Research Papers - Dept of Information Technology

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Author: search.filters.author.Nugaliyadde, A

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Now showing 1 - 9 of 9
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    PublicationEmbargo
    An ultra-specific image dataset for automated insect identification
    (Springer Nature, 2022-01) Abeywardhana, L; Dangalle, C; Nugaliyadde, A; Mallawarachchi, Y
    Automated identifcation of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from diferent sources, angles and on diferent scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing diferent unique patterns and features in images. The dataset was evaluated on diferent classifcation algorithms including deep learning models based on diferent approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classifcation algorithms. However transfer learning models using softmax classifer performed well on the current dataset. The tiger beetle classifcation can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classifcation algorithms to develop, to identify features which human eyes have not identifed.
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    PublicationEmbargo
    Evolutionary Algorithm for Sinhala to English Translation
    (IEEE, 2019-10-08) Nugaliyadde, A; Joseph, J. K; Chathurika, W. M. T; Mallawarachchi, Y
    Machine Translation (MT) is an area in natural language processing, which focuses on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules, and therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it into English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results.
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    PublicationOpen Access
    Solving Sinhala Language Arithmetic Problems using Neural Networks
    (arxiv logo > cs > arXiv:1809.04557, 2018-09-11) Chathurika, W. M. T; De Silva, K. C; Raddella, A. M; Ekanayake, E. M. R. S; Nugaliyadde, A; Mallawarachchi, Y
    A methodology is presented to solve Arithmetic problems in Sinhala Language using a Neural Network. The system comprises of (a) keyword identification, (b) question identification, (c) mathematical operation identification and is combined using a neural network. Naïve Bayes Classification is used in order to identify keywords and Conditional Random Field to identify the question and the operation which should be performed on the identified keywords to achieve the expected result. “One vs. all Classification” is done using a neural network for sentences. All functions are combined through the neural network which builds an equation to solve the problem. The paper compares each methodology in ARIS and Mahoshadha to the method presented in the paper. Mahoshadha2 learns to solve arithmetic problems with the accuracy of 76%.
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    PublicationOpen Access
    Evolutionary algorithm for sinhala to english translation
    (arXiv preprint arXiv:1907.03202, 2019-07-06) Joseph, JK; Chathurika, W. M. T; Nugaliyadde, A; Mallawarachchi, Y
    Machine Translation (MT) is an area in natural language processing, which focus on translating from one language to another. Many approaches ranging from statistical methods to deep learning approaches are used in order to achieve MT. However, these methods either require a large number of data or a clear understanding about the language. Sinhala language has less digital text which could be used to train a deep neural network. Furthermore, Sinhala has complex rules therefore, it is harder to create statistical rules in order to apply statistical methods in MT. This research focuses on Sinhala to English translation using an Evolutionary Algorithm (EA). EA is used to identifying the correct meaning of Sinhala text and to translate it to English. The Sinhala text is passed to identify the meaning in order to get the correct meaning of the sentence. With the use of the EA the translation is carried out. The translated text is passed on to grammatically correct the sentence. This has shown to achieve accurate results
  • Thumbnail Image
    PublicationOpen Access
    An ultra-specific image dataset for automated insect identification
    (Springer US, 2022-01-09) Abeywardhana, D. L; Dangalle, C. D; Nugaliyadde, A; Mallawarachchi, Y
    Automated identifcation of insects is a tough task where many challenges like data limitation, imbalanced data count, and background noise needs to be overcome for better performance. This paper describes such an image dataset which consists of a limited, imbalanced number of images regarding six genera of subfamily Cicindelinae (tiger beetles) of order Coleoptera. The diversity of image collection is at a high level as the images were taken from diferent sources, angles and on diferent scales. Thus, the salient regions of the images have a large variation. Therefore, one of the main intentions in this process was to get an idea about the image dataset while comparing diferent unique patterns and features in images. The dataset was evaluated on diferent classifcation algorithms including deep learning models based on diferent approaches to provide a benchmark. The dynamic nature of the dataset poses a challenge to the image classifcation algorithms. However transfer learning models using softmax classifer performed well on the current dataset. The tiger beetle classifcation can be challenging even to a trained human eye, therefore, this dataset opens a new avenue for the classifcation algorithms to develop, to identify features which human eyes have not identifed.
