Faculty of Computing

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    Oxygen: A Distributed Health Care Framework for Patient Health Record Management and Pharmaceutical Diagnosis
    (IEEE, 2022-12-09) Wickramarathna, M; De Silva, K; Lekamalage, V; Senanayake, J; Perera, J; Ruggahakotuwa, L
    With the COVID-19 pandemic, the world is confronting various healthcare issues, and healthcare automation is more crucial than ever. The pandemic has revealed the limitations of existing digital healthcare systems to manage public health emergencies. There is no registered population for many healthcare institutions in Sri Lanka, as a result, there is a communication gap. Electronic Health Record systems (EHRs) are becoming popular to share patient details but accessing scattered data across several EHRs while safeguarding patient privacy remains a challenge. Most of these medical records are in printed format and manually entering those into EHR systems is time-consuming and error prone. Not only that pharmaceutical error is a critical healthcare problem, but it is even riskier to visit doctors for pharmaceutical diagnosis during a pandemic. This research introduces a Blockchain-based patient health record system, an Optical Character Recognition (OCR) and Natural Language Processing (NLP) based Medical Document Scanner, a Drug Identifier based on Image Processing and a Medical Chatbot powered by NLP as four novel approaches to address these issues. Altogether with the results, this research aims at introducing a solution for the limitations in healthcare while providing a distributed healthcare framework for the healthcare community worldwide.
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    DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates
    (IEEE, 2022-12-09) Jayasekara, R.T.R; Kudarachchi, K.A.N.D; Kariyawasam, K.G.S.S.K; Rajapaksha, D; Jayasinghe, S.L; Thelijjagoda, S
    The HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.
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    Digital Forensic Investigation Framework for Marine Industry
    (IEEE, 2022-07-18) Fernando, V; Senarathne, A
    A massive 400% increase of cyber-attacks was reported in the Marine industry since February 2020 up to date. Experiments and research are being conducted towards safeguarding data in various technological aspects of the marine environment with regards to navigation, transportation and system management. At present, generic investigation processes are developed for computer incidents and forensic readiness in this sector, but those do not describe the investigation process precisely. Although there are incident analysis technologies implemented for the Marine environment, it lacks reliable digital forensic tools for evidence extraction and analysis. Therefore, in this research, a comprehensive digital forensic investigation framework is proposed, answering the issues of existing frameworks, and a tool is implemented for the examination of navigational evidence. Forensic procedures of existing investigation methodologies were taken into consideration when developing the fusion framework. The Marine Geo Navigator was implemented with the technologies of Machine Learning and Visualization to evaluate geolocations and paths of vessels. Through expert analysis, the proposed investigation framework was verified as a suitable process to be utilized in the Marine industry and the tool was validated resulting an acceptable level of accuracy.
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    IELTF: An ICT-based Framework to Leverage English Language Education in Sri Lanka
    (IEEE, 2018-08-08) Weerakoon, U; Manage, R; Wijekoon, J
    This paper aims to develop an ICT based English Language Teaching Framework (IELTF) for the students of Sri Lanka to overcome the complications of learning English language in secondary education. The English language is an international language and proper English education is essential for the betterment of the students lives. According to a survey (using both students and English teachers) and several research findings, we observed that there are several issues in the English education in Sri Lanka for the students who are following their secondary education. Such phenomenon make the most students struggle learning and understanding the English language, and hence, the teachers struggle teaching the English Language effectively. Recently Information and Communication Technology (ICT) is gaining momentum upgrading societies to the smart societies. To this end, this paper proposes a novel notion of using ICT for effective English education and thereby improve the teaching and learning quality of the English language in Sri Lankan secondary education.
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    Knowledge management framework for achieving quality of healthcare in the developing countries
    (IEEE, 2013-01-20) Amararachchi, J. L; Perera, H. S. C; Pulasinghe, K
    A severe dearth of medical experts in health institutions in the rural and remote areas in developing countries has directly affected the quality of healthcare. This problem can be alleviated by providing facilities to access up to date medical Information and knowledge for doctors who are stationed in these areas to update their knowledge. Since Knowledge Management System (KMS) consists of most related Information and knowledge, medical KMSs could be utilized to enhance the quality of clinical activities. This study was aimed to identify the factors that affect the knowledge management initiatives. Findings of the research have shown that there is a strong association between accessing and using Information/ knowledge in clinical activities and the quality of healthcare. Moreover, attitudes of Medical Practitioners (MP), Infrastructure facilities, patient Information systems, patient treatment, staff benefits etc., have contribute positively towards the success of knowledge management in Health organizations. The research has used the case study methodology for accomplishing the research objectives. Remote and rural areas in Sri Lanka have considered for the case study which is one of the developing countries in the Asian region.