Department of Information Technology
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Publication Open Access 2D Pose Estimation based Child Action Recognition(Institute of Electrical and Electronics Engineers Inc., 2022-11) Mohottala, S; Abeygunawardana, S; Samarasinghe, P; Kasthurirathna, D; Abhayaratne, CWe present a graph convolutional network with 2D pose estimation for the first time on child action recognition task achieving on par results with LRCN on a benchmark dataset containing unconstrained environment based videos.Publication Embargo Accuracy of Diabetes Patient Determination: Prediction Made from Sugar Levels Using Machine Learning(Springer, Cham, 2022) Krishnananthan, S; Puvanendran, S; Puvanendran, RThis study focuses on the prediction of the Diabetic Patients through the sugar levels. The Dataset is analyzed using the data mining techniques such as feature extraction, associate rule mining and classification. The Fast Blood Sugar (FBS) and Post-Prandial Blood Sugar (PPBS) sugar levels are selected as the important features, identification of a rule depending on the selected feature is identified and the performance metric for three classifiers is analyzed based on the selected attributes and choose the classifier with high accuracy. Classification algorithms like random forest, decision tree (J48), and Naïve Bayes were utilized to identify the patients with diabetes disease. The performance of these techniques is considered using the factors relating to the accuracy from the applied techniques. The accuracy is seeming to be higher for Naïve Bayes. The outcomes acquired demonstrated that Naïve Bayes outflanks from different strategies with most noteworthy precision of 74.8%.Publication Embargo Anonymo: Automatic Response and Analysis of Anonymous Caller Complaints(IEEE Computer Society, 2022-08-17) Azhar, A; Maweekumbura, S; Gunathilake, R; Maddumaarachchi, T; Karunasena, A; Nadeeshani, MCustomers are considered as the most valued asset in any business organization. Therefore, attending especially to negative feedback provided by customer in form of complaints is important for an organization to identify areas to improve and retain customers. To quickly respond to customer complaints many business organizations have made hotlines available. Such caller hotlines are dedicated for the purpose of receiving complaints or allowing whistleblowers to reveal information. Due to the fear of being identified, there is a hesitancy in the public to use these hotlines. From the perspective of the organizations when a customer complaint is received it is required to evaluate the validity of the call made to hotlines. Furthermore, when complaints are made, it is required to handle them efficiently by transferring them to relevant departments and prioritize complaints This research proposes 'Anonymo', a system to handle customer complaints in a secure and an efficient manner. To do so, the system analyses the complaints obtained by a caller and provides the end users with the appropriate responses and output, that includes the following: i. Conversational AI agent to respond to callers, ii. Wanted and unwanted call classification, iii. Department-based Complaint classification, iv. Caller Emotion detection and caller complaint analysis while establishing the caller's anonymity. An accuracy of 88.26% was obtained for identification of wanted complaints using SVM algorithm, an accuracy of 85% was obtained for department-based classification using SVM algorithm and 67% accuracy was obtained for emotion analysis by LSTM algorithmPublication Embargo An Approach of Enhancing the Quality of Public Transportation Service in Sri Lanka using IoT(Institute of Electrical and Electronics Engineers, 2022-10-15) Weligamage, H. D; Wijesekara, S. M; Chathwara, M.D.S.; Isuru Kavinda, H.G.; Amarasena, N; Gamage, NTraveling is one of the necessary and common behavior of any society. Thus, there are many ways of human travel. Due to the fact that Sri Lanka is still a developing country, the vast majority of the population rely on public transit as opposed to private transportation options. In this situation, public and private bus services are the most common means of transportation for people. People who use bus service for daily traveling face lot of issues due to the delays in bus arrivals, missing the bus or excessive crowd in the bus. This proposed system is intended to make bus travel more efficient and convenient for those who rely on buses as their primary means of public transit. This system provides a mobile application for passengers to utilize in order to observe the real-time position of the buses, as well as their anticipated arrival time, current passenger count, and a visualization of the available seat locations within the vehicle prior to the arrival of the bus. Besides, traditional manual ticketing procedure also cause many difficulties for the passengers like need of carrying changed money each time they travel. To avoid this serious problem, this system introduces a non-interactive automated ticketing system which has a smart card that can be tracked in a RFID zone and an automated fee calculating system using a logical conceptual algorithm considering environmental factors. Along with this a digital ticket is issued including all the required details of a journey. In addition to that, this system has a two-factor authentication process that makes use of face recognition to validate the user's identity before granting access to their smart card. The goal of this application is to