ICACT 2019

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3D Visualization of Human EEG Signals
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Pradeep, H.B.A.C.; Meegama, R.G.N.; Kalinga, S.
The brain is the most important and the most complex human organ that is responsible for all the functions that we do in our routine life. Moreover, the brain consists of millions of neurons that utilize electro-chemical signals to transmit information to other parts of the body. Whenever a neuron triggers an electrical impulse to another neuron, it generates electricity, referred to as an EEG wave that can be measured by a sensitive device. Using such brain patterns, it is possible to identify normal day-to-day human behavior. The brain commences its work before birth and works continuously until death during which brain waves are constantly generated according to what we perceive from the environment. By analyzing brain wave patterns, we can predict and identify valuable information on human or animal health. For examples we can monitor coma and brain death in human or animals, various effects of drugs on sleep disorder, day-to-day life human behavior, post-traumatic stress disorders (PTSD), etc. In the experiments conducted, we took the potential differences between the respective channels to identify the variations in brain wave data among the individuals. We used linear interpolation to generate 3D views of the potential data between the locations where the electrodes were placed. A color code is then applied to indicate the range of potential values projected on the human skull. High frequency components were observed near the right parietal and right occipital lobes of the brain. Significant variations were not observed near the frontal or the left region of the brain for a specific activity. The proposed project will introduce a technique to visualize human brain waves in 3D over the skull that will enable us to interpret how these brain waves are associated with various regions on the human brain.
Analyzing the E-Learning Satisfaction Factors Among University Students’ in Software Engineering Domain
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Perera, L.
E-learning has become popular within science faculties in Sri Lanka involve access to computers and significant knowledge of information technology. E-Learning provides a Web-based learning platform with a representative and more flexible framework which could support learning and teaching. Elearning provides various services that are customized by the students ‘needs, knowledge, expertise, and experience. This study focused on the undergraduates‘ analyzing the eLearning as an effective tool. , this study focused on analyzing the e-learning satisfaction factors among university student’s in the software engineering domain at the University of Kelaniya, Sri Lanka. This research aimed to provide a set of factors to be well-thought-out when an E-learning activity is planned and proposed to E-learners in the university of Kelaniya, Sri Lanka. The linear regression techniques were used to test the proposed research hypotheses. The technique run with online course satisfaction as the dependent variable, perceived usability, perceived quality, perceived value and computer self-efficacy as the independent variables. This study was based on a total sample of 150 students who are following Software Engineering degrees in the university Kalaniaya Sri Lanka. Point toward the results, that the effectiveness of e-learning is related to how confident students are while using the computer and the web-based learning software. The results of the study indicate that developers need to consider selfefficacy issues while developing e-learning systems.
An Application of Artificial Neural Networks to Predict the Milk Yield of a Typical Dairy Farm in Sri Lanka
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Hewage, S.S.; Chandrasekara, N.V.
It is quite interesting that milk and dairy products play an important role in a healthy, balanced diet thus contributing to certain indispensable nutritional benefits. Hence, the need for dairy is crucial, which means dairy farms provide a vital necessity to the people in both rural and other areas across the country. Therefore, accurate forecast of milk yield is important for dairy farmers to utilize and optimize their production process. The present study is aimed at using Artificial Neural Networks (ANN) for predicting the milk yield of a dairy farm by considering the potential factors that affect the milk production. Further, it is important to note that this dairy farm has kept records of the daily milk yield, the amount of food given to cows, and weather condition. Data from January 2016 to June 2018 were used for the study. In this regard, a feedforward neural network (FFNN), non-linear auto regressive neural network (NAR), and a non-linear auto regressive exogenous neural network (NARX) were fitted. Analysis was done using Matlab software and all three implemented models took around 30 seconds for execution. While all the three models exhibited quite strong model performances, the NARX model exhibited prominently outstanding results. The best forecasting performance was shown by the NARX neural network which contained one hidden layer with five neurons having saturating linear transfer function. Normalized Mean Squared Error (NMSE) was 0.0247 for the overall model while the Mean Absolute Error (MAE) value was 6.6245.
