Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo IoT Based Smart Pillow for Improved Sleep Experience(IEEE, 2022-12-09) Ravishani, W.M.S; Ganepola, G.A.S; .Silva, E.D.M; Chamodi Jayanika, G.H.G.; Rajapaksha, U.U.S; Swarnakantha, N.H.P.R.SMaintaining appropriate health by avoiding illnesses brought on by stress, heart disease, stroke, insomnia, and hormonal imbalance is made possible by managing the quality of sleep necessary for brain and memory-related tasks. In order to reduce these phenomena, we concentrated on recognizing them and developing strategies to do so. As a result, we decided to use smart pillows and bands that are Internet of Things (IoT)-based. To connect the touch sensor and relay module for improving sleep quality with the help of an automatic alarm system and light treatment system, an ESP-32 (microcontroller) was built into the pillow. The band will also have a second ESP 32 that can be connected to an oximeter, gyro, and accelerometer to improve the sleepwalk alert and health monitoring systems’ accuracy. The mobile application will also be created so that the patient and the doctor may review the patient’s sleeping patterns, and the CNN-based deep learning architecture was used to develop the emotion recognition function that uses music to improve sleep quality. For a better sleep experience, we will refer to the smart band and pillow as ”MAGICAL PILLOW” and ”MAGICAL BAND” as the ultimate products.Publication Embargo Interactive Mobile Application for Initial Skills Development of Primary Students in Sri Lanka(IEEE, 2022-12-09) Liyanage, C.; Kavinda, U. A. D. S.; Dasanayaka, D. S.; Shehara, P. G. J.; De Silva, D. I.In many cases, children between this age are using smartphones and other technology devices, to play games, watch cartoons, take photos and sometimes the chance is getting higher than we think that children access unnecessary contents due to lack of guidance and unawareness of parents. This interactive mobile application is used as an adaptive learning tool for the primary school students. Utilizing children’s comfort with technology allows for the development of their talents. In math skills development, some attractively designed gamified activities to solve basic math questions are given according to the skill level the child is currently in. The accuracy was much higher in the Convolutional Neural Network approach as it recorded a value of 0.9919. In environmental skills development component, the app will ask child to identify the surroundings according to a flow, starting from the house and towards the garden using object detection and the results were detected with a higher accuracy level around 0.9-0.99 after training the Machine Learning model. And in the language skills development component the child is given activities to develop pronunciation skills using audio processing and finally the verification of online achievements of a child by Non-Fungible Token technology, is fulfilled via the app.Publication Embargo Evaluating the Success of Digital Learning in Sri Lankan Tertiary Education(IEEE, 2022-12-09) Weerapperuma, J; Nawinna, D; Gamage, NThis paper takes a social capital perspective to explain the underlying mechanisms that drive the success of digital learning in tertiary education in an emerging economy. It is crucial to explore ways in which the success of tertiary education can be maximized since these students will immediately contribute to the economy. Although digital-learning initiatives have advanced in developed countries, it is still in its early phases in many developing countries, including Sri Lanka. This study focuses on structural, relational, and cognitive dimensions of social capital and provides a new theoretical framework to examine its relationship to digital educational success. The study uses a quantitative approach where the data is collected from University students in Sri Lanka using a survey deployed online. The model is validated using the structural equation modeling technique. The findings of this study indicated that the three dimensions of social capital positively influence the success of digital education at the tertiary level. Further, this paper contributes to the existing literature on Social Capital Theory and provides valuable insights and recommendations for policymakers in the educational sector on improving digital learning achievements.Publication Embargo Emission Activity Parts Extraction using custom Named Entity Recognition(IEEE, 2022-12-09) Mannavarasan, M; Gamage, A; Sivarajah, V; Chandrasiri, SDental 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%.Publication Embargo 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, JDental 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%.Publication Embargo Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing(IEEE, 2022-12-09) Sumeera, S; Pesala, N; Thilani, M; Gamage, A; Bandara, PThe fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.Publication Embargo AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka(IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.Publication Embargo A Surveillance System Controlling Covid-19 in Office Environments(IEEE, 2022-12-09) Bandara, P A D; Perera, P D D S; Perera, N P D D D; De Silva, P. N.; Kasthuriarachchi, S; Rajapaksha, U.U.SCOVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office environment an identification system that uses a deep learning model that prioritizes object recognition for the identification of employees who wear a face mask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. Loss of Smell after the next component. One person can use one disposable card to check the smell of sniffing. Each disposable card has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence and offers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person.Publication Embargo An Online Dashboard Platform for Weather Data of Major Sri Lankan Cities, and Global Climate Trends(IEEE, 2022-12-08) Pemasinghe, S; Dayarathna, D; Panditharathna, P M R A; Saparamadu, S; Wickramarathne, JHaving easy access to vital weather information and latest climate trends can be of utmost use for a myriad of stakeholders specially for sectors such as fishing community and the agricultural sector. In this paper we have mainly focused on major Sri Lankan cities and providing a one-stop station for easily accessing useful weather information for all the major Sri Lankan cities scattered over 25 administrative districts via a series of dedicated dashboards for each of the cities. The parameters that are displayed in the dashboards have been decided via surveys covering major stakeholders. Steps have been taken to disseminate not only the weather information but also information about latest climate trends regarding stratospheric ozone concentration and global land and ocean temperature anomalies, and providing all this information in one place with a lot of potential to extend the breadth of information provided in terms of weather and climate changes in the years to come.Publication Embargo A Novel Ranked Emission-Factor Retrieval for Emission Calculation(IEEE, 2022-11-22) Paskaran, S; Gamage, A; Chandrasiri, SEmission Factors (EF) selection is a vital task during Carbon Management Systems (CMS) emission calculation. Due to Carbon footprint reduction regulations, there is a demand increase for CMS with better usability and scalability. However, most CMS assumes users know emission technologies well. To circumvent these problems, authors have proposed an approach to building an EF ranking system with a combined scoring approach. It has considered each EF as a document unit and emission activity information provided by the user as the search query. This system uses a linear combination of the Vector Space Model (VSM) and Natural Language Processing (NLP) Word Embedding techniques to rank EF documents for exact and non-exact search queries. This approach's user satisfaction measured with Mean Average Precision (MAP) for “glove-wiki-gigaword-300” at 0.41 linear combination parameter was nearly 30% better than the VSM model and 127% more than the word embedding. In addition, the paper discusses performance metrics such as speed, future EFs scalability, and system resource utilization concerning the solution's overall scalability. This approach can provide better usability and scalable for EF selection tasks compared to single-ranking approaches (VSM or Word Embedding).