Digit recognition using decimal coding and artificial neural network
Kuwait Journal of Science
Current artificial neural network image recognition techniques use all the pixels of an image as ... more Current artificial neural network image recognition techniques use all the pixels of an image as input. In this paper, we present an efficient method for handwritten digit recognition that involves extracting the characteristics of a digit image by coding each row of the image as a decimal value, i.e., by transforming the binary representation into a decimal value. This method is called the decimal coding of rows. The set of decimal values calculated from the initial image is arranged as a vector and normalized; these values represent the inputs to the artificial neural network. The approach proposed in this work uses a multilayer perceptron neural network for the classification, recognition, and prediction of handwritten digits from 0 to 9. In this study, a dataset of 1797 samples were obtained from a digit database imported from the Scikit-learn library. Backpropagation was used as a learning algorithm to train the multilayer perceptron neural network. The results show that the pr...
Classification of Iris Plant Using Perceptron Neural Network
Classification is a prediction technique from the field of supervised learning where the goal is ... more Classification is a prediction technique from the field of supervised learning where the goal is to predict group of membership for data instances. It is one of the fundamental tools of machine learning. Perceptron Neural Network is the first model of Artificial Neural Network implemented to simplify some problems of classification. In this paper we present an approach based on perceptron Neural Network to classified Iris Plant on the basis of the following measurements: sepal length, sepal width, petal length, and petal width. The architecture used in this work is multiclass perceptron with the One-Versus-All (OVA) strategy and the Stochastic gradient descent algorithm learning for training the perceptron.
In recent years, due to information technology such as a computer, Internet, software and the dev... more In recent years, due to information technology such as a computer, Internet, software and the development of sensor technology, make present life appeared "everything can be a digital", big data is becoming a hot research topic in the field of all kinds of industrial [1]. Big Data has been created in the fields of business applications such as marketing, social networking, science, and smartphone applications. Nowadays, better computing power is more necessary in the area of data mining [2]. Image comparison is largely used in different fields: shape matching [3], registration [4]. However, comparing binary images that represent the same content is not easy because an image can undergo transformations like rotation, translation, resolution change. In data mining, there are many frequent patterns (FP) mining algorithms e.g FP-growth [5], CFP-growth [6], MISFP-growth (Multiple Item Support Frequent Patterns) [7], Apriori [8]. A comparison of FP mining algorithms is given in [9]. Apriori algorithm is the best-known basic algorithm proposed by R. Agrawal and R. Srikant in 1994. The Apriori algorithm is one of the most important algorithms which is used to retrieve frequent itemsets. For large databases, there exists the algorithms FTWeight-edHashT [10] and MISFP-growth [9]. It essentially requires two important things: minimum support and minimum confidence [11, 12]. Most of the commonly used association rule discovery algorithm that utilise the frequent itemset strategy, and which is exemplified by the Apriori algorithm [13]. The Apriori algorithm searches
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