Research Papers - Dept of Information Technology
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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 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).Publication Embargo GreenEye: Smart Consulting System for Domestic Farmers(IEEE, 2022-12-09) Mendis, O; Perera, A; Ranasinghe, S; Chandrasiri, SAlways it is challenging for typical domestic farmers to maintain a good homestead in today’s world and with the ever-growing economic concerns. To save time, money, and energy, they must keep up with the advancements of incorporating technology in their farming practices to ensure that their crops are up to standard and optimized for the maximum yield. Domestic farmers may grow crops for economic gain, pleasure, stress relief, decorative purposes, Etc. However, regardless of the purpose, everyone must be aware of good farming practices. No matter the intention, challenges, and outcomes, everyone engaged with plant growth is the same. In today’s highly advanced technological world, a lot of domestic farmers are using modern technology in their growing practices. Experimenting with intelligent growth mechanisms and intend to use modern technologies to provide advice that is useful for all gardeners who prefer home gardening. Additionally, the most crucial aspects of plant care are recognizing the ideal plants for each season, identifying stress factors, identifying diseases, identifying soil moisture levels, and predicting the harvest based on the current environmental conditions. Green Eye mobile application aims to provide a comprehensive solution to technologized domestic farmers using image processing technologies for their most common concerns.Publication Embargo Carbon Emission Optimization Using Linear Programming(IEEE, 2022-12-09) Magenthirarajah, V; Gamage, A; Chandrasiri, SIn this fast-growing modernization, excess carbon emission plays a crucial role in climate change. Targeting and experimenting with sustainable ways of Carbon neutrality and management is the pathway toward a greener society. Data show that factories and industries take a high market stake in carbon emission and management. In actions, Governments defined a limit for carbon emissions to each organization which is called carbon credit. Every organization must focus on reducing carbon emissions. This is a critical task for each organization, In some cases, it is still not possible to explore other sustainable options. An innovative solution proposed for the above scenario is to implement a real-time platform that can provide insights into the most up-to-date emission statistics of the organization. This paper provides advanced analytics and precise proactive planning and actions in the simplest form and a discussion on future elaborations and insights about conclusions. By finding the minimum optimal emission values of each emission source, organizations can maintain carbon emissions without exceeding their carbon credit. Also, how industries and factories can create a smart carbon optimization system that can create an even greener society.Publication Embargo Amazon Biology: An Augmented Reality-Based E-Book for Biology(IEEE, 2020-12-10) Somakeerthi, D. C. S; De Silva, G. W. I. U; De Silva, L. D. T; Chandrasiri, S; Joseph, J. KBiology is a conventionally struggling subject to learn from both high school and college students due to its complexity. Students are used to learning Biology from various methods such as reading textbooks, attending lectures. Biology is based on more practical and most of the schools not available proper lab facilities, anatomic structures, and resources to learn the module easily. And teachers who teach the module face a considerable number of issues when delivering the concepts. Some of them face unavailability of teaching aids, time-consuming, lack of lecture materials. Apart from that, the nature of the topic and the teaching style are the main learning problems faced by the students. Therefore, students do not learn the concepts perfectly and interest in the module has been reduced day by day. To overcome these difficulties “Amazon Biology,” mobile application has been proposed. The application consists of three major modules including image processing for the plant classification, augmented reality for human anatomy, and gamification. The proposed application has used the techniques in augmented reality and game-based learning. The developed system delivers nearly 85% level of accuracy and provides more advantages for students. They are effective and efficient learning, teaching via visual materials, and practical.Publication Open Access User Awareness System to Diagnose Dermatological Diseases(Foundation of Computer Science, 2020-12-18) Chandrasiri, S; Weerasooriya, T; Pathivarathan, V; Thavabalasingham, N; Philipreman, K; Gunasekaran, SNowadays, humans' health is deteriorating by dermatological diseases, and the spreading rate is high. Most people are not aware of skin diseases. As they do not realize these diseases' seriousness, they try to treat with some remedies by themselves, even without knowing what the actual disease is. Nevertheless, it is not a suitable way to cure the disease, leading to future complications. So still the dermatological diseases remain as one of the main categories of common health issues. A few people prefer to use computerized systems to evaluate the disease conditions these days. Moreover, it is essential to know about the diseases to manage that condition and prevent escalation. Therefore, the proposed system is implemented to give users some knowledge about dermatological diseases as much as possible. The users can get awareness and predict skin diseases and complications from the data mining technique. The user can identify the stage of the dermatological disease by applying the classification algorithm. Furthermore, this system will also scrap web pages related to that disease from known or system verified websites. The content analysis is based on the machine learning process, especially using Neural Language Processing. Hence, the