Deep learning has gained high popularity in the field of image processing and computer vision applications due to its unique feature extraction property. For this characteristic, deep learning networks used to solve different issues in computer vision applications. In this paper the issue has been raised is classification of logo of formal directors in Iraqi government. The paper proposes a multi-layer convolutional neural network (CNN) to classify and recognize these official logos by train the CNN model on several logos. The experimental show the effectiveness of the proposed method to recognize the logo with high accuracy rate about 99.16%. The proposed multi-layers CNN model proves the effectiveness to classify different logos with various conditions.

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<p>Technological development is a revolutionary process by this time, it is<br />mainly depending on electronic applications in our daily routines like<br />(business management, banking, financial transfers, health, and other essential<br />traits of life). Identification or approving identity is one of the complicated<br />issues within online electronic applications. Person’s writing style can be<br />employed as an identifying biological characteristic in order to recognize the<br />identity. This paper presents a new way for identifying a person in a social<br />media group using comments and based on the Deep Neural Network. The<br />text samples are short text comments collected from Telegram group in Arabic<br />language (Iraqi dialect). The proposed model is able to extract the person's<br />writing style features in group comments based on pre-saved dataset. The<br />analysis of this information and features forms the identification decision.<br />This model exhibits a range of prolific and favorable results, the accuracy that<br />comes with the proposed system reach to 92.88% (+/- 0.16%).</p>