Grayscale Image Authentication using Neural Hashing

Many different approaches for neural network based hash functions have been proposed. Statistical analysis must correlate security of them. This paper proposes novel neural hashing approach for gray scale image authentication. The suggested system is rapid, robust, useful and secure. Proposed hash function generates hash values using neural network one-way property and non-linear techniques. As a result security and performance analysis are performed and satisfying results are achieved. These features are dominant reasons for preferring against traditional ones.Comment: international journal of Natural and Engineering Sciences (NESciences.com) : Image Authentication, Cryptology, Hash Function, Statistical and Security Analysi

Mentoring to reduce antisocial behaviour in childhood

The effects of social interventions need to be examined in real life situations as well as studie

Retinal biomarkers of Alzheimer’s disease: insights from transgenic mouse models

In this paper, we use the retina as a window into the central nervous system and in particular to assess changes in the retinal tissue associated with the Alzheimer’s disease. We imaged the retina of wild-type (WT) and transgenic mouse model (TMM) of Alzheimer’s disease with optical coherence tomography and classify retinas into the WT and TMM groups using support vector machines with the radial basis function kernel. Predictions reached an accuracy over 80% at the age of 4 months and over 90% at the age of 8 months. Texture analysis of computed fundus reference images suggests a more heterogeneous organization of the retina in transgenic mice at the age of 8 months in comparison to controls.This study was supported by the Neuroscience Mantero Belard Prize 2015 (Santa Casa da Misericórdia)(MB-1049-2015), by The Portuguese Foundation for Science and Technology (PEst-UID/NEU/04539/2013), by FEDER-COMPETE (POCI-01-0145-FEDER-007440) and Centro 2020 Regional Operational Programme (CENTRO-01-0145-FEDER-000008: BrainHealth 2020).info:eu-repo/semantics/publishedVersio