Sabaragamuwa University Journal of Computer Science Volume 1 Issue 1(2021) http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/1917 2026-04-22T02:12:30Z 2026-04-22T02:12:30Z Gunathilaka, N.V. Tennakoon, W.D.N.S.M. W.J.A.J.M., Lasanthika http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/1919 2022-06-15T04:31:17Z 2021-10-01T00:00:00Z

Factors Affecting to the Success of Computerized Maintenance Management System in Multinational Beer Companies Gunathilaka, N.V.; Tennakoon, W.D.N.S.M.; W.J.A.J.M., Lasanthika A computerized maintenance management system or CMMS is software that centralizes maintenance information and facilitates the processes of maintenance operations. It helps optimize the utilization and availability of physical equipment like vehicles, machinery, communications, plant infrastructures and other assets. CMMS essential because they make it easier and more efficient for maintenance managers and departments to meet their primary objective: reliable uptime. Purpose of the study was to identify the factors affecting to success CMMS in multinational beer companies. A quantitative study was performed and structural modeling was used to analyze data with the aid of SmartPLS software. Results suggested that the Data standard, role assignment and Continues improvement are significant predictors successful CMMS and findings contribute to the existing literature on computer science. Further managerial implications are encouraged for better implementation of CMMS.

2021-10-01T00:00:00Z Heenkenda, H.M.S.R. Karunaratna, D.D. Arunatilake, S.M.K.D. http://repo.lib.sab.ac.lk:8080/xmlui/handle/susl/1918 2022-06-15T03:58:42Z 2021-10-01T00:00:00Z

Digital signatures for singers to identify their songs Heenkenda, H.M.S.R.; Karunaratna, D.D.; Arunatilake, S.M.K.D. Counterfeiting is the process of imitating the voice of a popular artist, with the intention of selling or passing the imitations as genuine. This research is aimed at identifying the imitated songs by generating unique digital signatures for each singer by using songs sung by the artists. Songs typically contain vocal signals surrounded with instrumental signals. In order to generate signatures for the voice of a singer, the vocals have to be isolated. Voice isolation and Artist classification had been addressed as two different problems throughout the past decades. Our research combines these two problems, to build a unique signature model for each singer. This study proposes a technique to isolate vocal signals from instrumental signals by using REPET filter, Harmonic - percussive source separation, Butterworth band-pass filter and silence removal. This technique show better results than the prevailing techniques for vocal isolation. The signatures are then generated by extracting features from the isolated vocal signals and represented as GMM Models. The results of this research are evaluated through quantitative and qualitative approaches by using a sample of songs sung by Sri Lankan artists. The outcome of this research has demonstrated the possibility of generating digital signatures for singers by using their songs. The technique proposed has the ability to distinguish singers having similar voices accurately. Finally, the technique proposed in this paper could be used to generate unique signatures for singers who sung similar songs.

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