Recent Submissions
Integrating AI and IoT for Smart Home Energy Management
(Institute of Electrical and Electronics Engineers Inc., 2025-12-08) Siriwardhana S.M.D.S; Pivithuru N.H.A.S; Welikalage R.Y.W; Balasuriya B.L.I.S; Pandithage, D; Iddamalgoda, A; Wijayasekara, S
This research introduces a smart IoT (Internet of Things) system for enhancing household energy efficiency by integrating real-time electricity monitoring, predictive analytics, and AI (Artificial Intelligence)-driven personalized guidance. Traditional home energy management often lacks a level of detail, leading to uninformed usage patterns and increased energy waste. The developed system consists of a developed IoT-enabled monitoring unit, time-series forecasting, and generative AI to deliver proactive and intelligent electricity management experience. The IoT-enabled unit monitors individual appliance consumption and implements threshold-based control mechanisms to prevent overuse. The machine learning algorithm analyzes historical usage data to forecast future electricity demand, supporting preemptive consumption planning. The Large Language Model (LLM) generates personalized energy-saving recommendations based on the overall electricity usage data collected from the system. Power monitoring portal enhances user interaction through real-time energy movements and feedback mechanisms. This study contributes to sustainable living by promoting energy-conscious behavior, reducing household energy expenditure, and aligning domestic energy usage through a data-driven solution.
Enhancing Trust in Brain-Computer Interfaces: Interpretable Deep Learning for EEG Signal Classification
(Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bakmeedeniya A.H.M.T.C; Chamini, L; Ranaweera, K
Brain signal is one of the methods in healthcare to identify brain activities. Mathematical modeling to machine learning and modern deep learning architectures are used to analyze these signals. These methods have been tested with EEG signals to capture meaningful insights and uncover patterns. Deep learning often outperforms conventional machine learning. But the black box nature of these signals has become a concern, especially in the healthcare sector. Hence, this study focused on explaining the Deep Learning architecture by using methods such as Permutation Feature Importance, Saliency Maps, and Gradient-weighted Class Activation Mapping. To test this, a model for sleep-stage classification using Electroencephalography (EEG) has been developed, as EEG is easier to interpret. The model was created by fusing different types of deep learning architecture. To capture sleep-related frequency activity, Power Spectral Density (PSD) features were extracted. Further, jitter ing was applied. Accuracy and F1-score used to measure the performance of the model. An average accuracy of 0.85 and the weighted F1-score of 0.87 were given on the test set. The results display that combining deep learning with explanation methods and careful info handling can develop EEG-based systems extra transparent and trustworthy.
Evaluation of Machine Learning Models in Student Academic Performance Prediction
(Institute of Electrical and Electronics Engineers Inc., 2025-02-19) Sandeepa A.G.R; Mohottala, S
This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach.
Comparative Analysis of Transformer and CNN Architectures for OCT-Based Detection of Circular Leaf Spot Disease in Diospyros kaki
(Institute of Electrical and Electronics Engineers Inc., 2025) Bandara, L.M; Seneviratne, O; Kalupahana, D; Silva, B.N; Wijenayake, U; Wijesinghe, R.E
Circular Leaf Spot (CLS) disease caused by Mycosphaerella nawae poses a significant threat to the cultivation of Diospyros kaki (persimmon). Early and accurate identification is essential to reduce yield loss, yet conventional diagnostic methods are often invasive, subjective, or time-consuming. Optical Coherence Tomography (OCT) offers a non-destructive imaging technique capable of revealing detailed leaf structures, allowing the use of deep learning models for automated disease classification. This study investigates the performance of transformer-based architectures, including ViT, Swin, CoaT, ConViT, LeViT, and MobileViT, for the classification of CLS disease OCT images. The same dataset as in previous CNN-based research was used, applying identical preprocessing techniques for fair comparison. Research was conducted using a 5-fold cross-validation, and transformer models were compared with traditional CNNs from the previous research, such as DenseNet121, VGG16, and Xception. The results show that Transformer architectures, particularly the hybrid CoaT model, achieve superior overall accuracy (87.6%) and micro-averaged AUC (0.974), surpassing the best CNN-based models. The findings highlight the strong potential of transformer architectures in the diagnosis of plant diseases using OCT, especially for early-stage detection. This research underscores the suitability of advanced vision models for high-resolution non-destructive classification tasks in agriculture.
AI Dependency in Sri Lankan Higher Education: Effects of Chatbot Overuse on Student Learning and Problem-Solving
(Institute of Electrical and Electronics Engineers Inc., 2025) Balalojanan, V; Vigneswaran, V; Sukirthan, T; Thayalan, N; Thiruthanigesan, K; Samarakoon, U; Ragel, R. G
Artificial Intelligence (AI) chatbots have rapidly transformed computer science education in Sri Lanka by offering instant coding assistance and conceptual explanations. While these tools enhance learning efficiency, their widespread adoption raises concerns about over-reliance and its impact on students' critical thinking and independent problem-solving abilities. This study investigates AI dependency among 80 computer science students from three Sri Lankan institutions during an open-book programming assignment conducted in May 2025. Students were allowed unrestricted access to AI chatbots and online resources, and their behavior was analyzed using screen monitoring, post-task interviews, and performance assessments. Results revealed that 88% of participants used AI chatbots even for basic tasks, indicating a significant trend of diminished self-reliance. Chatbot-reliant students demonstrated higher accuracy in syntax-based tasks but performed worse in algorithmic design and conceptual articulation. This study introduces the AI Dependency Index (AIDx), a novel metric to quantify the level of reliance across different task types. Findings show that 62% of students fall into the high dependency category, particularly among first- and second-year undergraduates. These insights highlight the need for pedagogical strategies that promote responsible AI usage rather than replace critical thinking and problem-solving skills in higher education.
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The collection comprises the research output of SLIIT staff and postgraduate research students, including research publications, conference and symposium papers, books, book chapters, theses, and other scholarly materials. Access to full texts may be restricted depending on the access and licensing terms.