Browsing by Author "Kumari, N"

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    Arogya -An Intelligent Ayurvedic Herb Management Platform
    (IEEE, 2020-11-04) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, I
    Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.
  • Thumbnail Image
    PublicationEmbargo
    Arogya-An Intelligent Ayurvedic Herb Management Platform
    (IEEE, 2020-10-15) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, I
    Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.
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    PublicationOpen Access
    An investigation of the usage of capital budgeting techniques by small and medium enterprises
    (Springer Nature, 2020-09-08) Alles, L; Jayathilaka, R; Kumari, N; Malalathunga, T; Obeyesekera, H; Sharmila, S
    This paper examines the extent or usage of capital budgeting techniques in Small and Medium Enterprises (SMEs) and the efect of non-fnancial factors on the choice of capital budgeting techniques adopted by SMEs. A qualitative research method of content analysis as well as an econometric quantitative analysis have been employed for this study. The study has been conducted in several divisional councils within the district of Colombo, Sri Lanka. Stratifed random sampling has been used to collect a sample of SMEs from each divisional council within these divisions. Information has been gathered through questionnaires and personal interviews. Results of the study reveal that Payback Period (PBP) is the dominant capital budgeting technique used in SMEs. Results of the Multinomial logistic regression indicate that the probability of selecting Net Present Value as the capital budgeting technique is higher in foreign SMEs and in SMEs who operate in the industry for 11 to 15 years. Furthermore, being a SME decision maker with less than 10 years of experience increases the probability of selecting PBP as the capital budgeting technique. Finally, qualitative techniques used in this study indicate that cost, time and knowledge are the main reasons that deter SMEs from using capital budgeting techniques.