Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Correlation based ADALINE neural network for commodity trading Subject The topic of the resource Adaptive Linear Neuron (ADALINE); Artificial Neural Network (ANN); Correlation Based Feature Selection (CBFS); Mean of Magnitude Relative Error (MMRE); Multilayer Perceptron (MLP); Return on Investments (ROI) Description An account of the resource Commodity trading is one of the most popular resources owning to its eminent predictable return on investment to earn money through trading. The trading includes all kinds of commodities like agricultural goods such as wheat, coffee, cocoa etc. and hard products like gold, rubber, crude oils etc.,. The investment decision can be made very easily with the help of the proposed model. The proposed model correlation based multi layer perceptron feed forward adaline neural network is an integrated method to forecast the future values of all commodity trading. The correlation based adaline neuron is used as an optimized predictor in the multi layer perceptron feed forward neural network. The correlation is used for feature selection before building the predictive model. The aim of the paper is to build the predictive model for commodity trading. The model is created using correlation based feature selection and adaline neural network to prognosticate all future values of commodities. The adaptive linear neuron is formed with the help of linear regression. To implement the proposed model the live data is captured from mcxindia. The mcxindia is considered as one the popular website for doing commodities and derivatives in India. To train the proposed model, few random samples are used and the model is evaluated with the help of few test samples from the same data set. 2015 Chandra, J., M. Nachamai and Anitha S. Pillai. Creator An entity primarily responsible for making the resource Chandra J.; Nachamai M.; Pillai A.S. Source A related resource from which the described resource is derived Journal of Computer Science, Vol-11, No. 7, pp. 863-871. Publisher An entity responsible for making the resource available Science Publications Date A point or period of time associated with an event in the lifecycle of the resource 2015-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3844/jcssp.2015.863.871" target="_blank" rel="noreferrer noopener">https://doi.org/10.3844/jcssp.2015.863.871</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954216730&amp;doi=10.3844%2Fjcssp.2015.863.871&amp;partnerID=40&amp;md5=86bdafed531f346e55498f6d90980e33" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-84954216730&amp;doi=10.3844%2fjcssp.2015.863.871&amp;partnerID=40&amp;md5=86bdafed531f346e55498f6d90980e33</a> Rights Information about rights held in and over the resource All Open Access; Hybrid Gold Open Access Relation A related resource ISSN: 15493636 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chandra J., Department of Computer Applications, Christ University, Bangalore, Karnataka, India; Nachamai M., Department of Computer Applications, Christ University, Bangalore, Karnataka, India; Pillai A.S., Department of Computer Sciences, Hindustan University, Chennai, India