Browsing by Author "Fernando, K"

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Embargo
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.
Open Access
Effect of Social Media Influencers’ Attributes on Customer Purchasing Behavior in Sri Lankan Context (Special References to Facebook and Instagram)
(Emerald Publishing, 2022-12-01) Bandara, G; Jayasuriya, N; Nimnajith, M; Withanage, N; Fernando, K; Jayawardana, S
This study aims to identify how social media influencer’s attributes can be useful to tune the customer purchasing behavior. Since social media influencer highly affects the day-to-day life of people, he/she highly impacts on decision making of customers to purchase products in the market. Therefore, it is essential to identify how these significant attributes support him/her to influence on customer purchasing behavior. Through the literature, attractiveness, expertise, prestige, follower base, and trustworthiness are identified as major attributes and are considered as independent variables, while customer attitude and customer mimicry desire act as the mediating factors of the relationship between these attributes and customer purchasing behavior. The research is designed as a quantitative study and primary data were collected from a sample of 405 participants through questionnaire. All the Facebook and Instagram users in the country are considered as the population. Reliability and validity of data are ensured through pilot test and data were analyzed through factor analysis, correlation analysis developing multiple regression models and hypothesis testing. Considering the findings, all the attributes show positive correlation, and all the correlations are significant at the point of P = 0.001. The conceptual framework is acceptable since all the hypotheses are supported. The conclusion is that there is a positive and significant impact of social media influencer’s attractiveness, expertise, prestige, follower base, and trustworthiness on customer purchasing behaviour while customer attitude and customer mimicry desire act as mediators. Policy implication of the study is to identify the suitable social media influencer and determine the criteria for selecting the social media influencer for social media marketing. The selected influencer will highly support the company by marketing its products and ensuring the customer attraction. He can apply these results and improve his follower base earning more through effective marketing campaigns. Hence, he can ensure high demand for the product, maintain competitiveness, and contribute to profit maximization. Novelty of the study is that it shows the significance of social media influencer’s attributes on social media marketing. By utilizing these attributes, he can attract new customers, retain existing customers, change customer perception towards the brand. Eventually, he will cause the brand to become the market leader.
Embargo
Genetic Algorithm Based Hybrid Clustering Technique for the Retinal Blood Vessels Segmentation
(IEEE, 2022-12-09) Dasanayake, D; Athuraliya, N; De Silva, H; Fernando, K; Haddela ., P.S
Important details about the visual anomaly can be found in the retinal fundus imaging. The segmentation of the blood vessels is crucial and necessary for diagnosing different ocular fundus. The primary and most common causes of blindness are diabetic retinopathy and its effects on the retinal vascular structures. The study suggested a genetic algorithm combined with the K-means clustering technique for unsupervised retinal segmentation. An essential pre-processing step for vessel identification applications is vessel enhancement. The CLAHE filtering method is employed in this work as a preprocessing step for vessel improvement. The improved vessels were grouped together using a genetic approach, and K-means clustering was applied for superior clustering outcomes. DRIVE and IOSTAR databases that are accessible to the public are used to evaluate the suggested strategy. According to the experimental findings, the proposed algorithm successfully separates clusters that are more dense and well-separated than those of other previous findings. Both the Calinski-Harabasz I ndex S core and the Silhouette Index Score are used to validate the proposed algorithm.