SLIIT Conference and Symposium Proceedings

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All SLIIT faculties annually conduct international conferences and symposiums. Publications from these events are included in this collection.

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A Parallel Version of the In-Close Algorithm
(Faculty of Graduate Studies and Research, 2017-01-17) Kodagoda, N.; Andrews, S.; Pulasinghe, K.
This research paper presents a new parallel algorithm for computing the formal concepts in a formal context. The proposed shared memory parallel algorithm Parallel-Task-In-Close3 parallelizes Andrews's In-Close3 serial algorithm. The paper presents the key parallelization strategy used and presents experimental results of the parallelization using the OpenMP framework
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E-GEO: Geography based E-learning system for sri lanka
(Faculty of Graduate Studies and Research, 2017-01-26) Rajapaksha, P. H. A. S.; Wijenayake, G. K. S. V.; Diroshan, R.; Eranga Harshani, M. W.; Mallawaarachchi, Y.; Ishara, W. G. A.
Geography is an enthralling subject and an additional qualification for day to day life of human beings. Because human beings are socialized and they are usually dealing with geographical related activities. As a person he needs to have a basic sense of where he lives and what is the surrounding environment of his living area. Therefore, to provide the basic geographical knowledge for these persons who are in any age category, standalone e-learning application is introduced as final year research. The E-GEO e-learning application which can be worked in totally offline environment consists of four main components in Map Marking and Validation, Cross Section Drawing Platform with Mountain Type Identifier, Virtual Tours and Speech Recognition and Generation with Geo Master. To accomplish with these features we have used different technologies and mechanisms respectively Geographical Information System (GIS) based Map Marking and Offline Validation, Image Processing, 3D Modeling and Natural Language Processing. The entire application based on e-learning concept called gamification to provide motivational environment for users and could work without internet connectivity. This e-learning application would be achieved high accuracy level at the end of the project duration.
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A wireless continuous patient monitoring system for dengue; Wi-Mon
(Faculty of Graduate Studies and Research, 2017-01-26) Nubenthan, S.; Kanagasabapathy, R.
The improvements in the wireless networking technologies and the integrated electronic circuits have allowed the advancement in the Wireless Body Area Network. WBAN offers many applications in remote health monitoring and medicine. IEEE 802.15.4j and IEEE 802.15.6 are standards for the medical WBAN. It allows the integration of intelligent and miniaturized sensor nodes in or on a human body to monitor the human body functions. It has great potential to make a huge transformation in the future of medical industry. The WBAN concept provides plentiful new innovative ideas to enhance the health care systems. The paper presents a wireless monitoring system for patients who need continuous monitoring, using WBAN concept. This wireless monitoring system contains sensor network and remote monitoring application. It contributes to collection of the vital information of the patients such as temperature, pulse rate, ECG (electrocardiogram), oxygen saturation and blood pressure. Moreover, the system also provides management of information collected from the sensors, alert the administration in severe condition of the patients. The design and implementation of system are discussed in this paper.
Open Access
Determinants of stock market index movements: Evidence from New Zealand stock market
(Faculty of Graduate Studies and Research, 2017-01-27) Dassanayake, W.; Jayawardena, C.
his study examines the impact of a selected macroeconomic variables on the New Zealand stock market(S&P/NZX 50) index. We use exchange rate, interest rate, inflation rate and foreign stock market index (S&P 500 index) to evaluate their influence on the New Zealand stock market (S&P/NZX 50) index. Daily data from January 2014 to September 2016 are evaluated. Unit root tests, cointegration tests, vector error correction model (VECM) and Granger causality test are employed to examine both long run and short run dynamic relationship between these variables. The study finds that there is no statistically significant long run causality from inflation rate, exchange rate, interest rate and S&P 500 index on the New Zealand stock market index. However, S&P 500 index has a strong significant short run Granger causality to the New Zealand stock market index.
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Plus Go: Intelligent Complementary Ride-Sharing System
(IEEE, 2019-11-21) Wickramasinghe, V; Edirisinghe, A; Gunawardena, S; Gunathilake, A; Kasthurirathna, D; Wijekoon, J
Currently the world population is gathering to the cities making huge traffic congestion throughout the day. This has drawn serious attention to the society incurred to implement smart solutions for traffic management. One of the prominent problems for traffic congestion is the number of vehicles entering the cities is high. It is a popular fact that the solitary travelers coming to a defined destination make the vehicles underutilized. Therefore, this study proposes a solution to implement a new ride-sharing platform: Plus Go, to reduce this underutilization. Plus Go matches the travelers by considering the designation, traveler preferences, shortest path details, and the ratings of the users. Moreover, Plus Go intelligently estimates the traveling cost based on the fuel consumption of the vehicle, distance traveled, and the time taken to reach the destination. The proposed solution matches the travelers with 98% accuracy ensuring that ride-sharing is an effective solution to reduce the number of vehicles entering the cities.
