Research Papers - Dept of Software Engineering

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1022

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Absorbing Markov Chain Approach to Modelling Disruptions in Supply Chain Networks
(IEEE, 2019-08) Perera, S; Bell, M; Kurauchi, F; Kasthurirathna, D
Recent developments in the area of network science has encouraged researchers to adopt a topological perspective in modelling Supply Chain Networks (SCNs). While topological models can provide macro level insights into the properties of SCN systems, the lack of specificity due to high level of abstraction in these models limit their real-world applicability, especially in relation to assessing the impact on SCNs arising due to individual firm or supply channel level disruptions. In particular, beyond the topological structure, a more comprehensive method should also incorporate the heterogeneity of various components (i.e. firms and inter-firm links) which together form the SCN. To fill the above gap, this work proposes using the idea of absorbing Markov chains to model disruption impacts on SCNs. Since this method does not require path enumeration to identify the number of supply chains which form the SCN, it is deemed more efficient compared to the other traditional methods.
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Adaptivo: A Personalized Adaptive E-Learning System based on Learning Styles and Prior Knowledge
(IEEE, 2022-12-09) Rishard, M.A.M; Jayasekara, S.L; Ekanayake, E.M.P.U; Wickramathilake, K.M.J.S; Reyal, S; Manathunga, K; Wickramarathne, J
The rapid advancement of technology and the internet has resulted in an increase in the number of learners seeking e-learning. Though E-Learning is widely used most e-learning systems provide the same set of learning resources and learning paths to each student, regardless of their personal preferences. In recent years there has been increasing attention towards the characteristics of learners such as the learning styles and the knowledge level of the learner. This research paper proposes a personalized adaptive E-learning system called “Adaptivo” that provides a personalized learning experience to the learners based on their learning style and knowledge level. To make the learning process more efficient and engaging, Adaptivo takes into account the specific differences between learners in terms of time, online interactions and learning duration. It then builds a personalized learning path depending on each learner's learning style and knowledge level. The main aim of this study is to investigate the impact of the proposed adaptive learning approach on learners. The results show that the students appreciate the approach, are highly satisfied, and performed better when content is personalized according to their learning style and prior knowledge.
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Advancing Canine Health and Care: A Multifaceted Approach using Machine Learning
(IEEE, 2023-06-26) Wimukthi, Y; Kottegoda, H; Andaraweera, D; Palihena, P; Fernando, H; Kasthurirathnae, D
This research paper proposes a comprehensive approach to enhance the well-being of dogs through a range of innovative technologies. Firstly, we develop an automated system for dog breed and age identification using a Convolutional Neural Network (CNN) and a transfer learning model. This system aims to provide an efficient and reliable solution for dog owners and new adopters who are interested in discovering more about their canine companions. Secondly, we propose the development of a system that uses Reinforcement Learning to generate personalized meal plans based on a variety of factors such as the dog's breed, age, weight, health status, and emotional state. The system aims to provide dog owners with a reliable and effective tool for generating personalized meal plans that will enhance their pets' overall health and well-being. Thirdly, we present a dog disease recognition application that utilizes an artificial neural network (ANN) for identifying dog diseases based on their symptoms. Lastly, we introduce a real-time remote dog monitoring system using loT devices with edge computing to detect aggressive and anxious sounds. Our system provides an accurate classification of dog sounds related to aggression and anxiety, which can help dog owners detect and respond to potential issues early on. This research aims to provide dog owners and veterinarians with a range of technologies that can help them better understand and care for their furry friends.
Open Access
Agro-Genius: Crop Prediction Using Machine Learning
(2019-10) Gamage, A; Kasthurirathna, D
This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.
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AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka
(IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.
Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.
