Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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Now showing 1 - 20 of 290

Embargo
Parade in the Virtual Dressing Room
(IEEE, 2018-08) Priyadharsun, S; Lakshigan, S; Baheerathan, S. S; Rajasooriyar, S; Rajapaksha, U. U. S. K; Harshanath, S. M. B
Fashion has always been in the forefront especially with the youngsters. The interest in fashion can vary according to the country, region, culture, age, seasons, climates, places visited, attitude, personal interests etc. Some of them, however, have difficulties finding out about suitable dressing styles for them. Meeting this need is the purpose of this application. On the other hand, social networks are an easy way to interact with the teenagers. In this new age social network site, users create a profile and enter their body measurements to create a virtual model of the particular user. They can also upload their photos to create a complete virtual model which includes face as well. It was necessary to add business value to the application along with the usual entertainment factors. Adding business value to entertainment factors is the main attraction in Fashion Fit to suit a new age of social networking.
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Location based garbage management system with iot for smart city
(IEEE, 2018-08-08) Lokuliyana, S; Jayakody, A; Dabarera, G. S. B; Ranaweera, R. K. R; Perera, P. G. D. M; Panangala, P. A. D. V. R
Smart cities integrate multiple ICT and IOT solutions to build a comfortable human habitation. One of these solutions is to provide an environmentally friendly, efficient and effective garbage management system. The current garbage collection system includes routine garbage trucks doing rounds daily or weekly, which not only doesn't cover every zone of the city but is a completely inefficient use of government resources. This paper proposes a cost-effective IOT based system for the government to utilize available resources to efficiently manage the overwhelming amounts of garbage collected each day, while also providing a better solution for the inconvenience of garbage disposal for the citizens. This is done by a network of smart bins which integrates cloud-based techniques to monitor and analyze data collected to provide predictive routes generated through algorithms for garbage trucks. An android app is developed for the workforce and the citizens, which primarily provides the generated routes for the workforce and finds the nearest available smart bin for citizens.
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Tempcache: A Database Optimization Algorithm for Real-Time Data Handling in Indoor Spatial Environments
(IEEE, 2018-08-08) Jayakody, A; Murray, I; Hermann, J; Lokuliyana, S; Dunuwila, V. R
The unstable arrangement of modern indoor environments has made navigation within buildings a difficult task. Hence, this paper introduces the AccessBIM framework, which is an efficient real-time indoor navigation system that facilitates in generating a real-time indoor map by crowdsourcing spatial data through the sensors available in mobile devices of navigators. The framework is equipped with a database optimization algorithm known as “Tempcache” which reduces the time and cost of searching data by examining the AccessBIM database for previously navigated paths, thus enabling faster data retrieval through efficient query processing. A simulation of a virtual environment similar to an actual indoor environment was used to test the algorithm. The significance of the algorithm was validated by comparing the total map generation time before and after the algorithm was applied for which the results demonstrated a reduction in map generation time with the use of the algorithm. The framework is also capable of capturing localization information with the support of i-Beacons which is then stored in a cloud server.
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DNS Cache Poisoning: A Review on its Technique and Countermeasures
(IEEE, 2018-10-02) Dissanayake, I. M. M
IP address is the basis of Internet server communication. Many attackers try to spoof Domain Name System using different techniques to introduce false IP addresses to client servers. Use of cache memory is the most commonly seen type, which is known as DNS cache poisoning. This attack has become a serious threat to information security at individual and organizational levels. The purpose of this article is to study Domain Name System and the mechanism of DNS cache poisoning. Further, this paper will discuss countermeasures used against DNS cache poisoning. Many scholars have presented different methods of cache poisoning and the most recent technique was introduced by Kaminsky in 2008. Many researches have been done throughout the world to invent more secure countermeasures against DNS cache poisoning and the most commonly used countermeasure is Domain Name System Security Extensions. However, there are different countermeasures against DNS cache poisoning; none of the countermeasure could certify the action against DNS cache poisoning. Hence, future researches should be oriented towards to introducing more trustworthy countermeasures against DNS cache poisoning and new identical infrastructure for internet browsing other than DNS.
