An Approach for IRIS Plant Classification Using Neural Network

Madhusmita Swain1, Sanjit Kumar Dash1, Sweta Dash2 and Ayeskanta Mohapatra3, 1College of Engineering and Technology, India, 2Synergy Institute of Engineering and Technology, India and 3Hi-tech Institute of Technology, India

ABSTRACT

Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm. 

KEYWORDS

IRIS dataset, artificial neural networks, Back-propagation algorithm

Original Source URL: https://airccse.org/journal/ijsc/papers/2112ijsc07.pdf

https://airccse.org/journal/ijsc/current2012.html

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Accuracy Driven Artificial Neural Networks in Stock Market Prediction

Selvan Simon1 and Arun Raoo2,1SNDT Women's University, India and 2National Institute of Industrial Engineering, India

ABSTRACT

Globalization has made the stock market prediction (SMP) accuracy more challenging and rewarding for the researchers and other participants in the stock market. Local and global economic situations along with the company’s financial strength and prospects have to be taken into account to improve the prediction accuracy. Artificial Neural Networks (ANN) has been identified to be one of the dominant data mining techniques in stock market prediction area. In this paper, we survey different ANN models that have been experimented in SMP with the special enhancement techniques used with them to improve the accuracy. Also, we explore the possible research strategies in this accuracy driven ANN models. 

KEYWORDS

Artificial Neural Networks, Multilayer Perceptron, Back Propagation, Stock market prediction & Prediction accuracy. 

Original Source URL: https://airccse.org/journal/ijsc/papers/3211ijsc03.pdf

https://airccse.org/journal/ijsc/current2012.html

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#ArtificialNeuralNetworks #multilayerperceptron #Backpropagation #stockmarketprediction #predictionaccuracy

AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORK

AN APPROACH FOR IRIS PLANT CLASSIFICATION USING NEURAL NETWORK

Madhusmita Swain1, Sanjit Kumar Dash2, Sweta Dash3 and Ayeskanta Mohapatra4

1, 2 Department of Information Technology, College of Engineering and Technology,Bhubaneswar, Odisha, India

3Department of Computer Science and Engineering, Synergy Institute of Engineering and Technology, Dhenkanal, Odisha, India

4Department of Computer Science and Engineering, Hi-tech Institute of Technology,Bhubaneswar, Odisha, India

ABSTRACT

Classification is a machine learning technique used to predict group membership for data instances. To simplify the problem of classification neural networks are being introduced. This paper focuses on IRIS plant classification using Neural Network. The problem concerns the identification of IRIS plant species on the basis of plant attribute measurements. Classification of IRIS data set would be discovering patterns from examining petal and sepal size of the IRIS plant and how the prediction was made from analyzing the pattern to form the class of IRIS plant. By using this pattern and classification, in future upcoming years the unknown data can be predicted more precisely. Artificial neural networks have been successfully applied to problems in pattern classification, function approximations, optimization, and associative memories. In this work, Multilayer feed- forward networks are trained using back propagation learning algorithm.

KEYWORDS

IRIS dataset, artificial neural networks, Back-propagation algorithm

ORIGINAL SOURCE URL : http://airccse.org/journal/ijsc/papers/2112ijsc07.pdf

http://airccse.org/journal/ijsc/current2012.html