Automatic Motion Artefacts Recognition in Resting ECG/EKG to Identify Failed Tests using Machine Learning
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Nanayakkara, S.A.; Meegama, R.G.N.
Although an ECG is able to identify certain heart diseases, an uninterrupted and a clear signal is essential to accurately diagnose any abnormalities in the heart functions. Obtaining such a crisp ECG is a challenging task due to several artifacts such as motions because muscle movements are inevitable even in resting ECGs due to medical conditions such as anxiety, Parkinson’s disease and body tremors. In addition, skin stretching too, produces electricity that disturbs the potentials involved in an ECG. There are numerous experiments have been conducted to find effective and efficient motion artifact removal methods from ECGs. In this study, we use cleaned and disturbed ECGs to implement more effective and efficient method to remove motion artifacts and evaluation mechanism for ECGs. The first stage of the proposed technique involved gathering more than 500 ECGs having 12 leads data from public sources available on PhysioNet online database. These data contained cleaned ECGs and disturbed ECGs of healthy and unhealthy patients. The data set is cleaned to remove noise and undesirable effects such as baseline wander. A technique based on multi-resolution thresholding is used to recognize and remove motion artifacts and further, the Savitzky-Golay filter is used to reduce the mean squared error of this process. In the second stage, a convolution neural network (CNN) is implemented on the cleaned ECG dataset. Initially, datasets of 12 leads are shuffled under two categories: with and without noise. These shuffled images, numbering more than 36,000, are then categorized for training, validation and testing of data with and without motion artifacts. Results indicate a 98.7% accuracy in predicting whether a given ECG can be used or not by examining more than 500 cleaned ECGs.
Behavior & Biometrics Based Masquerade Detection Mobile Application
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Chandrasekara, P.; Rajapaksha, S.; Abeywardana, H.; Sanjeevan, P.; Abeywardena, K. Y.
Mobile phone has become an important asset when it comes to personal security since one’s mobile is now a virtual safe for that person. This is due to the sensitivity of the details which are stored in these devices. To protect the information inside a mobile phone the manufacturers use conventional technologies such as password protection, face recognition or finger print protection. Nevertheless, it is clear that these security methods can be bypassed by several other techniques as shoulder surfing, finger print or face recognition by pass with 3D printing. Due to these concerns post authentication is an increasingly discussed topic in the security domain. However, there are very few applied researches done on the post authentication of mobile platforms. In order to protect the phone from an unauthorized user a novel method is proposed by the authors. The aim of the research is to detect the illegitimate user by monitoring the behavior of the user. In order to detect the behavior four main parameters are proposed. Namely, Key stroke dynamics using a customized keyboard, location detection, voice recognition and App usage. Initially machine learning is used to train this mobile application with the authentic user’s behavior and they are stored in a central database. After the initial training period the application is monitoring the usage comparing it with the existing data of the legitimate user. Another unique feature is the inbuilt prevention mechanism which is designed to be executed when an illegitimate user is detected. The entire storage content will be encrypted and a current location alert along with a captured photo of the intruder will be sent to a pre-defined account of the real user in a cloud platform. The real user can log into the account remotely and obtain the phone’s location and the photo of the intruder. Furthermore, this application is proposed as an inbuilt application in order to avoid the deletion of app or uninstallation of the app by the intruder. With this proposed post authentication application “AuthDNA”, a user is able to protect sensitive information of the mobile device in case of theft and bypassing of initial authentication.
Comparative Analysis between K-mean and EM Clustering for Investigate Appropriate Algorithm for Landslide Risk Evaluation
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Madawala, C.N.; Kumara, B.T.G.S.
