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Deep Learning Framework for Locating Physical Internet Hubs using Latitude and Longitude Classification

This article proposes framework for determining the optimal or near optimal locations of physical internet hubs using data mining and deep learning algorithms. The framework extracts latitude and longitude coordinates from various data types as data acquisition phase. These coordinates has been extracted from RIFD, online maps, GPS, and GSM data. These coordinates has been class labeled according to decision maker’s preferences using k-mean, density based algorithm (DB Scan and hierarchical clustering analysis algorithms. The proposed algorithm uses haversine distance matrix to calculate the distance between each coordinates rather than the Euclidian distance matrix. The haversine matrix provides more accurate distance surface of a sphere. The framework uses the class labeled data after the clustering phase as input for the classification phase. The classification has been performed using decision tree, random forest, Bayesian, gradient decent, neural network, convolutional neural network and recurrent neural network. The classified coordinates has been evaluated for each algorithms. It has been found that CNN, RNN outperformed the other classification algorithms with accuracy 97.6% and 97.9% respectively. Keywords—Physical internet hubs (π hubs); deep learning; convolutional neural network (CNN); recurrent neural network (RNN); latitude and Longitude classification