1,459 research outputs found
Signature Verification Approach using Fusion of Hybrid Texture Features
In this paper, a writer-dependent signature verification method is proposed.
Two different types of texture features, namely Wavelet and Local Quantized
Patterns (LQP) features, are employed to extract two kinds of transform and
statistical based information from signature images. For each writer two
separate one-class support vector machines (SVMs) corresponding to each set of
LQP and Wavelet features are trained to obtain two different authenticity
scores for a given signature. Finally, a score level classifier fusion method
is used to integrate the scores obtained from the two one-class SVMs to achieve
the verification score. In the proposed method only genuine signatures are used
to train the one-class SVMs. The proposed signature verification method has
been tested using four different publicly available datasets and the results
demonstrate the generality of the proposed method. The proposed system
outperforms other existing systems in the literature.Comment: Neural Computing and Applicatio
Undecimated Wavelet Transform for Word Embedded Semantic Marginal Autoencoder in Security improvement and Denoising different Languages
By combining the undecimated wavelet transform within a Word Embedded
Semantic Marginal Autoencoder (WESMA), this research study provides a novel
strategy for improving security measures and denoising multiple languages. The
incorporation of these strategies is intended to address the issues of
robustness, privacy, and multilingualism in data processing applications. The
undecimated wavelet transform is used as a feature extraction tool to identify
prominent language patterns and structural qualities in the input data. The
proposed system may successfully capture significant information while
preserving the temporal and geographical links within the data by employing
this transform. This improves security measures by increasing the system's
ability to detect abnormalities, discover hidden patterns, and distinguish
between legitimate content and dangerous threats. The Word Embedded Semantic
Marginal Autoencoder also functions as an intelligent framework for
dimensionality and noise reduction. The autoencoder effectively learns the
underlying semantics of the data and reduces noise components by exploiting
word embeddings and semantic context. As a result, data quality and accuracy
are increased in following processing stages. The suggested methodology is
tested using a diversified dataset that includes several languages and security
scenarios. The experimental results show that the proposed approach is
effective in attaining security enhancement and denoising capabilities across
multiple languages. The system is strong in dealing with linguistic variances,
producing consistent outcomes regardless of the language used. Furthermore,
incorporating the undecimated wavelet transform considerably improves the
system's ability to efficiently address complex security concern
A proposal of a suspicion of tax fraud indicator based on Google trends to foresee Spanish tax revenues.
pre-print583 K
Multiresolution FIR neural-network-based learning algorithm applied to network traffic prediction
Published versio
Fingerprint Recognition in Biometric Security -A State of the Art
Today, because of the vulnerability of standard authentication system, law-breaking has accumulated within the past few years. Identity authentication that relies on biometric feature like face, iris, voice, hand pure mathematics, handwriting, retina, fingerprints will considerably decrease the fraud. so that they square measure being replaced by identity verification mechanisms. Among bioscience, fingerprint systems are one amongst most generally researched and used. it\'s fashionable due to their easy accessibility. during this paper we tend to discuss the elaborated study of various gift implementation define strategies together with their comparative measures and result analysis thus as realize a brand new constructive technique for fingerprint recognition
Complex Data: Mining using Patterns
There is a growing need to analyse sets of complex data, i.e., data in which the individual data items are (semi-) structured collections of data themselves, such as sets of time-series. To perform such analysis, one has to redefine familiar notions such as similarity on such complex data types. One can do that either on the data items directly, or indi- rectly, based on features or patterns computed from the individual data items. In this paper, we argue that wavelet decomposition is a general tool for the latter approac
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