Irregular development activities on mountains and inadequate attention to construction aspects have led to increasing of landslide and sustaining damages to lives and properties. Within the study area, nearly 3275 sq.km of the area expanded over the Ratnapura District; and is to be highly prone to land sliding of 2178 sq.km. Landslides transpired in many regions of this area, and nearly 90 deaths have reported by National Research Building Organization (NBRO), Sri Lanka. If the suitable investigations were performed at the right time, most of the landslides could be predicted relatively and accurately. The main objective behind this of study is to evaluate the landslide risk levels to discover the real extent, timing and the intensity of landslide processes in Ratnapura district, such knowledge will present vital benefit to government officials, and the general public to avoid landslide hazards and mitigate the losses. Clustering Approaches can be used to developed the Risk Analysing model using actual data. This method was based on K-mean and Expectation Maximization (EM) Algorithms by concerning triggering factor; rainfall and causative factors; slope angle, elevation, and intensity. Such data were collected and applied to the Clustering algorithms. In this study, comparing the multiple Clustering algorithms and investigate the most appropriate risk evaluation approach where it can be used to advance hazard monitoring, early warning, and disaster mitigation. The results indicate that EM clustering algorithm showed accuracy over 84% with the highest speed. The highest accuracy over 92% was acquired by the K-means algorithm, but it was more time-consuming than EM algorithm. Therefore, this research proposed that an EM clustering has a strong capability to fit for the Landslide risk evaluation and producing a more relevant and accurate prediction of the landslide vulnerability within the study area.
Comparing Geant4 Simulated and TALYS-1.8 Code Evaluated Cross-Section Data for 4.438 MeV Gamma ray Line of 12C
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Ramanathan, V.
In present, Monte-Carlo transport code plays a major role in developing detectors, particularly the Geant4 Monte-Carlo code due to its versality and flexibility for different applications. Although, the Geant4 has been an invaluable tool for the development of devices, the discrepancies in prompt gamma cross-section data for prominent elements of human body has been reported in the range of proton therapy (50 -250 MeV). Even though, the binary cascade model has been suggested in proton therapy range, the problems with prompt gamma production cross-section have been reported. The aim of this study is to compare Geant4 simulated and TALYS evaluated prompt gamma cross-section data of 4.438 MeV photo peak of 12C to identify the inconsistency in the cross-section data. TALYS is a nuclear reaction study software which can be used to simulate nuclear reactions in the energy range of 1 keV to 200 MeV. The Geant4 model of AFRODITE detector system has been modeled to mimic the iThemba LABS AFRODITE detector system. The Geant4 AFRODITE model was validated using three standard gamma emitting sources (60Co, 137Cs, and 152Eu). The absolute detector efficiency of the Geant4 AFRODITE model also was determined. In the cross-section measurement simulation study, 1012 proton histories were used to collide the carbon and mylar target in the proton energy range of 66 – 125 MeV. The same procedures were performed experimentally using AFRODITE clover detector system. Further, TALYS 1.8 code was used to simulate the proton interaction with carbon target in the range of 5 to 150 MeV. As with the 4.438 MeV cross-section data comparison, there is a significant inconsistency between Geant4 simulated and TALYS simulation and also with experimental data set. To improve the accuracy of Monte-Carlo simulation study, more experimental cross-section data and the evaluation of proper physics models of Geant4 Monte-Carlo transport code in proton therapy range are future need.
Content in Box - Extending Moodle
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Senthuja, K.; Gnanakeethan, B.; Sarveswaran, K.
Aim of this research is to propose a systematic way to share Moodle-based learning materials such as video to remote schools that have limited Internet access. This has become a need in Sri Lanka. Because, serving multimedia contents over the internet is heavily limited by bandwidth and the link speed and this is a common problem everywhere, especially in the rural areas where the penetration of the Internet is very limited. On the other hand, the use of Multimedia has become an inevitable practice in classrooms. Further, self-paced learning has become a focal point in recent time. The government has also taken initiatives to promote self-learning and has invested a lot in setting up the environment and developing content for self-paced learning. In addition, there are studies in the region show that the use of multimedia content would increase the performance of students in national examinations. Shortage of school teachers in remote schools is also another serious issue. On the other hand, now there are several computer laboratories around the nook and corners of the country which are rarely utilized. Some of these laboratories have the Internet, however, students are usually not permitted to use the Internet due to the concern of Internet data cost. Aki.coach has been developed as an online course delivery platform for secondary education in Sri Lanka. To break the obstacles in taking this school level, a portable Aki box is introduced. This box will act as an integrated Content Delivery Network using squid proxy for Moodle. The box comes with all the video and the bandwidth consuming content. If this box is plugged to a laboratory, all the students can do self-paced learning using video and other materials. However, when students access a newly updated video, it will be downloaded via the Internet and stored in the Aki box. Students can also do an online examination. More importantly, students can continue their activities when going home. However, the content will be served from the main server when they access from home which students will feel any differences. Aki box has a Moodle installation which will periodically update the main server. Even if there is an Internet problem still the content can be served from the Aki box and the data will be synced when there is an internet connection. Moodle is altered to always get the video and other bandwidth consuming content from the squid cache if it is available. Now, this box is piloted in one location and soon it will be made available to other schools.
