Browsing by Author "Ogwueleka, Francisca Nonyelum"

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Citation Count: 20
An Artificial Neural Network Model for Road Accident Prediction: a Case Study of a Developing Country
(Budapest Tech, 2014) Ogwueleka, Francisca Nonyelum; Mısra, Sanjay; Misra, Sanjay; Ogwueleka, Toochukwu Chibueze; Fernandez-Sanz, L.; Computer Engineering
Road traffic accidents (RTA) are one of the major root causes of the unnatural loses of human beings all over the world. Although the rates of RTAs are decreasing in most developed countries, this is not the case in developing countries. The increase in the number of vehicles and inefficient drivers on the road, as well as to the poor conditions and maintenance of the roads, are responsible for this crisis in developing countries. In this paper, we produce a design of an Artificial Neural Network (ANN) model for the analysis and prediction of accident rates in a developing country. We apply the most recent (1998 to 2010) data to our model. In the design, the number of vehicles, accidents, and population were selected and used as model parameters. The sigmoid and linear functions were used as activation functions with the feed forward-back propagation algorithm. The performance evaluation of the model signified that the ANN model is better than other statistical methods in use.
Citation Count: 26
Neural Network and Classification Approach in Identifying Customer Behavior in the Banking Sector: a Case Study of an International Bank
(Wiley, 2015) Ogwueleka, Francisca Nonyelum; Mısra, Sanjay; Misra, Sanjay; Colomo-Palacios, Ricardo; Fernandez, Luis; Computer Engineering
The customer relationship focus for banks is in development of main competencies and strategies of building strong profitable customer relationships through considering and managing the customer impression, influence on the culture of the bank, satisfactory treatment, and assessment of valued relationship building. Artificial neural networks (ANNs) are used after data segmentation and classification, where the designed model register records into two class sets, that is, the training and testing sets. ANN predicts new customer behavior from previously observed customer behavior after executing the process of learning from existing data. This article proposes an ANN model, which is developed using a six-step procedure. The back-propagation algorithm is used to train the ANN by adjusting its weights to minimize the difference between the current ANN output and the desired output. An evaluation process is conducted to determine whether the ANN has learned how to perform. The training process is halted periodically, and its performance is tested until an acceptable result is obtained. The principles underlying detection software are grounded in classical statistical decision theory.