Latest Research on Artificial Neural Network: March – 2020

Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences

Artificial neural networks are appearing as useful alternatives to traditional statistical modelling techniques in many scientific disciplines. This paper presents a general introduction and discussion of recent applications of the multilayer perceptron, one type of artificial neural network, in the atmospheric sciences. [1]

Logistic regression and artificial neural network classification models: a methodology review

Logistic regression and artificial neural networks are the models of choice in many medical data classification tasks. In this review, we summarize the differences and similarities of these models from a technical point of view, and compare them with other machine learning algorithms. [2]

Electric load forecasting using an artificial neural network

An artificial neural network (ANN) approach is presented for electric load forecasting. The ANN is used to learn the relationship among past, current and future temperatures and loads. In order to provide the forecasted load, the ANN interpolates among the load and temperature data in a training data set. The average absolute errors of the 1 h and 24 h-ahead forecasts in tests on actual utility data are shown to be 1.40% and 2.06%, respectively. This compares with an average error of 4.22% for 24 h ahead forecasts with a currently used forecasting technique applied to the same data [3]

Classification of Soya Beans Based Image Processing Techniques and Artificial Neural Network

The benefits of using technology in agriculture cannot be overemphasised because of its impact that results in an increase in the quality and quantity of crops produced, minimising cost of farming, and providing suggestions for prompt action among others. Traditionally, to know the state of soya beans, farmers rely on observation to note the change in colour of the leaves so as to provide appropriate action to the crop. [4]

Assessing Infant Mortality in Nigeria Using Artificial Neural Network and Logistic Regression Models

Aim: To examine the suitability of Artificial Neural Network (ANN) in predicting infant mortality and compare its performance with Logistic Regression (LR) model.

Study Design: A cross-sectional population based study was conducted. The 2013 Nigeria Demographic Health Survey (NDHS) data were used. [5]

Reference

[1] Gardner, M.W. and Dorling, S.R., 1998. Artificial neural networks (the multilayer perceptron)—a review of applications in the atmospheric sciences. Atmospheric environment32(14-15), pp.2627-2636.

[2] Dreiseitl, S. and Ohno-Machado, L., 2002. Logistic regression and artificial neural network classification models: a methodology review. Journal of biomedical informatics35(5-6), pp.352-359.

[3] Park, D.C., El-Sharkawi, M.A., Marks, R.J., Atlas, L.E. and Damborg, M.J., 1991. Electric load forecasting using an artificial neural network. IEEE transactions on Power Systems6(2), pp.442-449.

[4] Abdulhamid, U., Daniel, S. and Babawuro, U. (2018) “Classification of Soya Beans Based Image Processing Techniques and Artificial Neural Network”, Journal of Advances in Mathematics and Computer Science, 26(6), pp. 1-9. doi: 10.9734/JAMCS/2018/39611.

[5] Jaiyeola, M. O., Oyamakin, S. O., Akinyemi, J. O., Adebowale, S. A., Chukwu, A. U. and Yusuf, O. B. (2016) “Assessing Infant Mortality in Nigeria Using Artificial Neural Network and Logistic Regression Models”, Journal of Advances in Mathematics and Computer Science, 19(5), pp. 1-14. doi: 10.9734/BJMCS/2016/28870.

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