Chaitanya Bapat

Chaitanya Bapat

Academic credentials are documents that attest to successful completion of any test, exam or act as a validation of an individual's skill. Currently, the domain of academic credential management suffers from large time consumption, high cost, dependence on third-party and a lack of transparency. A blockchain based solution tries to resolve these pain-points by allowing any recruiter or company to verify the user credentials without dependence on any centralized third party. Our decentralized application is based off of BlockCerts, an MIT project that acts as an open standard for blockchain credentials. The project talks about the implementation details of the decentralized application built for BlockCerts Wallet. It is an attempt to leverage the power of the blockchain technology as a global notary for the verification of digital records.

S. Kolkur, D. Kalbande, P. Shimpi et al.

Human Skin detection deals with the recognition of skin-colored pixels and regions in a given image. Skin color is often used in human skin detection because it is invariant to orientation and size and is fast to process. A new human skin detection algorithm is proposed in this paper. The three main parameters for recognizing a skin pixel are RGB (Red, Green, Blue), HSV (Hue, Saturation, Value) and YCbCr (Luminance, Chrominance) color models. The objective of proposed algorithm is to improve the recognition of skin pixels in given images. The algorithm not only considers individual ranges of the three color parameters but also takes into ac- count combinational ranges which provide greater accuracy in recognizing the skin area in a given image.