In computer systems, especially with the advancement of the Internet and databases, big data is increasingly expanding and is advancing exponentially [1-4]. This is mostly true in medical big data and images. Therefore, the issue of exploding data shows the concept and power of the big data. In the field of medicine, especially magnetic resonance imaging (MRI) images, the issue of big data with high data dimensions is investigated [5]. As people grow older in the community, an untreated disease would be common, which is called Alzheimer's, and it has been proven that it has no treatment, but it can be prevented from development with timely diagnosis. Alzheimer is known as the most common disease among the various causes of dementia and with each passing decade, the number of people infected with the disease is almost doubled. For this reason, timely
Recognizing handwritten letters is one of the important issues that have always been a major challenge in the field of computer vision. To have a better performance of letter identifying systems, one of the primary requirements is to select characteristics that explain a good word picture. Another challenge is the choice of an appropriate method for machine learning, which could be able to separate explanatory features of the characters, effectively. On the other hand, when the data set is small, the training process will be very difficult and error rate will increase. In this paper, Deep Belief Network Learning method is applied to identify Persian numbers. In deep learning method, raw data can be used as network's input; in fact deep learning can perform feature extraction and classification of data, at the same time. Every picture's pixels is changed into a horizontal vector and used for the training step of deep belief network. Although, utilized datasets for training and testing of the network are not huge, in the evaluation section, acceptable results obtained.
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