Indicator Selection for Topic Popularity Definition Based on AHP and Deep Learning Models

Purpose. The purpose of this article is to predict the topic popularity on the social network accurately. Indicator selection model for a new definition of topic popularity with degree of grey incidence (DGI) is undertook based on an improved analytic hierarchy process (AHP). Design/Methodology/Approach. Through screening the importance of indicators by the deep learning methods such as recurrent neural networks (RNNs), long short-term memory (LSTM), and gated recurrent unit (GRU), a selection model of topic popularity indicators based on AHP is set up. Findings. The results show that when topic popularity is being built quantitatively based on the DGI method and different weights of topic indicators are obtained from the help of AHP, the average accuracy of topic popularity prediction can reach 97.66%. The training speed is higher and the prediction precision is higher. Practical Implications. The method proposed in the paper can be used to calculate the popularity of each hot topic and generate the ranking list of topics’ popularities. Moreover, its future popularity can be predicted by deep learning methods. At the same time, a new application field of deep learning technology has been further discovered and verified. Originality/Value. This can lay a theoretical foundation for the formulation of topic popularity tendency prevention measures on the social network and provide an evaluation method which is consistent with the actual situation.

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A Single-Terminal Fault Location Method for HVDC Transmission Lines Based on a Hybrid Deep Network

Electronics ◽

10.3390/electronics10030255 ◽

2021 ◽

Vol 10 (3) ◽

pp. 255

Author(s):

Lei Wang ◽

Yigang He ◽

Lie Li

Keyword(s):

Deep Learning ◽

Transmission Lines ◽

Fault Location ◽

Short Term Memory ◽

Engineering Application ◽

Energy Operator ◽

Sampling Frequency ◽

Learning Technology ◽

Long Distance ◽

Hvdc Transmission

High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.

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Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimation

10.5194/hess-2020-224 ◽

2020 ◽

Author(s):

Mohammad Taghi Sattari ◽

Halit Apaydin ◽

Shahab Shamshirband ◽

Amir Mosavi

Keyword(s):

Deep Learning ◽

Minimum Temperature ◽

Meteorological Parameters ◽

Short Term Memory ◽

Sunshine Duration ◽

Gaussian Process Regression ◽

Support Vector ◽

Ann Model ◽

Learning Methods ◽

Average Maximum

Abstract. Proper estimation of the reference evapotranspiration (ET0) amount is an indispensable matter for agricultural water management in the efficient use of water. The aim of study is to estimate the amount of ET0 with a different machine and deep learning methods by using minimum meteorological parameters in the Corum region which is an arid and semi-arid climate with an important agricultural center of Turkey. In this context, meteorological variables of average, maximum and minimum temperature, sunshine duration, wind speed, average, maximum, and minimum relative humidity are used as input data monthly. Two different kernel-based (Gaussian Process Regression (GPR) and Support Vector Regression (SVR)) methods, BFGS-ANN and Long short-term memory models were used to estimate ET0 amounts in 10 different combinations. According to the results obtained, all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others. In kernel-based GPR and SVR methods, Pearson VII function-based universal kernel was the most successful kernel function. Besides, the scenario that is related to temperature in all scenarios used, including average temperature, maximum and minimum temperature, and sunshine duration gave the best results. The second-best scenario was the one that covers only the sunshine duration. In this case, the ANN (BFGS-ANN) model, which is optimized with the BFGS method that uses only the sunshine duration, can be estimated with the 0.971 correlation coefficient of ET0 without the need for other meteorological parameters.

