Symposia & Conferences
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/10213
Browse
4 results
Search Results
Item Minimization of fraudulent activities in land authentication through Blockchain-based system(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayabodhi, L.W.D.C.; Rajapakse, Chathura; Senanayake, J.M.D.In Sri Lanka, the land administration process has not been digitized, which has led to plenty of conflicts in determining the real ownership of lands and drastically increasing the land transaction processing time, which has resulted in many fraudulent activities concerning land management. The existing system is majorly paper-based and centralized. Administrators who have the powers to alter the system according to their wishes hold a huge threat for information security and is a centralized system that bears the risk of a single point of failure. Hence, transitioning into another system that could mitigate the drawbacks in the current land authentication system has become a vital need and currently, the knowledge regard this area is very much limited. The decentralized nature of the blockchain-based system has the potential of diluting major limitations in the current system. The overall objective of this research is to mitigate fraudulent activities in land authentication systems through the blockchainbased technological approach. A prototype of a smart contractbased model has been created and verified with the involvement of the actual users. Since the smart contract-based land authentication model verifies the land ownership within a short period, the transaction processing time narrows down from a few months to a couple of minutes. The results show signs of considerable improvements in the efficiency and the security of the land authentication process, the users who interacted with the prototype and presented positive comments.Item Prediction of the incubation period of COVID-19 patients using machine learning techniques(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Rathnayake, P.P.P.M.T.D.; Wickramaarachchi, D.N.; Senanayake, J.M.D.Coronavirus Disease 2019 (COVID-19) is a respiratory infection caused by a newly discovered coronavirus. As of September 2020, within about eight months of this infectious disease being identified, more than thirty million cases and 950,000 deaths have been reported within two hundred countries and territories. The incubation period of COVID -19, is the time range between exposure to symptom onset. During this period, affected persons may not show symptoms of being infected but are still capable of transmitting the virus to others. It is very important to identify the incubation period accurately to decide quarantine periods and to develop policies. Based on the current findings, the incubation period ranges between 2 to 14 days. Since there is a range to the incubation period, almost all the suspected cases should undergo a quarantine period of 14 days, which sometimes leads to inefficient allocation of resources in some cases. Although there are many studies on assessing the incubation period, studies regarding the factors affecting the incubation period are limited. This study is primarily aimed at identifying the factors affecting the incubation period and to develop a model to classify the incubation period of suspected cases, using machine learning techniques. Publicly available patient records within different countries were used for the study. The gathered dataset consist of 500 patients records with the age ranging from 5 to 80 years. Out of those records, 285 were male and 215 were female. The dataset includes 205 patients from China, 51 patients from Japan, 36 patients from Malaysia, 24 patients from the United States, 41 patients from South Korea, 31 patients from France, 24 patients from Taiwan, 46 patients from Singapore, and 42 patients from other countries. The results indicate that factors such as patients' age, gender, geographic location, immunocompetent/immunocompromised state, direct/indirect contact with the affected patients, cause deviations to the incubation period. Chisquare test of independence and correlation analysis were used to identify the relationship among variables and to identify the factors which have the strongest relationship with the incubation period. Supervised learning classification algorithms such as Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, and Random Forest were compared in this study. Overall model performance was evaluated using the weighted average of the incubation classes. Random forest was selected as the best algorithm to classify the incubation period since it performed better than other algorithms achieving a 0.78 precision score, 0.84 recall score, and 0.80 F1 score. As the final step, AdaBoost algorithm was used to improve the performance of the Random Forest algorithm.Item 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.Item Analysing mobility patterns of people to determine the best transportation method(Faculty of Science, University of Kelaniya, Sri Lanka, 2016) Senanayake, J.M.D.; Wijayanayake, W.M.J.I.With the technological enhancements related to Internet, Wireless Communication, Big Data Analytics, Sensor-based Data, and Machine Learning; new paradigms are enabled for processing large amount of data which are collected from various sources. In the past decades, both coarse and fine-grained sensor data had been used to perform location-driven activity inference. In recent years, GPS phone and GPS enabled PDA become prevalent in people’s daily lives. With such devices people become more capable than ever of tracing their outdoor mobility and using locationbased applications. Based on the collected data from these GPS enabled devices with the help of IoT related to user mobility lots of research areas are opened. In this research the data related to user locations when users do any outdoor movements is collected using the mobile devices that are connected to the Internet and is mined using data mining techniques and come up with an algorithm to model & analyse those big data to identify mobility pattern, traffic prediction, transportation method satisfaction etc. The data for this research will be collected using a mobile application which has to be installed in smart devices like smart phones, tablet PCs etc. In this application the user has to enter the activity that he or she currently doing and the method of transportation & the users' opinion on the transportation method if he is doing some sort of travelling. The GPS coordinates (longitude & latitude) as GPS trajectories along with the time stamp and the date will be automatically acquired from the users' IoT device. A cloud based storage will be used to store collected data. Since the dataset is going to be a huge one, there can be data which contains outlier values due to the uncertainty of the mobile network coverage and the GPS coverage of the devices. Therefore, these data should be properly cleaned when doing data mining activities otherwise these data will lead to incorrect results such as wrong traffic prediction in certain places if several users are stuck in the same GPS coordinates for a while. Not only that but also when it comes to the user satisfaction, it might lead to generate incorrect outcome if the users in the sample will not enter their satisfaction accurately. This can be avoided by comparing cluster wise users with the consideration of the location and the transportation method. We can get the average opinion of the users and take it as the satisfaction of the transportation method in that cluster. Using the final results of this research the government can also be benefited if we selected the sample users well with mixing all the types of people and by providing necessary information for planning smart cities.