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Neural Network – IRIS data Classification Model, Dr. Arunachalam Rajagopal

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Neural Network – IRIS data Classification Model by Dr. Arunachalam Rajagopal Artificial Neural Network (ANN): A typical artificial neural network is shown in Figure 1. ANN is a sub-domain of artificial intelligence, AI. ANNs attempt to imitate the behaviour of human brain for problem solving by creating an abstract model. ANN models which are basically non-linear in nature could abstract and represent any non-linear function. Activation function: introduces the non-linear characteristics into the neural network. The activation function is selected based on the type of data to be modeled.   The often used activation functions are: Rectified Linear Unit (ReLu) Logistic function Hyperbolic tangent (tanh) Figure 1 Artificial Neural Network Example Demo: Neural Network The iris data set has been made use of for demonstrating the development of neural network classification model. The Figure 2 shows the three flowers in the iris family which are Setosa, V
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Electricity Generation Forecast using ANN with R, Dr. Arunachalam Rajagopal

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Electricity Generation Forecast using Artificial Neural Network with R  Arunachalam Rajagopal The artificial neural network (ANN) is based on the concept of machine learning. ANN attempts to mimic the human brain into the machine (computer). ANNs attempt to learn the patterns that are exhibited in the training dataset (input - independent variable and output - dependent variable). Then based on the previous learning, ANN attempts to give generalized solution for the total dataset. The solution could be forecast in a time series dataset or causal regression model. It could also be used for classification of the dataset like what is done in a discriminant analysis or logistic regression. Artificial neural network (ANN) are data-driven and self-adaptive. There is no need to make any a priori assumption on the input data. The neural network model is formed in an adaptive manner based on the pattern exhibited by the dataset. The artificial neural network is basi
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 Wordcloud2 - Top Three Budget Speech Analysis Wordcloud2 has been generated for top three budget speeches in the recent past three decades starting from 1991-92. It was a historical budget sppech of our recent political history by Dr.Manmohan Singh. Then, came another high profile budget speech in the year 2004-05 by Shri.P.Chidambaram. Then, now the running honourable finance minister Nirmala Seetharaman has made a budget speech 2020-21 for which there was high expectations from the people of India. Wordcloud is too basic a tool for analysing the budget speech. The intent of this blog is demonstration of wordcloud2 generation and budget analysis with wordcloud is only a byproduct.  #------------------------------------------------------------------------- #R Code: #step-by-step approach to wordcloud2 for budget speech in pdf #step1: save budget speech 1991-92, 2004-05, and 2020-21 as pdf in working directory #step2: load NLP, tm, RColorBrewer, SnowballC, wordcloud2, pdftools, stringr
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