Faculty of Computing
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Publication Embargo Assessing the Efficacy of Machine Learning Algorithms in Predicting Critical Properties of Gold Nanoparticles for Pharmaceutical Applications(Springer, 2025-07-08) Fernando, H; Mohottala, S; Jayanetti, M; Thambiliyagodage, CAu nanoparticles are increasingly used in pharmaceuticals, but their synthesis is costly and time-intensive. Machine Learning can help optimize this process. In this research, eight distinct Machine Learning models were implemented and optimized on a dataset comprising 3000 records of gold nanoparticles. The performance of these models was assessed using four accuracy metrics and the time required for training and inference. The results are promising, with all seven models demonstrating high accuracy and low time requirements. Notably, the XGBoost and Artificial Neural Network models exhibited exceptional performance, with Mean Squared Error values of 0.0235 and 0.0098, Mean Absolute Error values of 0.1021 and 0.0674, Mean Absolute Percentage Deviation values of 0.4945 and 0.3590, R2 scores of 0.9995 and 0.9998, and inference times of 0.0029 and 0.4299 s, respectively. The Explainable Artificial Intelligence analysis of the resulting models revealed some interesting insights into how the models make the predictions and what factors heavily contribute to the nanoparticle AVG_R, allowing chemists to optimize the synthesis for gold nanoparticles better. The key contributions of the research include the design and development of eight Machine Learning models using industry-standard frameworks, the training, tuning, and evaluation of these eight models using five different metrics, and further assessment of these trained models using Explainable Artificial Intelligence. The findings indicate a substantial potential for applying neural networks in the design phase of nanoparticle synthesis, which could lead to significant reductions in both the time and cost required for synthesizing Au nanoparticles for pharmaceutical applications.Publication Open Access Exploring nontoxic perovskite materials for perovskite solar cells using machine learning(Discover, 2025-07-06) Pabasara W.G.A; Wijerathne H.A.H.M; Karunarathne M.G.M.M.; Sandaru D.M.C; Abeygunawardhana, Pradeep K. W; Sewvandi, GPerovskite solar cells are promising renewable energy technology that faces significant challenges due to the Pb induced toxicity. The current study addresses this issue by leveraging machine learning techniques to explore Pb-free perovskite materials that ensure environmental sustainability and human safety. A highly accurate machine learning model was developed to predict Goldschmidt factor and the band gap, aiming to discover lead-free perovskites. Extreme Gradient Boost (XGBoost), Random Forest (RF), Gradient Boost Regression (GBR), and Ada Boost Regression (ABR) models were employed for this purpose. The findings exhibit that XGBoost delivers the most precise and reliable results for Goldsmith tolerance factor prediction with an accuracy of 98.5%. Furthermore, GBR model, combined with K-nearest neighbors (KNN) model delivers an impressive accuracy of 98.7% for the band gap predictions. 49 Pb-free perovskite materials were screened out considering the toxicity and the abundance. Utilizing Principal Component Analysis (PCA) and K-means clustering, six optimal materials (KBiBr3, KZnBr3, RbBiBr 3, RbZnBr3, MAGeI3, and FAGeI3null) were identified as the potential environment-friendly materials for photovoltaic applications. These results show the crucial role of machine learning and statistical analysis in discovering nontoxic and environmental-friendly perovskite materials, advancing the development of sustainable energy solutionsPublication Embargo Revolutionalize Your Learning Experience with EQU ACCESS(IEEE, 2024-07-25) Raveenthiran, G; Sivarajah, K; Kugathasan, V; Chandrasiri, S; Mohamed Riyal, A. A; Rajendran, KThis paper introduces a novel approach aimed at enhancing online education by placing a central focus on students' emotional well-being and improving their learning experiences. The approach integrates four key machine learning technologies: behavioral expression analysis, a personalized chatbot for emotional support, voice stress detection, and visual content description. Through empirical findings, the study illustrates the effectiveness of these methods in bolstering students' emotional well-being and academic performance. By providing a roadmap for the advancement of online education and emotional support, this research holds promise for delivering substantial benefits to learners worldwide. The study showcases notable advancements in online education, reporting a 30% rise in perceived emotional support and a 25% increase in overall satisfaction. The personalized emotional support chatbot achieved an 85% accuracy in addressing students' emotional needs, while voice stress detection boasted a 90% accuracy in identifying anxiety. Additionally, visual content description led to a 20% improvement in comprehension. These findings highlight the approach's potential to elevate both emotional well-being and academic performance in online learners.Publication Embargo 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, SHigher 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.Publication Embargo EasyChat: A Chat Application for Deaf/Dumb People to Communicate with the General Community(Springer, Cham, 2022-07-07) Sriyaratna, D; Samararathne, W. A. H. K.; Gurusinghe, P. M.; Gunathilake, M. D. S. S.; Wijenayake, W. W. G. P. A.Sign Language is closely associated with the deaf and dumb community to communicate with each other. However, not everyone understands sign language or verbal languages, so these communities need proper ways to communicate online. Therefore, this paper presents EasyChat, a sign language chat application that can translate three main sign languages into Simple English text as well as Simple English text into sign language, which would benefit for deaf/dumb community to express their ideas with the general community by simply capturing their British Sign Language (BSL) or Makaton gestures/symbols or lip movements. These steps are handled by four components. The first component, Convert BSL into Simple English, and the second component, handles Lip Reading conversion. The Makaton gesture and symbol conversion component produces a simple English text-formatted output for identified Makaton hand signs. Finally, the Text/voice to Sign Converter works on converting entered English text back into the sign language-based images. By using these components, EasyChat can detect relevant gestures and lip movement inputs with