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Journal : International Journal of Electrical and Computer Engineering

Deep Belief Networks for Recognizing Handwriting Captured by Leap Motion Controller Abas Setiawan; Reza Pulungan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 6: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (677.119 KB) | DOI: 10.11591/ijece.v8i6.pp4693-4704

Abstract

Leap Motion controller is an input device that can track hands and fingers position quickly and precisely. In some gaming environment, a need may arise to capture letters written in the air by Leap Motion, which cannot be directly done right now. In this paper, we propose an approach to capture and recognize which letter has been drawn by the user with Leap Motion. This approach is based on Deep Belief Networks (DBN) with Resilient Backpropagation (Rprop) fine-tuning. To assess the performance of our proposed approach, we conduct experiments involving 30,000 samples of handwritten capital letters, 8,000 of which are to be recognized. Our experiments indicate that DBN with Rprop achieves an accuracy of 99.71%, which is better than DBN with Backpropagation or Multi-Layer Perceptron (MLP), either with Backpropagation or with Rprop. Our experiments also show that Rprop makes the process of fine-tuning significantly faster and results in a much more accurate recognition compared to ordinary Backpropagation. The time needed to recognize a letter is in the order of 5,000 microseconds, which is excellent even for online gaming experience.
Development of a Spatial Path-Analysis Method for Spatial Data Analysis Wiwin Sulistyo; Subanar Subanar; Reza Pulungan
International Journal of Electrical and Computer Engineering (IJECE) Vol 8, No 4: August 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1162.002 KB) | DOI: 10.11591/ijece.v8i4.pp2456-2467

Abstract

Path analysis is a method used to analyze the relationship between independent and dependent variables to identify direct and indirect relationship between them. This method is developed by Sewal Wright and initially only uses correlation analysis results in identifying the variables' relationship. Path analysis method currently is mostly used to deal with variables with non-spatial data type. When analyzing variables that have elements of spatial dependency, path analysis could result in a less precise model. Therefore, it is necessary to build a path analysis model that is able to identify and take into account the effects of spatial dependencies. Spatial autocorrelation and spatial regression methods can be used to develop path analysis method so as to identify the effects of spatial dependencies. This paper proposes a method in the form of path analysis method development to process data that have spatial elements. This study also discusses our effort on establishing a method that could be used to identify and analyze the spatial effect on data in the framework of path analysis; we call this method spatial path analysis.