scholarly journals Business Analytics in Telemarketing: Cost-Sensitive Analysis of Bank Campaigns Using Artificial Neural Networks

2020 ◽  
Vol 10 (7) ◽  
pp. 2581
Author(s):  
Nazeeh Ghatasheh ◽  
Hossam Faris ◽  
Ismail AlTaharwa ◽  
Yousra Harb ◽  
Ayman Harb
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Geometric Mean ◽  
Imbalanced Data ◽  
Original Data ◽  
Type I ◽  
Business Analytics ◽  
Neural Network Models ◽  
Artificial Neural

The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

Prediction Modeling of PM-10 in Chiangmai City Moat by Using Artificial Neural Networks

2015 ◽  
Vol 781 ◽  
pp. 628-631 ◽  
Author(s):  
Rati Wongsathan ◽  
Issaravuth Seedadan ◽  
Metawat Kavilkrue
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Chiang Mai ◽  
Adverse Health Effects ◽  
High Pollution ◽  
Artificial Neural ◽  
Input Variables ◽  
Pm 10 ◽  
Respiratory Conditions

A mathematical prediction model has been developed in order to detect particles with a diameter of 10 micrometers or less (PM-10) that are responsible for adverse health effects because of their ability to cause serious respiratory conditions in areas of high pollution such as Chiang Mai City moat area. The prediction model is based on 3 types of Artificial Neural Networks (ANNs), including Multi-layer perceptron (MLP-NN), Radial basis function (RBF-NN), and hybrid of RBF and Genetic algorithm (RBF-NN-GA). The model uses 8 input variables to predict PM-10, consisting of 4 air pollution substances ( CO, O3, NO2 and SO2) and 4 meteorological variables related PM-10 (wind speed, temperature, atmospheric pressure and relative humidity). These 3 types of ANN have proved efficient instrument in predicting the PM-10. However, the performance of RBF-NN was superior in comparison with MLP-NN and RBF-NN-GA respectively.

Predicting the exchange rate in Sudan using neural networks models during the period (1960 - 2017): التنبؤ بسعر الصرف في السودان باستخدام نماذج الشبكات العصبية خلال الفترة (1960م – 2017م)

2020 ◽  
Vol 4 (14) ◽  
Author(s):  
Fathi Ahmed Ali Adam, Mahmoud Mohamed Abdel Aziz Gamal El-Di
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Exchange Rate ◽  
Network Models ◽  
Neural Network Models ◽  
Classical Models ◽  
Artificial Neural ◽  
The Exchange Rate ◽  
Long Term Forecasting ◽  
Artificial Neural Network Models

The study examined the use of artificial neural network models to predict the exchange rate in Sudan through annual exchange rate data between the US dollar and the Sudanese pound. This study aimed to formulate the models of artificial neural networks in which the exchange rate can be predicted in the coming period. The importance of the study is that it is necessary to use modern models to predict instead of other classical models. The study hypothesized that the models of artificial neural networks have a high ability to predict the exchange rate. Use models of artificial neural networks. The most important results ability of artificial neural networks models to predict the exchange rate accurately, Form MLP (1-1-1) is the best model chosen for that purpose. The study recommended the development of the proposed model for long-term forecasting.

Artificial Neural Networks Based Dengue Diagnosis Prediction Model

2021 ◽  
Author(s):  
Lathesparan Ramachandran ◽  
Rm Kapila Tharanga Rathnayaka ◽  
Wiraj Udara Wickramaarachchi
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Prediction Model ◽  
Artificial Neural

Artificial Neural Networks

2011 ◽  
pp. 222-243
Author(s):  
Joarder Kamruzzaman ◽  
Ruhul Sarker
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Network Models ◽  
Neural Network Models ◽  
Network Applications ◽  
The Neural Network ◽  
Artificial Neural ◽  
Neural Network Applications

The primary aim of this chapter is to present an overview of the artificial neural network basics and operation, architectures, and the major algorithms used for training the neural network models. As can be seen in subsequent chapters, neural networks have made many useful contributions to solve theoretical and practical problems in finance and manufacturing areas. The secondary aim here is therefore to provide a brief review of artificial neural network applications in finance and manufacturing areas.

Comparing Conventional and Artificial Neural Network Models for the Pricing of Options

2002 ◽  
pp. 220-235 ◽  
Author(s):  
Paul Lajbcygier
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Artificial Neural Networks ◽  
Input Parameter ◽  
Network Models ◽  
Neural Network Models ◽  
Artificial Neural ◽  
Conventional Models ◽  
Artificial Neural Network Models

The pricing of options on futures is compared using conventional models and artificial neural networks. This work demonstrates superior pricing accuracy using the artificial neural networks in an important subset of the input parameter set.

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