Department of Information Technology

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Start date: 2024End date: 2024

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Now showing 1 - 4 of 4
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    PublicationOpen Access
    Dense Convolutional Neural Network-Based Deep Learning Pipeline for Pre-Identification of Circular Leaf Spot Disease of Diospyros kaki Leaves Using Optical Coherence Tomography
    (Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Kalupahana, D; Kahatapitiya, N.S; Silva, B.N; Kim, J; Jeon, M; Wijenayake, U; Wijesinghe, R. E
    Circular leaf spot (CLS) disease poses a significant threat to persimmon cultivation, leading to substantial harvest reductions. Existing visual and destructive inspection methods suffer from subjectivity, limited accuracy, and considerable time consumption. This study presents an automated pre-identification method of the disease through a deep learning (DL) based pipeline integrated with optical coherence tomography (OCT), thereby addressing the highlighted issues with the existing methods. The investigation yielded promising outcomes by employing transfer learning with pre-trained DL models, specifically DenseNet-121 and VGG-16. The DenseNet-121 model excels in differentiating among three stages of CLS disease (healthy (H), apparently healthy (or healthy-infected (HI)), and infected (I)). The model achieved precision values of 0.7823 for class-H, 0.9005 for class-HI, and 0.7027 for class-I, supported by recall values of 0.8953 for class-HI and 0.8387 for class-I. Moreover, the performance of CLS detection was enhanced by a supplemental quality inspection model utilizing VGG-16, which attained an accuracy of 98.99% in discriminating between low-detail and high-detail images. Moreover, this study employed a combination of LAMP and A-scan for the dataset labeling process, significantly enhancing the accuracy of the models. Overall, this study underscores the potential of DL techniques integrated with OCT to enhance disease identification processes in agricultural settings, particularly in persimmon cultivation, by offering efficient and objective pre-identification of CLS and enabling early intervention and management strategies. © 2024 by the authors.
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    PublicationOpen Access
    Real-time Multi-spectral Iris Extraction in Diversified Eye Images Utilizing Convolutional Neural Networks
    (IEEE, 2024-07-03) Rathnayake, R; Madhushan, N; Jeeva, A; Darshani, D; Pathirana, I; Ghosh, S; Subasinghe, A; Silva, B N; Wijenayake, U
    Iris extraction has gained prominence due to its application versatility across many domains. However, achieving real-time iris extraction poses challenges due to several factors. Learning-based algorithms outperform non-learning-based iris extraction methods, delivering superior accuracy and performance. In response, this article proposes a Convolutional Neural Networks (CNN)-based, accurate direct iris extraction mechanism for a broad spectrum of eye images. The innovation of our approach lies in its proficiency with varied image types, including those where the iris is partially obscured by the eyelid. We enhance the method’s reliability by introducing a modified Circular Hough Transform (CHT). Extensive testing demonstrates our method’s excellent real-time performance across diverse image types, even under challenging conditions. These findings underscore the proposed method’s potential as a cost-effective and computationally efficient solution for real-time iris extraction in varied application domains.
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    PublicationEmbargo
    IoT-Based Solution for Fish Disease Detection and Controlling a Fish Tank Through a Mobile Application
    (IEEE, 2024-04-05) Bodaragama, B.D.T; Miyurangana, E.H.A.D.M; Jayakod, Y.T.W.S.L; Vipulasiri, D.M.H.D; Rajapaksha, U. U. S; Krishara, J
    This research project seeks to enhance fish tank management and improve the well-being of aquatic life by leveraging modern technological solutions. It focuses on four key areas: monitoring water quality, detecting fish diseases, preventing algae growth, and developing an automatic fish feeder with remote control capabilities. The project’s first goal is to establish a comprehensive water quality monitoring and control system that predicts future water conditions, continuously assesses key parameters, and provides real-time data to users for proactive interventions. Additionally, the research project aims to develop an image-processing-based mobile application for early detection of fish diseases, eliminating the need for manual inspection and improving overall fish health management. The project also involves the creation of a mobile app to predict and prevent algae growth by analyzing factors like lighting, nutrient levels, and water flow, providing personalized recommendations for algae control. Lastly, an automatic fish feeder with remote control capabilities will be designed, allowing fish owners to schedule and adjust feeding times and portion sizes through a mobile app. This innovative approach ensures fish receive consistent and appropriate nutrition even when owners are away from home.
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    PublicationEmbargo
    Corrigendum to “Meta-heuristic optimization based cost efficient demand-side management for sustainable smart communities” [Energy Build. (2024) 113599] (Energy & Buildings (2024) 303, (S0378778823008290),
    (Elsevier Ltd, 2024-04-15) Silva, B.N; Khan, M; Wijesinghe, R.E; Wijenayake, U
    The monetary value of grid electricity is inflating significantly due to the staggeringly broadening gap between electricity demand and supply, which arise from the unceasing growth of consumption demands. Although heuristic optimization based demand side management has its merits, incorporating Ant Colony Optimization remains disputable due to its tendency to converge at a local optimum. Therefore, this work presents a hybridized algorithm of Ant Colony Optimization and Genetic Algorithm, which alleviates the drawbacks of Ant Colony Optimization through Genetic Algorithm. The proposed work promotes sustainable energy utilization simultaneously with demand-side optimization. The performance of the proposed algorithm is compared with no scheduling instance, Ant Colony Optimization based energy management controller, and mutated Ant Colony Optimization based appliance scheduling. The proposed algorithm successfully curtails 35.4% from community peak load demand and achieves 33.67% cumulative cost saving for the community. In other words, comparative analysis confirms the supremacy of the proposed algorithm in terms of minimizing peak load, total cost, peak-to-average ratio, and waiting time, while providing prevailing insights about proposed algorithm as a sustainable solution approach.