http://drr.vau.ac.lk/handle/123456789/254 2026-09-03T20:58:50Z http://drr.vau.ac.lk/handle/123456789/2214 Impact of Multiple CPU Cores to the Forensic Insights Acquisition From Mobile Devices Using Electromagnetic Side-Channel Analysis Lojenaa, N.; De Zoysa, K.; Sayakkara, A.P. ModernprocessorstendtoincorporatemultipleCPUcores.ThesemultipleCPUcores,running at the same or different clock frequencies, enable the effective distribution of workload and efficiency in energy consumption. Although Electromagnetic Side-Channel Analysis (EM-SCA) has been shown to be an effective and non-invasive method to acquire forensic insights from smartphones and Internet of Things (IoT) devices, the presence of multiple CPU cores has the potential to cause disruptions in this process. This research focuses on analysing the impact of multi-core CPU emissions — specifically the iPhone 13 and iPhone 14 Pro — on the EM-SCA-based forensic insights acquisition procedure. To achieve this, we developed a novel multi-core EM-SCA model specifically for iPhone models by integrating electromagnetic (EM) radiation traces captured from different core clusters of a single device. The developed multi-core model is then subjected to three transfer learning processes: inductive learning, feature extraction, and fine-tuning. The model is tested using individual single-core datasets collected at specific system-clock frequencies of the device. The findings of both martphones indicate that inductive transfer learning consistently yields poor results, ranging between 5% and 20%, regardless of the core cluster. Although feature extraction provides moderate accuracy for certain datasets — around 50% to 70% for the iPhone 13 and 20% to 92% for the iPhone 14 Pro — it is the fine-tuning process that proves to be the most effective. Fine-tuning supports a wide range of datasets across different system-clock frequencies, achieving classification accuracy as high as 99%. This highlights fine-tuning as the most reliable transfer learning technique for multi-core forensic nvestigations. We also tested for catastrophic forgetting to evaluate the robustness of the multi-core model when using single-core datasets from the same devices. The results demonstrate that the accuracy of the multi-core model remains unchanged, even after the transfer learning process across various datasets. 2025-01-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/2206 hnhcmj Lojenaa, N.; De Zoysa, K.; Sayakkara, fdh 2025-01-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/2177 An Ensemble Method for Fatigue Detection based on Multiple Facial Expressions Venuja, N.; Nadarajah, S.; Thavayoganathan, A.; Senthooran, V. People who spend a lot of time staring at screens need to be aware of their own level of fatigue because it can lower productivity and increase the risk of accidents. In this research work, we suggest a novel method for detecting fatigue based on a variety of facial expressions. We collected 12,185 facial images of people using digital screens, which contains 6108 normal face images and 6077 stress face images. In this framework, we used different multiple facial expressions techniques and Artificial Neural Network (ANN) to label neutral faces as normal, while sad and disgusted faces were labeled as stressed. We used this dataset to train a deep learning model to classify facial images as normal or stressful. Our model achieved highest accuracy rates of 88% for testing and showed good performance on a trained dataset. We also validated the accuracy of our approach by comparing the predicted stress/non-stress labels with ground truth labels obtained through self-report. The results of this study demonstrate the potential of using multiple facial expressions for fatigue detection in people who are using digital screens. This approach could be used in the future to develop real-time fatigue detection systems that could alert people when they are showing signs of stress or fatigue and help them take a break or adjust their work schedule. 2023-01-01T00:00:00Z http://drr.vau.ac.lk/handle/123456789/2176 Banana Classification using Deep Learning Techniques in the Sri Lankan Context Kumara, D.; Sonali, H.G.M.; Lakmini, W.; Lakshan, R.M.M.; Mayumika, P.; Athukorala, A.; Fernando, W.S.S.D.; Nadarajah, S.; Suthaharan, S. S. Bananas are one of the most widely consumed fruits worldwide, and they come in various shapes, sizes, and colors. Bananas are one of the few tropical crops that have not been bred successfully, and all presently cultivated varieties are natural selections. Traditionally, human experts perform the classification based on visual inspection, which can be subjective and time-consuming. Therefore, developing an automated system that can accurately classify bananas based on visual features would be highly beneficial. Feature engineering became easier after the Convolutional Neural Network (CNN) was developed. Five distinct varieties of bananas were categorized in this proposed work using the CNN model. To improve classification performance, many training images are needed when using the CNN model for classification. Both the original and enhanced images were used to train and evaluate the suggested CNN model. During training, the CNN model achieved an overall validation accuracy of 87%. 2025-01-01T00:00:00Z