Browsing by Author "Kuruwitaarachchi, N"

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Open Access
Application Layer Challenges And Adoption Barriers to Internet Based Advanced Communication Technologies In SMEs
(IEEE, 2018-10-12) Kuruwitaarachchi, N
Successful integration of advance communication technologies with different business models are playing a significant role in business development in various industries today. This integration gives business agility other numerus benefits. Therefore, organizations are focus on adopting to well-known key technologies quickly and experience the main advantages. Out of many applications Electronic business (E-business) though Electronic commerce (E-commerce) transactions has special attention from all the industries as it has becoming one of the primary level requirements to industrial development in different domains. But when analyzing the empirical studies and similar projects, organizations are faced different incompatibility issues with stakeholders without having comprehensive e-business models. This makes various barriers for business development. In this study focus on how organizations should start moving to new advance communication technologies and how to address key technical challenges in the deployment process. As this is critical in developing countries than developed many studies required to analyze this issue and start e-business operations. This research present literature in developing countries with strong theoretical and empirical background analysis. Finally, this study suggests main identical barriers to move in to information and communication technological solutions or models and showing a research avenue to overcome those through modeling a testable framework.
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Emergency Patient Identification System
(IEEE, 2019-12-05) Sandamal, T; Fernando, N; Jayasinghe, I; Xavier, J; Kuruwitaarachchi, N; Rupasinghe, L
Emergency patient identification system (EPIS) will enable the more powerful quality system in the health industry. This research study was conducted to develop an EPI system, which is a complete patient-based medical information recording system. This system will help to identify the patients uniquely. Doctors can get patients' latest situation and can make quick decisions to do the treatments in emergencies. To achieve this approach, authors use the patient's fingerprint, face recognition technology, and eye recognition to identify the person. The patient can view his medical records; system reminds the notifications. The system helps the patients in storing and tracking the diet weight, medications, allergies, health history, fitness, lab results, x-rays, blood pressure, ongoing surgeries, drug reminders, doctor visits, doctor's appointments, images and more. The system helps the user in receiving and sending necessary health-related information to the doctors and hospitals. This system has many unique features; an emergency will help to find nearest hospitals. This can add patient's family member's history so it can help to remind his present medical situation. The main system is working on web interface; another part is offered as a mobile application.
Open Access
Green Cloud Computing: A Review on Adoption of Green-Computing attributes and Vendor Specific Implementations
(researchgate.net, 2019-03) Jayalath, T; Chathumali, E. J. A. P. C; Kothalawala, K. R. M; Kuruwitaarachchi, N
With cloud computing emerging as a trending topic, it has been a major point o f discussion for the last few years. In regards to technological advancements, the associated shortcomings like environmental footprint caused by them also become an affair o f high significance. Cloud computing itself is a much greener alternative to individual data centers with lesser number o f servers being used and cloud data centers being far more efficient than those o f traditional thereby reducing the carbon impact. Nonetheless, it cannot be neglected the fact that the data centers utilized by the cloud vendors are still a major source o f carbon emissions due to the dirty energy usage. Therefore, the discussion o f the paper is based on how green the foremost cloud providers are and the implementations o f green IT attributes in the cloud infrastructure.
Open Access
“iSAY”: Blockchain-based Intelligent Polling System for Legislative Assistance
(2021-01) Wattegama, D; Silva, P. S; Elapatha, K; Yapa Abeywardena, K; Kuruwitaarachchi, N; Jayathilake, C. R
“iSAY”' is a Blockchain-based polling system created for legislative assistance. Sri Lanka is a democratic country. Country follows a representative democracy and voters in Sri Lanka vote for their preferred government based on their election mandate. However, governments implement legislative decisions that are not stated in the election mandate. People won’t get a chance to state their opinion on this legislative matter and the government also doesn’t know whether people like this or not.  To solve this issue, in this paper the authors propose a blockchain-based intelligent polling application for legislative assistance.  “iSay” is an application where blockchain technology gets together with machine learning to add value into the public opinion. The government can create a poll about a legislative decision and people can state their opinion which could be further discussed in the legislature. Adding a significant change to the blockchain based e-voting solutions this paper proposes a novel feature where users can add their idea to a relevant poll. Using machine learning algorithms all these user ideas will be classified and analyzed before presenting to the government. Through this research, it is expected to deploy scalable elections among the general public and get their vote and ideas about specific legislations to generate an overview of general public opinion about legislative decisions.
Open Access
Modern Solution for Human Elephant Conflict
(IEEE, 2021-05-21) Wijesekera, D. T. S; Amarasinghe, M. C. S. T; Dassanaike, P. N; De Silva, T. H. H; Kuruwitaarachchi, N
Human elephant conflict (HEC) has become a serious problem in forest border areas of Sri Lanka. There is a high vulnerability for humans where they are attacked by elephants daily. Sri Lanka is one of countries that reports highest number of elephant deaths due to HEC. Many solutions have introduced throughout the decades to mitigate this rising socio-economic issue, but still, it remains as an unresolved problem. This research will look at those conflicts and issues faced by both elephants and humans, the solutions that needs to take and practically brings out necessary precautions. In this comprehensive discussion, researchers will address the problems that are associated with existing solutions and propose a reliable solution to mitigate HEC with the help of modern technologies. Researchers take advantage of elephant’s unique physical characteristic, the gigantic body to detect elephants using seismic vibrations along with Infrared based system and Microwave radar system. Researchers use Wireless Sensor Network (WSN) and Low-Power Wide Area Network (LPWAN) technologies to interconnect systems. A cost effective modern electric fence with flashlight system will use to scare-off elephants while an alarm system will be used to alert villages about the attacks from elephants.
