, …). The comparison was performed on two available Arabic datasets. Using AraBERTv0.2-base model for dialogue representations outperformed all other pretrained models. Moreover, we compared the performance of AraBERTv0.2-base model to the state-of-the-art approaches applied on the two datasets. The comparison showed that this representation model outperformed the performance both state-of-the-art models.

Financial Risks of the Bank: Assessment and Mechanism of Neutralization

2020 ◽  
Vol 22 (1) ◽  
pp. 6-12
Author(s):  
Nelia Volkova ◽  
◽  
Alina Mukhina ◽  
Keyword(s):  
Risk Assessment ◽  
Financial Stability ◽  
Financial Risk ◽  
Stable Operation ◽  
Financial Risks ◽  
Negative Consequences ◽  
Conceptual Approach ◽  
Relevant Today ◽  
Banking Institution ◽  
The Impact

Abstract. Introduction. The issue of financial risk management of commercial banks is quite relevant today, because the activity of banks is the most risky of all. The presence of risks in banking can lead to unexpected losses, namely the loss of own resources. That’s why for the stable operation of the bank without loss the priority is to assess the financial risks, which is the basis for their further neutralization. Purpose. The purpose of the article is to develop conceptual provisions for assessment financial risks and justifying the need to neutralize them. Results. The article analyzes the impact of risks on the financial stability of a banking institution. The main methods of bank risk assessment are considered. All these include the statistical method, the analytical method, the expert method, the analogue method and the combined method. The necessity of neutralization of financial risks in order to avoid negative consequences is substantiated. Also the methods of bank risks neutralization are considered. It should be noted that these methods of neutralization can not only be used, but also supplement the list with new methods must be done, which in the future will protect the bank from the influence of undesirable factors. A conceptual approach to the assessment and neutralization of financial risks is proposed. This conceptual approach aims to ensure effective assessment of the level of risk with their subsequent neutralization Conclusions. Use of a conceptual approach will allow an effective risk assessment and decision-making to avoid or accept risk. Thanks to using this approach, the banking institution will be able to react swiftly to the presence of financial risks and to prevent the occurrence of negative consequences, which may lead to a violation of the financial stability of the bank.

Deep Learning for text in limted data settings

2020 ◽  
Author(s):  
Pathikkumar Patel ◽  
Bhargav Lad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Transfer Learning ◽  
Text Classification ◽  
State Of The Art ◽  
Time Series Forecasting ◽  
Text Data ◽  
Performance Levels

During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.

River-Basin Planning for Control of Nitrate Pollution

1982 ◽  
Vol 14 (4-5) ◽  
pp. 245-252 ◽  
Author(s):  
C S Sinnott ◽  
D G Jamieson
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
River Basin ◽  
Water Resource ◽  
Potable Water ◽  
Potential Problem ◽  
Nitrate Pollution ◽  
Optimal Mix ◽  
Regional Basis ◽  
The Impact

The combination of increasing nitrate concentrations in the River Thames and the recent EEC Directive on the acceptable level in potable water is posing a potential problem. In assessing the impact of nitrates on water-resource systems, extensive use has been made of time-series analysis and simulation. These techniques are being used to define the optimal mix of alternatives for overcoming the problem on a regional basis.

Análise da relação entre O3, NO e NO2 usando técnicas de regressão linear múltipla.

GEOgraphia ◽  
2018 ◽  
Vol 20 (43) ◽  
pp. 124
Author(s):  
Amaury De Souza ◽  
Priscilla V Ikefuti ◽  
Ana Paula Garcia ◽  
Debora A.S Santos ◽  
Soetania Oliveira
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Nitrogen Dioxide ◽  
Multiple Linear Regression ◽  
Quality Parameters ◽  
Environmental Pollutant ◽  
Environmental Research ◽  
Linear Regression Models ◽  
Mato Grosso ◽  
The Impact

Análise e previsão de parâmetros de qualidade do ar são tópicos importantes da pesquisa atmosférica e ambiental atual, devido ao impacto causado pela poluição do ar na saúde humana. Este estudo examina a transformação do dióxido de nitrogênio (NO2) em ozônio (O3) no ambiente urbano, usando o diagrama de séries temporais. Foram utilizados dados de concentração de poluentes ambientais e variáveis meteorológicas para prever a concentração de O3 na atmosfera. Foi testado o emprego de modelos de regressão linear múltipla como ferramenta para a predição da concentração de O3. Os resultados indicam que o valor da temperatura e a presença de NO2 influenciam na concentração de O3 em Campo Grande, capital do Estado do Mato Grosso do Sul. Palavras-chave: Ozônio. Dióxido de nitrogênio. Séries cronológicas. Regressões. ANALYSIS OF THE RELATIONSHIP BETWEEN O3, NO AND NO2 USING MULTIPLE LINEAR REGRESSION TECHNIQUES.Abstract: Analysis and prediction of air quality parameters are important topics of current atmospheric and environmental research due to the impact caused by air pollution on human health. This study examines the transformation of nitrogen dioxide (NO2) into ozone (O3) in the urban environment, using the time series diagram. Environmental pollutant concentration and meteorological variables were used to predict the O3 concentration in the atmosphere. The use of multiple linear regression models was tested as a tool to predict O3 concentration. The results indicate that the temperature value and the presence of NO2 influence the O3 concentration in Campo Grande, capital of the State of Mato Grosso do Sul.Keywords: Ozone. Nitrogen dioxide. Time series. Regressions. ANÁLISIS DE LA RELACIÓN ENTRE O3, NO Y NO2 UTILIZANDO MÚLTIPLES TÉCNICAS DE REGRESIÓN LINEAL.Resumen: Análisis y previsión de los parámetros de calidad del aire son temas importantes de la actual investigación de la atmósfera y el medio ambiente, debido al impacto de la contaminación atmosférica sobre la salud humana. Este estudio examina la transformación del dióxido de nitrógeno (NO2) en ozono (O3) en el entorno urbano, utilizando el diagrama de series de tiempo. Las concentraciones de los contaminantes ambientales de datos y variables climáticas fueron utilizadas para predecir la concentración de O3 en la atmósfera. El uso de múltiples modelos de regresión lineal como herramienta para predecir la concentración de O3 se puso a prueba. Los resultados indican que el valor de la temperatura y la presencia de NO2 influyen en la concentración de O3 en Campo Grande, capital del Estado de Mato Grosso do Sul.Palabras clave: Ozono. Dióxido de nitrógeno. Series de tiempo. Regresiones.

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