Browsing by Author "Nawinna, D. P"

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Open Access
Analysis and improvement of a construction permit approval process: A teaching case for developing business process development capabilities, targeting developing nations
(ACIS, 2014) Weerasinghe, K; Bandara, W; Dharmasena, T; Kuruppubandara, M; Nawinna, D. P
With the increasing competitiveness in global markets, many developing nations are striving to constantly improve their services in search for the next competitive edge. As a result, the demand and need for Business Process Management (BPM) in these regions is seeing a rapid rise. Yet there exists a lack of professional expertise and knowledge to cater to that need. Therefore, the development of well-structured BPM training/ education programs has become an urgent requirement for these industries. Furthermore, the lack of textbooks or other self-educating material, that go beyond the basics of BPM, further ratifies the need for case based teaching and related cases that enable the next generation of professionals in these countries. Teaching cases create an authentic learning environment where complexities and challenges of the ‘real world’ can be presented in a narrative, enabling students to evolve crucial skills such as problem analysis, problem solving, creativity within constraints as well as the application of appropriate tools (BPMN) and techniques (including best practices and benchmarking) within richer and real scenarios. The aim of this paper is to provide a comprehensive teaching case demonstrating the means to tackle any developing nation’s legacy government process undermined by inefficiency and ineffectiveness. The paper also includes thorough teaching notes The article is presented in three main parts: (i) Introduction - that provides a brief background setting the context of this paper, (ii) The Teaching Case, and (iii) Teaching notes.
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Android based e-Learning solution for early childhood education in Sri Lanka
(IEEE, 2013-04-26) Priyankara, K. W. T. G. T; Mahawaththa, D. C; Nawinna, D. P; Jayasundara, J. M. A; Tharuka, K. D. N; Rajapaksha, S. K
Preschool age is critical for a child's development. The parents of competitive society today are challenged to meet learning needs of children. They are unable to dedicate time and are not up-to-date with change of technology. The need for easy to use and effective learning aids has become vital. This research investigates how to support self-learning of modern-day preschoolers. Kids Training e-Learning System (KTeLs) is a learning tool that facilitate self learning of preschool kids. It is based on a strong theoretical foundation and allows kids to develop cognitive and psychomotor skills such as drawing, writing, recognition of numbers, basic shapes and colors and logical thinking. It incorporates a special algorithm to detect and guide the kid to write a letter in the correct direction without guidance of parents. It comes with kids-friendly navigation. The tool was designed as an Android application for tablets and was tested with a focus group. The backgrounds, sounds and colors are especially designed to maintain the attention of kids.
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Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant
(IEEE, 2019-12-05) Jayalath, A. D. A. D. S; Nadeeshan, P. V. D; Amarawansh, T. G. A. G. D; Jayasuriya, H. P; Nawinna, D. P
Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don't have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user's questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.
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CoviDefender: Digital Personal Guard For Defending Against COVID19
(IEEE, 2021-09-30) Dayarathna, p; Kumara, I; Ranaweera, D; Nawinna, D. P; Karunaratne, G; Wijekoon, J
In late 31 December 2019, a cluster of unexplained pneumonia cases was reported in Wuhan, China [1]. A few days later, the causative agent of this mysterious pneumonia was identified as the new COVID-19 virus. Currently, it has been spreading for more than one and a half years and has lost a huge number of lives all over the world. Most people faced this disaster because of their ignorance, carelessness and lack of updates. By the way most people are in lack of knowledge regarding COVID-19 pandemic, symptoms and what should do to survive from that. Those issues are great problems nowadays. “CoviDefender” is set to offer a solution to this worldwide COVID-19 pandemic problem. This is a new technological solution from a mobile application. “CoviDefender” is a Smart Assistant for Defending against COVID-19 Pandemic. This can be described as a solution to the ignorance and carelessness of the people who have been the main cause of the spread of this epidemic.
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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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Data-driven Online Decision Support for Hotel Site Selection
(IEEE, 2021-12-22) Kumarage, I; Weerakeshara, N; Chamika, T; Rathnapala, H; Nawinna, D. P; Gamage, N
Location is one of the fundamental factors that determine hotel success. The location, once selected, cannot be changed without a significant investment. This research aims to identify the location-specific factors that affect Sri Lankan coastal hotels. The factors that affect the location rating have been assessed under location attraction, accessibility, and popularity. An ensemble learning model has been trained to predict the location score of a hypothetical location, assess the manner accessibility affects hotel performance, and predict location popularity based on the surrounding competition. The results show that this method can assess hotel location and performance, with significant accuracy and the identified location-related factors that contribute to a hotel's success can be used by hoteliers and investors to improve decision making.
