Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Wijekoon, JStart date: 2020

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Now showing 1 - 10 of 21
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    Effectiveness of Using Radiology Images and Mask R-CNN for Stomatology
    (IEEE, 2022-12-09) Jayasinghe, H; Pallepitiya, N; Chandrasiri, A; Heenkenda, C; Vidhanaarachchi, S; Kugathasan, A; Rathnayaka, K; Wijekoon, J
    Dental health-related disorders have proliferated worldwide due to the excessive intake of fast food and sugary foods, which was followed by bad oral hygiene practices. The cost of dental examinations may change based on how critical the condition is, regardless of whether they are not regular. For a person, diagnosing an oral health problem, particularly locating the disease’s underlying cause, can be challenging. To properly diagnose and treat such conditions, advanced dental diagnostic techniques may be necessary. By offering convenience and enhancing their oral health knowledge, the system seeks to serve as a prediction tool that regular people can utilize to detect potential tooth illnesses at an early stage. It is encompassed as a mobile application where a Mask R-CNN model is used in the core that accepts a dental radiograph as the input. The trained model will be able to identify diseases related to the bone and teeth. Based on the performance evaluations, the accuracy of the results that are obtained in tooth type, restoration quality, dental caries, and periodontal disease identification falls in the range of 75%-80%.
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    Smart Driver Assistance for Traffic Sign, Pothole, Vehicle Malfunction, and Accident Detection
    (IEEE, 2022-11-30) Vithanage, W; Madushan, H; Madushanka, T; Lokuliyana, T; Wijekoon, J; Chandrasiri, S
    Reducing ever-increasing road accidents is a big concern worldwide. Sri Lanka had the highest rate of road fatalities in the past few years, rapidly increasing daily. Among many factors, traffic signs, potholes, and vehicle mechanical malfunctions significantly impact road safety. Most accidents result from a lack of awareness, ignorance, and negligence of drivers. While many high-end vehicles are equipped with technologies such as intelligent road sign recognition systems and air suspension systems, most cars in the market only come with basic driving instruments. Therefore, there is a need for a universal driver assistance system that can be plugged into any vehicle to assist drivers in minimising road casualties. To this end, this study discusses Neural Networks, Machine Learning and IoT technologies to develop an intelligent system that is capable of detecting and analysing road signs, road potholes, vehicles’ internal system malfunctions, and road accidents and notifying drivers in real-time and inform authorities such as hospitals and police stations to be aware of accidents to minimise further casualties. This portable device is based on a Raspberry Pi microprocessor. It uses a web camera, an onboard diagnostic tool (OBD) and an accelerometer to process traffic sign footages, vehicle sensor data and movement data of the vehicle. Yielded results showed that the proposed system was 90% accurate.
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    IoT-Based Disease Diagnosis and Knowledge Dissemination System for Coconut Plants
    (IEEE, 2022-12-09) Ekanayaka, S; Anawaratne, A; Ayeshmanthi, T; Dilanka, M; Aratchige, N.S.; Wijekoon, J; Lunugalage, D
    The coconut plant plays a significant role in the Sri Lankan domestic and export industries. It is a major livelihood crop of which more than 65% is consumed locally. However, most coconut trees suffer from various pest and disease outbreaks, which have an impact on the economy of coconut production. Out of them, infestations of Whiteflies, Plesispa Beetle, and Red Palm Weevil are destructive to the coconut plant at different stages, so early detection of those infections is a major task. To this end, the paper describes an IoT-based prediction system for detecting and classifying infections in the coconut industry.; Internet of Things (IoT), image processing, audio processing, and deep learning were used as techniques to utilize for the detection of those infestations. Audio and Image-capturing devices are developed to collect audio and image data. Additionally, there’s a knowledge dissemination system to identify the main coconut pests in Sri Lanka and share this knowledge with farmers. With the audio and image datasets gathered from the mentioned diseases, performance evaluation of the Deep Learning (DL) models revealed that the accuracy of the identifications of Red Palm Weevil infestation Plesispa beetle and Whitefly infestations is 88, 96, and 98% respectively.
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    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.
