https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4147 2026-03-26T15:13:24Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4364 Title: Remote management framework for IoT devices utilizing Blockchain Authors: Deshapriya, R.S Abstract: In recent times, the Internet of Things has become a major driving force in the development of a connected world. Potentially millions of devices featuring sensors and actuators would form a complete, cohesive network of things that would generate billions of bits of data. There are many issues related to the management and security of IoT devices, posing risks and difficulties in authentication, and remote management of devices belonging to a single owner. Blockchain has become popular with the rise of cryptocurrencies as a distributed, peer-to-peer ledger. With its features of verification, security and immutability, it has also become highly useful in areas other than cryptocurrency as a secure storage mechanism which is immune to attacks. As such, the possibility of implementing private blockchains which fulfill use cases related to secure storage, nonrepudiation and maintenance of reliable records has been explored. This research was targeted at bringing the security of blockchain as a viable means to manage and maintain details of IoT devices including their credentials, as well as the enablement of remote management of multiple devices belonging to a single owner. The implementation focuses on two main flows: the creation and management of devices and the storage of messages on blockchain. There are three main components of the developed system, the backend application which consists of a generic MQTT message broker and a RESTful web service API, the blockchain component which is implemented using Hyperledger Fabric, and an AngularJS web application which provides a standard interface for the user. The backend application impelements chaincode to interface with the blockchain to form smart contracts on the creation of device details and device message details on the ledger in the form of transactions. Evaluation of the system was carried out through the implementation of Behaviour-Driven Development-based testing methodologies, and have conclusively proven that blockchain can be utilized as a secure storage mechanism for maintaining IoT device details. 2021-08-03T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4363 Title: Free hand interaction therapy for Parkinson’s disease using leap motion Authors: Wijethunga, I.A. Abstract: This project is based on developing a system for Parkinson’s disease patients to give them free hand interaction therapies using the leap motion controller. Increasing demand for health applications for the more helpless people like Parkinson’s patients has led to develop an important system of this nature. Primary signs of Parkinson’s disease include tremor of hands, arms, legs, jaw and face. Physical therapies and exercise programs are recommended in people with Parkinson's disease to control the disease and to give them a better life. Rehabilitation methods using serious game without any device attached to the body is proposed for Parkinson's patients with limited mobility in order to restore their ability to independently perform the basic activities of daily living or to recover a lost or diminished function by performing exercises on a regular basis. Leap Motion is an optical sensor specially designed for acquisition of 3D positions and orientations of hands and fingers. The main purpose of the sensor was to extend current input devices with 3D control for VR environments. The Leap Motion Controller is capable of detecting and tracking hands, fingers, and tools in its field of view. The device captures data one frame at a time and the rate at which this occurs, or frame-rate, can vary based on the lighting conditions, but typically occurs at approximately 100 frames per second (fps). The device is capable of recording the three dimensional fingertip positions, which is done in millimeters relative to the device’s origin. The Leap Motion Controller follows a righthanded Cartesian coordinate system, with the origin centered at the top and middle of the Leap Motion Controller In this project, the Leap Motion sensor is used to give hand and finger therapies for Parkinson's patients using serious game application developed using three js. The scope of this project is to track the motion of the fingers of the hand and to exercise the fingers of the hand. Further to track the progress of the patient by giving an analysis of the output. Each patient should use a separate login and it will help to keep the records and analyze to monitor the progression of the Parkinson’s patient. 2021-08-03T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4362 Title: Bug Prediction Model Using Code Smells Authors: Ubayawardana, D.L.G.M. Abstract: The term ‘Code Smells’ was first coined in the book by Folwer [1]. A code smell is a surface indication that usually corresponds to a deeper problem in the system. These poor design choices have the potential to cause an error or failure in a computer program. The objective of this study is to use ‘Code Smells’ as a candidate metric to build a bug prediction model. Bug prediction models are often very useful. When bugs of a software can be predicted, the quality assurance teams can identify error prone components in advance and effectively allocate more resources to validate those components thoroughly. Bug prediction is an active research area in the community and various bug prediction models have been proposed using different metrics such as source code, process, network and code smells etc. In this study we have built a bug prediction model using both source code metrics and code smell based metrics proposed in the literature. We cannot use code smell based metrics only as a single predictor to predict buggy components of a software. There can be files in the source code which do not contain code smells. Therefore we will not be able to predict bug proneness of such components if we use code smell based metrics only. Therefore we initially built a basic model using source code metrics and then enhanced the basic model by using code smell based metrics. We used Naive Bayes, Random Forest and Logistic Regression as our candidate algorithms to build the model. We have trained our model against multiple versions of thirteen different Java based open source projects. The trained model was used to predict bugs in a particular version of a project and a particular project. We have also trained our model among different projects and trained model was used to predict bugs in an entirely different project. We were able to demonstrate in this study, that code smell based metrics can significantly improve the accuracy of a bug prediction model when integrated with source code metrics. Random Forest algorithm based model showed higher accuracy within a version, within a project and among projects when compared to other algorithms. 2021-08-03T00:00:00Z https://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4252 Title: Anomalies Detection System for Stock Market Authors: Sumanaweera, U. A. U. Abstract: Stock market is the place to trade company stocks among market participants at an agreed price. Investors have to have a good knowledge about fluctuations of parameters of market and there is a possibility of novel investors get in trouble due to lack of awareness of fluctuations in market. Rule based patterns are widely used in practice in the existing manipulation detection methods. However manipulators constantly change their strategies and they find new ways to manipulate markets. Therefore rule based or static detection methods fail to detect these new evolving manipulation attempts. The main project objective is to research and implement a method to detect these evolving stock abusing patterns. Artificial immune system theories based on stock manipulation detection system is implemented as an advanced detection mechanism. This project approaches the problem by analyzing daily price,volume values of transactions along with the behavior of customer. Natural immune system techniques are used such as danger theory, negative selection, clonal selection and immune network theory which have approach to identify unknown signatures of anomalies in stock market transactions. Novelty of this research is having a learning phase to train the system along with the usage of Artificial Immune System theories. Previous work does not have a learning phase based on AIS theories in detecting stock market anomalies. Unlike simple statical evaluations, system is capable of identifying stock market anomalies in a better rate due to supervised learning techniques. System was tested based on real transaction data collected from Saudi Stock Exchange. First stage is supervised learning for price, volume anomaly detection and second stage is optimize results using customer behavior. More than 30,000 real transactions are used for testing in various models. Degree of anomaly of a transactions is marked based on conclusions of three domain experts and system output is evaluated based on them. Best System output was 96% of Precision, 100% of recall, 75% of Accuracy and 88% of F1 score. All the percentages are calculated with respect to conclusions of domain experts. 2021-07-27T00:00:00Z