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Policies based container migration using cross-cloud management platform
(IEEE, 2018-12-21) Janarthanan, K; Peramune, P. R. L. C; Ranaweera, A. T; Krishnamohan, T; Rupasinghe, L; Sampath, K. K; Liyanapathirana, C
Over the last decade, cloud computing has helped in variety of ways to humanity. Mainly in the ways of, achieving Disaster Recovery (DR) and in protecting the end users' data and Anywhere, Any device, Anytime access to the users' data. This research further helps people and organization to overcome common problems related to clouds such as, vendor-lock in and legal regulation. In today's world, more and more organizations are adopting the cloud services mainly because of the reliability and affordability provided by them. However, there are several drawbacks faced by the cloud users and cloud service providers. Apart from the security perspective, the cloud users are facing challenges in control and visibility, lack of standard service interfaces, difficulty in deploying applications across multiple clouds and vendor lock-in. Also, cloud service providers are facing challenges in degradation of the quality of service provided because of the distance between cloud data center and the end user and unexpected interruption of services etc. The above problems can be reduced to a greater extent or mitigated by adopting Multi Cross Cloud Infrastructure. This benefits the cloud users to receive the best quality services to increase their productivity. Hence, the main aim of this research is to build a common platform to manage the cross-cloud environment particularly Microsoft AZURE cloud and Amazon Web Services (AWS) with multiple features such as policies based container migration among the clouds and finding the best virtual machines (VM) across the clouds to deploy new containers. Cross-cloud management platform can be implemented within an organization or Enterprise and is used by the 3rd level support team such as Infrastructure team to provide multiple services (E.g. - Delivering application containers, Migration of containers on request) to end users based on some service level agreements (SLA) with more control and visibility.
Open Access
Review on vibration quality improvement of a passenger seat
(researchgate.net, 2019-03) Perera, M; Rikaz, M; Abeysinghe, A
Seats are one of the most significant components of vehicles. Customer’s expectations for the comfort in vehicle seats rise continuously. Designing of automobile seats has always been a challenge for engineers as design parameters for automobile seats are complex. When it comes to the design of automobile seats, three design objectives, namely comfort, safety and health need to be fulfilled simultaneously. Vibration analysis plays a major role in engineering including the area of design for automobile comfort. Human structure, as a mechanical system, is immensely complex and the mechanical properties of human body readily undergo change. In addition, the vibration can initiate the development of pressure ulcer and other long-term diseases. Comfort measurement is challenging because of such factors as user subjectivity, seat geometry, occupant anthropometry and amount of time spent sitting. The purpose of this study is to identify the most significant parameters that make vibration in the vehicle seat which reduce passenger comfort and to identify the design parameters that can impact on appropriate seat design. Suspension systems, seat cushion and sitting postures are some of the vital parameters that have huge impacts on comfort analysis. This review could help in design of seat cushions with appropriate material properties and in analysing the comfort levels of human body to reduce the vibration transmissibility at the critical frequency bands
Open Access
Factors Influencing the Private Cost of Higher Education; the Case of Sri Lanka
(researchgate.net, 2019-03) Gobinath, S; Tharshan, K; Dheerasekara, W. R. H; Gunawardena, M.M.D de S; Jayakody, S. G; Lokeshwara, A. A
The research aims to identify and analyze the cost elements that impact the private cost of university education in Sri Lanka. It focused on determining the private cost of the Bachelor’s degree programs and also the cost elements affecting the total private cost and their significance. Twenty one cost categories were identified through a pilot study and analyzed in order to assess their impact on the private cost and their variability based on field of study, gender, programme duration, and the socio-economic group of undergraduates. The population comprised of students enrolled in private higher education institutes offering Bachelor’s degree programs in Sri Lanka. The study was conducted during the 2016/2017 academic year and the sample contained 419 respondents drawn utilizing the purposive and stratified random sampling procedures. The data were analyzed using descriptive statistics, while the hypotheses were tested using the Chi-square test for independent sample statistics at 0.05 level of significance. It was found that majority of cost categories (15) had significantly varied between fields of study undertaken while minority of cost categories (10) varied significantly between students’ gender. The study revealed that the identified factors influenced the private cost of university education in the Sri Lankan context.
