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 Image Processing and IoT-based Fish Diseases Identification and Fish Tank Monitoring System(IEEE, 2022-12-09) Ranaweera, I.U.; Weerakkody, G.K; Balasooriya, B.M.Eranda Kasun; Swarnakantha, N.H.P.Ravi SupunyaEvery person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world.Publication Embargo Monitoring System for Underage Smart Phone Users(IEEE, 2022-12-09) Jayawardena, M.A.P; Mahadi Hassan, M.H.F.M; Aflal, M.I.A; Weerathunga, W.A.A.S; Harshanath, S. M. B.; Rajapaksha, U. U. SIn today’s world, it is very common among children to use a smartphone or a handheld digital device such as a tablet to entertain themselves and as a medium of socializing with people easily. The COVID-19 pandemic forced many people to stay in their homes and rely on these digital devices to do their day-to-day work and communication. The latter caused the increase in reliance on digital devices to acquire information about the outside world and as a source of entertainment. This new tendency increased the likelihood of children being exposed to pornography, cyberbullying, cyberstalking, excessive gaming, sexting, and behavioral traits related to narcissism. These habits caused many children to develop psychological and physiological illnesses, which affected them in the short term and, for some, which affected them and their families in the long run, such as suicide. Our research proposes to constantly monitor behavioral patterns such as this, notify the relevant individuals, and prevent the children from being prone to such ill fates. According to the findings, using machine learning and natural language processing, sexting, phonographic words, and cyberbullying can all be recognized with pinpoint accuracy. Also, by using two machine learning models, depression and anxiety are detected with an accuracy of 0.84 and 0.86. To prevent and analyze computer vision syndrome caused by improper face-screen distance. An image processing-based algorithm is used to measure the distance from face to screen, and results are narrowed down to an accuracy of 1 inch.Publication Embargo Occupancy Monitoring System for Workplace Washrooms(IEEE, 2019-03-28) Godakandage, V. M. P; Kothalawala, K. R. M; Chathumali, E. J. A; Madhubhashana, A. WWith regard to rapid technological advancements majorly influencing our daily lives, Internet of Things (IoT) has been a topic of broad and current interest in the recent years. The capabilities of IoT can assist in revolutionizing the way people live and work, thereby improving quality of life. With the impact of IoT only continuing to propagate in the future, it can be used as a means of easing our day-to-day struggles. Therefore, with the assistance of IoT along with a few hardware, the proposed system, addresses the displeasing reality of queues and several visits for the washrooms due to them coming forth occupied. Thus, the focus of the intended system is on delivering a pleasant washroom experience for employees in an office environment providing them with an at-desk indication on the occupancy of the washroom cubicles reducing queues and disappointments.Publication Embargo IoT-based Monitoring System for Oyster Mushroom Farming(IEEE, 2021-12-09) Surige, Y. D; Perera, W. S; Gunarathna, P. K; Ariyarathna, K. P; Gamage, N; Nawinna, D. PAgriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.Publication Open Access IOT-based Monitoring System for Oyster Mushroom Farms in Sri Lanka(KDU IRC, 2022-01-10) Surige, Y. D; Perera, W. S. M; Gunarathna, P. K. M; Ariyarathna, K. P. W; Gamage, N. D. U; Nawinna, D. POyster Mushrooms are a type of a fungus which is very sensitive to the environmental factors and vulnerable to diseases and pest attacks which directly effects local trade and export strength. Mushroom is a climacteric type of food which continues its cycle even after harvesting. The mushroom farming process still uses manual mode such as the identification of diseases uses a farmers eye visually, harvesting of mushrooms are decided based on the visual appearance while the environmental factors are decided based on gut feelings. These methods has its limitations which requires more potential to improve both the quality and capacity of mushroom production. With the advancements of technology, this farming process can be performed with the aid of an IoT device and deep learning model. This research applies Convolutional Neural Networks (CNN) with Mobile Net V2 model to detect mushroom harvest time and any disease spread with an accuracy of 92% and 99% respectively. Long Short-Term memory (LSTM) to analyze the detected environmental factors with an accuracy of 89% and this system predicts the yield of mushroom production with the support of LSTM model with an accuracy of 97%. This developed system which aids mushroom farming activities is connected with the farmers through s mobile applicationPublication Embargo AI Based Monitoring System for Social Engineering(IEEE, 2021-12-09) Abeywardana, K. Y; Udara, S. W. I; Wijayawardane, U. P. B; Kularatne, K. N. P; Navaratne, N. M. P. P; Dharmaphriya, W. G. V. USocial media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.Publication Embargo OMNISCIENT: A Branch Monitoring System for Large-scale Organizations(IEEE, 