Browsing by Author "Attanayaka, B"

Now showing 1 - 12 of 12

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Blockchain based Patients' detail management System
(IEEE, 2020-12-10) Abeywardena, K. Y; Attanayaka, B; Periyasamy, K; Gunarathna, S; Prabhathi, U; Kudagoda, S
In the data technology revolution, electronic medical records are a standard way to store patients' information in hospitals. Although some hospital systems using server-based patient detail management systems, they need a large amount of storage to store all the patients' medical reports, therefore affecting the scalability. At the same time, they are facing several difficulties, such as interoperability concerns, security and privacy issues, cyber-attacks to the centralized storage and maintaining adhering to medical policies. Proposed Flexi Medi is a private blockchain based patient detail management system which is expected to address the above problems. Solution proposes a distributed secure ledger to permits efficient system access and systems retrieval, which is secure and immutable. The improved consensus mechanism achieves the consensus of the data without large energy utilization and network congestion. Moreover, Flexi Medi achieves high data security principles based on a combination of hybrid access control mechanism, public key cryptography, and a secure live health condition monitoring mechanism. The proposed solution results in successfully deployed smart contracts according to the roles of the system, real time patient health monitoring with more scalable and access controlled system. The overall objective of this solution is to bring the entire medical industry into a common platform using a decentralized approach to store, share medical details while eliminating the need to maintain printed medical records.
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Data-driven Business Intelligence Platform for Smart Retail Stores
(IEEE, 2021-12-09) Eheliyagoda, D. R. M. R. R. D. R. S; Liyanage, T. K. G; Jayasooriya, D. C; Nilmini, D. P. Y. C. A; Nawinna, D. P; Attanayaka, B
The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client delivery.This decision support system used Machine Learning (ML) technologies such as LSTM (Long short-term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule Mining Algorithms as key technologies. Data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system.
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Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks
(IEEE, 2021-09-30) Mihiranga, A; Shane, D; Indeewari, B; Udana, A; Nawinna, D. P; Attanayaka, B
Heart attack is one of the most frequent causes of death in adults. The majority of heart attacks lead to death before any treatment is given to patients. The conventional mode of healthcare is passive, whereby patients themselves call the healthcare services requesting assistance. Consequently, if they are unconscious when heart failure occurs, they normally fail to call the service. To prevent patients from further harm and save their lives, the early and on-time diagnosis important. This paper presents an innovative web and mobile solution designed using it as Internet of Things (IoT) technology and Machine learning concepts to effectively manage heart patients, the ‘CARDIIAC’ system. This system can predict potential heart attack based on a set of identified risk factors. The system also can identify an actual heart attack using the readings from a wearable IoT device and notify the patient. The system is also equipped with emergency event coordination functionalities. Therefore, ‘CARDIIAC’ provides a holistic care for heart patients by effectively monitoring and managing emergencies related to heart diseases. This would be a socially important system to reduce the number of heart patients who die due to the inability to get immediate treatment.
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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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Expert Prediction System for Spice Plants Grown in Sri Lanka: An Incentive for Planters
(IEEE, 2021-12-09) Gunasekara, R; Withanage, H; Wimalachandra, N; Hettiarachchi, L; Attanayaka, B; Thelijjagoda, S
Spice is an element that brings unique identification to Sri Lanka. The taste that is inherent in Sri Lankan spices is the main reason for this unique identification. The demand for Sri Lankan spices is growing day by day in local markets as well as in markets overseas. The plantation of spice crops needs to be planned carefully as those add a significant contribution not only to the domestic consumption but also to Sri Lanka’s export income. Hence, the cultivation of spices should be done systematically to provide a supply that meets the demand. In most cases, large scale and small scale of these crop plantations are not successful. Therefore, assisting these spice planters to identify the most suitable location for crop growth has become a critical requirement in the agriculture sector of Sri Lanka. As there are no applications developed yet in Sri Lanka to support this requirement, researchers try to give a reasonable solution to fill this gap. ‘Mr. Masala’ mobile application was developed with aim of encouraging planters to cultivate spices successfully. This mobile application can be used to identify whether a selected location by a planter is suitable to grow the spice plant they expect to grow. This is done by measuring environmental conditions such as temperature, rainfall, humidity, sunlight and soil pH. Also, users would be able to get an approximate amount of crop productivity, production costs & income for the size of their land, measure the amount of fertilizers needed for soil preparation & maintenance, and amount of pesticides needed to control pests and diseases. Furthermore, spice planters can measure factors required for the growth of spices regularly, helping them obtain expected yields and profits.