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AI Solution to Assist Online Education Productivity via Personalizing Learning Strategies and Analyzing the Student Performance
(Institute of Electrical and Electronics Engineers, 2022-10-29) Liyanage, M.L.A.P.; Hirimuthugoda, U.J; Liyanage, N.L.T.N.; Thammita, D.H.M.M.P; Koliya Harshanath Webadu Wedanage, D; Kugathasan, A; Thelijjagoda, S
Higher productivity in online education can be attained by consistent student engagement and appropriate use of learning resources and methodologies in the form of audio, video, and text. Lower literacy rates, decreased popularity, and unsatisfactory end-user goals can result from unbalanced or inappropriate use of the aforementioned. Prior studies mainly focused on identifying and separating the elements affecting the quality of online education and pinpointing the students' preferred learning styles outside of in-person and online instruction. This has not been able to clearly show how to enhance and customize the online learning environment in order to benefit the aforementioned criteria. This case study will primarily concentrate on elements that can be personalized and optimized to improve the quality of online education. With the aid of various algorithms like logistic regression,Support Vector Machines (SVM), time series forecasting (ARIMA), deep neural networks, and Recurrent Neural Networks (RNN), which make use of machine learning and deep learning techniques, the ultimate result has been attained. To increase application and accuracy, the newly presented technique will then be presented as a web-based software application. Contrary to what is commonly believed, this applied research proposes a new all-in-one Learning Management System (LMS) for students and tutors that acts as a central hub of all the learning resources.
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AI-Driven Smart Bin for Waste Management
(IEEE, 2020-12-10) Abeygunawardhana, A. G. D. T; Shalinda, R. M. M. M; Bandara, W. H. M. D; Anesta, W. D. S; Kasthurirathna, D; Abeysiri, L
With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.
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Analysis and enhancements of a cognitive based complexity measure
(IEEE, 2017-06-25) De Silva, D. I; Kodagoda, N; Kodituwakku, S. R; Pinidiyaarachchi, A. J
As stated by Tom DeMacro, something that cannot be measured is uncontrollable. Thus, a number of metrics have been developed to measure the complexity associated with software by considering various aspects such as size, control flow and data flow between modules, cognitive informatics etc. Amongst these aspects, cognitive informatics is recognized as a promising aspect in measuring software complexity. Thus, majority of the complexity metrics that were proposed after the introduction of cognitive informatics have been proposed mainly based on the cognitive aspect. Amongst them, Chhillar and Bhasins' weighted composite complexity measure is one of the few metrics that had attempted to measure the complexity of a program by considering more than three or more complexity factors. After a thorough analysis, in a previous study, the authors identified that the weighted composite complexity measure could be further improved by considering more complexity factors. This paper extends the previous study to identify the most appropriate factors that could be considered by the weighted composite complexity measure. Using the opinions of the industry experts, the authors were able to discover that compound conditional statements, threads and recursion could also be considered by the weighted composite complexity measure. Accordingly, the weighted composite complexity measure was enhanced to capture the complexities that arise due to those factors. The paper also includes a demonstration of the complexity calculation method of the improved weighted composite complexity measure with the use of three sample java programs, which were written by incorporating the above mentioned factors. In addition, an application of the weighted composite complexity measure to the same programs are also given in the paper, to illustrate the changes in complexity values of the two measures.
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Applicability of three complexity metrics
(IEEE, 2012-12-12) De Silva, D. I; Kodagoda, N; Perera, H
Over the years a number of complexity metrics have been proposed. However, there have been only a few studies conducted to compare those proposed metrics in terms of their practical applicability. The few researches that have been conducted to assess the applicability of a complexity metric have also used the five properties proposed by Briand et al. However determining whether a complexity measure satisfies some theoretical complexity properties is not a reliable method of determining the practical applicability of it. Thus, the main intent of this study was to compare three proposed code complexity metrics: McCabe's cyclomatic complexity, Halstead's software science and Shao and Wangs' cognitive functional size and identify which metric is the most suitable metric that can be used in the current state of the art with the help of thirty programmers. To conduct this empirical study ten freely available java programs were used as the base. From this study it was identified that Shao and Wangs' cognitive functional size is the best complexity metric that can be used in the real world.
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Applicability of Weyuker's properties using three complexity metrics
(IEEE, 2013-04-26) De Silva, D. I; Kodagoda, N
Although a number of complexity metrics have been proposed, only a few studies have been conducted to compare those proposed metrics to find the most effective and comprehensive metric. In general, to find the effectiveness and the comprehensiveness of a metric Weyuker's nine properties are used. However determining whether a complexity metric satisfies some theoretical framework is not enough to determine the effectiveness and comprehensiveness of that particular metric. Thus, this empirical study was conducted to find the effectiveness and comprehensiveness of three complexity metrics - McCabe's cyclomatic complexity, Halstead's software science and Shao and Wangs' cognitive functional size using the help of thirty software programmers with five or more years of working experience and thereby demonstrate the applicability of Weyuker's properties.