Development of a Solvent System for Effective Leaching of Extractable Proteins in Dipped Product Surfaces
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Kulathilaka, C.U.N.; Jayasuriya, C.K.; Premachandra, B.A.J.K.
Allergic conditions caused by natural rubber latex (NRL) proteins have become a vast problem in the natural rubber latex industry. Leaching is one of the protein removal methods which have been used in the industry. The objective of this study was developing a leaching solvent system to remove surface NRL proteins from dipped product surfaces using urea and sodium dodecyl sulphate (SDS). In this research NRL samples were prepared and leached using 4 different solvent systems namely distilled water, urea, SDS and a mixture of urea and SDS. At a time, one sample set (3 latex sheets to triplicate the results) was leached in previously mentioned solvent systems for a particular time and then washed with flowing water. Nine sample sets were used for the study. One sample set was kept without leaching. After leaching, Attenuated Total Reflectance Fourier Transform Infrared spectroscopy (ATR-FTIR) was used for qualitative determination of remaining surface protein content and modified Lowry method was used for quantitative determination of surface proteins. Antigenic proteins on sample surfaces were quantified using enzyme linked immunosorbent assay (ELISA) which is determined by reactions between specific NRL antibodies and NRL antigenic proteins. Without leaching, the average remaining extractable protein content was above the detection limit (> 200 µg/g). Therefore, water leached sample set was used as the control. When the urea concentration in leaching was increased, the removal efficiency of surface proteins was higher when a mixture of urea and SDS was used compared to when urea alone was used. This was observed in all the concentrations of urea: SDS ratios used. The maximum removal efficiency (74.36%) was observed for the leaching solvent mixture containing urea: SDS ratio 3:1. This could be due to the fact that both urea and SDS influence in deproteination and that increases the solubility of extractable proteins. In addition, ELISA suggested that after leaching, the antigenic protein content was below the detection limit for all the solvent systems used. However, since the removal of extractable protein content was maximized when mixture of urea and SDS was used it is expected that the antigenic protein content might also be further reduced compared to other solvents used.
Establishing Embodied Carbon Coefficients for Building Materials in Sri Lanka
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Kumanayake, R.P.
Buildings are identified as a major energy user and carbon emitter throughout their lifecycle. Carbon emission associated with energy consumption and chemical processes of material production is termed as ‘embodied carbon’. Material production stage or cradle-to-gate building lifecycle includes processes of raw material extraction, transportation and material production which are responsible for about 2040% of building lifecycle carbon emission. As embodied carbon of building materials highly vary with raw material quality, energy sources and production technologies, development of embodied carbon coefficients in the specific context of a country is necessary. Currently, Sri Lanka lacks such data inventories. This study was aimed at establishing embodied carbon coefficients of commonly used building materials in Sri Lanka. The process is made up of 3 stages; scoping, data collection and calculation. The data were collected through on-site surveys of material production facilities. In determining embodied carbon coefficient of a building material, aggregation decomposition hierarchy method was used. The embodied carbon coefficients obtained in the study were compared with values given in Inventory of Carbon and Energy (ICE) database. As the linkage between material production, energy use and carbon emission is dependent on many country specific factors, differences in values can be observed. A country-specific database ensures reliability and accuracy of building carbon emission studies. The process of establishing material embodied carbon coefficients should be standardized and data should be collected throughout the country so that computed values will represent national averages. This study will lead to future development of an embodied carbon coefficient database in the context of Sri Lanka, which will be useful in assessing embodied carbon of building materials and identifying appropriate strategies for mitigating embodied carbon of Sri Lankan buildings.