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Location Intelligence Based Smart E-Commerce Platform for Residential Real-Estate Industry
(Institute of Electrical and Electronics Engineers, 2022-10-22) Balasooriya, T; Kavishka, L; Dananjana, I; Yumna, M; Thelijjagoda, T; Attanayaka, B; Marasinghe, L
Making the decision to purchase or invest in real estate can be a very crucial process due to its high financial risk. The purchasing decision of residential real estate properties can be even more decisive because, apart from the financial risk, the choice of a property can have a great impact on the future lifestyle of the buyer. When considering residential real estate, one major factor to be considered is the property location. This research sought to determine the applicability of modern technologies such as location intelligence and machine learning in the development of an e-commerce system that may assist users in making optimal residential real estate location decisions. Third-party web APIs were used to obtain location data, and as a result of the study, methodologies were defined to convert location data into meaningful insights by using statistical methods like weighted sum and the analytical hierarchy process. The functionalities in the proposed system have been designed, considering the roles of both buyers and sellers in the real estate business. In the proposed system, the location quality index framework provides overall insights on the location, the personalized insights and alternative locations are generated via the personal preference-based suitability analyzer and the price prediction system provides the current and future price fluctuations. The usefulness of image processing technologies and machine learning for making the sellers' journey easier on a real estate platform has also been assessed.
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A Mobile Gamified Application to Improve Mathematics Skills of Students from Age 9 to 11 Years
(Institute of Electrical and Electronics Engineers, 2022-09-16) Karunasekara, K.K.R; Olinka, P.L.T; Kodithuwakku, K.T.D; Jayashan, B.V.P; Kahandawaarachchi, C; Attanayaka, B
The revolutionary change in technology has directly involved with e-learning systems. Mathematics is recognized as essential yet difficult for most of students. The main problem is considered as existing solutions were not provided a better solution for knowledge level enhancement of mathematics by detecting the exact knowledge levels of students. Current applications have offered permission to select students' knowledge levels by themselves or initiated from the initial level which has led to wrong identification of mathematics knowledge. As a solution for these problems gamified mobile application is provided by introducing a knowledge level detection with a whole syllabus coverage question classification and answer classification by using a Logistic Regression extra trees classifier. The knowledge improvement is performed from the exact knowledge level detected and positive emotions of students. Convolutional neural network is utilized for emotional recognition. Further mental health is improved via a Chabot as an educational encouragement and gamified application is developed based on psychology and preference around ten years by adhering to the best practices of Human-Computer Interaction. The proposed solution is considered as a multi-valued and cost-effective solution which will improve the mathematics knowledge level and good mental wellbeing of students with an appealing gamification environment.
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A New Approach for Consumer Protection with Business Intelligence and Data Visualization
(IEEE, 2021-12-09) Kariyawasam, K. S. T. U. S; Liyanaarachchi, L. A. A. S; Chathurabhani, H. M. N. N; Jayakody, A; Attanayaka, B
According to the current market usage in Sri Lanka, there is no proper system to manage the buying and selling process of consumer goods and services. This paper presents a possibility of developing a systematic and essential food items management system using a mobile application with public and private interventions benefiting both the trade and the consumer is being explored. The authors discussed a methodology for managing essential food items through business intelligence and data visualization. It connects the trade and consumer sectors and the public and responsible private sectors related to this sector through a mobile application and presents data related to this sector through business intelligence forecasting and visualization methods. This research will also help reduce consumer problems by building transparency in the essential foodstuff sector. It will also systematically update the future of the essential food and beverage industry. The findings contribute to the body of knowledge on the New Approach for Consumer Protection with Business Intelligence and Data Visualization.
