Browsing by Author "Mallawarachchi, Y"

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Open Access
Arboreal Tiger Beetles Recorded from Lowland Crop Cultivations in Sri Lanka
(UOC e-Repository > Science Department of Zoology, 2021-01) Abeywardhana, D. L; Dangalle, C. D; Mallawarachchi, Y
Purpose : Thirty-one species of arboreal tiger beetles are known from Sri Lanka of which 25 species are endemic. However, their habitat types are poorly documented and the available records are far outdated. Therefore, a survey of tiger beetles was carried out to determine their present occurrence with emphasis on agricultural habitat types. Research Method : Forty-six locations of the country, covering eighteen districts, all provinces, representing a majority of bioclimatic zones except those in Montane Sri Lanka were surveyed for arboreal tiger beetles. Sampling was conducted using the visual encounter method. Collected beetles were identified using taxonomic keys and descriptions. Findings : Eight species of arboreal tiger beetles were collected from the survey. Majority of the species (06) were collected from crop cultivations of coconut and also from tea, fruit farms, betel leaf, cinnamon and pepper. Four species of Derocrania and two species of Tricondyla were recorded from the cultivations and all had fused elytra and hence unable to fly. Derocrania scitiscabra was the dominant arboreal tiger beetle species in the crop cultivations. Originality/ Value : The study documents hitherto unrecorded habitat types for a poorly documented important beetle group of Sri Lanka. It further provides information for future research on the possibility of using arboreal tiger beetles as bio-control agents of insect pests of agricultural crops.
Open Access
Cicindelinae of Sri Lanka: New record of the arboreal tiger beetle Tricondyla gounellei Horn, 1900
(NSF, 2020-05-29) Abeywardhana, D. L; Dangalle, C. D; Mallawarachchi, Y
Information is provided on the newly recorded Tricondyla gounellei Horn, 1900, an arboreal tiger beetle, hitherto known only from Southern India, with this being its fi rst from Sri Lanka. Following fi eld surveys conducted from 2017 to 2019 in forty-one locations in the country, this species was recorded from two locations namely, Vellankulam in Mannar District and Kirinda in Hambantota District. Tricondyla gounellei, closely resembles Tricondyla granulifera Motschulsky, 1857 previously recorded from Sri Lanka. However, T. gounellei can be distinguished from T. granulifera by the smaller body size, short elytra that are narrower in the middle and palpi with black terminal joints which in T. granulifera is red.
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Decentralized ledger for land and property transactions in Sri Lanka acresense
(IEEE, 2018-12-06) Yapa, I; Heanthenna, S; Bandara, N; Prasad, I; Mallawarachchi, Y
Land & property can be identified as one of the core assets in any county in the world. Governments spend significant amount of money to identify, define, manage and maintain the transactional information around land and property in a country. Creating a proper mechanism to make this information available to the public users can be considered as one of the key indications that shows the development of a country. The system currently in use to record land and property transactions in Sri Lanka is a book-based system where all the information is stored in handwritten folios. This method is inefficient, time consuming, unsecured and complex when compared to the advanced systems which are used by developed countries in the world. This study is carried out to find a secured, efficient, cost effective solution with regards to the concerns and other problems related to land and property transactions in Sri Lanka. Proposed solution includes a decentralized ledger to record land and property transactions which runs on top of blockchain technology. All new transaction entries will be validated by smart contracts to verify the validity of the transactions. After performing a proper consensus mechanism, the transaction records will be stored in cryptographically secured blocks. So, the data integrity could not be violated. Additionally, investors and the land buyers can get information about the current market trends & predictions before buying or investing into a land. Also, the proposed system supports the users to view the land information graphically and allows the system users to view the desired land parcels without physically going to the actual location via the geographical information system.
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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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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.
Open 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
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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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Hastha: Online Learning Platform for Hearing Impaired
(IEEE, 2022-11-30) Wanasinghe, D; Maddugoda, C; Ramawickrama, H; Munasinghe, T; Abeywardhana, L; Mallawarachchi, Y
Sign language is the primary means of communication for the hearing-impaired community. Introducing a learning platform can result in many ways to make learning more accessible for the hearing-impaired community of Sri Lanka. Although many approaches are being made to build such systems, the learning platform “Hastha” aims to provide a more interactive outcome with a component that converts Youlhbe videos to sign language and a Chatbot component that acts as an intermediary between a hearing-impaired user and a Google Search Engine. Furthermore, it includes a game-based learning platform and a gesture translation component from Sri Lankan to American Sign Language while the results are displayed to the users in the form of an animation. The proposed methodology is achieved by using Natural Language Processing, speech recognition, and machine learning techniques. This web-based application enables increased interaction between the student and the system making it an effective learning environment for the hearing impaired.
Open 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.
Open Access
New record of Tricondyla gounellii Horn 1900 (Coleoptera, Cicindelinae), an arboreal tiger beetle from Sri Lanka
(: https://www.researchgate.net/publication/336107511, 2019-09) Abeywardhana, L; Dangalle, C; Mallawarachchi, Y
Arboreal tiger beetles belong to tribe Collyridini of order Coleoptera, family Carabidae, subfamily Cicindelinae and can be found predominantly in the tropical and subtropical regions of Asian countries mainly in forest habitat types (Toki et al., 2017). Tribe Collyridini is divided in to five genera - Collyris, Neocollyris, Protocollyris, Derocrania and Tricondyla. According to records provided by Fowler (1912) from his studies in the Fauna of British India’ five species of genus Tricondyla reside in Sri Lanka - Tricondyla femorata , Tricondyla tumidula , Tricondyla coriacea , Tricondyla nigripalpis , Tricondyla granulifera ). Three of these species, T. coriacea, T. nigripalpis, T. granulifera are endemic to the country, while the other two species also reside in India. However, the sources of this information is far outdated and unreliable and requires current investigations and revision. Thus, the present study was conducted to investigate the current species of arboreal tiger beetles of Sri Lanka, their morphology, locations, habitats and habitat preferences.
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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.
Open 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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Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection
(IEEE, 2021-12-09) Kariyawasam, S; Lakshan, A; Liyanage, A; Gimhana, K; Piyawardana, V; Mallawarachchi, Y
Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.
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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.
Open 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.