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Architectural description based Overlay Networks
(2011-09-01) Kasthurirathna, D; Keppetiyagama, C
Overlay Networks are heavily used in Distributed computing applications. They often have heterogeneous architectures, such as Client Server, Peer to Peer or Hybrid. In this work, we try to abstract the Architecture of an Overlay Network into a document called an Architectural Description (AD). The Architectural Description document may contain the Roles and the Relationships of a particular Overlay Architecture. The Architectural Description documents may be exchanged among the nodes and parsed by the nodes themselves, enabling the nodes to adopt different roles and relationships. By introducing a new AD, a new Overlay Network can be formed dynamically. AD based Overlay Networks may open many new possibilities in Overlay Networking. This approach would allow heterogeneous Overlays to work collaboratively, while maintaining their respective Security settings using 'Security Roles'. It would also allow multiple overlays to be dynamically 'super-imposed' on top of each other. Apart from that, the AD based approach would allow the same set of nodes to switch between heterogeneous overlays at different time intervals. Architectural Descriptions can also be used as an efficient means of Security key management. A prototype framework was developed to explore these features, using sample distributed file sharing applications. Moreover, the possible enhancements and future directions of AD based approach in developing Overlay Networks are also discussed.
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Arogya-An Intelligent Ayurvedic Herb Management Platform
(IEEE, 2020-10-15) 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.
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Artificial Intelligence-Based Centralized Resource Management Application for Distributed Systems
(IEEE, 2022-12-26) Hettiarachchi, L.S; Jayadeva, S. V; Bandara, R.A.V; Palliyaguruge, D
Due to the decentralized nature and emergence of new practices, tools, and platforms, microservices have become one of the most widely spread software architectures in the modern software industry. Furthermore, the advancement of software packaging tools like Docker and orchestration platforms such as Kubernetes enable developers and operation engineers to deploy and manage microservice applications more effectively and efficiently. However, establishing and managing microservice applications are still cumbersome due to the infrastructure configuration and array of disjoint tools that fail to understand the application’s dynamic behavior. As a result, developers need to configure multiple tools and platforms to automate the deployment and monitoring process to provide the optimal deployment strategy for microservices. Even though many tools are available in the industry, the fully automated product which comprises deployment, monitoring, resiliency evaluation and optimization were not developed yet. In response to this issue, we propose an artificial intelligence (AI)-based centralized resource management tool, that provides an automated low latency container management, cluster metrics gathering, resiliency evaluation and optimal deployment strategy behave in dynamic nature.
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Assistant Zone – Homeschooling Assistance System based on Natural Language Processing
(IEEE, 2022-12-09) Premendran, K; Bopearachchi, S.B.D.D.; Senevirathna, S.D.M.; Giridaran, S; Archchana, K; Ganegoda, D; Thelijjagoda, S
As a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.
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Assisting Wheelchair: Assist W
(IEEE, 2021-12-07) Ranaweera, D; Athalage, C; Sri Virajamana, M; Kaveesha, C; De Silva, D. I; De Silva, H
Traditional wheelchairs used by disabled people are required to be controlled manually. Hence, continuous monitoring and assistance of a caretaker is a mandatory requirement. This paper introduces an autonomous assisting wheelchair - Assist W, which would facilitate disabled people to do their day-to-day activities independently in a very safe manner, thereby managing their mental and physical health. Assist W can scan the location and design a 2D map of the house using SLAM algorithm and LIDAR sensor. After generating the map, Assist W is able to move automatically according to the commands (Voice and touch) given by the user, with the help of the map data. There is an AR (Augmented Reality) chat-bot that acts as a good companion to manage the mental health of the disabled person. Assist W is also able to manage the security and physical health of the disabled person by providing a fall detection system and automatic lifting system, and sending emergency alerts to the caretakers. This system was tested using simulation.
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Augmented Reality Based Platform for Simulation of 3D Models, Generated with a Series of 2D Images, on Real Environment
(IEEE, 2014-10-21) Ashinshanie, S. A. D. N. N; Hazari, A; Rupasinghe, H. N; Hettiarchchi, D. P; De Silva, D. I
With the need of an Augmented Reality based platform for simulation of three dimension models, generated with a series of two dimension images, on real environment, the main objective of this project is to build a platform for users to use both these functionalities. This is a mobile application which helps people to decide when they are buying household items, whether that item fits their desired place in the house. Three dimension models can be downloaded using the application and it will be simulated in the real time environment using the concept of Augmented Reality. A desktop application will be implemented for sellers to upload their three dimension models and to generate three dimension models using photographs. With the use of the technology people can make their lives easier, and this is another step the development team has taken to help that cause. Main goal is that with the use of this application people will be able to make their decisions easily when they are going to buy large household items.
