Science
Permanent URI for this communityhttp://repository.kln.ac.lk/handle/123456789/1
Browse
4122 results
Search Results
Item Hybrid Deep Learning for Portable Weather Forecasting: Real-Time Predictions Using CNN- LSTM-Transformer Models(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Perera, K. P. V. D. U. P.; Fernando, K. J. P.; Lakmak, H. K. I. S.; Nirmal, W. C.Accurate weather forecasting is critical for agriculture, disaster management, and transportation sectors. However, traditional forecasting systems often require extensive computational resources and centralized infrastructure, limiting their accessibility in remote and underserved regions. This study introduces a Portable Weather Forecasting Station that combines real-time sensor-based data acquisition with advanced deep learning techniques. The station integrates sensors to measure parameters such as temperature, humidity, wind speed, and solar radiation, processed by a Raspberry Pi for localized predictions. A hybrid deep learning model comprising Convolutional Neural Networks (CNNs), Long Short-Term Memory Networks (LSTMs), and Transformers is developed to capture both short-term patterns and long-term dependencies in the data. The system's performance is enhanced through hyperparameter optimization using Optuna, with metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Skill Score used for evaluation. The hybrid model demonstrated superior accuracy compared to standalone architectures. Designed for autonomy with battery backup, the station operates independently of external infrastructure, making it ideal for deployment in resource-constrained environments. This research offers an innovative approach to localized, real-time weather forecasting, addressing the limitations of traditional methods while ensuring accessibility and scalability.Item Deep Learning based Screen Display Fault Detection System for Vehicle Infotainment Applications(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Ramesh, B.; Dheeba, J.; Raja Singh, R.Modern vehicles are integrated with in-vehicle infotainment systems and are subject to software faults. This paper explores the application of deep learning algorithms to identify visual defects in infotainment systems and automatically document the issues. A real-time capable framework is deployed, delivering immediate feedback on detected defects. The proposed system performs thorough analysis, automatically summarizes detected defects, and generates detailed reports, significantly reducing manual documentation effort and supporting faster decision-making. The performance of the developed models is evaluated using Convolutional Neural Networks (CNN) and Artificial Neural Network (ANN) classifiers. Experimental results demonstrate the superior performance of the CNN model, achieving a training accuracy of 82.21% with an F1 score of 0.85, and a testing accuracy of 80.51% with an F1 score of 0.811. In comparison, the ANN model achieves a training accuracy of 70.18% with an F1 score of 0.7314, and a testing accuracy of 69.32% with an F1 score of 0.705.Item FITSTYLE: An Application Revolutionizing Online Shopping by Enhancing the Virtual Try-On Experience(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Divyanjalee, G.; Ilmini, K.; Uwanthika, I.In the last decade, the fashion industry has been significantly influenced by the rise of e-commerce and mobile commerce. As online consumers, we often face the challenge of selecting the right clothing size without the ability to physically try on garments, leading to frustration, uncertainty, and high return rates. These issues negatively impact customer satisfaction and online sales productivity, highlighting the need for advanced virtual shopping solutions. To address this problem, FITSTYLE proposes a sophisticated virtual try-on tool based on Generative Adversarial Networks (GANs) to simulate how clothes fit a consumer’s body. The FITSTYLE system tackles challenges in online clothing shopping by utilizing advanced technologies such as ResNet101 for image preprocessing, OpenPose for pose estimation, image segmentation to isolate users from backgrounds, and garment deformation algorithms for accurate fitting. Designed for ease of use and realistic visualization, it enhances customer satisfaction and reduces return rates. The research employed a mixed quantitative and qualitative methodology, collecting data from online shoppers to understand user needs and preferences. Based on these insights, the system was implemented using the most suitable technologies, with initial results showing improved customer decision-making and engagement. The system enhances satisfaction, reduces return rates, and boosts productivity in online sales, while also paving the way for future advancements in virtual try-on technologies and the broader fashion e-commerce landscape.Item Computer Vision Technique for Quality Grading of Cardamom Spice(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Ahnaf, M. R. A.; Nizzad, A. R. M.; Mafaza, N. F.In the agriculture industry, spices such as cardamom must be graded and their quality evaluated to maintain product standards and increase market value. Traditionally, cardamoms are graded manually, but this process is time-consuming, inconsistent, and prone to human error due to fatigue and subjectivity. This study proposes computer vision-based techniques to detect and categorize cardamom into four grades—premium, standard, substandard, and defective—based on size, shape, and texture. Researchers prepared 880 images representing these categories under uniform lighting conditions. Preprocessing techniques, including resizing, noise addition, and augmentation, were applied to enhance the dataset for robust model training. YOLOv8 was utilized for the detection and grading of cardamoms due to its capability for real-time object detection and classification. The proposed method effectively differentiated between quality grades with an accuracy of 92%, demonstrating reliability and efficiency. A user-friendly interface was developed using the Streamlit library, allowing users to upload images and obtain grading results instantly. This system offers a practical and scalable solution for improving quality control processes in the agricultural sector. Future work aims to incorporate larger datasets, texture analysis, and integration with automated machinery to further enhance its applicability in industrial settings. The proposed solution can also be generalized to other use cases in the agricultural industry.Item AI-Driven Approach for Measuring and Classifying Diabetic Retinopathy Severity(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Shaik, A.; Little Flower, K.; Veerabhadraiah, S.; Nandini, A.; Sai Kiran, C.; Yashwanth Goud, K.Diabetic Retinopathy is one of the most common complications affecting people with diabetes and is a leading cause of blindness worldwide. Advanced technological methods through image analysis and artificial neural networks have become major assets in addressing the escalating problem of DR. This paper discusses various approaches to implementing automation in DR detection, focusing on image acquisition and preprocessing, feature extraction, and classification using AI. We review the utility of these systems