provide a systematic solution for the typical challenges that public transportation users face in order to improve the service quality by using IoT-based technologies and image processing.Publication Embargo The Automated Temporal Analysis of Gaze Following in a Visual Tracking Task(Springer, Cham, 2022-05-15) Dhanawansa, V; Samarasinghe, P; Gardiner, B; Yogarajah, P; Karunasena, AThe attention assessment of an individual in following the motion of a target object provides valuable insights into understanding one’s behavioural patterns in cognitive disorders including Autism Spectrum Disorder (ASD). Existing frameworks often require dedicated devices for gaze capture, focus on stationary target objects, or fails to conduct a temporal analysis of the participant’s response. Thus, in order to address the persisting research gap in the analysis of video capture of a visual tracking task, this paper proposes a novel framework to analyse the temporal relationship between the 3D head pose angles and object displacement, and demonstrates its validity via application on the EYEDIAP video dataset. The conducted multivariate time-series analysis is two-fold; the statistical correlation computes the similarity between the time series as an overall measure of attention; and the Dynamic Time Warping (DTW) algorithm aligns the two sequences, and computes relevant temporal metrics. The temporal features of latency and maximum time of focus retention enabled an intragroup comparison between the performance of the participants. Further analysis disclosed valuable insights into the behavioural response of participants, including the superior response to horizontal motion of the target and the improvement in retention of focus on the vertical motion over time, implying that following a vertical target initially proved a challenging task.Publication Embargo BlossomSnap: A Single Platform for all Anthurium Planters Based on The Sri Lankan Market(Institute of Electrical and Electronics Engineers, 2022-10-15) Rathnayake, R.M.S.T; Tharika Pramodi, M.L.A.D.; Gayathree, I. R; Rashmika, L.K.R; Gamage, M; Gamage, AThe popular and extensively grown flowering plant known as the Anthurium is prized for its beauty. In Sri Lanka, anthuriums have a substantial international market. Although it is a significant field that can be further developed by expanding the market, but it has led to a lack of attention, resources, and a moderate cost of production, as well as from the absence of an appropriate market channel, all of which have led to lower productivity and quality. As a result, Anthurium growers have numerous challenges both in terms of production and marketing. This paper introduces a novel mobile application 'BlossomSnap' which involves automating and significantly enhancing the outdated manual process. Using natural language processing, machine learning, and deep learning approaches, the proposed system analyzes the diseases, pests, varieties, and the highest quality plants to create a more secure growing environment. It will provide high-quality, cost effective, and timely services. The first step of anthurium plant disease and pest diagnosis is carried out using image processing, deep learning, and machine learning technologies. In order to identify the infection stage, the following steps involve extracting, classifying, and detecting images of Anthurium flowers and leaves. The accuracy was checked by comparing actual results taken from experts with the predicted results obtained from the proposed system. 'BlossomSnap' achieves an average accuracy of more than 80% and produces a better overall result. An in-place chatbot technology is intended to assist new planters with their problems. The Anthurium plant variety and quality detection methodology is used in concert with to determine the optimum market opportunity.Publication Embargo CertiMart: Use Computer Vision to Digitize and Automate Supermarket with Fruit Quality Measuring and Maintaining(IEEE, 2022-12-09) Rathnayake, W.P.D.N. P; Geeth Dulanjana, D; Punchihewa, A.V.B.W. G; Anjana, N.W. G; Suriya Kumari, P. K.; Samarakoon, USri Lanka has a tropical environment, which makes it easy for fruit and vegetable plants to thrive. Vitamins, proteins, and other nutrients are abundant in fruits. However, there is a time when the fruit is considered to be fresh. During this time, many fruit supplier firms continue to supply fruit that is unsafe for ingestion due to inaccuracy in the sorting process when the fruit is taken from the plantation and the introduction of other fruit into an incorrect packing. As a result, detecting food rotting from the point of production to the point of consumption is critical. Inside the market we realize that there is unavailability of sorting of fruits. Just after receiving the fruit into the supermarket, we should have a way to measure freshness of fruit and maintain it. In addition to this ripened method identification and disease identification will be great help to this help.Publication Embargo Child Head Gesture Classification through Transformers(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Wedasingha, N; Samarasinghe, P; Singarathnam, D; Papandrea, M; Puiatti, A; Seneviratne, LThis paper proposes a transformer network for head pose classification (HPC) which outperforms the existing SoA for HPC. This robust model is then extended to overcome the limited child data challenge by applying transfer learning resulting in an accuracy of 95.34% for child HPC in the wild.Publication Embargo Children's Behavior Analysis