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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.
Open Access
A gamified learning tool for sri lankan primary schools
(SLIIT, 2014-09-26) Ranathunga, R. A. G. K; Rajakaruna, L. A. V. N; Karunarathna, S. A. T. N; Abeywardena, L. N. Y; Nawinna, D. P; Halloluwa, T
One of the critical problems in primary education sector particularly in Sri Lanka is inadequate resources for children who has special needs and Attention Deficit-Hyperactivity Disorder (ADHD) is a commonly observed among primary level children with symptoms like inabilities to pay attention and to stay on task. Such children are less interested in mathematics and have problems in learning the subject. To overcome above difficulties, the need for easy to use and effective learning aids has become vital and this research investigates on a possible way to support them using gamified learning infrastructure. ‘Gamified Learning Environment for the Children with Special Needs in Primary Schools’ is a learning tool that improves children’s attitude towards mathematics in an interactive manner and the performance of children with learning difficulties. It comes with a gamified architecture with an interactive background, attractive graphics, colours, images and sounds. This system is designed as a web based learning environment that facilitates effective learning of mathematical concepts and badges, medals and rewards are distributed considering their achievements as a motivation technique. The intention of this automated tool is to deliver a user friendly automated learning tool grounded with proven effective pedagogy for affordable cost.
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Identification of Medicinal Plants by Visual Characteristics of Leaves and Flowers
(IEEE, 2019-12-18) Jayalath, A. D. A. D. S; Amarawanshaline, T. G. A. G. D; Nawinna, D. P; Nadeeshan, P. V. D; Jayasuriya, H. P
In Ayurveda medicine, correct identification of medicinal plants is of great importance. Plants are identified by human experts using their visual features and aroma. Incorrect identification of medicinal plants may lead to adverse results. Plant identification can be automated using visual morphological characteristics such as the shape, color, and texture of the leaves and flowers. This paper presents how rare medicinal plants were identified with high accuracy by applying image processing and machine learning capabilities. For this study, a database was created from scanned images of leaves and flowers of rare medicinal plants used in Sri Lankan Ayurveda medicine. Both the front and back sides of leaves and flowers were captured. The leaves are classified based on the unique feature combination. Identification rates up to 98% have been obtained when tested over 10 plants.
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Impact of non-functional requirements on the success of ubiquitous systems
(IEEE, 2020-12-10) Sandeepani, S; Nawinna, D. P
With the recent advancements of technology, Ubiquitous Systems have rapidly become popular all over the world. It is a new paradigm that focuses on smooth integration of technology in human environments enabling users to access information and functionality anytime and anywhere. Software development companies nowadays increasingly invest in the ubiquitous system development projects in order to stay competitive and survive in the IT Industry. Success of ubiquitous system development projects heavily depends on Nonfunctional user requirements. Identification of the nonfunctional requirements is challenging since it represents the quality attributes of the system and are not directly measurable. This quantitative research aims to evaluate the different types of non-functional requirements that significantly contribute to the success of ubiquitous system development projects. This study was based on the data collected from the software industry in Sri Lanka. The results of this study indicate that both the product-related and organizational-related nonfunctional requirements strongly affect the ubiquitous systems success. The findings provide insights to the vendors of ubiquitous system development companies in the software industry.
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An Integrated Platform of Water Quality Management for National Water Supply and Drainage Board
(IEEE, 2019-12-18) Hettiarachchi, S; Proboshena, D; Stembo, L; Rajapaksha, H; de Silva, R; Nawinna, D. P
With the growing rates of population and environmental pollution, the need for in-depth research on sustainable water-quality management systems has become evident. This paper presents a smart system for water quality management including predictive capabilities. The proposed system facilitates the regular monitoring of water quality parameters at water treatment plants using an easy to use IoT device and facilitates to identify water leakage points in the water distribution network using crowd-sourcing and visualization techniques. Most importantly, proposed system is capable of predicting of upcoming changes of water quality with an accuracy of 99% and calculating the respective purification costs. Digital dashboard in the system presents summarized information on leakages, customer feedback, water quality patterns and associated purification costs.
