Browsing by Author "Haddela, P.S"

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Classification of Documents and Images Using an Enhanced Genetic Algorithm
(IEEE, 2022-12-09) Athuraliya, N; De Silva, H; Dasanayake, D; Fernando, K; Haddela, P.S; Gunarathne, A
In 1975, John Holland proposed the Genetic Algorithm (GA). The algorithm is widely used to provide superior solutions for optimization and search problems by relying on biologically inspired operators including mutation, crossover, and selection. The fittest individuals are chosen for reproduction in this algorithm to generate the next generation’s offspring. Classification is a technique used in data mining to analyze the collected data and to divide them into different classes. The relationship between a known class assignment and the properties of the entity to be classed may serve as the foundation for the classification procedure. Through this research, it has mainly consider classification for documents and images using GA. In order to enhance the accuracy and to reduce the error rate of traditional models, a new approach is proposed which is based on GA. The primary benefit of using GA in conjunction with classification is the efficiency in which it can address optimization issues. The experiment results are used to verify the suggested algorithm using benchmark data sets gathered from the UCI machine learning repository.
Embargo
CricSquad: A System to Recommend Ideal Players to a Particular Match and Predict the Outcome of the Match
(IEEE, 2023-06-12) Lekamge, E. L.; Wickramasinghe, K. R.; Gamage, S. E.; Thennakoon, T. M. K. L.; Haddela, P.S; Senaratne, S
Selection of the cricket squad plays a very important role in the outcome of the match. This work is about selecting ideal players for a cricket match and predicting the outcome of the match according to the selected cricket team. A cricket squad consist of around 15 to 16 players, with different expertise in batting, bowling, fielding. To select players for the squad, points were calculated using a statistical approach considering player’s overall career data. And then for the further use of selecting players for the squad next match performance of each and every player were predicted using Machine Learning techniques. Association rule mining was used to find frequent winning player combinations with day/night, home/away, batting first/second, against different opponent combinations. Finally calculate points for each player in both teams, then predict the outcome of the match with classification algorithms by considering the calculated total points of each team and other factors such as toss outcome, batting inning, day night conditions and venue. As for the results, XG boost regressor has produced the highest R2 score of 0.92 for batsman runs prediction model while random forest regressor has produced the highest R2 score of 0.66 for bowler wickets prediction model. The Gradient Boost Classifier predicted the Outcome of a match with the highest accuracy of 0.92 while the K Nearest Neighbor achieved the lowest accuracy of 0.82 score.