In the field of text mining, many novel feature extraction approaches have been propounded. The following research paper is based on a novel feature extraction algorithm. In this paper, to formulate this approach, a weighted graph mining has been used to ensure the effectiveness of the feature extraction and computational efficiency; only the most effective graphs representing the maximum number of triangles based on a predefined relational criterion have been considered. The proposed novel technique is an amalgamation of the relation between words surrounding an aspect of the product and the lexicon-based connection among those words, which creates a relational triangle. A maximum number of a triangle covering an element has been accounted as a prime feature. The proposed algorithm performs more than three times better than TF-IDF within a limited set of data in analysis based on domain-specific data.
Vein mapping can be used to identify possible suspects using matching learning algorithms. Since vasculature deep in the skin cannot be visualized by naked eyes, the features extracted usually by converting to near infrared images which gives best track recovery with little noise. Two decades, ago the premise for the use of vein patterns for identification emerged in the forensic field. Researchers are proposing innovative approaches and methods utilized to improve the recognition, quality, classification, and extraction of viable vein patterns from images. Deep learning algorithms such as convolution neural network (CNN), K-nearest network, autoencoders are being used to extract venous features with ease especially when analyzing image forensic evidence. This paper provides an overview of recently proposed finger vein, dorsal hand vein, wrist vein and hybrid systems and highlights their performance and real-life application.
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