Faculty of Computing

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Now showing 1 - 9 of 9
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    Emission Activity Parts Extraction using custom Named Entity Recognition
    (IEEE, 2022-12-09) Mannavarasan, M; Gamage, A; Sivarajah, V; Chandrasiri, S
    Dental health-related disorders have proliferated worldwide due to the excessive intake of fast food and sugary foods, which was followed by bad oral hygiene practices. The cost of dental examinations may change based on how critical the condition is, regardless of whether they are not regular. For a person, diagnosing an oral health problem, particularly locating the disease’s underlying cause, can be challenging. To properly diagnose and treat such conditions, advanced dental diagnostic techniques may be necessary. By offering convenience and enhancing their oral health knowledge, the system seeks to serve as a prediction tool that regular people can utilize to detect potential tooth illnesses at an early stage. It is encompassed as a mobile application where a Mask R-CNN model is used in the core that accepts a dental radiograph as the input. The trained model will be able to identify diseases related to the bone and teeth. Based on the performance evaluations, the accuracy of the results that are obtained in tooth type, restoration quality, dental caries, and periodontal disease identification falls in the range of 75%-80%.
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    Effectiveness of Using Radiology Images and Mask R-CNN for Stomatology
    (IEEE, 2022-12-09) Jayasinghe, H; Pallepitiya, N; Chandrasiri, A; Heenkenda, C; Vidhanaarachchi, S; Kugathasan, A; Rathnayaka, K; Wijekoon, J
    Dental health-related disorders have proliferated worldwide due to the excessive intake of fast food and sugary foods, which was followed by bad oral hygiene practices. The cost of dental examinations may change based on how critical the condition is, regardless of whether they are not regular. For a person, diagnosing an oral health problem, particularly locating the disease’s underlying cause, can be challenging. To properly diagnose and treat such conditions, advanced dental diagnostic techniques may be necessary. By offering convenience and enhancing their oral health knowledge, the system seeks to serve as a prediction tool that regular people can utilize to detect potential tooth illnesses at an early stage. It is encompassed as a mobile application where a Mask R-CNN model is used in the core that accepts a dental radiograph as the input. The trained model will be able to identify diseases related to the bone and teeth. Based on the performance evaluations, the accuracy of the results that are obtained in tooth type, restoration quality, dental caries, and periodontal disease identification falls in the range of 75%-80%.
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    Effectiveness of rule-based classifiers in Sinhala text categorization
    (IEEE, 2017-09-14) Haddela, P. S; Lakmali, K. B. N
    In the recent past, the growth of Sinhala text usage on the web has been increasing rapidly due to the advancement in the field of information and communication technologies in Sri Lanka. With this change in society, automatic text categorization becomes important for many operations in computing. Therefore, the aim of this research is to assess commonly used rule based text classification algorithms against the Sinhala dataset. This study is limited to rule based classifiers as they are humanly interpretable by nature, which gives an added advantage to text classification. This paper presents a. The comparison of experiment results of rule based classifiers b. SinNG5 corpus and Sinhala stop word list named as SinSWL. The corpus and stop word list are freely available for academic researches.
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    On The Effectiveness of Using Machine Learning and Gaussian Plume Model for Plant Disease Dispersion Prediction and Simulation
    (IEEE, 2020-05-29) Miriyagalla, R; Samarawickrama, Y; Rathnaweera, D; Liyanage, L; Kasthurirathna, D; Nawinna, D; Wijekoon, J
    Agriculture plays a vital role in the economic development of the entire world. Similarly, in Sri Lanka, 6.9% of the national GDP is contributed by the agricultural sector and more than 25% of Sri Lankans are employed in the field of agriculture. But the frequent fluctuations of climate conditions have caused the spread of diseases such as late blight which eventually has led to the devastation of entire plantations of Sri Lankans. To this end, this paper proposes to forecast the possible dispersion pattern and assist the farmers in identifying the possibility of the disease getting dispersed to nearby crops to provide early warning. Eventually, it leads the farmers to take precautions to save the plants before reaching a critical stage. The yielded results show that the proposed method successfully performed disease diagnosis and disease progression level identification with 90-94 % accuracy and dispersion pattern analysis.
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    Effectiveness of artificial intelligence, decentralized and distributed systems for prediction and secure channelling for Medical Tourism
    (IEEE, 2020-11-04) Subasinghe, M; Magalage, D; Amadoru, N; Amarathunga, L; Bhanupriya, N; Wijekoon, J
    Good health and wellbeing, a sustainable development goal introduced by the United Nations to be achieved by 2030. Sri Lanka is a country that highly depends on tourism. A healthcare system which consists of high quality and low-cost services and an abundance of tourist attractions makes Sri Lanka to be one of the best medical tourism destinations. Tourism and travel have contributed to the GDP of Sri Lanka by 11.1 billion USD by 2018. Lack of technological advancements within the medical sector has drawn back the ability to smoothly cater medical tourism. The proposed system aims for an advanced technological improvement that would help in further developing and contributing to medical tourism. To this end, this paper introduces an Intelligent System for Secure Channeling platform that aids medical tourism with the help of artificial intelligence and blockchain technologies. System proposes a treatment prediction and suggesting the best doctor for it and a secured network to store and access electronic health records (EHR). The yielded results show that the proposed method successfully performed treatment prediction with 79-88% accuracy.