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SmartOne: IoT-based Smart Platform to Manage Personal Water Usage
(IEEE, 2019-11-21) Vithanage, J; de Silva, R; Karunaratne, K; Silva, M. D; Bogoda, P; Kankanamge, R; Kehelella, P; Jayakody, K. D; Wijekoon, J
The origin of life is water, and consistent water consumption is essential for the proper functioning of human organs. Thus, regular hydration is vital for human beings because improper hydration leads to diseases such as cholera, diarrhea, bladder stones, and kidney issues. However, maintaining a sufficient and regulated water intake is challenging for many personal as the livelihood, i.e., busy life schedule, is getting complicated, and sometimes due to limited access to clean water. To this end, the SmartOne water bottle was introduced to enable effective management of daily water requirements and to ensure users drink good quality water. The initial prototype of the water bottle was implemented as a combination of hardware and mobile application, and it was evaluated in terms of measuring water goal, quality, drink and sift event detection.
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Recognition and translation of Ancient Brahmi Letters using deep learning and NLP
(IEEE, 2019-12) Wijerathna, K. A. S. A. N; Sepalitha, R; Thuiyadura, I; Athauda, H; Suranjini, P. D; Silva, J. A. D. C; Jayakodi, A
Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very difficult work for present generation. There is no any automatic system for translating Brahmi letters to Sinhala language. However, they are using manual method for translating inscriptions. The method that used in epigraphy is being taken a long period to decipher, analyze and translate the inscribed text in inscriptions. This research mainly focuses on recognition of ancient Brahmi characters written the time period between 3 rd B.C and 1 st A. D. First, we remove the noise, segment the letters from the inscription image and convert it into the binary image using image processing techniques. Secondly, we recognize the correct Brahmi letters, broken letters and then identify the time period of the inscriptions using Convolution Neural Networks in deep learning. Finally, the Brahmi letters are translated into modern Sinhala letters and provide the meaning of the inscription using Natural Language Processing. This proposed system builds up solution to overcome the existing problems in epigraphy.
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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.
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Film-it: Virtual Location Scout and Movie Production Planning Assistant for Film Industry in Sri Lanka
(IEEE, 2019-12-05) Wijesekera, C; Kosgahakumbura, D; Alwis, J; Kaluarachchi, B; Thelijjagoda, S
The global multi-billion-dollar industry of film making is not that healthy in Sri Lanka. Film industry majors say that this happens because not enough local movies are made within the country by local artists. Therefore, there is no problem with the number of creative minds in the country. The reason for the lack of local movies made annually in the country is because it takes a lot of effort, time and money. And if the movie fails, millions get wasted. Movies mostly fail because they are not organized very well from the beginning. Everything starting from the script to the final tickets that are going to be sold needs to be planned during pre-production. If pre-production fails, production fails. If production fails, post-production fails. The success or the failure of a movie starts right at the beginning of the pre-production phase. This phase contains many processes that are very important to carry out the production phase. One of them is location scouting. Since film industry is mostly based on aesthetic pleasure of the people in the society, scenic beauty is a must when choosing locations for a movie. “Film-it” is an application that is capable of giving all kinds of assistance in location scouting and much more in movie planning. That is the reason it is called “The Virtual Location Scout and Movie Production Planning Assistant”. This application has already proved to be beneficial for Sri Lankan movie directors and producers to do months tasks in much less time which is a huge improvement in the industry that saves so much time and money. Major roles in the industry states that this application will take the Sri Lankan movie industry to a whole new level.
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FIMAA: Four-way Integrated Mobile Authentication API
(IEEE, 2019-12-05) Kariyawasam, L; Moonamaldeniya, M; Samarawickrama, V; Premalal, S. H; Rupasinghe, L; Abeywardena, K. Y
In this era of the digital world, mobile device users have been increased to a significant number. This proposed system presents a hybrid authentication approach which can be considered as a combination of silent authentication and input based authentication to enhance mobile security in android mobile applications. A four-way integrated mobile API introduces the gait patterns and location traces as well as the image context and face ID-based emotions of the user. This application silently authenticates the exposed location trace and gait patterns of the user while other ear and emotion inputs will be prompted to end-user accordingly with input authentication.
