Browsing by Author "Chandrasiri, S"

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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. K
Biology 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.
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Automatic anemia identification through morphological image processing
(IEEE, 2014-12-22) Chandrasiri, S; Samarasinghe, P
Though blood cell manipulation has been an interesting research area for many years, most of the techniques presented in literature produce poor segmentation results for images with high overlapped blood cells. In this paper, we introduce a fully automatic low cost and accurate system to identify four common types of anemia and report on blood cell count. The results of our system indicate a good impact with the manually processed results of 99.678% accuracy of Red Blood Cell count. The diagnosis of Elliptocytes, Microcytes, Macrocyte and Spherocytes anemia result in the range of 91%-97% accuracy.
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Carbon Emission Optimization Using Linear Programming
(IEEE, 2022-12-09) Magenthirarajah, V; Gamage, A; Chandrasiri, S
In 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.
Open Access
CityTour Bus Locator and Bus Booking Mobile Application
(NSBM, Colombo, 2018-08-25) Chandrasiri, S; Pitipana, H; Hettiwatte, S. N
The public bus transportation system has the direct impact on economic development of the country. Scheduling, tracking and monitoring of the public bus transportation is one of the major issues for any public transportation sector. Currently, there are many vehicles tracking systems available using Global Positioning Systems (GPS) technology. At present, bus passengers are unable to gather enough information which would lead up to a stress-free bus ride. Under current conditions a passenger has to wait in the bus stand without having any prior idea about the buses. ‘CityTour’ application assists passengers and the conductor to use bus service in more stress-free, well-organized and a suggestive manner. “CityTour”, is the cross-platform application implemented with the sole purpose of addressing these problems a passenger has. The application will predict the next Bus’s arrival time, seat reservation and a rating which will give an indication regarding the quality of the service offered. Bus conductor has a say in whether a passenger gets a seat or not. ‘CityTour’ application enables the “real-time” passenger bus communication that would lead to a better collaboration and make their day-to-day work easier for both the parties.
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Diagnostic Intervention for Mental Disorder
(IEEE, 2021-12-01) Senanayake, S; Karunanayaka, C; Dananjaya, L; Chamodya, L; Kumari, S; Chandrasiri, S
Mental 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.
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E-Learning Assistive System for Deaf and Mute Students
(IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, S
E-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.
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Emission Activity Parts Extraction using custom Named Entity Recognition
(IEEE, 2022-12-09) Mannavarasan, M; Gamage, A; Sivarajah, V; Chandrasiri, S
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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GreenEye: Smart Consulting System for Domestic Farmers
(IEEE, 2022-12-09) Mendis, O; Perera, A; Ranasinghe, S; Chandrasiri, S
Always 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.
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Home Bridge - Smart Elderly Care System
(IEEE, 2021-01-17) Sitparoopan, T; Chellapillai, V; Arulmoli, J; Chandrasiri, S; Kugathasan, A
This 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.
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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, S
Augmented 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.
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Morphology based automatic disease analysis through evaluation of red blood cells
(IEEE, 2014-01-27) Chandrasiri, S; Samarasinghe, P
Cell morphology has been an active area in the field of bio-medical research. When applied for blood microscopic images, one can study blood cell characteristics and detect abnormalities. In this paper, we introduce an automatic, cost effective and accurate way of red blood cell analysis and evaluation through Blob detection, Morphology operations and Hough circle transformation techniques for identification of four common types of anemia. Our research has filled the gaps in the existing literature by developing an integrated system to Count RBC, Diagnose Elliptocytes, Microcytic, Macrocyte and Spherocytes Anemia, Detect abnormalities and Separate overlapped cells, automatically, accurately and efficiently. The result shows an insight in the manually processed results with 99.545% accuracy of RBC count. Each sub method is closely running in the range 91%-97% of accuracy. The achievements are highlighted as efficiency through automation, cost effective, elimination of human error and easy to manipulate.