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Flutter speed prediction by using deep learning

Advances in Mechanical Engineering ◽

10.1177/16878140211062275 ◽

2021 ◽

Vol 13 (11) ◽

pp. 168781402110622

Author(s):

Yi-Ren Wang ◽

Yi-Jyun Wang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Short Term Memory ◽

Learning Technology ◽

Memory Space ◽

Flutter Speed ◽

Aeroelastic Flutter ◽

Speed Prediction ◽

Computational Fluid Dynamics Cfd ◽

Cfd Method

Deep learning technology has been widely used in various field in recent years. This study intends to use deep learning algorithms to analyze the aeroelastic phenomenon and compare the differences between Deep Neural Network (DNN) and Long Short-term Memory (LSTM) applied on the flutter speed prediction. In this present work, DNN and LSTM are used to address complex aeroelastic systems by superimposing multi-layer Artificial Neural Network. Under such an architecture, the neurons in neural network can extract features from various flight data. Instead of time-consuming high-fidelity computational fluid dynamics (CFD) method, this study uses the K method to build the aeroelastic flutter speed big data for different flight conditions. The flutter speeds for various flight conditions are predicted by the deep learning methods and verified by the K method. The detailed physical meaning of aerodynamics and aeroelasticity of the prediction results are studied. The LSTM model has a cyclic architecture, which enables it to store information and update it with the latest information at the same time. Although the training of the model is more time-consuming than DNN, this method can increase the memory space. The results of this work show that the LSTM model established in this study can provide more accurate flutter speed prediction than the DNN algorithm.

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PP15 Predicting variations of calls to an ambulance service in the UK caused by circulating infections using-deep learning methods

Emergency Medicine Journal ◽

10.1136/emermed-2019-999abs.15 ◽

2019 ◽

Vol 36 (10) ◽

pp. e7.2-e7

Author(s):

Thilo Reich ◽

Marcin Budka

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Prehospital Care ◽

Machine Learning Algorithms ◽

Ambulance Service ◽

Percentage Error ◽

Learning Methods ◽

Average Percentage ◽

Average Percentage Error ◽

The Uk

BackgroundDigital patient records in the ambulance service have opened up new opportunities for prehospital care. Previously it was demonstrated that prehospital pyrexia numbers are linked to an increase in overall calls to the ambulance service. This study aims to predict the future number of calls using deep-learning methods.MethodsTemperature readings for 280,447 patients were generously provided by the South Western Ambulance Service Trust. The data covered the time between 05/01/2016 and 30/04/2017 with overall 44,472 patients being pyretic. A rolling window of 10 days was applied to daily sums for both pyretic and apyretic patients. These windows were used as input features to train machine-learning algorithms predicting the number of calls 10 days ahead. Algorithms tested include Linear Regression (LR), basic Recurrent Neural Networks (RNN), Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures. A genetic approach was used to optimise the architecture, in which parameters were randomly modified and over several generations the best performing algorithm will be selected to be further manipulated. To assess performance the Mean Average Percentage Error (MAPE) was used.ResultsThe initial analysis showed that the total patient number and pyretic patient numbers are correlated. The best performing algorithms with varying numbers of hidden units had the following MAPE in comparison to simple LR: LR=19.4%, LSTM (104 units) = 6.1%, RNN (79 units)=6.01%, GRU (80 units)=5.97%.ConclusionsThese preliminary results suggest that deep-learning methods allow to predict the variations in total number of calls caused by circulating infections. Further investigations will aim to confirm these findings. Once fully verified these algorithms could play a major role in operational planning of any ambulance service by predicting increases in demand.

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Measuring the Impact of Social Media

Social Knowledge ◽

10.4018/978-1-60960-203-1.ch002 ◽

2011 ◽

pp. 24-36 ◽

Cited By ~ 8

Author(s):

Kimiz Dalkir

Keyword(s):

Social Media ◽

Social Network ◽

Social Network Analysis ◽

Network Analysis ◽

Evaluation Method ◽

Real Life ◽

Work Processes ◽

The Social ◽

Post Test ◽

The Impact

This chapter focuses on a method, social network analysis (SNA) that can be used to assess the quantity and quality of connection, communication and collaboration mediated by social tools in an organization. An organization, in the Canadian public sector, is used as a real-life case study to illustrate how SNA can be used in a pre-test/post-test evaluation design to conduct a comparative assessment of methods that can be used before, during and after the implementation of organizational change in work processes. The same evaluation method can be used to assess the impact of introducing new social media such as wikis, expertise locator systems, blogs, Twitter and so on. In other words, while traditional pre-test/post-test designs can be easily applied to social media, the social media tools themselves can be added to the assessment toolkit. Social network analysis in particular is a good candidate to analyze the connections between people and content as well as people with other people.