Global Positioning System Based Real-Time Traffic Monitoring System for Minneriya National Park
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Hansanie, M.H.M.; Manawadu, U.A.; Abeyratne, K.R.; Perera, P.; De Silva, R. S.
Located in the North Central Plains of Sri Lanka, Minneriya National Park is an ideal eco-tourism location. Annually during the dry season, August to September herds up to 300 elephants get attracted towards the Minneriya reservoir. Due to the large elephant herd that visits the reservoir throughout the year, mostly in the dry season, Minneriya National Park has become a large visitor attraction. The increasing visitor attraction to witness the elephant gathering in the banks of Minneriya Reservoir has caused adverse effects to the sensitive ecological areas, disturbing the natural habitats. The high number of visitor attraction has caused difficulties in the systematic management of safari rides in the park. The main objective of this project is to design and develop an android app that tracks the location of vehicles entering the park based on GPS data. The geo-location history of safari jeeps is updated in Firebase Real-Time Database. The park administrators are provided with a web-based system that consists of a customized map that shows the real-time location of all vehicles inside the park. Also, using Firebase Cloud Messaging facility, administrators can message the safari jeep drivers real-time and redistribute the vehicle traffic. Geo-fences are implemented in the app that marks protected zones. It indicates to the drivers that they are sensitive ecological areas and enter and exit to the areas are marked by an app notification. The app also shows the other safari vehicles in its proximity and the jeep drivers can have an idea of the realtime vehicle traffic. When implementing the app traffic data of 20 vehicles were collected and identified that grassland habitat occupied the maximum number of vehicles. There were concerns regarding network battery drain. It can be minimized by changing the CPU frequency when the app is inactive in run time. Through this real-time traffic monitoring system, the park administrators can easily manage and redistribute safari jeep traffic and improve the behavior of safari jeep drivers eliminating disturbance caused to flora and fauna of the national park due to increased vehicle traffic.
High Fidelity Simulation in Undergraduate Medical Curricula: Experience of Fourth Year Medical Students at a Sri Lankan Medical Faculty
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Kodikara, K.G.; Karunaratne, W.C.D.; Chandratilake, M.N.
Application of theoretical knowledge to management of critically ill patients is a challenging task faced by medical undergraduates where opportunities to learn clinical skills with regard to management of emergencies are few. High fidelity simulation (HFS) is widely used globally as an adjunct to clinical practice enabling students to learn clinical skills in a safe environment. However, research in the use of HFS in Sri Lanka is minimal. The purpose of this study was to explore the response of medical undergraduates to a high-fidelity simulator (HFS) in the context of management of emergencies. A pilot group of 30 fourth year medical students underwent a high-fidelity simulator session. They completed a self-administered evaluation, which included both open and close ended questions and participated in a focus group discussion post-simulation. Descriptive statistics were employed to analyze the responses to close-ended questions and the responses of the focus group discussion and open-ended questions were analyzed for recurring themes. All participating students responded to the evaluation. Students rated the simulation-based learning experience with high positivity. The self-competency of 29 (96.6%) students had increased following the sessions. The session provided a safe learning environment to all students. 19 (63.3%) students felt it helped put theory into practice while 21 (70.7%) students identified it as good practice for internship. 25 (83.3%) students wished to participate in more sessions. 17 (56.6%) students commented on the realistic nature of the experience. This study confirmed findings of previous studies conducted using HFS among medical undergraduates, confirming that the students highly valued high-fidelity simulation and find the opportunity to apply theoretical knowledge to practice in a safe environment. A high-fidelity simulator is a valuable learning tool in undergraduate medical education.
Identification of Papaya Fruit Diseases using Deep Learning Approach
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Munasingha, L.V.; Gunasinghe, H.N.; Dhanapala, W. W. G. D. S.
