Implementation of an Object Recognizer Through Image Processing and Backpropagation Learning Algorithm

In the past decade development of machine learning algorithm for network settings has witnessed little advancements owing to slow development of technologies for improving bandwidth and latency. In this study we present a novel online learning algorithm for network based computational operations in image processing setting

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IMPLEMENTASI BACKPROPAGATION NEURAL NETWORK DALAM PRAKIRAAN CUACA DI DAERAH BALI SELATAN

E-Jurnal Matematika ◽

10.24843/mtk.2016.v05.i04.p131 ◽

2016 ◽

Vol 5 (4) ◽

pp. 126 ◽

Cited By ~ 1

Author(s):

I MADE DWI UDAYANA PUTRA ◽

G. K. GANDHIADI ◽

LUH PUTU IDA HARINI

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Learning Algorithm ◽

Weather Forecast ◽

Weather Forecasts ◽

Forecast Models ◽

Artificial Neural Network Method ◽

Artificial Neural ◽

Hidden Layer ◽

Backpropagation Learning

Weather information has an important role in human life in various fields, such as agriculture, marine, and aviation. The accurate weather forecasts are needed in order to improve the performance of various fields. In this study, use artificial neural network method with backpropagation learning algorithm to create a model of weather forecasting in the area of ??South Bali. The aim of this study is to determine the effect of the number of neurons in the hidden layer and to determine the level of accuracy of the method of artificial neural network with backpropagation learning algorithm in weather forecast models. Weather forecast models in this study use input of the factors that influence the weather, namely air temperature, dew point, wind speed, visibility, and barometric pressure.The results of testing the network with a different number of neurons in the hidden layer of artificial neural network method with backpropagation learning algorithms show that the increase in the number of neurons in the hidden layer is not directly proportional to the value of the accuracy of the weather forecasts, the increase in the number of neurons in the hidden layer does not necessarily increase or decrease value accuracy of weather forecasts we obtain the best accuracy rate of 51.6129% on a network model with three neurons in the hidden layer.

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Customizing parallel formulations of backpropagation learning algorithm to neural network architectures: a summary of result

Proceedings Sixth International Conference on Tools with Artificial Intelligence. TAI 94 ◽

10.1109/tai.1994.346497 ◽

2002 ◽

Cited By ~ 2

Author(s):

M.B. Amin ◽

S. Shekhar

Keyword(s):

Neural Network ◽

Learning Algorithm ◽

Network Architectures ◽

Neural Network Architectures ◽

Backpropagation Learning

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Detection of Masses in Mammograms using Bayesian Method and Machine learning

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i4.6.20441 ◽

2018 ◽

Vol 7 (4.6) ◽

pp. 108

Author(s):

Priyadarshini Chatterjee ◽

Ch. Mamatha ◽

T. Jagadeeswari ◽

Katha Chandra Shekhar

Keyword(s):

Machine Learning ◽

Image Processing ◽

Bayesian Method ◽

Learning Algorithm ◽

Correct Diagnosis ◽

Machine Learning Algorithms ◽

Human Detection ◽

Breast Masses ◽

Learning Machine ◽

Vast Area

Every 100th cases in cancer we come across are of breasts cancer cases. It is becoming very common in woman of all ages. Correct detection of these lesions in breast is very important. With less of human intervention, the goal is to do the correct diagnosis. Not all the cases of breast masses are futile. If the cases are not dealt properly, they might create panic amongst people. Human detection without machine intervention is not hundred percent accurate. If machines can be deeply trained, they can do the same work of detection with much more accuracy. Bayesian method has a vast area of application in the field of medical image processing as well as in machine learning. This paper intends to use Bayesian probabilistic in image segmentation as well as in machine learning. Machine learning in image processing means application in pattern recognition. There are various machine learning algorithms that can classify an image at their best. In the proposed system, we will be firstly segment the image using Bayesian method. On the segmented parts of the image, we will be applying machine learning algorithm to diagnose the mass or the growth.

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