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Smart Monitor for Tracking Child's Brain Development
(researchgate.net, 2019-03) Anparasanesan, T; Mathangi, K; Seyon, S; Kobikanth, S; Gamage, A
This paper provides a way to track the brain development of children and improving it via gamification. Machine Learning and Gamification are the key technologies used here. As the population rises, the demand for cost-effective methods to reduce the rate of cognitive decline becomes higher. A mobile application is developed to track and develop the brain development of children. In the mobile application, the child initially undergoes an evaluation phase to determine the current level of the cognitive skills of the child. Milestones particular to that age category are also tracked in this evaluation phase. The results of this evaluation phase are analyzed by the machine learning model and suitable brain games are suggested. K-means algorithm is used to develop the model which is an unsupervised learning algorithm. The dataset is prepared by storing the results of each game category in the evaluation phase. Data preprocessing is done to clean up the dataset. During this period, data undergoes a series of steps. The dataset is divided into 80% and 20%. 80% of the dataset is used as the training dataset and the remaining 20% as the test dataset. The accuracy of the model is checked several times against the test data. Model accuracy is improved through model training and finally, the model got an accuracy of 88.49%. For the child, proper training is given to improve his cognitive skills and thus the brain development using Gamification. Games are developed using the UNITY game engine. The system generates a report and notifies parents about their child's statistics periodically. This paper elaborates the procedure of model development, model training, model testing and development of suitable brain games in details. The results of the research work and future works are also discussed in the following sections.
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Occupancy Monitoring System for Workplace Washrooms
(IEEE, 2019-03-28) Godakandage, V. M. P; Kothalawala, K. R. M; Chathumali, E. J. A; Madhubhashana, A. W
With regard to rapid technological advancements majorly influencing our daily lives, Internet of Things (IoT) has been a topic of broad and current interest in the recent years. The capabilities of IoT can assist in revolutionizing the way people live and work, thereby improving quality of life. With the impact of IoT only continuing to propagate in the future, it can be used as a means of easing our day-to-day struggles. Therefore, with the assistance of IoT along with a few hardware, the proposed system, addresses the displeasing reality of queues and several visits for the washrooms due to them coming forth occupied. Thus, the focus of the intended system is on delivering a pleasant washroom experience for employees in an office environment providing them with an at-desk indication on the occupancy of the washroom cubicles reducing queues and disappointments.
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On the scaling of virtualized network functions
(IEEE, 2019-05-20) Rankothge, W; Ramalhinho, H; Lobo, J
Offering Virtualized Network Functions (VNFs) as a service requires automation of cloud resource management to allocate cloud resources for the VNFs dynamically. Most of the existing solutions focus only on the initial resource allocation. However, the allocation of resources must adapt to dynamic traffic demands and support fast scaling mechanisms. There are three basic scaling models: vertical where re-scaling is achieved by changing the resources assigned to the VNF in the host server, horizontal where VNFs are replicated or removed to do rescaling, and migration where VNFs are moved to servers with more resources. In this paper, we present an Iterated Local Search (ILS) based framework for automation of resource reallocation that supports the three scaling models. We, then, use the framework to run experiments and compare the different scaling approaches, specifically how the optimization is affected by the scaling approach and the optimization objectives.
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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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Analyzing Payment Behaviors And Introducing An Optimal Credit Limit
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Bandara, H.M.M.T.; Samarasinghe, D.P.; Manchanayake, S.M.A.M.
Identifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.
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Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 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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AI Based Depression and Suicide Prevention System
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Kulasinghe, S.A.S.A.; Jayasinghe, A.; Rathnayaka, R.M.A.; Karunarathne, P.B.M.M.D.; Silva, P.D.S.; Anuradha Jayakodi, J.A.D.C.