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MOOCs Recommender Based on User Preference, Learning Styles and Forum Activity
(IEEE, 2019-12-05) Hilmy, S; De Silva, T; Pathirana, S; Kodagoda, N; Suriyawansa, K
With the development of MOOCs (Massive Open Online Courses) as a major source of e-learning materials, the number of MOOCs available today has become dauntingly high. Furthermore, MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the course throughout. However, due to the sheer number of MOOCs available today, it is becoming increasing difficult to find the MOOCs that suits your personal preferences and the learning style. This paper describes how thousands of MOOCs that belong to different styles are identified efficiently while each consumer's preferences are identified to provide personalized MOOC recommendations. Furthermore, the paper describes how forums can be analyzed to identify how consumers feel about MOOCs that they followed, which is a crucial metric in recommending MOOCs to consumers.
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Air Visio: Air Quality Monitoring and Analysis Based Predictive System
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Dissanayaka, A.D.; Taniya, W.A.D.; De Silva, B.P.A.N.; Senarathne, A.N.; Wijesiri, M.P.M.; Kahandawaarachchi, K.A.D.C.P.
Sri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.
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Smart Platform for Cloud Service Providers
(IEEE, 2019-12-05) Dharmapriya, W. A. S. P; Supipi, K. G; Ravindu Nimesh, G. G; Muhandiram, M. A. B. K; Rankothge, W. H; Gamage, N
Cloud computing offers many types of computer related services without the direct active management of their users. Cloud Service Providers (CSPs) are responsible to manage these services such as placement of services in the cloud, resource allocation, network monitoring etc. The cloud service provider is required to monitor the network traffic, predict the dynamic traffic changes, and scale out the resources accordingly. We have proposed a platform for cloud service providers that automates the cloud management related services with following modules: (1) traffic monitoring, (2) traffic prediction, (3) virtual service instances placement and (4) traffic load balancing. We have used continuous and periodic approaches for traffic monitoring, Auto-Regressive Integrated Moving Average (ARIMA) model for traffic prediction, Randomized Weighted Majority Algorithm (RWMA) for virtual service instances placement and a threshold-based approach for load balancing. In this paper, we are presenting the performances of our cloud management platform, specially an evaluation of the algorithms used in above mentioned modules. Our results show that, using our proposed modules, the cloud management related services can be automated efficiently and reliably.
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Crowd-sourced Approach to Generate Real-time Passenger Train Time Table
(IEEE, 2019-12-05) Weerathunga, D. C. B; Jayawickckrama, M. M. M; Jayasekara, U; Kasthurirathna, D; Wijetunga, P. S
Availability of real-time public train transportation information can help to improve the commuters' transportation needs. Most of the time the guaranteed information is only supported in a closed system. Due to the administrative issue, there is no infrastructure to provide real-time data to any interested party. This paper proposed a framework that aims to provide a multiple-sourced crowdsourcing approach to generate Real-time Public Train information. The proposed system will enhance the accuracy as well as efficiency of the current system to provide accurate real-time train Status by crowdsourcing train location GPS (Global Positioning System) data from the passenger's smartphones. The tracing data are used to update the arrival/departure time using a predictive data source. The basic information is collected and distributed of each train route, stops and schedules. The `User Report Information' includes information related to trains and can be shared among the other interested parties through our System.
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gCodex: A tool to analyze software repositories over time (visualization)
(IEEE, 2019-12-05) Nuzrath, S; Amarasinghe, N. H; Liyanage, K. T; Suriyawansa, K; Madanayake, D. P; Kodagoda, N
gCodex is a novel tool for analyzing and visualizing the code base in a manner that it allows its users to get an idea of the insights of the codebase. This tool was built to analyze code bases and it supports any language. In addition, it provides a visualization of the file structure, rate of change of complexity and defects rate. In order to improve the quality of the software and the controllability of the project, it is necessary to control the complexity of the software by measuring the associated aspects and visualize those in a descriptive and attractive dashboard. Using the existing tools, it is not possible to visualize the rate of change of code complexity with the time. This tool uses Cyclomatic complexity, line of code and Halstead complexity metric and their impact on the software quality, and visualize those in a descriptive dashboard which provides analytics that describes and summarize past trends.
