Browsing by Author "Dissanayake, D"

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Balancing Maintenance Costs and Degradation Levels in Building Elements
(American Society of Civil Engineers (ASCE), 2025-10-06) Dissanayake, D; Dias W.P.S
Maintenance strategies require a balance between maintaining assets at acceptable levels of service and minimizing maintenance costs. The latter is imperative especially for building elements, since they require less maintenance than machinery, and carrying out such maintenance can be disruptive and costly. This paper reports an inquiry into optimal maintenance strategies for 12 building elements, ranging from structural through finishes and opening to services ones, each with a differing service life. Element degradation was predicted using Markov chain models, while repair costs were based on lost value ratios associated with five discrete condition ratings, separately defined for each of the elements. The strategies considered were based on three different criteria, namely, minimizing maintenance cost, achieving the best the overall condition rating at end of service life, and optimizing a rating that combined both cost and quality. The maintenance cycles considered were 5, 10, and 20 years, and also the zero-maintenance case, costed at both zero and 5% net discount rates. The repair combinations involved upgrading elements in a single given Condition 2, 3, 4, or 5 to the as new Condition 1 or upgrading elements in all such conditions to Condition 1. It was found that performing regular maintenance on concrete slabs, floor tiles, timber windows, and especially ceiling fans and fan regulators was in fact cheaper than not maintaining them (and having to renovate at the end of service life), while a 20 year cycle was found to be optimal for the other elements studied.
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Machine Learning Based Solution for Improving the Efficiency of Sugar Production in Sri Lanka
(IEEE, 2022-12-26) Kulasekara, S; Kumarasiri, K; Sirimanna, T; Dissanayake, D; Karunasena, A; Pemadasa, N
Although sugar is a popularly used commodity in Sri Lanka, sugar manufactured within the country fulfill only a very small portion of the demanded amount. Sugar production is an intricate process which requires a considerable amount of expertise especially in the areas of cultivation, production and revenue prediction which may not exist in novice farmers. This research proposes a methodology which provides novice sugarcane farmers with expert knowledge on four main areas related to farming including weather forecast, sugarcane maturity estimation, production forecast and prediction of return sugarcane amounts from lands. ARIMA model is used for weather forecast whereas machine learning methods and multiple regression models were used for sugarcane maturity estimation and production of forecasts and returns respectively. The final ARIMA time series model was validated with p-value greater than 0.05 for Ljung-Box test with three different lag values. The Support Vector Machines model was identified as the best model with an accuracy of 81.19% for the sugarcane maturity estimation. The SVM model was trained using the HSV and texture features extracted from sugarcane stalk images using image processing techniques. The prediction of sugar production received a testing R-squared score of 87.75% and mean squared error of 0. Prediction of yield received a mean squared error of approximately 0 and R squared score of 98% on test data. The methodology used in this research could be used by novice farmers to increase their cultivation as well as sugar production.
Open Access
Organizational Culture Impact on Employee Loyalty in Private Banking Sector in Sri Lanka
(Emerald Publishing, 2022-12-01) Wisenthige, K; Samarakoon, A; Lakmali, R; Samarakoon, D; Dissanayake, D
Companies now recognize the need of investing in their people if they want to continue providing solid value to their clients. This research set out to quantify how much of an effect company culture had on loyalty among private bank employees in Sri Lanka's Western Province. Employee loyalty was used as the dependent variable, while the organization's culture was examined as the independent variable along four dimensions: managerial culture, open working environment, incentives and perks, and training and development. Data was gathered by self-administered questionnaires on a 5-point Likert scale from 374 bank employees chosen via simple random sampling. The effect was evaluated by a multiple-regression study. Based on the findings, improving the openness of management choices; providing value to a person's professional development; the positive culture with opportunities will help bank decision-making and planning; associated with people-centred ideas, which guide managers and to act in the best employees' interests of stakeholders. Schedule job-related training to satisfy the employer's and workers' goals. Government and Central Bank of Sri Lanka policymakers recognize that company culture must be used to increase employee loyalty. The procedures and approaches that are decided upon to carry out the study are discussed in the section of the paper titled "Methodology” and the population as well as the sample size of this study are considered, together with the technique of collecting data, the type of data collected, and the method of analysing data. The findings of this study revealed that organizational culture has an impact on employee loyalty.
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Platform Independent Browser Forensic Tool for Advanced Analysis of Artifacts and Case Management
(IEEE, 2021-12-09) Dissanayake, D; Rajakaruna, S; Ranasinghe, D; Wijesooriya, A; Jayakody, A; Rajapaksha, S. K
A web browser is a major attack vector which cyber-criminals utilize to land in an environment. The evidence related to the malicious browsing activities can be found in the host which gives valuable information related to the case. These digital footprints involve history, cookies, bookmarks, saved credentials and downloads etc. This paper presents a sophisticated tool aiding the conventional manual investigation process from evidence collection to the final v e rdict b y a u tomating h u man dependent functions, resulting a fast and unbiased analysis of browser forensic artifacts. This tool states its unique value over the existing tools by working operating systems independently, collecting all browsing evidence including deleted artifacts and encrypted saved credentials, automatically analysing the reputation of the extracted evidence, integrating evidence collected from different web browsers into a single timeline, and correlating the adjacent distrustful events inside and outside the host. Eventually, this tool calculates a browsing reputation scorecard and creates a profile for the host, condensing the findings g a thered t h roughout the investigation. The paper presents another important methodology to predict the future browsing reputation score based on the past browsing patterns. Furthermore, multiple cases management feature and dashboard provide a concise overview of overall findings to the forensic investigator.
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Platform Independent Browser Forensic Tool for Advanced Analysis of Artifacts and Case Management
(IEEE, 2021-12-09) Dissanayake, D; Rajakaruna, S; Ranasinghe, D; Wijesooriya, A; Jayakody, A; Rajapaksha, S
A web browser is a major attack vector which cyber-criminals utilize to land in an environment. The evidence related to the malicious browsing activities can be found in the host which gives valuable information related to the case. These digital footprints involve history, cookies, bookmarks, saved credentials and downloads etc. This paper presents a sophisticated tool aiding the conventional manual investigation process from evidence collection to the final v e rdict b y a u tomating h u man dependent functions, resulting a fast and unbiased analysis of browser forensic artifacts. This tool states its unique value over the existing tools by working operating systems independently, collecting all browsing evidence including deleted artifacts and encrypted saved credentials, automatically analysing the reputation of the extracted evidence, integrating evidence collected from different web browsers into a single timeline, and correlating the adjacent distrustful events inside and outside the host. Eventually, this tool calculates a browsing reputation scorecard and creates a profile for the host, condensing the findings g a thered t h roughout the investigation. The paper presents another important methodology to predict the future browsing reputation score based on the past browsing patterns. Furthermore, multiple cases management feature and dashboard provide a concise overview of overall findings to the forensic investigator.