Open Access
Authoring and enactment of mobile pyramid‐based collaborative learning activities
(Wiley Online Library, 2018-03) Manathunga, K; Hernández‐Leo, D
Collaborative learning flow patterns (CLFPs) formulate best practices for the orchestration of activity sequences and collaboration mechanisms that can elicit fruitful social interactions. Mobile technology features offer opportunities to support interaction mediation and content accessibility. However, existing mobile collaborative learning research has mostly focussed on simple activity orchestrations from the perspective of collaborative flow orchestration and flexibility requirements, predominantly in face-toface pre-university educational contexts. This paper proposes a particularisation of the Pyramid CLFP to support flexible face-to-face and distance mobile learning scenarios in which learners interact in increasingly larger groups along a sequence of activities (Pyramid levels). PyramidApp implements this Pyramid particularisation that provides both a web-based authoring tool and an enactment tool accessible through web or mobile devices. The authoring tool was evaluated in workshops where teachers appreciated its design and applicability to their educational contexts. PyramidApp flows were enacted in three higher education settings. Learners enjoyed the activities but usage and satisfaction varied depending on several design and contextual factors like the epistemic tasks given, the education level and application mode (face-to-face or distance).
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Automated Programming Assignment Marking Tool
(IEEE, 2022-07-18) Thenuwara, T. B. K. P; Vimalaraj, H; Wijekoon, V. U; Sathurjan, T; Reyal, S; Kuruppu, T. A; Tharmaseelan, J
Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student’s ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking.
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Automated Programming Assignment Marking Tool
(IEEE, 2022-07-18) Vimalaraj, H; Thenuwara, T. B. K. P.; Wijekoon, V. U; Sathurjan, T; Reyal, S; Kuruppu, T. A; Tharmaseelan, J
Due to the enrolment of a very high number of students to programming modules, marking of programming modules is becoming a very tedious and time-consuming process. Programming assignments mainly test for the student’s ability to think logically and approach a solution to the problem. In that case, just running the script and checking the output will not be sufficient enough to award a grade to the student. Marking criteria of programming modules provide certain marks for programs which are not syntactically correct but still have a good approach. Therefore, the code has to be read line by line and the implementation should be checked carefully to provide marks. Source code analysis has become mandatory in the current scenario. This leads to immense pressure and heavy workload on the staff who mark these programs. Considering all these aspects manual marking can lead to inconsistency, biasness, waste of time and less accuracy. Therefore, the main objective of this research is to minimize these problems by implementing an automated programming module marking tool by converting source codes to parse trees, extracting features, generating feature vectors, comparing them and generating a mark along with a feedback and plagiarism report. The solution focuses on automation marking by source code analysis and plagiarism checking.
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Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language
(IEEE, 2022-10-04) Goonawardena, M; Kulatunga, A; Wickramasinghe, R; Weerasekara, T; De Silva, H; Thelijjagoda, S
Sinhala is a native language spoken by the Sinhalese people, the largest ethnic group in Sri Lanka. It is a morphologically rich language, which is a derivation of Pali and Sanskrit. The Sinhala language creates a diglossia situation, as the language’s written form differs from its spoken form. With this difference, the written form requires more complex rules to be followed when in use. Manually proofreading the content of Sinhala material takes up much time and labor, and it can be a tedious task. Hence, a system is necessary which can be used by different industries such as journalism and even students. At present, there are a handful of systems and research that have automated Sinhala spelling analysis and grammar analysis. In addition, the existing systems are mainly focused on either spelling analysis or grammar analysis. However, the proposed system will cover both aspects and improve upon existing work by either optimizing or re-building the process to provide accurate outputs. The proposed system consists of a suffix list built for verbs and subjects, which helps the system stand out from the current proposed solutions. This research intends to implement a service for spell checking and grammar correctness of formal context in Sinhala. The research follows a rule-based approach with some components adopting a hybrid approach. As per the literature survey, many papers were analyzed, related to different aspects of the proposed system and complete systems. The proposed system would be able to overcome most barriers faced by previous papers whilst it takes a fresh take on providing a solution.