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    On the effectiveness of IP-routable entire-packet encryption service over public networks (november 2018)
    (IEEE, 2018-11-20) Tennekoon, R; Wijekoon, J; Nishi, H
    The Internet is an unsecured public network accessed by approximately half of the world population. There are several techniques, such as cryptography, end-to-end encryption, and tunneling, used to preserve data security and integrity and to reduce information theft. This is because the security of data transmission over public networks is an ever-questionable issue. However, none of the above techniques are capable of providing the flexibility of changing either the algorithm or its key at the intermediary routers according to the requirements of stakeholders, e.g., ISPs or Internet users. Although the transmitted data are encrypted and unreadable, the metadata contained in the packet headers are readable during traversal. Nonetheless, service-based Internet architectures, e.g., IoT architectures, demand the analysis the data streams at the intermediary routers to provide smart services such as strengthening the security of the data streams. To this end, this paper proposes a method to use service-oriented routers for providing secure data transmission by encrypting data packets including the header and trailer information. A prototype of the proposed method is implemented on the ns-3 simulator, and this paper discusses the implementation notes and evaluation of the test results. The test results demonstrate that there is only an average processing cost of 180.14/191.35, 213.96/257.41, 157.56/170.68, and 235.48/ 249.49 μs for encrypting the total encrypted combined packets/total encrypted separate packets using IDEA, DES, AES-GCM, and AES-CTR encryption algorithms, respectively, with a 256-bit key space. This is significantly lower than the tolerable transmission delay (150 ms) defined by the ITU-T.
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    Effectiveness of a service-oriented router in future content delivery networks
    (IEEE, 2015-07-07) Wijekoon, J; Harahap, E; Takagiwa, K; Tennekoon, R; Nishi, H
    Content Delivery Networks (CDNs) constitute a major portion of Internet traffic. To cope with increasing demand for content, CDNs have deployed distributed infrastructures on Internet Service Providers (ISPs') networks. Most CDN systems optimize their traffic flow using Domain Name Systems. However, they do not collaborate with the ISPs, and the lack of collaboration limits performance such as end-user latency. Meanwhile, in future networks, it is anticipated that network routers will be equipped with more processing power and storage modules for providing most effective end-user services. From this viewpoint, a Service-oriented Router (SoR) is introduced to accelerate content-based services. In this paper, the benefits of introducing an SoR to an ISP network for maintaining ISP-CDN collaboration is outlined. Furthermore, a prototype design of the proposed system is presented. Simulations clearly demonstrate the effectiveness of the proposed ISP-CDN collaboration, which yields a 30-50% reduction in end-user latency.
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    PublicationOpen Access
    Effectiveness of Service-oriented router for ISP-CDN collaboration
    (Information Processing Society of Japan, 2017-01) Wijekoon, J; Harahap, E. H; Tennekoon, R; Nishi, H
    This article discusses a novel method to strengthen the collaboration between Internet service providers (ISPs) and content delivery networks (CDNs). CDNs are becoming the primary data delivery method in information communication technology environments because information sharing via networks is becoming the driving force of the future Internet. Moreover, it is anticipated that network routers will be equipped with additional processing power and storage modules for providing efficient end-user services. Consequently, this article studies the effectiveness of introducing a Service-oriented Router (SoR) to strengthen the ISP-CDN collaboration to leverage DNS-based request redirection in CDNs. In contrast, the proposed method yields better performance in user redirection and network resource utilization, suggesting that using SoR may a future business model which addresses adequate ISP-CDN collaboration.
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    On the effectiveness of using machine learning and Gaussian plume model for plant disease dispersion prediction and simulation
    (IEEE, 2019-12-05) Miriyagalla, R; Samarawickrama, Y; Rathnaweera, D; Liyanage, L; Kasthurirathna, D; Nawinna, D; Wijekoon, J. L
    Agriculture plays a vital role in the economic development of the entire world. Similarly, in Sri Lanka, 6.9% of the national GDP is contributed by the agricultural sector and more than 25% of Sri Lankans are employed in the field of agriculture. But the frequent fluctuations of climate conditions have caused the spread of diseases such as late blight which eventually has led to the devastation of entire plantations of Sri Lankans. To this end, this paper proposes to forecast the possible dispersion pattern and assist the farmers in identifying the possibility of the disease getting dispersed to nearby crops to provide early warning. Eventually, it leads the farmers to take precautions to save the plants before reaching a critical stage. The yielded results show that the proposed method successfully performed disease diagnosis and disease progression level identification with 90-94 % accuracy and dispersion pattern analysis.