International Postgraduate Research Conference (IPRC)
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/155
Browse
3 results
Search Results
Item A Trust Framework for Social Networks in MANET Environment(International Postgraduate Research Conference 2019, Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2019) Mayadunna, H.; Liyanage, S.R.The improvement of online social networks such as Facebook, Twitter, Instagram has been expanded the idea of using social networks wider. The utilization of mobile phones of general public that given access to social networks makes such platforms popular. Node to node communication in a network gives a discussion to their individuals to associate with different individuals in the systems and share hobbies, opinions, and educational involvements including daily experiences. A significant number of these online social networks are operated with the point of associating to connect many people. Hence, it is important to enhance trustworthiness in social networks. This research is focusing on implementing a trust factor in the device layer. Information within the social networks can be used to get additional trust value for the devices. Hence trust can be calculated at the upper layers to be used at the device level. Thereby, research has developed a social trust framework to allow MANET (Mobile Ad-hoc Network) environment to move cross layer to find trust-related information which can be used at the device level for decision making. The captured social network behavior will provide an indication of how trustworthy the same device by capturing upper layer information. The intent of this research project is to create a trust layer on top of a social environment, in order to achieve the advantages of trustworthy connections. A network structure has been developed in order to complete that achievement. Prior to that, information of Facebook personal friend networks has been extracted and analyzed. Analyzing the parameters which are related to security of the social network is done through a literature survey. While examining the information from social networks, appropriate security-related parameters were selected with their possible states and values. A social network is a group of people or organizations or other entities that connected by a social relationship including friendship, information exchange or corporative working. Social network analysis is the process of mapping and measuring relationships, interactions and flows between people, groups, organizations or other social entities. In general, social network trust can be defined as a measure of confidence that an entity or entities behaves in an expected manner. The research work is reviewing the definitions and measures of trust by focusing on social networks where it can be utilizing within further achievements such as improving security within any kind of networkItem Recommendations for Students in Higher Education: A Machine Learning Approach.(2017) Kasthuriarachchi, K.S.T.; Liyanage, S.R.Educational Data Mining is a rising discipline in Data Mining setting which concentrated on creating systems for investigating one of a kind data that starts from educational settings, and utilizing those procedures to better comprehend students and the settings which they learn in. There were numerous potential circumstances for applying data mining in education, such as; predicting the performance of students in education domain, advancement of student models, making methodologies for instructive help, settling on decisions to growing better learning systems, upgrading the execution of students and lessening the dropout rate of students and so on. There were sure examinations directed in dissecting students' data to foresee the execution in light of data mining approaches utilizing machine learning algorithms. However, a few of them were guiding the students using the recommendations of educators to success in their academic lives. The key objective of this research is to provide educators‘ recommendations to students in higher education through data analysis using machine learning algorithms. In this experiment, the data about more than 3000 students with eight attributes; age, gender, A/L Stream, A/L English Grade, does the student has repeat modules, GPA of Semester1, GPA of Semester 2 and Pass status of year 1 were included into the research sample who registered and were following their first academic year of an Information Technology degree in an institute. Three classification type machine learning algorithms were used to build the predictive model. They were Naïve Bayes algorithm, Decision Tree algorithm and Support Vector Machine algorithm. The accuracy of the models built by each algorithm have been tested against each other to identify the best model and extracted the most influencing/ important attributes in the model to predict the final grade (pass/ fail) in the end of first year of the students. Accordingly, the accuracy measures of Naïve Bayes, Decision tree andSupport Vector Machine were recorded as 74.67%, 74.01% and 74.01% respectively and it was clear that all three algorithms were holding almost same accuracy level. However, the model generated by Naïve Bayes algorithm has been selected since it was outperformed the rest. Then rank features by importance method was used as the feature selection method to identify the most influencing factors of the predictive model. As the result of it, past repeat modules, GPA of Semester1, GPA of Semester 2 were extracted as the most influencing attributes. Furthermore, these attributes were tested using correlation analysis to measure the significance of the relationship with the target attribute. According to this study, the educators will be able to recommend the students to score good marks for assessments of the subjects to obtain a better GPA to semester 1 and semester 2 without failing the modules to successfully complete the first year of the degree course which make more beneficial for educators as well as students to be success.Item A Review of Data Mining Methods for Educational Decision Support(Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Kasthuriarachchi, K.T.S.; Bhatt, C.M.; Liyanage, S.R.Data mining is a computer based information system which is devoted to scanning huge data repositories, generate information and discover knowledge. It attempts to uncover data patterns, organize information of hidden relationships, structure association rules and many more operations that cannot be performed using traditional computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries and education is one domain that can benefit from data mining. Application of data mining in education is living in its spring time and preparing for a hot summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. Educational Data Mining develops and adopts statistical methods, machine- learning and data mining methods to study educational data generated basically by students and educational instructors. The main goal of applying data mining in education is largely to improve learning by enabling data driven decision making for improve current educational practices and learning materials. Educational knowledge discovery, in data mining point of view can be seen as a similar process of applying the general knowledge discovery and data mining process and in experimental point of view, it can be seen as an iterative cycle of hypothesis formation, testing and refinement which not just turn data into knowledge but, also to filter the mined knowledge for decision making. There are many applications in education arena that have been resolved using data mining. There are more research studies have also been conducted under various educational problem categories and also there are a number of frequently used data mining methods use in Educational Data Mining. Various open source and commercial tools are available to apply data mining methods on the educational data. This study focuses on the identification of various educational problem domains where data mining methods can be applied and to study the suitability of the available data mining methods and the tools to perform Educational Data Mining in Sri Lankan Educational Institutes. The knowledge discovered by this review is expected to generate meaningful insight and provide guidance for important decisions made by educators.