Browsing by Author "Perera, K. D. M"

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    Online Digital Cheque Clearance and Verification System using Block Chain
    (IEEE, 2021-04-02) Bogahawatte, W. W. M. K. A; Isuri Samanmali, A. H. L; Perera, K. D. M; Kavindi, M. A. T; Senarathne, A. N; Rupasinghe, P. L
    Cheque Truncation System (CTS) is an image-based cheque clearing framework used in Sri Lanka. This semi manual process has certain limitations and takes up to 3 working days to clear an inter-bank national cheque in Sri Lanka. Faced with the limitations of this system, cheque users and commercial banks must need an efficient and a secured system which can clear a cheque within less than 24 hours along with providing integrity and confidentiality to the system. This research portrays an automated solution, which is feasible for any commercial bank in Sri Lanka, to address above-mentioned issues. The proposed system is based on the blockchain where all banks willing to take an interest in this framework must connect the proposed blockchain based system to supply the quicker cheque clearance to its clients. Answers were proposed with a complete framework consisting of four main phases: (i) paper cheque clearing process, (ii) digital cheque issuing and clearing process, (iii) cheque fraud detection process and (iv) cheque transaction securing process. Python along with Flutter framework and Ethereum were the major technologies used for implementing the system. The proposed system is highly scalable as Ethereum provides added integrity to the system. The approach advocates the customer as well as the bank with much simpler and speedier cheque clearing process with increased security. It also contributes with a paper cheque fraud detection system with faster and reliable results. The proposed system provides benefits to the user as well as the bank by addressing the requirement of producing a secure, effective and environment friendly system. Finally, CheckMate permits a consistent stream of cheque clearance operation for the payer and the payee without any mediators.
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    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for Chronic Kidney Disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.
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    Performance evaluation on machine learning classification techniques for disease classification and forecasting through data analytics for chronic kidney disease (CKD)
    (IEEE, 2017-10-23) Gunarathne, W. H. S. D; Perera, K. D. M; Kahandawaarachchi, K. A. D. C. P
    Chronic Kidney Disease (CKD) is considered as kidney damage which lasts longer than three months. In Sri Lanka, CKD has become a severe problem in the present days due to CKD of unknown aetiology (CKDu) that can be seen popularly in North Central Province. Identifying CKD in the initial stage is important to provide necessary treatments to prevent or cure the disease. In this work main focus is on predicting the patient's status of CKD or non CKD. To predict the value in machine learning classification algorithms have been used. Classification models have been built with different classification algorithms will predict the CKD and non CKD status of the patient. These models have applied on recently collected CKD dataset downloaded from the UCI repository with 400 data records and 25 attributes. Results of different models are compared. From the comparison it has been observed that the model with Multiclass Decision forest algorithm performed best with an accuracy of 99.1% for the reduced dataset with the 14 attributes.