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An Effective Video Surveillance System by using CNN for COVID-19

10.4018/978-1-7998-7685-4.ch006 ◽

2022 ◽

pp. 88-102

Author(s):

Basetty Mallikarjuna ◽

Anusha D. J. ◽

Sethu Ram M. ◽

Munish Sabharwal

Keyword(s):

Deep Learning ◽

Video Surveillance ◽

Surveillance System ◽

Video Surveillance System ◽

Learning Technology ◽

Proposed Model ◽

The Social ◽

Scripting Language ◽

Python Programming ◽

Fully Connected

An effective video surveillance system is a challenging task in the COVID-19 pandemic. Building a model proper way of wearing a mask and maintaining the social distance minimum six feet or one or two meters by using CNN approach in the COVID-19 pandemic, the video surveillance system works with the help of TensorFlow, Keras, Pandas, which are libraries used in Python programming scripting language used in the concepts of deep learning technology. The proposed model improved the CNN approach in the area of deep learning and named as the Ram-Laxman algorithm. The proposed model proved to build the optimized approach, the convolutional layers grouped as ‘Ram', and fully connected layers grouped as ‘Laxman'. The proposed system results convey that the Ram-Laxman model is easy to implement in the CCTV footage.

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A Comparative Study of Bitcoin Price Prediction Using Deep Learning

Mathematics ◽

10.3390/math7100898 ◽

2019 ◽

Vol 7 (10) ◽

pp. 898 ◽

Cited By ~ 12

Author(s):

Suhwan Ji ◽

Jongmin Kim ◽

Hyeonseung Im

Keyword(s):

Neural Network ◽

Deep Learning ◽

Prediction Models ◽

Short Term Memory ◽

Learning Methods ◽

The Public ◽

Price Prediction ◽

Profitability Analysis ◽

The Media ◽

Long Short Term Memory

Bitcoin has recently received a lot of attention from the media and the public due to its recent price surge and crash. Correspondingly, many researchers have investigated various factors that affect the Bitcoin price and the patterns behind its fluctuations, in particular, using various machine learning methods. In this paper, we study and compare various state-of-the-art deep learning methods such as a deep neural network (DNN), a long short-term memory (LSTM) model, a convolutional neural network, a deep residual network, and their combinations for Bitcoin price prediction. Experimental results showed that although LSTM-based prediction models slightly outperformed the other prediction models for Bitcoin price prediction (regression), DNN-based models performed the best for price ups and downs prediction (classification). In addition, a simple profitability analysis showed that classification models were more effective than regression models for algorithmic trading. Overall, the performances of the proposed deep learning-based prediction models were comparable.

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Applying Deep Learning Methods on Time-Series Data for Forecasting COVID-19 in Egypt, Kuwait, and Saudi Arabia

Complexity ◽

10.1155/2021/6686745 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Nahla F. Omran ◽

Sara F. Abd-el Ghany ◽

Hager Saleh ◽

Abdelmgeid A. Ali ◽

Abdu Gumaei ◽

...

