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This booklet constitutes the refereed lawsuits of the second one foreign convention on complicated computer studying applied sciences and functions, AMLTA 2014, held in Cairo, Egypt, in November 2014. The forty nine complete papers offered have been conscientiously reviewed and chosen from a hundred and one preliminary submissions. The papers are equipped in topical sections on desktop studying in Arabic textual content popularity and assistive expertise; suggestion platforms for cloud companies; computing device studying in watermarking/authentication and digital machines; positive aspects extraction and category; rough/fuzzy units and functions; fuzzy multi-criteria selection making; Web-based software and case-based reasoning development; social networks and large information sets.

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Additional info for Advanced Machine Learning Technologies and Applications: Second International Conference, AMLTA 2014, Cairo, Egypt, November 28-30, 2014. Proceedings

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Visually impaired and blind people find it difficult to face their day-to-day problems because of their impairment. 6% of the world’s total population was visually imapried. In spite of the advancement in technology, there is no complete cure for such problems and they need some assistance to complete their daily tasks. Reading glasses, walking sticks, and Braille tools are few devices which could help them to complete their daily life. However, they don’t provide a sense of “seeing” for the blind person.

Computer Vision and Image Understanding 110(3), 346–359 (2008) 16. : ORB: an efficient alternative to SIFT or SURF. Paper presented at the IEEE International Conference on Computer Vision (2011) 17. : Multi- Probe LSH: Efficient Indexing for High-Dimensional Similarity Search. In: Proceedings of Very Large Database Conference, Vienna (2007) 18. : Approximate Nearest Neighbors: Towards Removing the Curse of Dimensionality. Theory of Computing 8(1), 321–350 (2012) 19. : Superpixel-Based Object Detection Using Local Feature Matching.

Soft Margin A separating hyper plane does not always exist. And if it exists, it doesn’t represent, usually, best solution for classification. In addition, an error class in training data can affects the choice of hyper plane. In the case where data are not linearly separable or incorrectly labeled constraints of equation (2) cannot be verified, and there is need to relax. This can be done by assuming a certain error of classification which is called Soft Margin. We introduce variables ξi called of relaxation; it can be added to allow mis-classification of difficult or noisy data.

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