The diseases are a major problem faced by all the farmers including fruit farmers. It is a threat for large farmlands because these diseases spread throughout the land and make the fruits inedible, which at the end impact badly on the farmer’s income. Hence early disease detection is very important for the farmers to prevent or to control the propagation of the diseases. The traditional method of fruit disease detection and identification is naked eye observation. Even if this method is sufficient for a home gardener, it is a very inefficient one that requires experience and expertise. As a solution for this problem several computerized approaches are being developed using Machine Learning and Image Processing techniques in the resent researches. In our proposed work, we considered Papaya fruit, as it is a very popular fruit cultivation in Sri Lanka. In this study we have implemented a computerized model for papaya disease identification using Convolutional Neural Network (CNN). Among various diseases of papaya fruit, anthracnose, black spot, powdery mildew, phytophthora and ringspot were chosen. These are commonly found in Sri Lankan papaya cultivation. We have collected diseased images using a digital camera in normal conditions from papaya farms. Some of the images were found from the publicly available images on the internet. Then we have trained a deep CNN for these images. The network is able to classify images into five major papaya diseases. The system can finally identify the disease once a new image fed to it. The model performed ~92% of classification accuracy for new images. With compared to previous research done using Support Vector Machine (SVM), there is an increase of ~2%. Furthermore, it could be seen that the Black Spot disease was identified very easily by the model. Powdery Mildew was the most difficult disease to recognize. The results of this study reveal that this method is an accurate, reliable and efficient where it could be useful as an aid for expertise.
Integrating Technology into Undergraduate Classroom; Studentled Video Production as an Effective Instructional Strategy
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Rupasinghe, T.P.; Wijesinghe, S.C.
In the current world context, successful integration of digital technology and education theory has led to new advents of teaching and learning. Current students, termed as “Digital natives” have grown up in a multi-media simulated world and are highly competent in adopting to new technologies and therefore, it is importance to utilize technology enabled pedagogical approaches to invoke students’ interest and engagement. Further in the current socio-economic context, it is of enormous importance to enhance students’ generic skills such as self-directed learning, critical thinking, problem-solving, collaboration and cooperation in addition to the domain-specific knowledge and skills. Utilizing digital technologies in the tertiary education can be named as a valuable approach to address above challenges. In the past decade, using digital videos in the teaching and learning have become an emerging instructional strategy, mainly being used in the content delivery. However, there is only a limited number of studies that have been conducted focusing on learning through student-produced digital videos. Current study focuses on investigating student-led video production as an effective active learning instructional strategy. Study was conducted as a part of an Engineering Technology degree program and students (N=72) were asked to create videos (10-15 minutes) to educate their peers on given topics in the course content. Then they were given the opportunity to teach their peers using produced videos. Students’ perception on the activity was evaluated using surveys and its’ impact on the learning process was evaluated through an in-class quiz and was compared with previous quizzes. Majority of the students (> 90 %) had agreed that the activity helped them to understand subject matter better and improved their confidence, communication skills, team work skills and technical skills. Further, according to statistical testing it was proved that the average mark (57 %) for the quiz after the activity was higher than previous quiz (39 %) proving that the activity has a direct impact on students learning. In conclusion, it can be stated that student-led video production has a vast impact as an instructional strategy which enhances students’ competence, generic skills as well as the subject knowledge and thereby enhance the quality of tertiary education.
Investigation of Lead Concentration in Road Dust Samples in Kiribathgoda Area, Sri Lanka
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Silva, K.D.M.; Premaratne, W.A.P.J.