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IoT-based Monitoring System for Oyster Mushroom Farming
(IEEE, 2021-12-09) Surige, Y. D; Perera, W. S; Gunarathna, P. K; Ariyarathna, K. P; Gamage, N; Nawinna, D. P
Agriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.
Open Access
IOT-based Monitoring System for Oyster Mushroom Farms in Sri Lanka
(KDU IRC, 2022-01-10) Surige, Y. D; Perera, W. S. M; Gunarathna, P. K. M; Ariyarathna, K. P. W; Gamage, N. D. U; Nawinna, D. P
Oyster Mushrooms are a type of a fungus which is very sensitive to the environmental factors and vulnerable to diseases and pest attacks which directly effects local trade and export strength. Mushroom is a climacteric type of food which continues its cycle even after harvesting. The mushroom farming process still uses manual mode such as the identification of diseases uses a farmers eye visually, harvesting of mushrooms are decided based on the visual appearance while the environmental factors are decided based on gut feelings. These methods has its limitations which requires more potential to improve both the quality and capacity of mushroom production. With the advancements of technology, this farming process can be performed with the aid of an IoT device and deep learning model. This research applies Convolutional Neural Networks (CNN) with Mobile Net V2 model to detect mushroom harvest time and any disease spread with an accuracy of 92% and 99% respectively. Long Short-Term memory (LSTM) to analyze the detected environmental factors with an accuracy of 89% and this system predicts the yield of mushroom production with the support of LSTM model with an accuracy of 97%. This developed system which aids mushroom farming activities is connected with the farmers through s mobile application
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Mobile-based Assistive Tool to Identify & Learn Medicinal Herbs
(IEEE, 2020-12-10) Senevirathne, L. P. D. S; Pathirana, D. P. D. S; Silva, A. L; Dissanayaka, M. G. S. R; Nawinna, D. P; Ganegoda, D
Sri Lanka is recognized and valued globally due to its rich heritage of tropical plants, herbs and trees. A need for the valuation of valuable herbs are identified among both Sri Lankans as well as tourists. This paper brings forth a solution in distinguishing medicinal herbs through leaves and flowers using deep learning and image processing algorithms via a mobile application. The proposed mobile application identifies a flower and leaf by its morphological features, such as shape, color, texture. The perspective is to achieve highest accuracy for plant identification using image processing. The proposed model revealed an accuracy of 92.5% in the classification of leaves and flowers. Accuracy of 6 different plants are identified using this method. This application also provides Sinhala virtual assistant which enables user to search herbs using the name, which is popular among people, to obtain information about herbs. The main outcome of the virtual assistant of the research is to develop an information retrieval method on medicinal herbs in a more accurate, easy and efficient way. In addition. this application also provides 3D structure of the selected medicinal herb in augmented reality (AR).
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Power Profiling: Assessment of Household Energy Footprints
(IEEE, 2021-03-06) Wijesinghe, V; Perera, M; Peiris, C; Vidyaratne, P; Nawinna, D. P; Wijekoon, J
Reduced energy footprint is considered an indicator of efficiency around the world. Having insights into electricity consumption behavior of individuals or families across the day is very useful in efficient management of electricity. In this paper, we present s study that focused on identifying patterns in the monthly electricity consumption profiles of a single household with the K-means clustering algorithm. The data required for this study was collected through a survey in the Sri Lankan context. The survey mainly captured the factors affecting electricity consumption. After proving the demand of electricity is dependable on the data that has been collected, they will be keyed into data models/ profiles that will be built using clustering algorithms. A load profile will be designed using K-means to identify usage patterns of a household on a monthly basis. The parameters that affect the electricity consumption were tested and trained using the SVM algorithm. The outcomes of this study include; identifying the factors contributing to the electricity consumption, identifying electricity consumption patterns, identifying the energy footprint of individuals or families and predicting the future electricity requirements. The results of this study provide many advantages for both consumers and suppliers in efficient management of electricity. It also provides significant impacts in both micro and macro levels through enabling efficient decision-making regarding management of electricity.
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Predictive Analytics Platform for Airline Industry
(IEEE, 2020-12-10) Tissera, P. H. K; Waduge, K. T; Perera, M. A. l; Nawinna, D. P; Kasthurirathna, D
The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.
