The ear recognition system is an attractive research topic in the area of biometrics. It involves building machine learning models to verify the identities of humans using their ears. In this article, an exploration of the performance of ear recognition using two features - local binary pattern and histogram of gradient - has been done using the famous dataset USTB. The finding is that there is a similarity in the performance of these two features in terms of accuracy with a difference in the number of false predictions. The achieved accuracy of the histogram of gradient based extreme learning machine was 99.86% while for local binary pattern based extreme learning machine it was 99.59%.
<span id="docs-internal-guid-5c723154-7fff-a7b2-3582-b7c2920a9921"><span>Arabic calligraphy is considered a sort of Arabic writing art where letters in Arabic can be written in various curvy or segments styles. The efforts of automating the identification of Arabic calligraphy by using artificial intelligence were less comparing with other languages. Hence, this article proposes using four types of features and a single hidden layer neural network for training on Arabic calligraphy and predicting the type of calligraphy that is used. For neural networks, we compared the case of non-connected input and output layers in extreme learning machine ELM and the case of connected input-output layers in FLN. The prediction accuracy of fast learning machine FLN was superior comparing ELM that showed a variation in the obtained accuracy. </span></span>
The exponential growth of information on the internet makes it troublesome for users to get valuable information. Text summarization is the process to overcome such a problem. An adequate summary must have wide coverage, high diversity, and high readability. In this article, a new method for multidocument summarization has been supposed based on a harmony search algorithm that optimizes the coverage, diversity, and readability. Concerning the benchmark dataset Text Analysis Conference (TAC-2011), the ROUGE package used to measure the effectiveness of the proposed model. The calculated results support the effectiveness of the proposed approach.
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