Suicide is a major issue in the world. The number one reason for suicide is untreated depression. That is why it was decided to focus on depression symptoms more and identify them in order to prevent suicidal attempts. To cure depression, the best way is to talk about their feelings with someone they trusted and release their pain inside of them. Because of that this system has a Chat-bot for the user to interact with. Chat-bot will gather information about the users feelings through text and voice analysis. Also by analyzing their Facebook statuses and recent web history, the application gather more information about their mental state so that the system take more accurate conclusions. After analyzing all the information from each component the back brain will decide on how the chat-bot should act on the user. At the end, the product was able to give more than 75% accurate results for each component.
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An Automated Tool for Memory Forensics
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Murthaja, M.; Sahayanathan, B.; Munasinghe, A.N.T.S.; Uthayakumar, D.; Rupasinghe, L.; Senarathne, A.
In the present, memory forensics has captured the world’s attention. Currently, the volatility framework is used to extract artifacts from the memory dump, and the extracted artifacts are then used to investigate and to identify the malicious processes in the memory dump. The investigation process must be conducted manually, since the volatility framework provides only the artifacts that exist in the memory dump. In this paper, we investigate the four predominant domains of registry, DLL, API calls and network connections in memory forensics to implement the system ‘Malfore,’ which helps automate the entire process of memory forensics. We use the cuckoo sandbox to analyze malware samples and to obtain memory dumps and volatility frameworks to extract artifacts from the memory dump. The finalized dataset was evaluated using several machine learning algorithms, including RNN. The highest accuracy achieved was 98%, and it was reached using a recurrent neural network model, fitted to the data extracted from the DLL artifacts, and 92% accuracy was reached using a recurrent neural network model,fitted to data extracted from the network connection artifacts.
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Automated Smart Checkup Portal Network System to Check the Vision and Hearing of the Patients.
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Dias, A.A.T.K.; Vithusha, J.; Liyadipita, L.A.M.T.J.; Abeygunawardhana, P.K.W.
The human eye and ear are impressive systems in the body. Vision and Hearing are the main functions of those organs. We should regularly check our vision and hearing, It's the most reliable ways to maintain good vision and hearing. Not only that, every patient must keep a medical history and previous checkup records, those related to vision and hearing and those results should be real-time processed. Therefore, we have built an Automated Centralized Smart EE (eye and ear) Checkup Portal Network System. We have designed and developed an automated centralized vision and hearing checkup rooms network, Automated centralized live traffic indicating cloud-based web application to establish in every hospital.
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EyeDriver: Intelligent Driver Assistance System
(IEEE, 2019-12-18) Gayadeeptha, P; Baddewithana, T. P; Pannegama, K. V; Samarakkody, C. S; Samarasinghe, P; Siriwardana, S
“EyeDriver” is a driver assistance system that analyzes and provides real-time driver assistant data from four separate components. These main components are drowsiness detection and head pose estimation, over-speed detection, lane departure, and front collision avoidance. It is a compact product that included a Raspberry pi board, a USB camera module, Pi camera, and a TFT LCD. Since the “EyeDriver” is a first affordable aftermarket solution in Sri Lanka, it can be mounted and configured in any vehicle without any professional knowledge in less effort. Drowsiness detection and head pose estimation component will monitor the driver's eyes and keep track of whether the driver's head's position is inconsistent or deviated from the optimal position. In accordance with the road's recommended speed, the vehicle's actual speed is analyzed and if it is more than the permitted, the system makes a notification. It is done by the over-speed detection component. Lane departure component consists of assisting in keeping the vehicle stable on the desired lane on the road. Also, when the driver makes an intended lane change, the system provides a notification. The Front collision avoidance part will detect the frontal obstacle on the road and provide pre-collision/proximity warning notification. The notification makes according to the vehicle speed and distance between the object and the vehicles. The whole system is based on the Raspberry Pi 3 Model B+ board and the implementation of the system has been done by using OpenCV and Python.
