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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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    PublicationEmbargo
    Anonymo: Automatic Response and Analysis of Anonymous Caller Complaints
    (IEEE Computer Society, 2022-08-17) Azhar, A; Maweekumbura, S; Gunathilake, R; Maddumaarachchi, T; Karunasena, A; Nadeeshani, M
    Customers are considered as the most valued asset in any business organization. Therefore, attending especially to negative feedback provided by customer in form of complaints is important for an organization to identify areas to improve and retain customers. To quickly respond to customer complaints many business organizations have made hotlines available. Such caller hotlines are dedicated for the purpose of receiving complaints or allowing whistleblowers to reveal information. Due to the fear of being identified, there is a hesitancy in the public to use these hotlines. From the perspective of the organizations when a customer complaint is received it is required to evaluate the validity of the call made to hotlines. Furthermore, when complaints are made, it is required to handle them efficiently by transferring them to relevant departments and prioritize complaints This research proposes 'Anonymo', a system to handle customer complaints in a secure and an efficient manner. To do so, the system analyses the complaints obtained by a caller and provides the end users with the appropriate responses and output, that includes the following: i. Conversational AI agent to respond to callers, ii. Wanted and unwanted call classification, iii. Department-based Complaint classification, iv. Caller Emotion detection and caller complaint analysis while establishing the caller's anonymity. An accuracy of 88.26% was obtained for identification of wanted complaints using SVM algorithm, an accuracy of 85% was obtained for department-based classification using SVM algorithm and 67% accuracy was obtained for emotion analysis by LSTM algorithm
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    PublicationEmbargo
    Machine learning based classification of ripening and decay stages of Mango (Mangifera indica L.) cv. Tom EJC
    (IEEE, 2022-06-21) Hippola, H. M. W. M; WaduMesthri, D. P; Rajakaruna, R. M. T. P; Yasakethu, L; Rajapaksha, M
    om EJC is a variety of Mango grown in tropical countries like Sri Lanka and India which has a very large demand and hence expensive. From the early stage of ripening, until the senescence stage, the process takes around 10–14 days. The fruit shows a yellowish color starting from the very early stage of ripening, throughout the period until it reaches the senescence stage. Unlike the other Mango varieties, it is difficult for a regular customer to determine the stage of ripening of the Tom EJC fruit, by observation. This paper focuses towards developing a vision-based classifier to rapidly identify ripening and decay stages of Tom EJC mango using surface image captures. A dataset of Tom EJC mango images was collated at different maturity levels. A Convolutional Neural Network (CNN) is proposed and tested using over 6000 Tom EJC images. The proposed model is shown to have a 76% testing accuracy in identifying four stages of maturity.
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    PublicationEmbargo
    Use of LIME for Human interpretability in Sinhala document classification
    (IEEE, 2019-03-28)
    With advancement of technology in Sri Lanka, use of Sinhala text usage has grown rapidly over the time where automatic categorization is helpful for efficient content management. As a result, experts tend to use machine learning application to categorize this large volume of data in an efficient and accurate manner. Most of these learning models are operating in a black-box where there is no way to understand how the model has decided which category an instance is assigned. Understanding the reason behind why learning model makes these predictions is very important to trust such models and to provide reasonable justifications in real world application. Intention of this research is to present the work carried on related to document classification model prediction interpretation where a set of text classifiers has been studied with use of SinNG5, freely available Sinhala Document corpus.