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Personalized Adaptive System for Enhancing University Student Performance in Sri Lanka
(Institute of Electrical and Electronics Engineers Inc., 2025) Dissanayake, N; Samarakoon, C; Wickramasinghe, D; Pathirana, M; Gamage N.D.U; Attanayaka, B
The growing need for personalized learning strategies has driven the development of data-driven solutions to meet the diverse needs of Sri Lankan university students. A key challenge lies in identifying optimal learning paths that align with individual capabilities, learning styles, and engagement behaviors to improve academic performance. While previous research has explored generalized learning models, these often fail to adapt to the specific demands of individual learners. Traditional strategies lack personalization, resulting in inconsistent learning progress. To address this gap, the research introduces an assistive, data-driven approach that leverages Self-Organizing Maps (SOMs), Adaptive Learning (AL), Content-Based Filtering, Graph Neural Networks (GNNs), and Social Network Analysis (SNA) to create optimized, personalized learning strategies. Clustering algorithms and predictive analysis were used to segment learners and deliver tailored interventions based on their behavior. The proposed system integrates advanced machine learning techniques to enhance student engagement and improve overall academic outcomes through personalized pathways.
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Success Factors of Requirement Elicitation in the Field of Software Engineering
(IEEE, 2022-12-09) Attanayaka, B; Nawinna, D; Manathunga, K; Abeygunawardhana, P. K. W
Requirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.
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UrbanGreen - E-Waste Detection and Analysis using YOLOv5
(Institute of Electrical and Electronics Engineers Inc., 2025) Madusanka A.R.M.S; Nawaratne D.M.R.S.; Gamage, N; Attanayaka, B
E-waste has become a global concern that challenges environmental sustain ability. The disposal of electronic devices is often poorly managed, especially in urban areas. This research aims to develop an innovative e-waste management system suitable for urban areas, focusing on accurately identifying electronic devices and their harmful components through advanced image processing techniques. (Y olov5) The system identifies various electronic devices, harmful components and materials and assesses their recyclability, improper disposal's environmental and health impacts, empowering users to make informed decisions about disposal and recycling. The system will integrate tools to identify E-waste, promote the reuse of electronic devices, educate the public through interactive educational platforms, and locate nearby e-waste collection centers. By addressing these critical aspects of e-waste management, the project aims to provide a useful platform to manage e-waste effectively in urban areas. This paper was developed to discuss E-waste detection and analysis using YOLOv5 object detection model.
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WONGA: The Future of Personal Finance Management – A Machine Learning-Driven Approach for Predictive Analysis and Efficient Expense Tracking
(IEEE, 2023-07-10) Uyanahewa, M.I.R; Jayawardana, G.V.H.D; Bandara, M.B.D.N; Hapugala, H.A.V.V; Attanayaka, B
The financial literacy of Sri Lankans is relatively low, leading to difficulties in managing personal finances. This research presents a smart solution to simplify the complexities associated with money management and assist individuals in managing their finances more efficiently to achieve better financial health without requiring a comprehensive knowledge of money management from the user. The proposed system automates personal finance management with minimal user effort, reducing manual data entry by tracking cash flow by utilizing SMS messages and expense bills to extract bank transaction data and cash expenditures. Each extracted expense will automatically be categorized into the correct expense category. The system also generates a custom budget plan for each user based on spending patterns to help them stay on the budget throughout the month and avoid irrational overspending. Furthermore, the system provides a mechanism to predict future expenses associated with upcoming events based on calendar events, allowing users to devise the most efficient budget plan and avoid facing financially unprepared events in the upcoming month. All these smart solutions are bundled up in the "Wonga" mobile application to help users make better financial decisions to achieve personal financial success.