2020-12-10) Jayasekara, T; Omalka, K; Hewawelengoda, P; Kanishka, C; Samarasinghe, P; Weerasinghe, LOmniscient is a system that enables higher-level management of massive organizations to remotely monitor and scrutinize the activities that take place in the branches from the head office itself by providing exclusive insight in the form of detailed reports on the employees' behaviour and performance daily, weekly and monthly. The system further monitors the branch and provides reports on any suspicious behaviour and also on the customers' activity within the branch premises. Omniscient rates the customer's level of satisfaction by capturing the customer's facial expressions and analyzing their emotions while they are being served. The employee face and dress recognition models have accuracies of 90.90% and 87.00% respectively while, employee activity detection has an accuracy of 89.00%. Customer emotion and miscellaneous activities detection models have the accuracies of 91.50% and 83.00% respectively. All of the aforementioned procedures were made possible by systematically analyzing the IP camera video footage obtained throughout the day to analyze the work productivity and performance of the branch as accurately as possible using deep learning and modern visual computing techniques like CNN, OpenCV, Haar Cascade classifier, face recognition, Dlib and Darknet.Publication Embargo Deep Learning Based Dog Behavioural Monitoring System(IEEE, 2020-12-03) Boteju, W. J. M; Herath, H. M. K. S; Peiris, M. D. P; Wathsala, A. K. P. E; Samarasinghe, P; Weerasinghe, LDogs are one of the most popular pets in the world. It is usual that pet owners are always concerned about the health and the wellbeing of their pets. The activity levels of the dogs vary from each other based on breed and age. Tracking the behavioral changes using image processing and machine learning concepts and notifying the pet owners via a mobile application is the main objective of this research. Breed recognition has been done applying deep learning concepts to the user-uploaded video or the photograph of the dog. This research mainly focuses on walking, running, resting, and barking activity patterns of the dog. A surveillance camera and sensors were the main equipment for data collection. The audio feature of the surveillance camera is used to identity the barking behavior of the dog. Dogs from different ages belonging to Pomeranian and German Shepherd breeds have been selected for this experiment. Transfer learning with ResNet50, Inception V3, and support vector machines have been used to recognize and classify the activities of the dogs. The research study was able to achieve the accuracy levels as follows: - breed recognition - 89%+, walking pattern recognition - 99.5%, resting pattern recognition - 97% and barking pattern recognition - 60%. With the above accuracy levels, the research was able to identify the unusual behaviour of the dogs.Publication Embargo DiabiTech-Non-Invasive Blood Glucose Monitoring System(IEEE, 2019-12-05) Udara, S. S. W. I; De Alwis, A. K; Silva, K. M. W. K; Ananda, U. V. D. M. A; Kahandawaarachchi, K. A. D. C. PDiabetes is one of the largest chronic diseases threatening 84 million patients in South East Asia alone. By the year 2045, this is expected to rise to 156 million. Lack of a permanent cure and impractical, expensive, painful measuring techniques are among the reasons behind these alarming statistics. Almost 58% of diabetes patients are not diagnosed in this South Asian region, mainly due to lack of motivation caused by the above issues. This research focuses on finding a solution, which is non-invasive, portable, practical, accurate, and cost-effective with the help of sensors and accessible technology platforms. The research started upon discovering the medical classifications, parameters, contributing factors, and external dynamics that could affect the result. Upon research, an algorithm was developed to calibrate and measure the blood glucose level, which is compatible with dynamic factors of the patients. Later on, the hardware device was built using NIR and red LEDs accompanied by a user-friendly mobile application, which can be accessible by patients of all ages. The results obtained were validated by using statistical techniques. The analysis showed a strong linear correlation between the voltage output and blood glucose level. The overall accuracy of the system accounts for above 90%. Since this solution is noninvasive, it provides a reusable and portable platform which can constantly monitor blood glucose levels conveniently in a painless manner, without any repetitive costs. Further, this solution will help patients to adjust medication based on their current blood glucose levels to reduce both the unnecessary damage of organs and additional costs being incurred.Publication Embargo E-Learn Detector: Smart Behaviour Monitoring System to Analyze Student Behaviours During Online Educational Activities(IEEE, 2021-12-09) Bamunuge, H. K. T; Perera, H. M; Kumarage, S; Savindri, P. A. P; Kasthurirathna, D; Kugathasan, AWith the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students' learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. "E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. "E-Learn Detector" is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.