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A Novel Ranked Emission-Factor Retrieval for Emission Calculation
(IEEE, 2022-11-22) Paskaran, S; Gamage, A; Chandrasiri, S
Emission 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).
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Real-time Smart Navigation System for Visually Impaired People
(IEEE, 2022-12-09) Sudaraka Keshara, S.R.D.; Weragoda, W.R.J.M.; Chandrasiri, S; Ellankovan, J; Madushan, W.A
Visual sense plays a primary role in guiding sighted people through an unfamiliar environment and assisting them to reach their destination safely. Visual impairment describes the actual damage that makes it difficult to accomplish visual tasks because it makes it difficult to see clearly. This paper proposes an approach to overcome the challenges faced by visually impaired people with the help of machine learning. This proposed system combines a smart cane and a wearable smart glass. The detection of obstacles and potholes helps to increase the safety and comfort of visually impaired users by detecting and displaying obstacles, and the Smart Walk-lane Navigation assists in navigating through the walk-lanes without letting them enter the main roads and helps to prevent accidents. Road sign detection allows users to follow road signs and cross the roads safely, while face and emotion detection allows users to recognize well-known people and their emotions.
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Revolutionalize Your Learning Experience with EQU ACCESS
(IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, K
This paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.
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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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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, S
The 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.
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, S
Nowadays, 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 dermatologist
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UveaTrack: Uveitis Eye Disease Prediction and Detection with Vision Function Calculation and Risk Analysis Publisher: IEEE Cite This PDF
(Institute of Electrical and Electronics Engineers, 2022-10-15) Perera, B. D. K; Wickramarathna, W.A.A.I.; Chandrasiri, S; Wanniarachchi, W.A.P.W; Dilshani, S.H.N; Pemadasa, N
Uveitis is an inflammatory infection that affects uvea tissue, the middle layer of the eyewall. It can result in swelling or damage to the eye and lead to vision impairments or blindness. Most Uveitis symptoms are associated with many other diseases localized to the eye. Thus, it is hard to determine the responsible symptoms for uveitis. Consequently, early detection of this disease can prevent a perilous situation in the future. The initial motivation behind the design of this mobile application is to help accurately diagnose uveitis with minimal time and effort and thereby minimize the shortage of human specialists in this field. The 'UveaTrack' is a hybrid mobile application that enables the keep tracking of uveitis eye illness and uses machine learning (ML) algorithms, deep learning (DL) architectures, and image processing techniques for developing the system. The 'UveaTrack' application could be able to achieve an average accuracy of more than 85% and had produced overall better results. Furthermore, the 'UveaTrack' application can use as a valuable instructional tool for freshly graduated clinicians, supporting their work with patients and assisting them in making diagnostics conclusions.
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VAPECA - Smart Agricultural and Analysis Monitoring System
(Institute of Electrical and Electronics Engineers, 2022-10-15) Jithmal Pitigala, P. K. D. U; Laksahan, T. M. K; Hewapathirana, S. S; Sadeepika Herath, H. M. H; Chandrasiri, S; Nadeesa Pemadasa, M. G
Agriculture dramatically contributes to the economy by creating a monetary future for developing nations. However, in Sri Lanka, the farmers have confined resources and encounter numerous challenges to enrich their crop productivity and prevail in the competitive business world. In the directive, the farmers' knowledge about export crops and weak decision- making needs to be exposed [1]. This study has built a mobile application with budget planning, determining plant conditions, weather forecasting, analyzing harvest quality, and a price prediction system to mitigate these hardships. This application would be utilized to manage three critical plants in Sri Lanka t for extraction and export. Those are Vanilla, Pepper, and Cardamom. The key technologies used for the system are deep learning and machine learning. The overall system obtained desirable outcomes with an accuracy rate higherthan 94%-97%. The ultimate intent of this study is to achieve the optimal growth of the agriculture sector by navigating the farmers to get maximum crop yield, quality, and effective decision-making through reliable market trends and to enhance the farmers' profit