Effect of Social Media Interactions on CLV in Telecommunications

2020 ◽  
Vol 19 (02) ◽  
pp. 447-468
Author(s):  
Oğuzhan Kivrak ◽  
Cüneyt Akar
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Network Models ◽  
Neural Network Models ◽  
Telecommunication Sector ◽  
Elman Neural Network ◽  
Customer Lifetime ◽  
Twitter Account ◽  
Network Approaches ◽  
Artificial Neural Network Models

The main goal of this study is to investigate whether social media, as a recent communication channel, has an impact on customer lifetime value (CLV). No studies have been done in Turkey with similar purposes in the telecommunication sector. To reach this goal, there has been an attempt to develop both artificial neural network models and sector-specific applicable models. Four years of data between 2011 and 2014 belonging to customers in the telecommunication sector who have a Twitter account are used in this study. The CLV is modeled through radial basis function (RBF), multilayer perceptron (MLP), and Elman neural network approaches, and the performance of such models is compared. According to the findings, calculated CLV error values are at an acceptable range in all formed models. Additionally, it is determined that the CLV was calculated with a lower error value in models where social media variables were used. The Elman neural network is determined to perform better compared to RBF and MLP.

scholarly journals Artificial Neural Network Models for Rainfall Prediction

2021 ◽  
Vol 5 (2) ◽  
pp. 30-35
Author(s):  
Emmanuel Gbenga Dada ◽  
Hurcha Joseph Yakubu ◽  
David Opeoluwa Oyewola
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Climatic Conditions ◽  
Feed Forward Neural Network ◽  
Neural Network Models ◽  
Elman Neural Network ◽  
Rainfall Prediction ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Nonlinear Nature

Rainfall prediction is an important meteorological problem that can greatly affect humanity in areas such as agriculture production, flooding, drought, and sustainable management of water resources. The dynamic and nonlinear nature of the climatic conditions have made it impossible for traditional techniques to yield satisfactory accuracy for rainfall prediction. As a result of the sophistication of climatic processes that produced rainfall, using quantitative techniques to predict rainfall is a very cumbersome task. The paper proposed four non-linear techniques such as Artificial Neural Networks (ANN) for rainfall prediction. ANN has the capacity to map different input and output patterns. The Feed Forward Neural Network (FFNN), Cascade Forward Neural Network (CFNN), Recurrent Neural Network (RNN), and Elman Neural Network (ENN) were used to predict rainfall. The dataset used for this work contains some meteorological variables such as temperature, wind speed, humidity, rainfall, visibility, and others for the year 2015-2019. Simulation results indicated that of all the proposed Neural Network (NN) models, the Elman NN model produced the best performance. We also found out that Elman NN has the best performance for the year 2018 (having the lowest RMSE, MSE, and MAE of 6.360, 40.45, and 0.54 respectively). The results indicated that NN algorithms are robust, dependable, and reliable algorithms that can be used for daily, monthly, or yearly rainfall prediction.

Automatic Electrocardiographic Analysis Using Artificial Neural Network Models

2011 ◽  
Vol 403-408 ◽  
pp. 3587-3593
Author(s):  
T.V.K. Hanumantha Rao ◽  
Saurabh Mishra ◽  
Sudhir Kumar Singh
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Network Models ◽  
Neural Network Models ◽  
Radial Basis ◽  
Artificial Neural ◽  
Artificial Neural Network Models ◽  
Self Organizing

In this paper, the artificial neural network method was used for Electrocardiogram (ECG) pattern analysis. The analysis of the ECG can benefit from the wide availability of computing technology as far as features and performances as well. This paper presents some results achieved by carrying out the classification tasks by integrating the most common features of ECG analysis. Four types of ECG patterns were chosen from the MIT-BIH database to be recognized, including normal sinus rhythm, long term atrial fibrillation, sudden cardiac death and congestive heart failure. The R-R interval features were performed as the characteristic representation of the original ECG signals to be fed into the neural network models. Two types of artificial neural network models, SOM (Self- Organizing maps) and RBF (Radial Basis Function) networks were separately trained and tested for ECG pattern recognition and experimental results of the different models have been compared. The trade-off between the time consuming training of artificial neural networks and their performance is also explored. The Radial Basis Function network exhibited the best performance and reached an overall accuracy of 93% and the Kohonen Self- Organizing map network reached an overall accuracy of 87.5%.

scholarly journals Flame Failures and Recovery in Industrial Furnaces: A Neural Network Steady-State Model for the Firing Rate Setpoint Rearrangement

2018 ◽  
Vol 2018 ◽  
pp. 1-15
Author(s):  
Tahmineh Adili ◽  
Zohreh Rostamnezhad ◽  
Ali Chaibakhsh ◽  
Ali Jamali
Keyword(s):  
Neural Network ◽  
Steady State ◽  
Optimization Problems ◽  
Network Models ◽  
Economic Loss ◽  
Neural Network Models ◽  
Firing Rates ◽  
State Models ◽  
Major Equipment ◽  
Artificial Neural Network Models

Burner failures are common abnormal conditions associated with industrial fired heaters. Preventing from economic loss and major equipment damages can be attained by compensating the lost heat due to burners’ failures, which can be possible by defining appropriate setpoints to rearrange the firing rates for healthy burners. In this study, artificial neural network models were developed for estimating the appropriate setpoints for the combustion control system to recover an industrial fired-heater furnace from abnormal conditions. For this purpose, based on an accurate high-order mathematical model, constrained nonlinear optimization problems were solved using the genetic algorithm. For different failure scenarios, the best possible excess firing rates for healthy burners to recover the furnace from abnormal conditions were obtained and data were recorded for training and testing stages. The performances of the developed neural steady-state models were evaluated through simulation experiments. The obtained results indicated the feasibility of the proposed technique to deal with the failures in the combustion system.

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