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Predictive Analytics Platform for Airline Industry
(IEEE, 2020-12-10) Tissera, P. H. K; Waduge, K. T; Perera, M. A. l; Nawinna, D. P; Kasthurirathna, D
The research is to develop accurate demand forecasting model to control the availability in Airline industry. The primary outcome of the model is that the Airline organization can maximize the revenue by controlling the availability. The product in airline industry is the seat, which is an expensive, unstock able product. The demand for the seats is almost uncertain, the capacity is constraint and difficult to increase and the variable costs are very high. Hence the priority of the expected demand forecast is very high for airline industry. An accurate mechanism to predict the revenue for future months of ODs (Origin destinations) is done using fare and passenger data. The revenue is derived by the number of passengers and the fares they pay which vary for each flight. Airline travel is very susceptible to the social, political and economic changes. Therefore, passenger buying patterns change quite dynamically. Hence, it is challenging to develop an accurate method to project the revenue for each route. To overcome this, we are going to use semi-supervised learning mechanism. We have the current ticketed revenue plus we have the current booked passengers. We also have the ticketed passenger details of previous flights. Hence most of the information is available, however changing market conditions is an unknown variable which can have a significant impact on passenger travel patterns. Through this research We are going to design and develop the best fit model to forecast flight OD level passenger demand based on the historical data.
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Predictive Analytics Platform for Organic Cultivation Management
(IEEE, 2019-12-05) Rathnayake, R. M. S. M; Ekanayake, E. W. L. M. B; Kahandawala, K. A. I. P; de Silva, W. G. S. C; Nawinna, D. P; Kasthurirathna, D
There is an increasing demand for organic farming as an environmentally friendly alternative to industrial agricultural system. It is a method of farming that does not involve pesticides, fertilizers, genetically modified organisms, and growth hormones. Organic farming yields vital benefits such as preservation of soil's organic composition, fertility, structure and biodiversity, reduce erosion and reduce the risks of human, animal, and environmental exposure to toxic materials. This paper presents design and development of a software platform for supporting sustainability of organic agriculture system, which has been implemented as a proof of concept in Sri Lanka. The predictive analytics based service platform that not only supports farming decisions of organic farmers but also offers an electronic market place for organic foods. The proposed system is capable of predicting organic harvests, prices and provide decision support on crop selection for upcoming cultivations. To implement this system, machine learning and optimization techniques have been used. In addition, it uses block chain technology to maintain authentication and identity management of organic farmers so that the consumers can trust they get genuine organic food.
Open Access
The Role of Social Capital and ICT in Inter-Firm Collaboration on Syndicated Development Loans: An Empirical Study of the Finance Industry in Sri Lanka
(Proceedings of the AIS SIGGlobDev Pre-ICIS Workshop, 2016-09-12) Nawinna, D. P; Venable, J. R
Social capital is an influential concept in understanding why and how some organisations do better in inter-organisational relations. It has been recognized as an important factor in developing relationships of trust, forming the foundation for greater collaboration among individuals, groups, and organisations. This paper presents findings from an empirical study that investigates the effect of multiple dimensions of Social Capital and Information and Communication Technologies (ICT) on inter-bank strategic collaboration in a developing context. Moreover, the study explores the moderating role of ICT capability in the inter-bank industry domain. This paper develops and presents a new theory on how social capital and ICT drive inter-firm partnerships. The theoretical model is validated using a quantitative approach to analyse survey and secondary data using Partial Least Squares Structural Equation Modelling. The findings of this study suggest that there is a strong positive combined effect of social capital and ICT towards inter-firm strategic alliances. The results contribute to both social capital theory and theories of ICT for development. It will also contribute to a more holistic perspective that incorporates social, technical, and organisational aspects for building effective strategies
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Singlish Sentiment Analysis Based Rating For Public Transportation
(IEEE, 2021-01-27) Sharounthan, B; Nawinna, D. P; De Silva, R
As the primary transportation mode in Sri Lanka, Public transportation is tightly attached with the daily life of the people. The important factor in transportation is providing a door to door mobility. This paper presents a study focused on measuring the service quality of public transportation based on the comments and feedbacks provided by the passengers with the aim of producing a better service for users. Comments were gained from social media and were used to produce a comprehensive rating mechanism to rate the transportation service providers. This tool was developed using natural language processing techniques using the data from the Sri Lankan context. With the popularity of Social Media in Sri Lanka, many people use Singlish (Sinhala written using English scripts) for online communication. The rating engine presented in this paper is capable of performing Sentiment analysis on comments given in Singlish Language in the Transportation domain.