Research Publications
Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4194
This main community comprises five sub-communities, each representing the academic contribution made by SLIIT-affiliated personnel.
Browse
2 results
Search Results
Publication Embargo Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kariyawasam, S.; Lakshan, A.; Liyanage, A.; Gimhana, K.; Piyawardana, V.; Mallawarachchi, Y.Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.Publication Embargo Robust Speech Analysis Framework Using CNN(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) RUPASINGHE, L.; Alahendra, A.M.A.T.N.; Ranathunge, R. A. D. O.; Perera, P.S.D.; Kulathunge, Y. N.Voice is the main component of human communication and learning about and recognizing somebody's behavior. By listening to people's voices, humans can recognize a person's identity, speech fluency, accent, emotions, and stress level. It is difficult to understand what the speaker is saying when Speech fluency is poor. It varies from person to person. With the help of specific information in a person's voice, we can recognize human emotion, stress level, and identity. Every person has a unique vocal feature that facilitates recognizing them from others. This proposed framework is developed to identify a person's identity, emotions, fluency in speaking, and stress level of the speaker using their voice. The proposed framework is developed using machine learning techniques, and deep learning algorithms are highlighted in this study. Convolution Neural Network (CNN) is the used deep learning algorithm, and Fast Fourier transform (FFT), (MFCC), and Random Forest are machine learning techniques. The proposed AI-based framework provides comparatively accurate results in a user-friendly way.