Open Access
Novel Sprinter Assistive Smart Agent for Continuous Performance Improvement
(IEEE, 2021-04-06) Subhashana, H; Bandara, C; Bandara, I; Devindi, A; Kuruwitaarachchi, N; Dharmasena, T
In the field of Sports, sprinting is the term used for introducing running over a short distance in a limited time. To date, a method to identify whether sprinters are getting enough speed during the accelerated period is not available so far. This paper proposes a smart agent to recognize the technical precision and performance of a sprinter using wireless hardware devices and a software solution. Smart shoe, track sensor, arm motion detection bracelet are the devices used to collect data from a sprinter. After required data collecting is complete the based web application provides feedback to the sprinter to improve sprinting techniques. This modern technology based system reduces human errors and workload of a trainer and would be highly beneficial for the sports community including coaches and sprinters as it could be accessed through mobile phones. The results of the study show the visualization of sprinter data effectively and an analysis on the obtained data regarding the performance.
Embargo
Predicting Diabetes Mellitus Using Machine Learning and Optical Character Recognition
(IEEE, 2021-04-02) Silva, W. A. J. R; Shirantha, H. M. K; Balalla, L. J. M. V. N; Ranasinghe, R. A. D. V. K; Kuruwitaarachchi, N; Kasthurirathna, D
Diabetes Mellitus is recognized as a chronic metabolic disease that is characterized by hyperglycemia. As stated by the International Diabetes Federation, the statistics reveal that the incidence of diabetes among adults in Sri Lanka is 8.5%. In hindsight, this indicates that an average of one in every twelve adults in Sri Lanka is at risk of being diagnosed with the disease. However, presently, due to the lack of knowledge or mediums concerning the disease and its symptoms, diabetes often goes undetected which has resulted in 1/3 rd of the constituent population being unaware that they possess the disease. The proposed system aims to implement an application to read and analyze medical reports which will generate data that predicts the probabilities of the contraction and onset of diabetes, with insurance of maximum system efficiency and data credibility. Machine learning classification algorithms and optimization techniques have been used to predict diabetes status with maximum accuracy. To extract data from medical reports Optical Character Recognition, Image Processing, and Natural Language Processing have been used
Embargo
Real-time credit card fraud detection using machine learning
(IEEE, 2019-01-10) Thennakoon, A; Bhagyani, C; Premadasa, S; Mihiranga, S; Kuruwitaarachchi, N
Credit card fraud events take place frequently and then result in huge financial losses [1]. The number of online transactions has grown in large quantities and online credit card transactions holds a huge share of these transactions. Therefore, banks and financial institutions offer credit card fraud detection applications much value and demand. Fraudulent transactions can occur in various ways and can be put into different categories. This paper focuses on four main fraud occasions in real-world transactions. Each fraud is addressed using a series of machine learning models and the best method is selected via an evaluation. This evaluation provides a comprehensive guide to selecting an optimal algorithm with respect to the type of the frauds and we illustrate the evaluation with an appropriate performance measure. Another major key area that we address in our project is real-time credit card fraud detection. For this, we take the use of predictive analytics done by the implemented machine learning models and an API module to decide if a particular transaction is genuine or fraudulent. We also assess a novel strategy that effectively addresses the skewed distribution of data. The data used in our experiments come from a financial institution according to a confidential disclosure agreement.
Embargo
SEAMS: A Symmetric Encryption Algorithm Modification System to Resist Power Based Side Channel Attacks
(Springer, Cham, 2018-11-02) Pathirana, K. P. A. P; Lankarathne, L. R. M. O; Hangawaththa, N. H. A. D. A; Abeywardena, K. Y; Kuruwitaarachchi, N
Side channel attacks which examine physical characteristics of a cryptographic algorithm, are getting much more popular in present days since it is easier to mount an attack in a short time with only a few hundred dollars’ worth of devices. Sensitive information of a cryptographic module can be easily identified by evaluating the side channel information, such as power consumption, heat and electromagnetic emissions that outputs from the cryptographic device. This creates a huge impact on the security of the cryptographic modules as it is an efficient technique to break cryptographic algorithms by analyzing the patterns of the side channel information without having any specialized knowledge in cryptography. The solution proposed in this paper is an algorithm modification system for symmetric algorithms in order to mitigate side channel attacks. This is achieved by injecting randomness to the algorithm following a comprehensive analysis of power fluctuations that outputs from a given algorithm. In the proposed solution, a hardware device tracks down the patterns in power consumption and analyze those meter readings by utilizing machine learning techniques. As a result of this analysis, it identifies the pattern generating source code positions. System will add random code fragments in to the identified positions in the algorithm without altering the output in order to resist side channel attacks.
Embargo
TreeSpirit: Illegal logging detection and alerting system using audio identification over an IoT network
(IEEE, 2018-02-19) Kalhara, P. G; Jayasinghearachchi, V. D; Dias, A. H. A. T; Ratnayake, V. C; Jayawardena, C; Kuruwitaarachchi, N
Illegal logging has been identified as a major problem in the world, which may be minimized through effective monitoring of forest covered areas. In this paper, we propose and describe the initial steps to build a new three-tier architecture for Forest Monitoring based on Wireless Sensor Network and Chainsaw Noise Identification using a Neural Network. In addition to detection of chainsaw noises, we also propose methodologies to localize the origin of the chainsaw noise.