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$3 USD / hour
Flag of INDIA
bhopal, india
$3 USD / hour
It's currently 6:10 PM here
Joined September 18, 2017
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Shona S.

@ShonaShrivastava

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$3 USD / hour
Flag of INDIA
bhopal, india
$3 USD / hour
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Maters in Computer Science and engineering.

My name is Saloni Shrivastava. I have done my MTech from an RGPV University. I have decided to pursue a career in Information Technology, having graduated with a degree in Computer Science. This is also the ideal career path for me since I have always been interested and good at Soft Computing, Neural Networks, database management, networking, Document writing, research, web design etc
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3806032
Coins have been integral a part of our day to day life. Coins are used nearly every place like in grocery stores, banks, trains, buses etc. Thus it's a basic would like that coins may be recognized, counted, sorted mechanically. For this, it is necessary that coins can be recognized automatically and check whether it’s real or fake In this paper we have developed an ANN Fast and Efficient coin recognition using 5 hidden layers Back-Propagation Neural Networks Algorithm for the recognition of Indian Coins of denomination `1, `2, `5 and `10 using Canny Edge Detection. We have taken images from both sides of the coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Labeling, Canny Edge Detection, and Image Processing etc. Then, the extracted features are passed as input to a trained Neural Network 84.3% recognition rate has been achieved during the experiments.
Fast & Efficient Coin Recognition using 5 Hidden Layers BPNN
3806032
Coins have been integral a part of our day to day life. Coins are used nearly every place like in grocery stores, banks, trains, buses etc. Thus it's a basic would like that coins may be recognized, counted, sorted mechanically. For this, it is necessary that coins can be recognized automatically and check whether it’s real or fake In this paper we have developed an ANN Fast and Efficient coin recognition using 5 hidden layers Back-Propagation Neural Networks Algorithm for the recognition of Indian Coins of denomination `1, `2, `5 and `10 using Canny Edge Detection. We have taken images from both sides of the coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Labeling, Canny Edge Detection, and Image Processing etc. Then, the extracted features are passed as input to a trained Neural Network 84.3% recognition rate has been achieved during the experiments.
Fast & Efficient Coin Recognition using 5 Hidden Layers BPNN

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