A Novel Text Encryption Algorithm

Acevedo, Elena; Martínez, Ángel; Acevedo, Marco; Martínez, Fabiola

doi:10.13053/rcs-68-1-8

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…The form of the vectors y to build the max and min memories is as follows, respectively. We applied the idea of Linear Associator, 19 and we selected the value of 500 because we found that a value greater than the greatest element in the dataset results in a better performance 21 :…”

Section: Datasetmentioning

confidence: 99%

Computational Intelligence for Shoeprint Recognition

et al. 2019

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Shoeprint marks present valuable information for forensic investigators to resolve a crime. These marks can be helpful to find the brand of the shoe and can make the investigation easier. In this paper, we present an associative model-based algorithm to match noisy shoeprint patterns with a brand of shoe. The shoeprints are corrupted with additive, subtractive and mixed noises. A particular case of subtractive noise are partial shoeprints such as toe, heel, left-half and right-half prints. The Morphological Associative Memories (MAMs) were applied. Both memories, max and min, recognize noisy shoeprints corrupted with 98% additive and subtractive noise, respectively, with an effectiveness of 100%. The images corrupted with mixed noise were recognized when the additive or subtractive noise applied was greater than the mixed noise; in this case, the recalling was around 70%, otherwise, both memories failed to recognize the shoeprints.

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Section: Datasetmentioning

confidence: 99%