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AI Based Cyber Threats and Vulnerability Detection, Prevention and Prediction System
(IEEE, 2019-12-05) Amarasinghe, A. M. S. N; Wijesinghe, W. A. C. H; Nirmana, D. L. A; Jayakody, A; Priyankara, A. M. S
Security of the computer systems is the most important factor for single users and businesses, because an attack on a system can cause data loss and considerable harm to the businesses. Due to the increment of the range of the cyber-attacks, anti-virus scanners cannot fulfil the need for protection. Hence, the increment of the skill level that required for the development of cyber threats and the availability of the attacking tools on the internet, the need for Artificial Intelligence-based systems, is a must to the users. The proposed approach is an automated system that consists of a mechanism to deploy vulnerabilities and a rich database with known vulnerabilities. The Convolutional Neural Networks detects the vulnerabilities and the artificial intelligence-based generative models do the prevention process and improves reliability. The prediction procedure implemented using the algorithm called “Time Series” and the model called “SARIMA”. These implementations give an output with considerable accuracy.
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Smart wheelchair to facilitate disabled individuals
(IEEE, 2019-12-05) Jayakody, A; Nawarathna, A; Wijesinghe, I; Liyanage, S; Dissanayake, J
This paper describes the design and implementation of a voice controlled smart wheelchair for disabled whom the manual operation is difficult due to lack of physical strength. The main objective of this research is to develop a smart wheelchair to facilitate disabled individuals which can be operated with lesser effort while operating the wheelchair. The proposed wheelchair can be controlled through voice commands which enables the user to control the wheelchair with less effort. This aids the disabled in carrying out daily activities independently within indoor environments. The proposed solution has five modules namely, speech recognition module, obstacle avoidance module, autonomous navigation module, health monitoring module, and central system controller. The wheelchair operates in two modes called manual mode and the autonomous mode. This paper presents a smart wheelchair that makes the disabled individuals' life easier with technology. Further this paper elaborates testing and evaluations carried out to prove the proposed title.
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A Gamified Approach for Screening and Intervention of Dyslexia, Dysgraphia and Dyscalculia
(2019 International Conference on Advancements in Computing (ICAC) -SLIIT, 2019-12-05) Kariyawasam, R.; Nadeeshani, M.; Hamid, T.; Subasinghe, I.; Ratnayake, P.
This paper aims to diagnose children with specific learning disabilities and provide treatments via a mobile game. Learning disabilities are neurological disorders that affect the brain. Children with learning disabilities have trouble with learning compared to their fellow peers and quite often fall back academically since a majority of them go undiagnosed. The specific learning disabilities for which this paper provides screening are dyslexia a reading disability, dyscalculia a mathematical disability, letter dysgraphia and numeric dysgraphia are both writing disabilities. Deep learning and machine learning techniques are used in the screening process of these specific learning disabilities. Trained convolutional neural networks are used to detect the spoken letter/word, detect the written letter/word and detect the written number on the mobile application. Outputs from the convolutional neural network are fed into the models used for screening learning disabilities. The machine learning algorithms used in building the models include k-nearest neighbors, random forest and support vector machine. Screening results from the models built in this research provided an accuracy of 89%, 90%, 92%, 92% for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia respectively. This is the first game based screening and intervention tool for dyslexia, letter dysgraphia, dyscalculia and numeric dysgraphia.
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Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction
(IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, C
Forecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.
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Enhanced Secure Solution for PoS Architecture
(IEEE, 2019-12-05) Samaranayake, C; Kuruppu Achchige, R. P; Shanaz, T; Ranasinghe, A; Senarathne, A. N
Today retail businesses expect to bring the utmost in sales and payment transactions by adapting new technologies. Therefore, Advanced Point of Sales (PoS) Systems are widely used in the industry. Regardless of how efficient and secure these systems or applications work, unexpected information security risks can arise. Such risks could be a threat to their business and organization. It is important to ensure that critical information such as payment card information, handled in PoS systems is kept secure from attacks that could bring financial loss. This research provides a solution by studying the overall infrastructure of a PoS System and identifies the key events that such data would be at risk. The major concern of it was to enhance the existing security features of the system to avoid any type of malicious activity. This research consists of four main sections under security related to PoS Systems that would address the risk; Studying of malware and classifying them, detecting possible attacks and means of preventing it, a robot (BOT) to predict and generate the system status with a Data Leakage Prevention(DLP) solution for all the events occurring at a PoS. The key objective of implementing this solution was to protect the confidential data that is being used in the PoS System and to avoid threats that lead to the unavailability of the system. The implemented security features using machine learning and Deep Learning methods to the existing PoS functions produced a 99.3% of accuracy in Malware Detection and 95% of accuracy in its Classification process while the DLP Solution was able to obtain an accuracy of 84.6%. The above results retrieved fulfilled the research objectives and aided to integrate an enhanced security solution for a PoS system.