Faculty of Computing

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Author: search.filters.author.Dissanayake, K

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    “Trust Pass”-Blockchain-Based Trusted Digital Identity Platform Towards Digital Transformation
    (IEEE, 2021-12-16) Dissanayake, K; Somarathne, P; Fernando, U; Pathmasiri, D; Liyanapathirana, C; Rupasinghe, L
    According to the United States Census Bureau, by June 2019 world population on earth was 7.5 billion, which exceeds the world population of 7.2 billion as of 2015. Each of these citizens needs to prove their identity to fulfil their day-to-day routine. In this current digital revolution whole world is transforming to digitalization. Therefore, proving someone's identity in the digital space is a must. Being able to track a person digitally can eliminate identity theft and most incidents related to online harassment. With the focus on data privacy and security of citizens, we have proposed “Trust Pass”: Cyber Security Intelligence-based trusted digital identity platform capable of registering and verifying service providers based on document validation neural network model (95.4% accuracy) and allowing citizens to authenticate themselves to service providers with three-factor biometrics authentication with liveness detection neural network model (99.8% accuracy). The requests of the whole system are secured with Cyber Security Threat Intelligence System, and unusual activities of users are monitored through Informative Data Analytics Engine. All the sensitive user data is saved using a blockchain to ensure user privacy while reducing the system's vulnerability.
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    Career Aura–Smart Resume and Employment Recommender
    (IEEE, 2021-12-09) Dissanayake, K; Mendis, S; Subasinghe, R; Geethanjana, D; Lunugalage, D; Kasthurirathna, D
    Recruitment and Job seeking are two major factors that are directly proportional to each other. Due to the competitive nature of the present world, the process of acquiring the best resource effectively and efficiently has become a challenging aspect for the companies. As a result, modern job portals have become increasingly popular to address the challenges identified in the early recruitment and job search process. The purpose of this research is to introduce an optimal solution to address the ineffective areas identified in the job and recruitment domain which can further enhance the recruitment and job seeking decisions by utilizing deep learning and sentiment analytic approach along with descriptive analysis. The proposed system recommends the relevant job opportunities by omitting the irrelevant job advertisements for job hunters who are interested in the IT job domain while they input their resume to the system and additionally, they can improve their career decisions by adhering to the prediction schemes. Moreover, the system facilitates recruiters to headhunt top talents efficiently once they input job requirements to the system and candidate suggestions are not only made depending on their resume information but also analyzing their LinkedIn endorsements.