Smart Computing and Systems Engineering (SCSE)

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    Data Mining Approach for Identifying Suitable Sport for Beginners
    (IEEE International Research Conference on Smart computing & Systems Engineering (SCSE) 2019, Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2019) Amarasena, P.T.; Kumara, B. T. G. S.; Jointion, S.
    Anthropometric measurements are generally used to determine and predict achievement in different sports. An athlete’s anthropometric and physical characteristics may perform important preconditions for successful participation in any given sport. Further, anthropometric profiles indicate whether the player would be suitable for the competition at the highest level in a specific sport. Recently, more researches have been carried out on Sport Data mining. In this study, we propose an approach to identify the most suitable sport for beginners using data mining and anthropometric profiles. We propose clustering base approach. We apply a spatial clustering technique called the Spherical Associated Keyword Space which is projected clustering result from a three-dimensional sphere to a two dimensional (2D) spherical surface for 2D visualization. Empirical study of our approach has proved the effectiveness of clustering results
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    A data mining approach for the analysis of undergraduate examination question papers
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Brahmana, A.; Kumara, B.T.G.S.; Liyanage, A.L.C.J.
    Examinations play a major role in the teaching, learning and assessment process. Questions are used to obtain information and assess knowledge and competence of students. Academics who are involved in teaching process in higher education mostly use final examination papers to assess the retention capability and application skills of students. Questions that used to evaluate different cognitive levels of students may be categorized as higher order questions, intermediate order questions and lower order questions. This research work tries to derive a suitable methodology to categorize final examination question papers based on Bloom’s Taxonomy. The analysis was performed on computer science related end semester examination papers in the Department of computing and information systems of Sabaragamuwa University of Sri Lanka. Bloom’s Taxonomy identifies six levels in the cognitive domain. The study was conducted to check whether examination questions comply with the requirements of Bloom’s Taxonomy at various cognitive levels. According to the study the appropriate category of the questions in each examination, the paper was determined. Over 900 questions which obtained from 30 question papers are allocated for the analysis. Natural language processing techniques were used to identify the significant keywords and verbs which are useful in the determination of the suitable cognitive level. A rule based approach was used to determine the level of the question paper in the light of Bloom’s Taxonomy. An effective model which enables to determine the level of examination paper can derive as the final outcome.
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    Analysis of historical accident data to determine accident prone locations and cause of accidents
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Ifthikar, A.; Hettiarachchi, S.
    Road traffic accidents causes great distress and destroy the lives of many individuals. Inspite of different attempts to solve this problem, it still resides as a major cause of death. This paper proposes a system to analyse historical accident data and subsequently identify accident-prone areas and their relevant causes via clustering accident location coordinates. This system, once developed, can be used to warn drivers and also to aid fully autonomous automobiles to take precautions at accident-prone areas.
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    Applicability of crowdsourcing for traffic-less travelling in Sri Lankan context
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Senanayake, J.M.D.; Wijayanayake, J.
    Traffic is one of the most significant problem in Sri Lanka. Valuable time can be saved if there is a proper way to predict the traffic and recommend the best route considering the time factor and the people’s satisfaction on various transportation methods. Therefore, in this research using crowdsourcing together with data mining techniques, data related to user mobility were collected and studied and based on the observations, an algorithm has been developed to overcome the problem. By using developed techniques, the best transportation method can be predicted. Therefore, people can choose what will be the best time slots & transportation methods when planning journeys. The algorithm correctly predict the best traffic-less traveling method for the studied area of each given day & the given time. Throughout this research it has been proven that to determine the best transportation method in Sri Lankan context, data mining concepts together with crowdsourcing can be applied. Based on a thorough analysis by extending the data set of the collection stage, it was shown that this research can be extended to predict the best transportation method with consideration of existing traffic in all the areas.
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    Social media mining for post-disaster management - A case study on Twitter and news
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Banujan, K.; Kumara, B.T.G.S.; Incheon Paik
    A natural disaster is a natural event which can cause damage to both lives and properties. Social media are capable of sharing information on a real-time basis. Post disaster management can be improved to a great extent if we mine the social media properly. After identifying the need and the possibility of solving that through social media, we chose Twitter to mine and News for validating the Twitter Posts. As a first stage, we fetch the Twitter posts and news posts from Twitter API and News API respectively, using predefined keywords relating to the disaster. Those posts were cleaned and the noise was reduced at the second stage. Then in the third stage, we get the disaster type and geolocation of the posts by using Named Entity Recognizer library API. As a final stage, we compared the Twitter datum with news datum to give the rating for the trueness of each Twitter post. Final integrated results show that the 80% of the Twitter posts obtained the rating of “3” and 15% obtained the rating of “2”. We believe that by using our model we can alert the organizations to do their disaster management activities. Our future development consists mainly of two folds. Firstly, we are planning to integrate the other social media to fetch the data, i.e. Instagram, YouTube, etc. Secondly, we are planning to integrate the weather data into the system in order to improve the precision and accuracy for finding the trueness of the disaster and location.
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    Data mining model for identifying high-quality journals
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Jayaneththi, J.K.D.B.G.; Kumara, B.T.G.S.
    The focus in local universities over the last decade, have shifted from teaching at undergraduate and postgraduate levels to conducting research and publishing in reputed local and international journals. Such publications will enhance the reputation on the individual and the university. The last two decades has seen a rapid rise in open access journals. This has led to quality issues and hence chossing journals for publication has become an issue. Most of these journals focus on the monetary aspect and will publish articles that previously may not have been accepted. Some of the issues include design of the study, methodology and the rigor of the analysis. This has great consequences as some of these papers are cited and used as a basis for further studies. Another cause for concern is that, the honest researchers are sometimes duped, into believing that journals are legitimate and may end up by publishing good material in them. In addition, at present, it is very difficult to identify the fake journals from the legitimate ones. Therefore, the objective of the research was to introduce a data mining model which helps the publishers to identify the highest quality and most suitable journals to publish their research findings. The study focused on the journals in the field of Computer Science. Journal Impact Factor, H-index, Scientific Journal Rankings, Eigen factor Score, Article Influence Score and Source Normalized Impact per Paper journal metrics were used for building this data mining model. Journals were clustered into five clusters using K-Means clustering algorithm and the clusters were interpreted as excellent, good, fair, poor and very poor based on the results.