Through Smart Toys(Institute of Electrical and Electronics Engineers Inc., 2022-11) Ramesha, M. D. D; Kavindi, M. V; Somawansa, R.P; Yadav, A; Samarasinghe, P; Wedasinghe, N; Jayasinghearachchi, VAnalyzing children's behavior is a major part of pediatric psychological studies. Here we are going to use the hand movements of the child to understand the behavioral pattern with the help of IoT-based toys. © 2022 IEEE.Publication Embargo Comparative Study of Parameter Selection for Enhanced Edge Inference for a Multi-Output Regression model for Head Pose Estimation(Institute of Electrical and Electronics Engineers Inc., 2022-11-04) Lindamulage, A; Kodagoda, N; Reyal, S; Samarasinghe, P; Yogarajah, PMagnitude-based pruning is a technique used to optimise deep learning models for edge inference. We have achieved over 75% model size reduction with a higher accuracy than the original multi-output regression model for head-pose estimationPublication Embargo Comparison of CPU Scheduling Algorithms: FCFS, SJF, SRTF, Round Robin, Priority Based, and Multilevel Queuing(IEEE, 2022-11-03) Rajapaksha, U. U. S. K; Pemasinghe, SIn this article, we are discussing various aspects of CPU scheduling. We first introduce the concept of CPU scheduling, different types of schedulers and the typical terminology used in relation to processes. Scheduling criteria, the optimization of which is the ultimate goal of a CPU scheduling algorithm, are also discussed. We then discuss various types of research studies that have been carried out with respect to CPU scheduling algorithms. Different CPU scheduling algorithms are examined with examples to highlight their characteristics. Advantages and disadvantages of each of these algorithms are also explored. The scheduling algorithms discussed are, first come first served, shortest job first, shortest remaining time first, priority based, round robin, multilevel queue, and multilevel feedback queue.Publication Embargo Deep Vision-Data Mining To Find Insights and Visualization in Code Repositories(Institute of Electrical and Electronics Engineers, 2022-09-16) Ariyarathne, I.G.P.S; Wimalasuriya, M.K; Abesinghe, N.D.N.S; Edirisinghe, E.A.S.H.; Kodagoda, N; Kasthurirathna, DDeep Vision is a code mining system for analyzing and visualizing a repository's codebase so that its users may obtain a sense of the repository's insights. This system will examine codebases and support as many languages as feasible. This system visualizes the file structure, vocabulary and length change rates, comprehensibility and defect rates, etc. It is vital to have a comprehensive grasp of the codebase to manage the program's complexity by calculating multiple factors and presenting them in a descriptive and engaging dashboard to enhance the quality of the software process and the project's controllability. Improved code visualization may help improve code understandability while lowering development costs. In addition, our visualization regions and methodologies are one-of-a-kinds. To get rapid and reliable results, we will create new machine learning models and algorithms for analysis and new categories of a code repository. Our dataset for this research will be GitHub open-source code repositoriesPublication Embargo E-Learning Education System For Children With Down Syndrome(Institute of Electrical and Electronics Engineers, 2022-09-16) Sampath, A.S.T; Vidanapathirana, M.W.; Gunawardana, T.B.A; Sandeepani, P.W.H.; Chandrasiri, L.H.S.S; Attanayaka, BThe World Health Organization assesses that Down Syndrome (DS) affects about 1 in 1000 births worldwide. Children with DS cannot learn, as usual, instigating numerous inadequacies that lead to formative issues such as trouble encoding information and low intelligence to interpret data for decision-making. As a superior technique for these kids' intercom-municating and logical intellect, free-hand sketch drawing, Voice training, and word prediction activities can be success-fully utilized. As the best way to express the mindset of such chil-dren, introducing an E-Learning system makes a friendlier ac-tivity than learning about the past. Because of the improvement of Artificial intelligence and its encouragement, E-Learning-re-lated exploration and applications are moving at an enormous advancement rate. The main objective of this project is to de-velop a reliable and efficient approach to predicting the devel-opment of DS children. Classifying and identifying those hand-written images and voice samples and those samples are given by children with DS compared to the teacher through the construction of a model structure. This research project specially considered local down syndrome children's hand-drawn images, voice samples, letters, numbers, and words as the input. As a result, it gives accuracy and similarity with the teacher's sam-ples and relates parts in the down syndrome children's samples. The system uses artificial intelligence technologies. Through that, the knowledge capacity of the DS children and their con-veyed articulation of that knowledge can be assessed for additional correlations and investigation.Publication Embargo Farming Through Technology Driven Solutions For Agriculture Industry Ceylon E-Agro mobile application-find technology based solutions for agricultural problems(Institute of Electrical and Electronics Engineers, 2022-09-16) Imalka, L.A.; Gunawardana, K.G.A.; Kodithuwakku, K.M.S.K; Arachchi, H.K.E; Harshanath, S.M.B; Rajapaksha, SMany developing countries are based on the agricultural sector. More than 60 percent of the population