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    PublicationEmbargo
    Deep learning approach to classify Tiger beetles of Sri Lanka
    (Elsevier, 2021-05-01) Abeywardhana, D. L; Dangalle, C. D; Nugaliyadde, A; Mallawarachchi, Y
    Deep learning has shown to achieve dramatic results in image classification tasks. However, deep learning models require large amounts of data to train. Most of the real-world datasets, generally insect classification data does not have large number of training dataset. These images have a large amount of noise and various differences. The paper proposes a novel architectural model which removes the background noise and classify the Tiger beetles. Here object location is identified using contours by converting the original coloured image to white on black background. Then the remaining background is eliminated using grabcut algorithm. Later the extracted images are classified using a modified SqueezeNet transfer learning model to identify the tiger beetle class up to genus level. Transfer learning models with fewer trainable parameters performed well than the total number of parameters in the original model. When evaluating results it was identified that by freezing uppermost layers of SqueezeNet model better accuracy can be gained while freezing lowermost layers will reduce the validation accuracy. The proposed model achieved more than 90% for the test set in 40 epochs using 701,481 trainable parameters by freezing the top 19 layers of the original model. Improving the pre-processing to localize insect has improved the accuracy.
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    PublicationEmbargo
    Dynamic 3D model construction using architectural house plans
    (IEEE, 2017-01-27) Ruwanthika, R. G. N; Amarasekera, P. A. D. B. M; Chandrasiri, R. U. I. B; Rangana, D. M. A. I; Nugaliyadde, A; Mallawarachchi, Y
    The paper presents a complete approach to a dynamic 3D model construction from 2D house plans. This tool assembles 3D models and overlays virtual model on the real 2D blueprint of a house (architectural or hand-drawn). Key content of this research covers three dimensions which are; Wall detection and Wall modeling, Roof detection and Roof modeling and Template matching of Doors/Windows. The end result will be mainly based on Image Processing and Augmented Reality technologies. This tool lets users easily manipulate 3D models in real time through their smartphones and to showcase architecture models are in an entirely new way.
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    PublicationOpen Access
    “Mahoshadha”, the Sinhala Tagged Corpus Based Question Answering System
    (Springer, Cham, 2016) Jayakody, J. A. T. K; Gamlath, T. S. K; Lasantha, W. A. N; Premachandra, K. M. K. P; Nugaliyadde, A; Mallawarachchi, Y
    “Mahoshadha” the Sinhala Question Answering Systems aims at retrieving precise information from a large Sinhala tagged corpus. This paper describes a novel architecture for a Question Answering System which summarizes a tagged corpus and uses the summarization to generate the answers for a query. The summarized corpuses are categorized according to a set of topics enabling fast search for information. K-Nearest Neighbor Algorithms is used in order to cluster the summarized corpuses. The query will be tagged, the tagged query will be used to get more accurate results. Through the tagged query the question will be identified clearly with the category of the query. Support Vector Machine is used in order to both automate the summarization and question understanding. This will enable “Mahoshadha” to answer any type of query as well as summarize any type of Sinhala corpus. This enables the Question Answering System to be more useable through many applications.
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    PublicationEmbargo
    Semantic video search by automatic video annotation using TensorFlow
    (IEEE, 2016-10-22) Ashangani, K; Wickramasinghe, K. U; De Silva, D. W. N; Gamwara, V. M; Nugaliyadde, A; Mallawarachchi, Y
    The paper discusses a tool for video structure analysis, feature extraction, classification and semantic querying suitable for an extremely broad scale of video data set. The tool analyses the video structure to detect shot boundaries where shots in each video are identified using image duplication techniques. A single frame from each shot is passed to a deep learning model implemented using TensorFlow, that is trained for feature extraction and classification of objects in each frame. Subsequently, an automatic textual annotation is generated for each video and finally with the aid of ontology, semantic searching is done using NLP, which allows receiving an efficient result other than manual video annotation of a large scale dataset. While maintaining accurate querying with automatic video content analysis and annotation with semantic searching with around seventy-four percent accuracy rate, this becomes a useful tool in video tagging and annotation.