Browsing by Author "Abeygunawardhana, P."

Now showing 1 - 2 of 2

Embargo
Code Vulnerability Identification and Code Improvement using Advanced Machine Learning
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Ruggahakotuwa, L.; Rupasinghe, L.; Abeygunawardhana, P.
Cyber-attacks are fairly mundane. The misconfigurations of the source code can result in security vulnerabilities that potentially encourage the attackers to exploit them and compromise the system. This paper aims to discover various mechanisms of automating the detection and correction of vulnerabilities in source code. Usage of static and dynamic analysis, various machine learning, deep learning, and neural network techniques will enhance the automation of detecting and correcting processes. This paper systematically presents the various methods and research efforts of detecting vulnerabilities in the source code, starting with what is a software vulnerability and what kind of exploitation, existing vulnerability detection methods, correction methods and efforts of best researches in the world relevant to the research area. A plugin will be developed which is capable of intelligently and efficiently detecting the vulnerable source code segment and correcting the source code accurately in the development stage.
Embargo
Hand Rehabilitation Using Robot-Assisted Physiotherapy
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madhushan, I.H.D.; Charnara, E.B.K.; De Zoysa, A.T.J.; Upeka, G.S.; Abhayasinghe, N.; Abeygunawardhana, P.
Robotics technology in the modern world is currently being implemented in medical fields to improve the quality of care and patient outcomes. In the proposed system, the robotics technology is used for physiotherapy. In the existing physiotherapy robot devices, there is no feature that provides exercise for every joint of the fingers and the wrist. Therefore, in this system, we used forward kinematics technologies to address each joint of the fingers and wrist thatcan access by the physiotherapist. We have designed the robot hand using the solid work and implemented 3D model then assembled system was tested again using different scenarios. Most existing robotic systems provide finger and wrist exercises separately, but our system can provide all exercises simultaneously. In here, we can predict the next exercises that are given for the patient and the progress of the rehabilitation of the patient. For the prediction, we developed the models using the FB prophet algorithm. When using this device, the patient's hand exercises are monitored in real-time and the physiotherapist can see the angles of the hand movement while controlling the robot device. To control this robot device, we used a mobile application.