Heavy metal contamination has become one of the major problems in metropolitan cities all around the globe. Kiribathgoda area in Sri Lanka is one such hot spot. Anthropogenic activities have resulted in the increment of heavy metal levels in the earth’s crust. Analysis of outdoor dust is a useful technique to determine the heavy metal content in an urban area and thereby predict the extent of air pollution. This could open opportunities to relate the threat for human health by such toxic heavy metals in an unhealthy environment. This investigation was carried out by selecting a section from the main road of Kandy-Colombo in Kelaniya area. Ten sampling sites were selected and samples were collected as triplicates for three consecutive months. Their pH level and organic matter content were tested. Also, concentration level of the heavy metal Pb was determined using atomic absorption spectroscopy. The preliminary factors that favor the persistence of heavy metals in the environment were investigated and analyzed. It was observed that slightly acidic (pH 5.89±0.41) dust favor Pb deposition. The Pb content was found to be fluctuating around 22.01-84.52 mg kg-1. A good positive correlation (Correlation Coefficient 0.878) was observed in between Pb- Organic matter. It was evident that Pb exists in the environment for a very long time but their escape from nature is very slow. Hence it is necessary to study and understand the health risks associated with heavy metal toxicity on future studies developing from the findings of this research that will benefit the mankind.
LaSi Spell: Language Agents for Sinhala Spellings
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Samarawickrama, L.; Premarathne, H.L.; De Silva, S.C.M.; Hettige, S.B.
A Spell Checker is a tool which can used as a learning tool for any language learner because meaningful words created through correct spellings. It’s an essential part of several computer softwares such as web browsers, word processors and others because spelling of words give there correct meaning. Sinhala is a language which contain so many spelling rules, while there are few spell checkers has been developed for this language.Multi-agent systems are capable of handling the complexity of the real world problems through its emerging features including communication ,coordination and negotiations. This paper presents design and implementation of multi agent-based spell checker, named LaSi Spell which implemented through the MaSMT framework. It consists of sub ordinary agent systems namely corpus agent,rules agent,gui agent,custom agent and internet agent.LaSi Spell, desktop application has been implemented through java and capable to run with windows. The LaSi Spell has been incrementally tested and has shown encouraging results in its performance.
MLP Model Approach for Driver Fault Identification
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Ariyathilake, S.N.; Rathnayaka, R.M.K.T.
The issue of the traffic accident has gain attention of the globe which has been a major challenge for the sustainable development of transportation and traffic. Crashes are events which occurred by involving different components: Driver, road, environment. Driver identification is directly connected to taking advanced actions on the road accident. Prevention of the road accident is the primary concern and necessary legal actions must be taken for the responsible party of the accident. In order to accurately predict the driver fault regarding an accident, this study aims to identify whether the driver is fault for the accident or not, by using a Multilayer Perceptron (MLP) model. The proposed model accurately predicts the driver fault while ensuring the accuracy of the decision. Proposed Multilayer perceptron model has achieved an accuracy of 97.77% with the accident data. To compare the results of the model, Decision Tree, Linear classifier and DNN classifier has used. Comparative results revealed that the most accurate model as the Multilayer perceptron approach. Necessary sensitivity analysis regarding the MLP was performed to find the best MLP model. Results revealed that by using 500 epochs with RMSprop accuracy was increased. T – Test was performed with 0.05 accuracy level for the selected methods and MLP method outperformed the other techniques. The research will provide the information needed to guide the relevant decision-makers in adopting suitable measures to prevent and to reduce the accident rate.
A Novel Technique to Digest Biochar for Metal Analysis
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Wathudura, P.D.; Peiris, C.; Navarathna, C.; Kaumal, M.N.; Gunatilake, S.R.
Biochar (BC) is a low cost carbonaceous adsorbent material widely used for the removal of toxic metal ions from aqueous systems due to their highly porous nature and presence of various functional groups. Depending on the feedstock used to produce these carbonaceous materials, the trace metal content may vary. Various digestion techniques have been incorporated to analyze the metal content of BC though a proper method has not yet been established. This study was focused on finding a suitable method to totally digest the carbonaceous material and to evaluate the matrix effect. Both open vessel and microwave digestion methods were carried out for BC derived from tea waste, king coconut husk, Douglas fir and steam activated coconut shell biochar (CSBC) using mixtures of 69% nitric acid (NA), fuming nitric acid (FNA), 98% sulfuric acid (SA) and 30% hydrogen peroxide (HP) and their turbidity were measured. Lowest turbidities for open vessel digestions were observed for SA/HP mixture for low-temperature pyrolyzed BC with no external heating (2.04 – 7.90 FNU). Microwave digestions provided satisfactory turbidity levels for NA, NA/SA mixture, FNA and FNA/SA mixture for all types of carbonaceous material (1.58 – 20.97 FNU). The matrix effects were compared using cadmium, copper, lead and zinc using flame atomic absorption spectrophotometry. Digestion mixture containing only fuming nitric acid showed the lowest matrix effect for cadmium (1.2) for CSBC and copper (2.4) for CSBC while the mixture containing only nitric acid shows lowest matrix effect (7.6) for zinc with respect to Douglas fir BC. Recovery study confirmed the suitability of FNA as a suitable digestion mixture incorporated with microwave energy.