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Aerodynamic modeling of simplified wind turbine rotors targeting small-scale applications in Sri Lanka
(Elsevier, 2020-09-11) Sugathapala, T. M; Boteju, S; Withanage, P. B; Wijewardane, S
A design and optimization procedure of simplified wind turbine rotors for small-scale applications is presented. The need for this research has arisen from the recent national initiative of the government of Sri Lanka titled ‘Battle for Wind Energy’ in promoting small scale grid connected wind plants for electricity customers under Net Metering scheme. The main objective of this research is to assist local developers to design optimum rotors for given electrical generators (as determined by customer requirements), suitable for wind characteristics at specific locations. Another objective is to enhance local manufacturing capabilities by providing a design option of a simplified rotor blade geometry. A study on the correlation between population density of electricity customers and wind energy potentials was carried out to categorize the demand centres based on wind energy potentials in proposing series of small-scale wind turbine designs. A unique and improved rotor design procedure is presented which attempts to match the point of maximum performance of a rotor (design tip speed ratio) with the design wind speed of a given location by considering generator performance. The new design procedure showed successful convergence on a unique blade diameter for each rotor configuration that allowed the design tip speed ratio to match the design wind speed. The performance evaluation of rotor designs showed that high solidity rotors work better on the low wind potential region while low solidity rotors dominate medium and high wind potential regions. The performance reductions of simplified rotor designs are not significant and therefore would be an effective way to enhance value addition through local manufacture.
Open Access
Comparative Analysis of Deep Learning Models for Multi-Step Prediction of Financial Time Series
(researchgate.net, 2020-10-21) Aryal, S; Nadarajah, D; Rupasinghe, P.L; Jayawardena, C; Kasthurirathna, D
Financial time series prediction has been a key topic of interest among researchers considering the complexity of the domain and also due to its significant impact on a wide range of applications. In contrast to one-step ahead prediction, multi-step forecasting is more desirable in the industry but the task is more challenging. In recent days, advancement in deep learning has shown impressive accomplishments across various tasks including sequence learning and time series forecasting. Although most previous studies are focused on applications of deep learning models for single-step ahead prediction, multi-step financial time series forecasting has not been explored exhaustively. This paper aims at extensively evaluating the performance of various state-of-the-art deep learning models for multiple multi-steps ahead prediction horizons on real-world stock and forex markets dataset. Specifically, we focus on Long-Short Term Memory (LSTM) network and its variations, Encoder-Decoder based sequence to sequence models, Temporal Convolution Network (TCN), hybrid Exponential SmoothingRecurrent Neural Networks (ES-RNN) and Neural Basis Expansion Analysis for interpretable Time Series forecasting (N-BEATS). Experimental results show that the latest deep learning models such as NBEATS, ES-LSTM and TCN produced better results for all stock market related datasets by obtaining around 50% less Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) scores for each prediction horizon as compared to other models. However, the conventional LSTM-based models still prove to be dominant in the forex domain by comparatively achieving around 2% less error values.
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Arogya -An Intelligent Ayurvedic Herb Management Platform
(IEEE, 2020-11-04) Pathiranage, N; Nilfa, N; Nithmali, M; Kumari, N; Weerasinghe, L; Weerathunga, I
Ayurvedic means a science of life and well-being with its unique approaches to social and spiritual life. Especially in Sri Lanka we have our own set of rare Ayurvedic herbs which have been utilized by generations as medicinal treatments for a variety of diseases. Absence of specialists in this area makes proper identification as well as classification of valuable herbal plants a tedious task, which is essential for better treatment. Hence, a fully automated system for herb detection and classification, information visualization regarding them is highly desirable. There are existing applications which can identify plants with low prediction accuracies, as well as to give information regarding them. However, these applications are based on foreign plant data sets that do not include valuable herbs and shrubs with medicinal qualities. Hence this research proposes an application unique to medicinal plants, which can perform all these functionalities in both online and offline approach. Here, a new Ayurvedic plant dataset prepared from scratch, and preliminary results for classification of 5 types of herbs, compared with several deep Convolutional Neural Network (CNN) models based on transfer learning are presented. Experimental results indicate Marker-based Watershed algorithm as the best object detection algorithm in a complex background, VGG-16 as the best deep CNN classification model which reached a promising testing accuracy of 99.53%, and Seq2Seq LSTM model as the best deep learning model with optimum accuracy in abstractive information summarization.