Browsing by Author "Sharmilan, S"

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    Blockchain & machine learning based secure personal medical record storage and sharing platform - datablock
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2019-12) Sharmilan, S; Farook, C; Sudantha, BH
    Data is the most important part of machine learning. In the bioinformatics field, the sensitivity of the data is high and due to that, the accessibility of the data for a secondary purpose (e.g.: research) consists of many legal and ethical issues. Due to that in many bioinformatics research collecting the data consume more time than the development phase. There are some researches done to solve the legal and ethical issues by anonymizing the data using encryption, de-identification and perturbation of potentially identifiable attributes. But for some extend those solutions restricted the data breach but on the other hand, anonymized data not performed well during the analysis and mining tasks and some researches done to generate fake data like the real data sets. But those researches not full fill the requirements because of the generated data more restricted to the knowledge of the training data. The evolution of Blockchain provided a secure and trusted way to transfer valuable assets between two unknown parties. This research used Blockchain technology to store and share personal medical data to data scientists. And that will help them to build more accurate and efficient models. It also proposed a machine learning model to predict the authenticity and validity of the personal data based on domain knowledge and the validator's trust percentage.
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    Blockchain based decentralized knowledge sharing system - jigsaw
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2019-12) Azeem, A; Jajeththanan, S; Sharmilan, S; Sudantha, BH
    Knowledge is formalized information that is cited or used in logical inference. The growth of knowledge sharing in the field of education helps people understand and grow their potential. Individuals are willing to share knowledge when they are certain to benefit from reciprocity or build a reputation. Individuals spend enormous amounts of time, resources and scarifications to gain knowledge. Individuals are currently sharing their expertise through forums, vlogs, videos, and trainings. Nevertheless, there is a variety of untrusted and unvalidated knowledge available for a subject from the recipient's point of view. To grasp what they need, it requires multiple resources. There are no direct benefits for people who make, comment or vote for these resources. In this research, the researcher implemented a distributed knowledge sharing system centered on Blockchain to allow multiple individuals to contribute their knowledge by building a resource that is moderated, verified, and community structured. Each creator, commenter and voter of knowledge will receive rewards, and the knowledge invested will gain it forever. The researchers used cryptography and knowledge economy in addition to stellar blockchain to make this process secure and more trustable. In this paper, the proposed system is realistic, efficient and has an ever-earning model.
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    Generate bioinformatics data using generative adversaria l network: a review
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa, Sri Lanka, 2017-12) Sharmilan, S; Chaminda, HT; Sudantha, BH
    Data is the most important part in machine learning. In bioinformatics field the sensitivity of the data is high and due to that the accessibility of the data for a secondary purpose (e.g.: research) is consist with many legal and ethical issues. Due to that in many bioinformatics researches collecting the data consume more time than the development phase. There are some researches done to solve the legal and ethical issues by anonymising the data using encryption, de-identification and perturbation of potentially identifiable attributes. For some extend those solutions restricted the data breach but in other hand anonymized data not performed well during the analysis and mining tasks. Recently Generative adversarial networks (GANs) have become a research focus of artificial intelligence. The goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that distribution. Here, researcher review GAN in bioinformatics to generate data sets, presenting examples of current research. To provide a useful and comprehensive perspective, Researcher categorize research both by the bioinformatics data and GAN architecture and flow. Additionally, discussed about the issues of GAN in bioinformatics to generate data sets and suggest future research directions. Researcher believes that this review will provide valuable insights for researchers to apply GAN to generate bioinformatics data sets.