system will undeniably be useful to the users to summarize skin diseases and get concerns from a dermatologistPublication Embargo Kidland: An Augmented Reality-based approach for Smart Ordering for Toy Store(IEEE, 2021-12-01) Wijayalath, W. M. C. D; Ranasinghe, R. M. T. T; Thennakoon, M. T. H; Vithanage, H. D; Chandrasiri, SAugmented reality (AR) is an iconic topic that can be applied in different domains in modern world technology. With the rapid development of technologies, eCommerce (Online Shopping) has become closer to human life. As a result, AR was started implemented with eCommerce platforms by the developers. With the busy lives and the pandemic situation, people are limited to visiting toy stores while providing a solution. An AR-based virtual toy store is proposed with 3D Toy generation for visualizing selected toys, a Virtual tour for enhancing the remote virtual shopping experience, and an Indoor navigation system visualizing the path within large scale shopping malls are new features of the proposed system. The majority of the existing eCommerce platforms are missing image search features. As a solution, “KidLand” has implemented an image search engine, suggesting add-on-related items and nearest branches using machine learning algorithms. An intelligent chatbot uses a reinforcement learning algorithm and Natural Language Understanding (NLU) to give possible solutions regarding the toy store. As a solution to the language literacy problem, developed a chatbot that can chat both English and Sinhala languages. “Kidland” was developed to provide the users the next level of shopping experience with attractive features of AR technology with marketing and use advanced technologies overcoming the issues of ordinary eCommerce platforms. In Sri Lanka, this system has been identified as a solution for the issues with ordinary shopping platforms.Publication Embargo Home Bridge - Smart Elderly Care System(IEEE, 2021-01-17) Sitparoopan, T; Chellapillai, V; Arulmoli, J; Chandrasiri, S; Kugathasan, AThis paper proposes a recommendation system for the elderly that runs on a consumer smartphone as an interactive mobile application. This study looks at the relationship between depression and the factors contributing to depression in the elderly. Obesity and depression have a bidirectional negative relationship. Older people experience symptoms of depression that have gone undetected and untreated. Therefore, the proposed solution provides the elderly with overcoming depression and loneliness in everyday life and looking after themselves. An elder's life revolves around eating well-balanced meals and staying healthy. This system will include a food picture recognition capability for determining calories and nutritional content. The system will assess the area, size, and volume of food and provide advice such as activities to reduce food's calories. It will also propose alternative healthy food if there is any harmful food in the image. The use of diet to keep improving both health issues is a cornerstone strategy to manage depression in patients. Because chronic diseases and depression have similar physiology, equivalent dietary recommendations could be beneficial. Malnutrition is a significant issue among the elderly. On the other hand, adjusting food-related habits is complicated. Therefore, provide an intelligent meal planning application that recommends appropriate low-calorie food recipes based on the image or voice command of the ingredient.Publication Embargo Smart Pest Management: An Augmented Reality-Based Approach for an Organic Cultivation(IEEE, 2021-01-17) Mahenthiran, N; Sittampalam, H; Yogarajah, S; Jeyarajah, S; Chandrasiri, SThe agricultural world faces more difficulties due to crop pests that damage or infliction cultivated plants. The main challenges to those interested in cultivation are pest attack and disease. Pests spread the disease, and the yield is decreased. However, it is possible to control pest attacks and infections in the early attack stage to reduce pesticide use and keep the farm safe. Mobile applications can provide accurate identification rather than manual detection. Mobile applications and technologies are created when considering the solution. The importance of the proposed solution is to increase the rate of the plant product and achieve high revenue without any cost. One of the main components used in this system is the image processing technique. The pest images will be taken, and they will be subjected to various preprocessing for noise reduction and enhancement of the pictures. Using image processing, the user can determine the pest's life cycle stage. The user can identify the stage of the damaged plant by applying the classification algorithm. The content analysis is based on the machine learning process, especially using a Convolutional Neural Network. Hence, the proposed system will help to get knowledge of organic pest prevention methods. In the system, we determined the type of pest with 90% accuracy by submitting a damaged leaf and a pest image. The pest's lifecycle stage and stage of the affected plant also can be identified in our system with high accuracy. Moreover, it shows the organic prevention methods.Publication Embargo Diagnostic Intervention for Mental Disorder(IEEE, 2021-12-01) Senanayake, S; Karunanayaka, C; Dananjaya, L; Chamodya, L; Kumari, S; Chandrasiri, SMental health is one of the essential factors in the topic of healthcare and wellbeing. However, mental health disorders could cause severe damage, even loss of life to the person or the surroundings, if mental health disorders were not identified and appropriately cured. Unfortunately, though there is good help there, some people have a hard time detecting whether they are suffering from mental health disorders or not. In this study, the team proposes a system to detect mental health issues using facial emotion recognition (FER), sleeping patterns, social media web scraping, and heart rate. The intention is to give an accurate prediction of the mental health status of a person using these three nodes.