Document verification is a complex domain that involves various challenging and tedious processes to authenticate. Moreover various types of documents for instance banking documents, government documents, transaction documents, educational certificates etc. might involve customized verification and authentication practices. The content for each type vary significantly, hence requires to be dealt in a distinct manner. For students, educational certificates are the most important documents issued by their universities. However, as the issuing process is not that transparent and verifiable, fake certificates can be easily created. A skilfully generated fake certificate is always hard to detect and can be treated as the original. With the increase of forged documents, credibility of both the document holder and the issuing authority is jeopardized. Blockchain technology has recently emerged as a potential mean for authenticating the document verification process and a significant tool to combat document fraud and misuse. This research aimed to enhance the document verification process using blockchain technology. In this research, authors have identified the security themes required for document verification in the blockchain. This research also identifies the gaps and loopholes in the current blockchain based educational certificate verification solutions. At the end, a blockchain based framework for verifying educational certificates focusing on themes including authentication, authorization, confidentiality, privacy and ownership is proposed using the Hyperledger Fabric Framework.

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The goal of compression techniques is to reducing the size of data and decreasing the communication cost while transferring data. Fractal based coding technique is widely used to compress images files which provides high compression ratio and good image quality. However, like a compression technique, it is still limited because of the difference of the human perceptions between audio and image files, the long time for searching the best possible domain blocks and many comparisons in the encoding process. For those reasons, Fractal Coding had not broadly studied on audio data. Few years ago, Fractal Coding has been extended to apply on the audio data. In this paper, the application of the Fractal Coding on different types of audio files is investigated. Moreover, the effect of block length on the audio quality and compression performance are highlighted since block length is considered the main factor in the Fractal Coding algorithm. A GTZAN dataset is adopted in the evaluation and the experimental results show that there is an inverse relationship between block length and audio quality and proportional relationship between block length and compression ratio and factor. Furthermore, it can be noticed that the Fractal Coding can be compressed any speech and music audio signal directly with acceptable quality, PSNR 39 dB on average with a high compression ratio around 90 % with compression factor around 10 when the block length is 20 samples.

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