depends on this sector. This project is focused on maize cultivation. In agriculture, farmers play the most important role. Currently, farmers are facing many problems related to maize cultivation in Sri Lanka. This mobile application will help the maize farmers to overcome these difficulties and provide a good consumer demand for maize cultivation. Through this mobile application, the farmer can find solutions for pest & diseases in maize, fire threat in the farm field. AI based Agri Agent will be provide real-time solutions, bring the farmers and the buyers into the one platform, and provide price prophesying, price index feature. IoT based smart farming features will be provided to remain soil moisture and quality of soil for maize plantation.Publication Embargo A Framework for Tracking And Summarizing Daily Activities with Mobile Phone for Healthy Life of a User(Institute of Electrical and Electronics Engineers, 2022-09-16) Wickramasinghe, K.E.T.; Albertjanstin, N; Wijethunga, L.P. P. L; Rajapaksha, U.U.S.K; Panduwawala, P.K.K.G; Harshanath, S.M.BPeople nowadays have incredibly busy lifestyles with little time to do their activities, and smartphones have become a need for many people all over the world as new features and controls have been developed, and smartphone usage has rapidly increased by individuals. People that lead complex lifestyles find it difficult to balance their activities. As a consequence, an automated method of tracking user activity on a smartphone can give a better solution for managing a user's daily life while also saving time. We take a look at some of the previous studies on user behaviour tracking methods approaches. Derived from the previous studies, we noticed some important challenges, including smartphone addiction and unhealthy posture, battery drainage concerns, managing educational activities, and finding necessary information from the huge data. In this paper, we proposed an automated monitoring approach to track user activities on smartphones and give solutions to the aforementioned difficulties. We developed different algorithms such as ANN to understand smartphone usages and battery interactions, CNN for detecting unhealthy neck postures and Word2Vec for generating similar meanings for fast searchPublication Embargo Intelligent System for Skin Disease Detection of Dogs with Ontology Based Clinical Information Extraction(Institute of Electrical and Electronics Engineers Inc., 2022-10-29) Rathnayaka, R. M. N. A; Anuththara, K. G. S. N; Wickramasinghe, R.J.P; Gimhana, P. S; Weerasinghe, L; Wimalaratne, GThe largest organ in dogs, the epidermis, is crucial in supplying immunological responses. Skin will preserve all the nutrients and safeguard the cells while warding off harmful or pathogenic substances. Most dog owners today are not aware that their pet dog has a skin condition. Although they were aware of these ailments, they had no notion of how to cure them. In such a situation, the dog may experience pain and an aggravation of the condition. Owners should therefore take their dogs to the vet, even if the skin condition is minor. It can, however, be a costly procedure. There aren't many forums where dog owners may get advice from professionals and ask inquiries regarding their pets. The solution suggests a fully functional mobile application which is a combination of disease identification feature, disease severity level detection feature, domain specific knowledge base with semantic web development and a domain specific AI based chat-bot to the dog owners to overcome this problem using Convolutional Neural Network (CNN) and natural language processing (NLP).System will extract the necessary features from the images of the lesion to classify the skin condition and Severity level of the disease. The results obtained show disease type classification is within the accuracy range of 77.78% to 100% which tested again 4 CNN base models. As for the severity level identification accuracy situated around 99.62%.Publication Embargo Intelligent Wheelchair with Emotion Analysis and Voice Recognition(IEEE, 2022-12-26) Perera, S; Gamage, S; Weerasinghe, C; Jayawardena, C; Pathinayake, K; Rajapaksha, SIntelligent wheelchairs are becoming more and more prevalent in contemporary life, and the peaceful interaction of humans with wheelchairs is one of the most popular research topics. The development of a voice recognition and emotion recognition based intelligent wheelchair framework is being addressed here for truly impaired/disabled people who are unable to operate the wheelchair by hand. The patient can operate the wheelchair using voice commands, and the wheelchair’s Emotion Analysis module recognizes the patient’s face and records the patient’s emotions before sending the information to a cell phone application. A portion of the intelligent wheelchair is made to gather crucial information given by other units and send out emergency calls or notifications to the caregivers. Face recognition technology uses image processing to identify facial expressions by detecting the patient’s face and facial expressions. This helps the other components collect and send data via Internet of Things technologies. Speech – to –Text and Text – to-Speech Methodology is used in the voice recognition module and it captures the voice command data set and extracts the features of the commands.The model is already built and trained to recognize the commands and to send action request to the relevant unit.The Responsive AI auto starts the timer when the patient moves away from