Faculty of Computing

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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, S
Lexis 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.
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Accuracy of Diabetes Patient Determination: Prediction Made from Sugar Levels Using Machine Learning
(Springer, Cham, 2022) Krishnananthan, S; Puvanendran, S; Puvanendran, R
This 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%.
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Identifying Objects with related Angles using Vision-based System integrated with Service Robots
(2022) Lakshan, K.K. Pasindu
Manipulation an object can be done with the collaboration of a human to a robot by introducing the object in a proper way. To do this in an easy way, we can model the object inside the robot head and add some sensors and cameras to identify the specific object. But when it comes to the real world, we cannot model all the objects in the world inside a robot head. If we can manipulate every object there can be more work would have done by the robots in efficient way. This research will present a strategy to identify the unknown objects using a visionbased system and with the perspective angles of the detected object and the system is integrated with service robots. This will go in a way when the robot should be able to identify the objects around the robot in an asynchronous manner with rotational angles and the pitch and roll angles, perspective to the robot standing surface. The research will be based on Artificial intelligence, Machine learning, and Robotics. Robotics operating system is used for simulating the robots and identification. For the identification process, a few ways can be used. Vision-based identification using color and depth images from an RGB camera, and this research is mainly based on this RGB, and depth feature integrated with YoloV5. And there are some other ways to identify objects like using a 3D-LiDAR laser scanner. However, this learning process, should have a stable object to model and train the object. After the object recognition, by using the proposed methodology robots can calculate and estimate the angles of the detected object. After the acquisition, the robot should be able to identify the object any time when it sees the object. Since this is a robot, we can use this to model unknown objects and retrieve the data from its database and manually name them if there is no one to name it in the time being
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Super Learner for Malicious URL Detection
(IEEE, 2022-02-23) Hevapathige, A; Rathnayake, K
Malicious Uniform Resource Locator (URL) detection is one of the prominent research areas in Cyber security. Machine learning and statistical models are mainly used for this task due to their ability to adapt complex patterns. This research study mainly focused on implementing a machine learning classifier model using Super Learner ensemble to classify malicious URLs. Static feature set is extracted using only the URL information with less latency and reduced computational complexity to support offline and real-time detection. Proposed binary classifier model is used to separate malicious URLs from benign ones whereas the proposed multi-class classifier model separates URLs into benign and multiple categories of attacks (phishing, malware, spam and defacement). These classifiers are tested on a dataset comprising around 750,000 URLs. The empirical results show that the proposed model works well in malicious URL detection. The binary classifier provides 95.145% accuracy and 96.844% precision whereas the multi-class classifier provides 94.69% accuracy and 96.234% precision. Also, the comparison results show that the proposed model outperforms leading supervised machine learning algorithms in malicious URL detection.
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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, A
The 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.
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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, M
Customers 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 algorithm
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SmartCare: Detecting Heart Failure and Diabetes Using Smartwatch
(IEEE, 2022-09-08) Colombage, L; Amarasiri, T; Sanjeewani, T; Senevirathne, C
Busy lifestyles of people which resulted in an increase in non-communicable diseases have demanded a revolution in the healthcare system. This has prompted active research in developing smart sensing devices to automatically monitor the health status of a user with less human intervention. This could be more challenging when the disease is asymptomatic, hence smart solutions for early detection of such diseases are vital to help people to maintain a healthy and long life. In this study, we focus on the most common non-communicable diseases, Heart Failure, and Diabetes which are asymptomatic in their early stages. We propose a SmartCare solution for the real-time detection of heart failure and diabetes disease using a smartwatch. Data collected through a smartwatch along with health data provided by the user are used to detect heart failure, severity levels of the heart failure, diabetes disease, and types of diabetes. Random Forest and Logistic Regression algorithms are used to develop the four prediction models. Extensive evaluations performed on patients' data collected from local hospitals show our SmartCare system can detect the heart failure, severity levels of the heart failure, diabetes disease, and types of diabetes with an F1 score of 0.72, 0.7, 0.72, and 0.86 respectively.
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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, D
Deep 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 repositories
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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, B
The 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.
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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.K
Learning 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%.
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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, S
Many 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.
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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.B
People 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 search
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Security Architecture Development in Internet of Things Operating Systems
(IEEE, 2022-10-04) Sachindra, U. G. T.; Rajapaksha, U. U. S.
Due to the widespread use of the Internet of Things (IoT) in recent years, the need for IoT technologies to handle communications with the rest of the globe has grown dramatically. Wireless sensor networks (WSNs) play a vital role in the operation of the IoT. The creation of Internet of Things operating systems (OS), which can handle the newly constructed IoT hardware, as well as new protocols and procedures for all communication levels, all of which are now in development, will pave the way for the future. When compared to other devices, these gadgets require a comparatively little amount of electricity, memory, and other resources. This has caused the scientific community to become more aware of the relevance of IoT device operating systems as a result of their findings. These devices may be made more versatile and powerful by including an operating system that contains real-time capabilities, kernel, networking, and other features, among other things. IEEE 802.15.4 networks are linked together using IPv6, which has a wide address space and so enables more devices to connect to the internet using the 6LoWPAN protocol. It is necessary to address some privacy and security issues that have arisen as a result of the widespread use of the Internet, notwithstanding the great benefits that have resulted. For the Internet of Things operating systems, this research has provided a network security architecture that ensures secure communication by utilizing the Cooja network simulator in combination with the Contiki operating system and demonstrate and explained how the nodes can protect from the network layer and physical layer attacks. Also, this research has depicted the energy consumption results of each designated node type during the authentication and communication process. Finally, proposed a few further improvements for the architecture which will enhance the network layer protection.
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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, N
Traveling 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.
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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
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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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Recommendation system based on Tamil-English code-mixed text analysis
(Institute of Electrical and Electronics Engineers, 2022-10-15) Vijayakumar, S; Murugaiah, G; Sivanesan, J; Archchana, K; Tissera, W; Vidhanaarachchi, S
The cinema industry has always been popular since its inception many years ago and is a preferred pastime of many people. It can be observed that even though online movie applications are popular in multilingual society, English is the preferred language. Naturally, people of other languages mix their native language with English during communications resulting in an abundance of multilingual data called code-mixed data, available in today's world. This research focuses on the movie recommendation system whose primary objective is to make a recommender system through Natural Language Processing (NLP) Tools for Tamil-English Code-mixed (Tanglish) Comments. Our recommendation system will be a filtering scheme whose primary objective is to predict a viewer's rating or preference towards a movie or web series.
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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, A
The 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.
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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, G
The 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%.
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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, V
Analyzing 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.