superior accuracy and translate. This can lead to more effective and efficient online communication between the community of deaf/dumb individuals and the general public.Publication Embargo ARCSECURE: Centralized Hub for Securing a Network of IoT Devices(Springer, Cham, 2021-07-06) Yapa Abeywardena, K; Abeykoon, A. M. I. S; Atapattu, A. M. S. P. B; Jayawardhane, H. N; Samarasekara, C. NAs far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.Publication Open Access Machine learning approach for predicting career suitability, career progression and attrition of IT graduates(IEEE, 2021-12-02) Bannaka, B. M. D. E; Dhanasekara, D. M. H. S. G; Sheena, M. K; Karunasena, A; Pemadasa, NThe IT industry in Sri Lanka is associated with a massive work force consisting of skillful professionals and it also provides many job opportunities for fresh graduates at the present. For a fresh graduate entering the IT industry there is a wide variety of job opportunities available and in order to have a satisfactory and rewarding career they should identify the most suitable career for them. On the other hand, employees change their careers and regularly seeking for career advancements and more benefits while the employers struggle to retain employees. Under such circumstances, this research focuses on developing a career mentoring system which comprises of the prediction of career suitability, career and salary progression, and employee attrition to assist IT employees to achieve career goals by overcoming barriers in their career path. For this purpose, data are collected from IT employees, and several models were implemented using classification algorithms such as XGBoost, Random Forest, Support Vector Machine, K-Nearest Neighbors, Decision tree, Naive Bayes, and their performance are compared using accuracy, precision, recall, and F1-Score to select accurate models. XGBoost resulted with higher accuracies for prediction of career suitability, initial salary, career and salary progression with values of 92.31, 90.35, 86.45 and 88.76 respectively. Furthermore, for the prediction of professional courses and employee attrition, Random Forest resulted higher accuracies of 93.52 and 89.70. The ultimate goal of this research is to guide IT graduates and employees to have better performances and to assist them in embracing responsibilities throughout their career life.Publication Embargo SEAMS: A Symmetric Encryption Algorithm Modification System to Resist Power Based Side Channel Attacks(Springer, Cham, 2018-11-02) Pathirana, K. P. A. P; Lankarathne, L. R. M. O; Hangawaththa, N. H. A. D. A; Abeywardena, K. Y; Kuruwitaarachchi, NSide channel attacks which examine physical characteristics of a cryptographic algorithm, are getting much more popular in present days since it is easier to mount an attack in a short time with only a few hundred dollars’ worth of devices. Sensitive information of a cryptographic module can be easily identified by evaluating the side channel information, such as power consumption, heat and electromagnetic emissions that outputs from the cryptographic device. This creates a huge impact on the security of the cryptographic modules as it is an efficient technique to break cryptographic algorithms by analyzing the patterns of the side channel information without having any specialized knowledge in cryptography. The solution proposed in this paper is an algorithm modification system for symmetric algorithms in order to mitigate side channel attacks. This is achieved by injecting randomness to the algorithm following a comprehensive analysis of power fluctuations that outputs from a given algorithm. In the proposed solution, a hardware device tracks down the patterns in power consumption and analyze those meter readings by utilizing machine learning techniques. As a result of this analysis, it identifies the pattern generating source code positions. System will add random code fragments in to the identified positions in the algorithm without altering the output in order to resist side channel attacks.Publication Embargo Behavior & Bio metric based Masquerade Detection Mobile Application(Springer, Cham, 2019-07-29) Chandrasekara, P; Abeywardana, H; Rajapaksha, S; Sanjeevan, pMobile phone has become an important asset when it comes to information security since it has become a virtual safe. However, to protect the information inside the mobile, the manufacturers use the technologies as password protection, face recognition or fingerprint protection. Nevertheless, it is clear that these security methods can be bypassed. That is when the urge of a post-authentication is coming to the surface. In order to protect the phone from an unauthorized or illegitimate user this method is proposed as a solution. The aim of the proposed solution is to detect the illegitimate user by monitoring the behavior of the user by four main parameters. They are: 1) Keystroke dynamics with a customized keyboard; 2) location detection; 3) voice recognition; 4) Application usage. In the initial state 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 and comparing it with the already saved data of the user. Another unique feature of this is the prevention mechanism it executes when an illegitimate user is detected. 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 Application which is introduced as “AuthDNA” will help you to protect the sensitive information of your mobile device in a case of theft and bypassing of initial authentication.Publication Embargo Code Vulnerability Identification and Code Improvement using Advanced Machine Learning(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Ruggahakotuwa, L.; Rupasinghe, L.; Abeygunawardhana, P.Cyber-attacks are fairly mundane. The misconfigurations of the source code can result in security vulnerabilities that potentially encourage the attackers to exploit them and compromise the system. This paper aims to discover various mechanisms of automating the detection and correction of vulnerabilities in source code. Usage of static and dynamic analysis, various machine learning, deep learning, and neural network techniques will enhance the automation of detecting and correcting processes. This paper systematically presents the various methods and research efforts of detecting vulnerabilities in the source code, starting with what is a software vulnerability and what kind of exploitation, existing vulnerability detection methods, correction methods and efforts of best researches in the world relevant to the research area. A plugin will be developed which is capable of intelligently and efficiently detecting the vulnerable source code segment and correcting the source code accurately in the development stage.