Browsing by Author "Kasthuriarachchi, S"

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Open Access
Assessing the Effectiveness of Curbside Pickup Process in Sri Lankan Restaurant Industry
(Emerald Publishing, 2022-12-01) Godakumbura, K. E; Kasthuriarachchi, S
Pandemic situation pushed people to explore many different paths to fulfill their basic requirements. Generally pandemic did a considerable progress to enable ecommerce/online capabilities on almost every supplier/producer. People tried the benefit of contactless delivery services and pickup options. As a result, most of the western countries get used to help their grocery shopping and food ordering with curbside pickup. Existing research found some limitations in online delivery methods. This research used actual data collected through survey questionnaires from customers who are using online ordering applications and accordingly descriptive and inferential statistical data has been used to conduct the data analysis. Throughout the research findings, there were strong positive relationships identified in Efficiency, Time, and Convenience factors with curbside pickup. Also, moderate strong positive relationship between Cost and Curbside pickup. This research concludes that the effectiveness of curbside pickup method has positive influence with the variable factors. In future curbside pickup will illuminate the existing issues related to the ongoing online delivery methods.
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Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques
(Institute of Electrical and Electronics Engineers Inc., 2020-12-09) Nesarajan, D; Kunalan, L; Logeswaran, M; Kasthuriarachchi, S; Lungalage, D
Coconut production is the most important and one of the main sources of income in the Sri Lankan economy. The recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. This paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. Thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. The outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector.
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A Data Mining Approach to Identify the Factors Affecting the Academic Success of Tertiary Students in Sri Lanka
(Springer, Cham, 2018-02-11) Kasthuriarachchi, S; Bhatt, C. M; Liyanage, S. R
Educational Data Mining has become a very popular and highly important area in the domain of Data mining . Application of data mining to education arena arises as a paradigm oriented to design models, methods, tasks and algorithms for discovering data from educational domain. It attempts to uncover data patterns, structure association rules, establish information of unseen relationships with educational data and many more operations that cannot be performed using traditional computer based information systems. It grows and adopts statistical methods, data mining methods and machine-learning to study educational data produced mostly by students, educators, educational management policy makers and instructors. The main objective of applying data mining in education is primarily to advance learning by enabling data oriented decision making to improve existing educational practices and learning materials. This study focuses on finding the key factors affecting the performance of the students enrolled for technology related degree programs in Sri Lanka. The findings of this study will positively affect the future decisions about the progress of the students’ performance, quality of the education process and the future of the education provider.
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Health Care – A Personalized Guidance for Non-Communicable Diseases
(IEEE, 2022-12-09) Dakshima, D.D.T.D; Seliya Mindula, K; Rathnayake, R.M.S.J; Kasthuriarachchi, S; Buddhi Chathuranga, A.K; Lunugalage, D
All people expect to live a healthy life. But today about eighty million people a year suffer from non-communicable diseases. Among non-communicable diseases, heart disease and diabetes are at the forefront, and the number of deaths due to heart disease is rising in people with diabetes. Changes in lifestyle, work-related stress and bad food habits, and smoking addiction contribute to the increase in the rate of several heart diseases and diabetes diseases. Therefore, a reliable and accurate system is needed to identify such diseases in time for proper treatment. The methodology proposed in this research is based on Machine learning classification techniques using Random Forest (RF), Logistic Regression, Gradient Boosting, etc. It is an android mobile application. The prognosis process gives a cardiac risk analysis percentage based on the patient’s heart condition and a diabetic risk analysis percentage based on the diabetic condition by the Kaggle dataset. Accordingly, a system was proposed with daily guidelines including calculation of risk level, Exercise recommendation, Meal planner, and stress-releaser. The accuracy of the proposed system was risk calculation of heart at 82,75%, risk calculation of Diabetics at 81.66%, Meal planner at 89.8%, the exercise scheduler Cardiac status prediction at 73.57%, diabetic status prediction at 78.57%, body performance prediction 74.68% and stress release 100%. This system helps to prevent the associated risk levels and keep healthy life.
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Mobile Application for Mental Health Using Machine Learning
(IEEE, 2022-12-09) Mendis, E.S; Kasthuriarachchi, L.W; Samarasinha, H.P.K.L; Kasthuriarachchi, S; Rajapaksa, S
In present era, mental health has become one of the most neglected, yet critically important, factors of our overall well-being. A large number of people are affected by various types of mental illnesses and mental health disorders. Stress, anxiety, and depression are the most common disorders among children and adolescents in Sri Lanka, and their prevalence has increased over the years, likely to require immediate medical attention. In today’s world, mobile phones and applications play an important role in everyone’s life. With the rapid growth of mental illness, mental health-focused apps and websites have gradually increased globally in recent years. This study aims to develop a mobile application that will primarily serve Sri Lankans with mental health problems, helping them identify their levels of stress, anxiety, and depression (ADS) and receiving advice on how to deal with them. This app’s main objective is to support those who are dealing with mental illnesses and raise awareness of them locally using machine learning and image processing techniques. It does this by serving as a constant reminder of how crucial mental health is and how much of an impact it has on daily life. The GSE Scale, DASS 21 scale has been used to find the users’ mental health illness and the severity of each mental health illness such and Anxiety depression and stress. These methods are put to our mobile application using machine learning techniques such as Decision tree and Random Forest classifiers and uses image processing technologies, CNN machine learning algorithm to offer a variety of activities for reliving stress, depression, and anxiety,
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A Surveillance System Controlling Covid-19 in Office Environments
(IEEE, 2022-12-09) Bandara, P A D; Perera, P D D S; Perera, N P D D D; De Silva, P. N.; Kasthuriarachchi, S; Rajapaksha, U.U.S
COVID-19 is one of the pandemic diseases that has hit the world including Sri Lanka. He has a virus that became the target of bids to stop its spread. Including the implementation of health protocols, to provide information about the spread of the virus emergency response, detection services for suspicious persons infected with the virus, and programs to contain the spread of the virus ensuring that the whole of Sri Lanka gets vaccinated. Here, the research focuses on the minimal spread of the face mask in the office environment an identification system that uses a deep learning model that prioritizes object recognition for the identification of employees who wear a face mask and detects social distancing and crowd gathering, if any if there is a violation, it will inform via a voice notification. Loss of Smell after the next component. One person can use one disposable card to check the smell of sniffing. Each disposable card has QR codes, and all QR codes are encrypted by adding data. The user scans the QR code on their ticket and then scratches off and smelled the smelling area and selected the corresponding scent on the disposable card. Employee company attendance is a proposed automated attendance system using facial recognition. Because it requires minimal human influence and offers a high level of accuracy and marking employee attendance and employee body temperature measurement, facial recognition will appear to be a practical option. This system aims to provide a high level of protection. Automated Attendance systems that detect and recognize are safe, fast, and time-consuming savings. This technique can also be used to identify an unknown person.