the wheelchair, recognizes time and responses back. This unit auto also sends the alert and calls to the guardian when the user has no response.Publication Open Access LEXISGURU: Mobile Application for Learning Basic Lexis in English for Kids(Springer Science and Business Media Deutschland GmbH, 2021-11-05) Jayasinghe, M. J. W; Hennayaka, W. H. M. A. D. H; Fernando, M. P. M; Thilakarathne, K. N. U; Samarakoon, U; Kumari, SLexis is an essential part of English vocabulary that puts a good foundation on a child’s English knowledge. In this rapidly globalizing world, it is fundamentally essential to learn English from a young age. In recent years eLearning, mobile applications have been developed for teaching Lexis to children. The market of educational mobile apps, especially for English language learning, has been rapidly growing. Especially in a country like Sri Lanka, English is not the mother tongue, it is the second language. So, when that second language is not taught right the child will lose interest in learning that language. The problem is that the existing lexical learning mobile applications does not aim at keeping the child interested and interactive in the learning process and in Sri Lanka, children find it difficult to understand these lexical parts. As a result, teachers and parents had to spend a lot of time to teach them those lexical parts. We designed and developed a mobile application called “LexisGuru” that uses interactive and effective ways to teach three lexical parts that are homophones, synonyms, and antonyms to children aged between 8–10 in Sri Lanka. This mobile application uses Machine Learning (ML), Image Processing (IP), gamification that includes collaborative environments, and speech recognition techniques. The developed mobile application was introduced to primary level learners, and they were all very attracted and interested while using this application. The attractive user interfaces, the pretests, and posttests, notifying the child when he loses focus while learning, using interesting stories and activities to teach lexis, playing a game with multiple players, and asking questions from the lesson and taking the voice inputs gave a new experience and showed that making the mobile application interactive as possible is an effective way to teach lexis to children.Publication Embargo Mobile and Simulation-based Approach to reduce the Dyslexia with children Learning Disabilities(Institute of Electrical and Electronics Engineers, 2022-09-16) Muthumal, S.A.D.M; Neranga, K.T.; Harshanath, S.M.B; Sandeepa, V.D.R.P; Lihinikaduwa, D.N.R; Rajapaksha, U.U.S.KLearning disabilties are frequently overlooked while treating various limitations. The primary causes for such a dropout might be a lack of awareness of the risks and access to adequate medical care. Because Learning Disabilities are neurological illnesses, their etiology is unknown. Learning Disabilities do not have a therapy or a cure. Dyslexia, Dysgraphia and Dyscalculia are the most common types of Learning Dis-abilities among school children. There are numerous approaches for diagnosing and treating this ailment available today. The optimal method is to make use of a mobile application. Existing applications either do not adequately handle the problem or have minor limitations in terms of meeting genuine expectations. We can mitigate this dysfunction by utilizing mobile and simulation-based technologies. Furthermore, earlier research has not given contact and curiosity among children a significant emphasis and children have not interacted with robots. Therefore, this paper, introduces interactive and collaborative mobile appli-cation called 'Helply' with a robotic based simulation that may foster learning and help children improve and encour-age Color identification skills, Reading skills and Short-term memory skills the learning process while reducing their other dyslexic disorders. Also, NAO Robot is used to taking inputs as voice clip using voice recognition technology and images using image processing technology which embedded in NAO robot. We constructed three models for three distinct disorders and obtained accurate findings. CNN model for colour disorder had a training accuracy of 93%, FFNN model for short-term memory disorder had a training accuracy of 98%, and CNN model for reading disorder had a training accuracy of 94%.Publication Embargo A Notion of Real-Time Anomaly Detection for IoT Devices Based on Hardware-Level Performance(Institute of Electrical and Electronics Engineers, 2022-11-03) Umagiliya, T; Senarathne, A; Rupasinghe, LInternet of Things (IoT) is becoming a considerable topic due to its benefits in the modern world. IoT devices carry out simple routine duties, but they can be valuable. IoT devices or a group of devices are connected to the internet, anomaly detection is essential, considering securing the IoT devices within the isolated environments. The most known and typical attacking modes for IoT devices are denial-of-service (DoS) and password brute-force attacks. The most dangerous attack is the Zero-day attack. The best mechanism for finding those issues as a solution is the concept of anomaly detection. Considering IoT device hardware-level anomaly detection mechanism uses the heat and the power consumption for detections. The results of those concepts can be misleading due to environmental situations. Here, it discusses the distinct approach to merely overcoming those problems using CPU and RAM utilization and driving the solution efficiently and effectively up to 99.9%.