Browsing by Author "Koggalage, R"

Now showing 1 - 3 of 3

item: Conference-Extended-Abstract
Optimization of newspaper pagination using the simulated annealing algorithm and the genetic algorithm
(2010) Sirisena, KJL; Perera, KAPS; Karunarathna, CDD; Hettimulla, HATD; Weerawarana, S; Koggalage, R
Newspaper pagination has become an NP-hard problem with the need to optimized the space of a newspaper. A well paginated newspaper is a newspaper which includes a high number of advertisements and articles along with pagination rules. The research problem is to find an efficient algorithm to generate a well paginated newspaper. Most of the literature related to newspaper pagination indicated the use of the Simulated Annealing (SA) algorithm to solve the problem. In this research study, we introduce an improved method of using the Genetic Algorithm (GA) to solve the problem along with a method of deriving as improved solution using SA. This research study also includes a comparison of statistical data from the two algorithms.
item: Conference-Full-text
Optimization of newspaper pagination using the simulated annealing algorithm and the genetic algorithm
(Computer Science & Engineering Society c/o Department of Computer Science and Engineering, University of Moratuwa., 2010-09) Sirisena, KJL; Perera, KAPS; Karunarathna, CDD; Hettimulla, HATD; Weerawarana, S; Koggalage, R; Gunasekara, C; Wijegunawardana, P; Pavalanathan, U
Newspaper pagination has become an NP-hard problem with the need to optimize the space of a newspaper. A well paginated newspaper is a newspaper which includes a high number of advertisements and articles along with specific pagination rules. The research problem is to find an efficient and suitable algorithm to generate a well paginated newspaper. Most of the literature related to newspaper pagination indicates the use of the Simulated Annealing algorithm to solve the problem. In this research study, we introduce an improved method of using the Genetic Algorithm to solve the newspaper pagination problem along with a method of deriving an improved solution using Simulated Annealing. We use some heuristic methods within the Genetic Algorithm and the Simulated Annealing algorithm to achieve the basic pagination rules. This research study includes a comparison of statistical data from the two algorithms.
item: Conference-Abstract
Patient alert and decision support system
Gunawardane, TSFW; Koggalage, R; Rodrigo, BKRP; Rajapakse, S
Safety of critically ill patients in intensive care units is an important aspect of medical care. There are many factors contributing to shortcomings and errors in patient care in the intensive care setting, such as long working hours, high levels of stress, lack of enough people, may cause human errors and affecting the effectiveness of the decisions of the physician. Several attempts have been made to increase the effectiveness of such decisions by issuing early alerts on adverse patient conditions. However, such alerts are based on single parameter variations but not on the relationship between multiple parameter variations. Thus, inability to provide an effective communication model causes a considerable bottleneck in intensive care unit (ICU) operations. The proposed model is an integrated solution which identifies the adverse patient conditions on multiple parameter variations and then provides predictive treatment suggestions on those identified conditions. It follows an interactive communication cycle in order to properly notify the responsible physicians. Results show that the system is capable of early identification of adverse conditions and providing suitable treatment suggestions compared to physicians themselves make decisions on same patient conditions.