Browsing by Author "Yapa, I"

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    Decentralized ledger for land and property transactions in Sri Lanka acresense
    (IEEE, 2018-12-06) Yapa, I; Heanthenna, S; Bandara, N; Prasad, I; Mallawarachchi, Y
    Land & property can be identified as one of the core assets in any county in the world. Governments spend significant amount of money to identify, define, manage and maintain the transactional information around land and property in a country. Creating a proper mechanism to make this information available to the public users can be considered as one of the key indications that shows the development of a country. The system currently in use to record land and property transactions in Sri Lanka is a book-based system where all the information is stored in handwritten folios. This method is inefficient, time consuming, unsecured and complex when compared to the advanced systems which are used by developed countries in the world. This study is carried out to find a secured, efficient, cost effective solution with regards to the concerns and other problems related to land and property transactions in Sri Lanka. Proposed solution includes a decentralized ledger to record land and property transactions which runs on top of blockchain technology. All new transaction entries will be validated by smart contracts to verify the validity of the transactions. After performing a proper consensus mechanism, the transaction records will be stored in cryptographically secured blocks. So, the data integrity could not be violated. Additionally, investors and the land buyers can get information about the current market trends & predictions before buying or investing into a land. Also, the proposed system supports the users to view the land information graphically and allows the system users to view the desired land parcels without physically going to the actual location via the geographical information system.
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    PredictiveOps : Advanced Predictive Maintenance System for Industrial Machinery
    (Institute of Electrical and Electronics Engineers Inc., 2025-07-03) Yapa, I; Gunawardana, J.V; Madhuwantha, H; Gunasekara, S; De Silva, H
    Predictive maintenance is transforming industrial equipment management through the application of machine learning (ML) and the Internet of Things (IoT) for pre-failure detection. This paper investigates the PredictiveOps system that minimizes downtime, lowers maintenance expenditures, and increases operational effectiveness. This research puts the IoT sensors' function for the monitoring of such key parameters as vibration, temperature and rotational speed into the limelight, making condition-based maintenance possible. Anomaly detection with explainable AI ensures actionable output for maintenance staff. Predictive models also ensure minimal spare parts inventory, preventing undue stockholding. Findings indicate that predictive maintenance improves equipment reliability, prolongs asset life, and minimizes industrial waste. Challenges such as sensor noise and data imbalance still exist, calling for additional research. Through the transition from reactive to proactive measures, PredictiveOps is advancing Industry 4.0, promoting efficiency and sustainability.