Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Perera, S

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    PublicationOpen Access
    Optimizing Asset Transfer Process in ERP Using Business Process Management Technique
    (Science and Information Organization, 2025-10-30) Yasarathne, R; Ranatunga, N; Herath, V; Chalinda, L; Kahandawaarachchi, C; Perera, S; Randula, C
    Enterprise Resource Planning (ERP) systems are critical for managing enterprise-wide business processes, including asset management. Yet, many ERP platforms lack efficient mechanisms for bulk asset transfers, leading to high manual effort, increased costs, and data inconsistencies. This study applies Business Process Reengineering (BPR) techniques as the methodology to optimize ERP asset management, focusing on workflow optimization and automation, contributing both practical and methodological insights. A mixed-method approach was adopted, analyzing a financial organization with 256 branches and over 450 Oracle ERP users. Data from 51 representative branches identified inefficiencies such as manual transfer delays, approval bottlenecks, and synchronization issues. The proposed solution introduces automated bulk asset transfers, optimized approval workflows, and real-time data synchronization, along with new metrics for evaluating efficiency, compliance, risk, and asset utilization. Compared to the As-Is system, the reengineered framework achieved a 100% reduction in operational costs per user ($7,500 annual saving), an 80% reduction in compliance incidents, a 67% reduction in asset transaction errors, and a 20% improvement in asset utilization. These results demonstrate a scalable, adaptable, and effective framework that enhances ERP operational efficiency, strengthens data integrity, and advances both academic understanding and industrial practice of asset management process reengineering.
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    PublicationEmbargo
    Supply and Demand Planning of Electricity Power: A Comprehensive Solution
    (IEEE, 2019-12-06) Perera, S; Dissanayake, S; Fernando, D; De Silva, S; Rankothge, W
    Electrical energy is one of the fastest growing energy demands in the world. Uncertainty in supplying the demand can threaten the social economic aspects of a country. The biggest driver of electrical demand is weather. Climatic changes not only affect the demand but also renewable energy supply. Wind and Solar are two alternative energy sources with less pollution. We have proposed a platform which helps energy providers, energy traders with services related to electricity supply and demand planning, with following modules. (1) Forecasting electricity consumption patterns (2) Forecasting wind power generation (3) Optimizing Load Shedding. Our platform has been implemented using statistical and machine learning techniques: Multi-Linear Regression for consumption prediction, Random forest regression for wind power forecast, and genetic algorithm to optimize load shedding. Our results show that, using our proposed module, we can minimize the imbalance between the supply and demand of electricity by predicting the consumption patterns of consumers, predicting the wind power generation and by selecting the best feeder to be selected for load shedding under given constraints.
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    PublicationEmbargo
    A Real-Time Cardiac Arrhythmia Classifier
    (IEEE, 2019-10-08) Abayaratne, H; Perera, S; De Silva, E; Atapattu, P; Wijesundara, M
    Cardiovascular diseases (CVD) have increased drastically among Non-Communicable diseases, which have peaked over the past recent years. In 2018, around 17.9 million which is an estimated 31% of the people have died worldwide due to CVDs. A novel machine learning algorithm for continuous monitoring, identification and classification of cardiac arrhythmias from Electrocardiogram (ECG) data is presented here. The proposed solution has two stages where the first stage is a rule based cardiac abnormality identification which has an individual 97.55% ± 0.3% of accuracy (Acc) for a dataset of 705,000 and the second stage is a Neural Network (NN) based classification model which is trained and tested to identify 15 different classes recommended by ANSI/AAMI standard [1], and has 97.1% of individual accuracy for MIT-BIH Arrhythmia dataset [2] of 96265 beat samples. The combined real-time cardiac arrhythmia classifier is parallelized with CUDA in order to utilize the GPU and increase the execution speed by 4.86 times.