Potential Use of Selected Macrophytes Based Constructed Wetlands for the Treatment of Landfill Leachate
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Perera, K.R.S.; Yatawara, M.D.M.D.W.M.M.K.
Phytoremediation using constructed wetlands (CWs) is widely practiced for the removal of contaminants in landfill leachate. The present study was planned to assess the potential of floating macrophytes (Eichhornia crassipes and Pistia stratiotes) and emergent macrophytes (Typha angustifolia and Chrysopogon zizanioides) in improving the quality of leachate discharged from a Sequencing Batch Reactor (SBR) system located at Dompe sanitary landfill, Gampaha, Sri Lanka. The batch type CWs were arranged to identify the suitable dilution of leachate (as 0%, 25%, 50% and 75%) for the optimum plant growths. Based on the preliminary investigations, the potential of improving leachate quality by (ia) E. crassipes (T501) (ib) T. angustifolia (T502) and C. zizanioides (T503) at 50% dilutions and (ii) T. angustifolia (T04) and C. zizanioides (T05) at 0% dilution were assessed in continuous flow CWs. Water quality parameters including temperature, pH, electrical conductivity, turbidity, BOD, COD, TSS, phosphate, ammonium nitrogen, nitrate, sulphate and color were tested once in five days for 40 days period. Results were subjected to One-way ANOVA followed by Tukey’s pair wise tests in Minitab 14. Two sample t-test at 95% CI was also applied as required. With respect to controls, percentage reductions of measured parameters increased in wetlands having either floating macrophyte, E. crassipes or selected emergent macrophytes. Nevertheless, among the emergent plants, C. zizanioides [(T503) and (T05)] showed the highest performance in improving leachate quality followed by T. angustifolia [(T502) and (T04)]at both dilutions. Although E. crassipes (T501) showed higher percentage reductions of the selected parameters at 50% dilutions, this is not recommended as leachate dilutions are impractical in CWs. P. stratiotes has proven unsuccessful in the present study. Therefore, among the plant species selected, C. zizanioides that performed at 0% dilution could be recommended as the best plant for the remediation of leachate draining from SBR system.
A Pragmatic Approach to Enhance the Economic Viability of the Road Construction Industry in Sri Lanka by Integrating Lean Construction Concepts
(4th International Conference on Advances in Computing and Technology (ICACT ‒ 2019), Faculty of Computing and Technology, University of Kelaniya, Sri Lanka, 2019) Karunanayake, S.G.S.; Ananda, H.S.R.
The interconnection of activities required for the design and construction of building and infrastructure involves the interplay between people, technology, situations, and decisions. It requires the astute coordination of labor, materials, and plant to realize the planned progress of work. Minimizing waste and maximizing value while continuous improvement is the concept of lean. Lean construction has proven to be an alternative for such improvements so as the satisfy client by creating customer value. Through its origins in the Toyota Production System, lean is now applied as an innovative way to manage the design and construction of projects with the use of tools which address project constraints, such as complexities and uncertainties, among others. This research is an effort to implement lean construction concept to the Sri Lankan road construction industry. Research approach involved the use of primary data, collected from Questionnaire survey and semistructured interviews with qualitative and quantitative mixed type research. The foremost objective was to optimize the cost, quality and time in road construction with the application of lean construction concept and identify most important lean tool among 5S, Construction process analysis, just in time, Value stream mapping, Kanban and last planner and adapted to road construction industry. Finally, the aim is to identify the most important lean construction tool for road construction improvement.