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    CoviDefender: Digital Personal Guard For Defending Against COVID19
    (IEEE, 2021-09-30) Dayarathna, p; Kumara, I; Ranaweera, D; Nawinna, D. P; Karunaratne, G; Wijekoon, J
    In late 31 December 2019, a cluster of unexplained pneumonia cases was reported in Wuhan, China [1]. A few days later, the causative agent of this mysterious pneumonia was identified as the new COVID-19 virus. Currently, it has been spreading for more than one and a half years and has lost a huge number of lives all over the world. Most people faced this disaster because of their ignorance, carelessness and lack of updates. By the way most people are in lack of knowledge regarding COVID-19 pandemic, symptoms and what should do to survive from that. Those issues are great problems nowadays. “CoviDefender” is set to offer a solution to this worldwide COVID-19 pandemic problem. This is a new technological solution from a mobile application. “CoviDefender” is a Smart Assistant for Defending against COVID-19 Pandemic. This can be described as a solution to the ignorance and carelessness of the people who have been the main cause of the spread of this epidemic.
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    Power Profiling: Assessment of Household Energy Footprints
    (IEEE, 2021-03-06) Wijesinghe, V; Perera, M; Peiris, C; Vidyaratne, P; Nawinna, D. P; Wijekoon, J
    Reduced energy footprint is considered an indicator of efficiency around the world. Having insights into electricity consumption behavior of individuals or families across the day is very useful in efficient management of electricity. In this paper, we present s study that focused on identifying patterns in the monthly electricity consumption profiles of a single household with the K-means clustering algorithm. The data required for this study was collected through a survey in the Sri Lankan context. The survey mainly captured the factors affecting electricity consumption. After proving the demand of electricity is dependable on the data that has been collected, they will be keyed into data models/ profiles that will be built using clustering algorithms. A load profile will be designed using K-means to identify usage patterns of a household on a monthly basis. The parameters that affect the electricity consumption were tested and trained using the SVM algorithm. The outcomes of this study include; identifying the factors contributing to the electricity consumption, identifying electricity consumption patterns, identifying the energy footprint of individuals or families and predicting the future electricity requirements. The results of this study provide many advantages for both consumers and suppliers in efficient management of electricity. It also provides significant impacts in both micro and macro levels through enabling efficient decision-making regarding management of electricity.
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    Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
    (IEEE, 2021-10-27) Gamage, R; Rajapaksa, H; Sangeeth, A; Hemachandra, G; Wijekoon, J; Nawinna, D. P
    Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm.
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    CoviDefender: Digital Personal Guard For Defending Against COVID19
    (IEEE, 2021-09-30) Dayarathna, P; Kumara, I; Ranaweera, D; Nawinna, D; Karunaratne, G; Wijekoon, J
    In late 31 December 2019, a cluster of unexplained pneumonia cases was reported in Wuhan, China [1]. A few days later, the causative agent of this mysterious pneumonia was identified as the new COVID-19 virus. Currently, it has been spreading for more than one and a half years and has lost a huge number of lives all over the world. Most people faced this disaster because of their ignorance, carelessness and lack of updates. By the way most people are in lack of knowledge regarding COVID-19 pandemic, symptoms and what should do to survive from that. Those issues are great problems nowadays. “CoviDefender” is set to offer a solution to this worldwide COVID-19 pandemic problem. This is a new technological solution from a mobile application. “CoviDefender” is a Smart Assistant for Defending against COVID-19 Pandemic. This can be described as a solution to the ignorance and carelessness of the people who have been the main cause of the spread of this epidemic.
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    Smart Plant Disorder Identification using Computer Vision Technology
    (IEEE, 2020-11-04) Manoharan, S; Sariffodeen, B; Ramasinghe, K. T; Rajaratne, L. H; Kasthurirathna, D; Wijekoon, J
    The soil composition around the world is depleting at a rapid rate due to overexploitation by the unsustainable use of fertilizers. Streamlining the availability of nutrient deficiency and fertilizer related knowledge among impoverished farming communities would promoter environmentally and scientifically sustainable farming practices. Thus, contributing to several Sustainable Development Goals set out by the United Nations. The most direct solution to the inappropriate fertilizer usage is to add only the necessary amounts of fertilizer required by plants to produce a significant yield without nutrition deficiencies. To this end this paper proposes a Smart Nutrient Disorder Identification system employing computer vision and machine learning techniques for identification purposes and a decentralized blockchain platform to streamline a bias-less procurement system. The proposed system yielded 88% accuracy in disorder identification, while also enabling secure, transparent flow of verified information.
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    A Geophone Based Surveillance System Using Neural Networks and IoT
    (IEEE, 2020-12-10) Hettigoda, S; Jayaminda, C; Amarathunga, U; Thaha, S; Wijesundara, M; Wijekoon, J
    Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.