Keyword(s):

Time Series ◽

Saudi Arabia ◽

Deep Learning ◽

Time Series Data ◽

Short Term Memory ◽

Disease Outbreaks ◽

Handling Time ◽

Series Data ◽

Learning Methods ◽

Death Cases

The novel coronavirus disease (COVID-19) is regarded as one of the most imminent disease outbreaks which threaten public health on various levels worldwide. Because of the unpredictable outbreak nature and the virus’s pandemic intensity, people are experiencing depression, anxiety, and other strain reactions. The response to prevent and control the new coronavirus pneumonia has reached a crucial point. Therefore, it is essential—for safety and prevention purposes—to promptly predict and forecast the virus outbreak in the course of this troublesome time to have control over its mortality. Recently, deep learning models are playing essential roles in handling time-series data in different applications. This paper presents a comparative study of two deep learning methods to forecast the confirmed cases and death cases of COVID-19. Long short-term memory (LSTM) and gated recurrent unit (GRU) have been applied on time-series data in three countries: Egypt, Saudi Arabia, and Kuwait, from 1/5/2020 to 6/12/2020. The results show that LSTM has achieved the best performance in confirmed cases in the three countries, and GRU has achieved the best performance in death cases in Egypt and Kuwait.

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Examination of ‘Interests’ and ‘Activities’ of Social Network users

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.b7448.129219 ◽

2019 ◽

Vol 9 (2) ◽

pp. 2688-2693

Keyword(s):

Machine Learning ◽

Social Media ◽

Social Network ◽

General Population ◽

Social Media Marketing ◽

Family Status ◽

Learning Methods ◽

Attribute Data ◽

Machine Learning Methods ◽

The Social

The present study relates to the analysis of attribute data related to users of the social network VK. The general population N = 52,614 users is the intersection of audiences from two communities for social media marketing. Based on the collected statistics on the “interests” attribute, one can compile a generalized portrait of an IT specialist and online marketer: this is a man aged about 30 years old, not married, or who defines his family status as “everything is complicated”. He speaks an average of two languages, works for an organization, or studies at a university. He has about 370 followers on VK. The result based on the data from the field 'activities' is very close to the data from the field 'interests', and gives a similar picture of the generalized portrait of a specialist. As part of the study, the authors have learned how to segment users into the users that identify themselves as „IT specialists or online marketers‟, and „other‟ users, using machine learning methods

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A Statistics and Deep Learning Hybrid Method for Multivariate Time Series Forecasting and Mortality Modeling

Forecasting ◽

10.3390/forecast4010001 ◽

2021 ◽

Vol 4 (1) ◽

pp. 1-25

Author(s):

Thabang Mathonsi ◽

Terence L. van Zyl

Keyword(s):

Neural Network ◽

Deep Learning ◽

Recurrent Neural Network ◽

Short Term Memory ◽

Hybrid Methods ◽

Prediction Interval ◽

Hybrid Approach ◽

Computational Cost ◽

Exponential Smoothing ◽

Learning Methods

Hybrid methods have been shown to outperform pure statistical and pure deep learning methods at forecasting tasks and quantifying the associated uncertainty with those forecasts (prediction intervals). One example is Exponential Smoothing Recurrent Neural Network (ES-RNN), a hybrid between a statistical forecasting model and a recurrent neural network variant. ES-RNN achieves a 9.4% improvement in absolute error in the Makridakis-4 Forecasting Competition. This improvement and similar outperformance from other hybrid models have primarily been demonstrated only on univariate datasets. Difficulties with applying hybrid forecast methods to multivariate data include (i) the high computational cost involved in hyperparameter tuning for models that are not parsimonious, (ii) challenges associated with auto-correlation inherent in the data, as well as (iii) complex dependency (cross-correlation) between the covariates that may be hard to capture. This paper presents Multivariate Exponential Smoothing Long Short Term Memory (MES-LSTM), a generalized multivariate extension to ES-RNN, that overcomes these challenges. MES-LSTM utilizes a vectorized implementation. We test MES-LSTM on several aggregated coronavirus disease of 2019 (COVID-19) morbidity datasets and find our hybrid approach shows consistent, significant improvement over pure statistical and deep learning methods at forecast accuracy and prediction interval construction.

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