in terms of cost, benefits, and performance, as well as the challenges related to data quality, model interpretability, and regulatory requirements. The study demonstrates that automation holds the key to delivering higher patient-impact opportunities in clinical applications such as screening programs and telemedicine. Finally, we discuss directions for future research and community implementation, emphasizing the importance of highly controlled, naturalistic designs and the translation of research findings into clinical practice.Item Optimizing Economic Predictors for Digital Growth in Hosting MSEs with Neural Boosted and KNN Models(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Manoli, N.; Takahashi, M.; Matsuura, Y.Mega sporting events (MSEs) have emerged as an effective instrument for driving economic growth in host countries. Digital capabilities have also influenced national development. This study addresses how economic indicators and digital advancement have a parallel transition to the sustainable growth of host nations. This study employs various machine learning techniques and combines information from selected economic indicators and digital capabilities. Therefore, two perspectives of validation are merged to produce economic indicators on digital advancement: (i) validation as the most critical predictor of digital advancement prediction and (ii) validation as the most effective machine learning model for prediction. A quantitative variance in the decision trees as a contribution value is an empirical illustration of the significant predictor, and performance metrics such as R², RASE, and SSE are used to explain the selected machine learning model. The period of change spans five years, from 2016 to 2020. The digital evolution of host countries is significantly influenced by three specific indicators: exports of goods and services, charges for using intellectual property payments, and exports as a percentage of GDP. Riding along with the K-Nearest Neighbours model, which has the best performance, especially in host nations, results in improved forecast accuracy. According to this study, a country's economic status can be significantly improved, and digital transformation can be accelerated by hosting MSEs. The results offer empirical insights that policymakers can use to strategically fund digital infrastructure and major sporting events, promoting technological and economic growth.Item AI-Driven Fault-Tolerant ETL Pipelines for Enhanced Data Integration and Quality(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Kaushalya, C.; Perera, S. K.; Thelijjagoda, S.The reliability and fault tolerance of ETL (Extract, Transform, Load) pipelines are crucial for ensuring data integrity in corporate environments. Traditional ETL systems often rely on manual interventions to resolve data inconsistencies, leading to inefficiencies and increased operational costs. This study introduces an AI-driven framework to enhance ETL fault tolerance by automating data cleaning, standardization, and integration. Leveraging machine learning models, the framework minimizes human intervention, improves data quality, and scales across diverse data formats. Using real-world datasets, the proposed solution demonstrates its ability to enhance operational efficiency and reduce errors in corporate data pipelines. The findings highlight the framework's ability to strengthen fault tolerance, ensure data quality, and provide organizations with a competitive edge in managing complex data ecosystems.Item Federated SCFGWO for Secure and High-Accuracy Brain Tumor Detection Across Multi-Center MRI(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Alphonsa, J.; Kumari, V. S.Privacy preservation and security concerns in medical imaging have led to the implementation of federated learning (FL) as an alternative to centralized machine learning approaches. This study introduces a novel Sine and Cosine Fitness Grey Wolf Optimization (SCFGWO) algorithm integrated into an FL framework to enhance the accuracy and efficiency of brain tumor detection across multi-center MRI datasets. SCFGWO addresses traditional Grey Wolf Optimization (GWO) limitations, such as early convergence and local optima, by incorporating adaptive sine–cosine strategies for improved global exploration and local exploitation. Compared to existing methods, the proposed SCFGWO-based framework achieves superior accuracy (97.2%), Dice Similarity Coefficient (0.94), and Intersection over Union (0.90) while maintaining computational efficiency (0.015s per slice). Innovative privacy-preserving methods, including differential privacy and homomorphic encryption, ensure data security across the dataset. These contributions establish SCFGWO as a robust optimization tool for federated learning applications in medical imaging, offering improved performance and enhanced data security.Item Optimizing Predictive Maintenance in Industrial Machinery with Data Smoothing and Machine Learning(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Nirmal, W. C.; Lakmak, H. K. I. S.; Fernando, K. J. P.Unplanned machinery breakdowns result in significant financial losses, making predictive maintenance essential in industrial operations. This study focuses on fault detection in a single-phase induction motor used in jewelry manufacturing by analyzing vibration data under normal and abnormal conditions. Data was collected using an accelerometer, and three preprocessing techniques—Kalman Filter, Moving Average Filter, and Fast Fourier Transform (FFT)—were applied to reduce noise and improve data quality. Six supervised classification algorithms were evaluated on both raw and preprocessed data. Results demonstrate that preprocessing significantly enhances model accuracy, with the Moving Average Filter enabling Random Forest to achieve the highest accuracy of 99.77%. Kalman Filter also improved model performance, while FFT was particularly beneficial for Logistic Regression. This research highlights the importance of combining machine learning with effective preprocessing to optimize predictive maintenance strategies, reduce downtime, and minimize maintenance costs in industrial environments. By demonstrating the practical implications of these methods, this study contributes to the advancement of reliable fault detection systems for critical machinery components.Item A Unified Pipeline for Improving Financial Aid Eligibility Predictions in Deep Learning Models(Department of Industrial Management, Faculty of Science, University of Kelaniya., 2025) Kishanthan, S.; Hevapathige, A.This paper systematically evaluates the performance of widely used deep learning architectures for financial aid prediction, identifying critical bottlenecks that hinder optimal performance. To address these limitations, we further propose a novel pipeline that enhances deep learning models by incorporating three key components: text vectorization, data equalization, and adaptive feature recalibration. This pipeline improves the models’ representational power and predictive accuracy, offering seamless integration with existing architectures. It significantly boosts performance in predicting financial aid eligibility, providing up to a 145% increase in balanced accuracy and a 45% increase in F1 score.