Software Engineering Teaching Unit

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Modeling and Forecasting Mortality in Sri Lanka
(2014) Aberathna, Wasana; Alles, Lakshman; Wickremasinghe, W. N.; Hewapathirana, Isuru
The purpose of this study is to develop sex-specific mortality estimation models using historical mortality data for Sri Lanka, based on the statistical time series techniques attributed to Lee and Carter (1992). Historical mortality data was analyzed in the light of significant historical episodes. Several alternative univariate time series models were examined for modeling males and females, as well as a bivariate vector autoregressive (VAR) model. The VAR model when fitted to the first differenced series performed better than the univariate models and hence used for forecasting purposes. From the estimated VAR model, mortality forecasts were generated for the period up to 2030 and life tables were generated for the selected periods of 2006-2008.
Change detection in dynamic attributed networks
(2018) Hewapathirana, I.U.
A network provides powerful means of representing complex relationships between entities by abstracting entities as vertices, and relationships as edges connecting vertices in a graph. Beyond the presence or absence of relationships, a network may contain additional information that can be attributed to the entities and their relationships. Attaching these additional attribute data to the corresponding vertices and edges yields an attributed graph. Moreover, in the majority of real-world applications, such as online social networks, financial networks and transactional networks, relationships between entities evolve over time. Change detection in dynamic attributed networks is an important problem in many areas, such as fraud detection, cyber intrusion detection, and health care monitoring. It is a challenging problem because it involves a time sequence of attributed graphs, each of which is usually very large and can contain many attributes attached to the vertices and edges, resulting in a complex, high-dimensional mathematical object. In this survey we provide an overview of some of the existing change detection methods that utilize attribute information. We categorize these methods based on the levels of structure in the graph that are exploited to detect changes. These levels are vertices, edges, subgraphs, communities, and the overall graph. We focus attention on the strengths and weaknesses of these methods, including their performance and scalability. Furthermore, we discuss some publicly available dynamic network datasets and give a brief overview of models to generate dynamic attributed networks. Finally, we discuss the limitations of existing approaches identifying key areas for future research.
Enterprise Ready Containerized And Microservices Architectural Devops Engine Designing
(2nd South Asia Conference on Multidisciplinary Research, 2019) Kithulwatta, W.M.C.J.T.; Jayawickrama, D.
Seamlessly software delivery and maintaining without any delay, is the major task of DevOps engineers in industrialization. In the traditional way, it is using bare metal hardware or cloud services to farm the computer system infrastructure. While using those modules, the main problems arising are, huge cloud service charges, disability to use infrastructure in the cross-platform, difficulty of infrastructure migration, system archiving problem, data persisting problems and smooth scalability issue. Main objectives of the research study are to create portable system infrastructure modules, to create technical and theoretical containerized DevOps engine, apply long-time data persisting approach to the enterprise applications and to apply high-velocity innovation to the computer systems infrastructure. The proposed DevOps engine was designed with the Docker container management system on top of the Linux operating system as the host. It was used Docker trusted images to deploy, isolated containers by using microservices architecture with advanced software engineering concepts with industrialized software applications. It was used enterprise-ready software applications and services on the proposed engine to validate the concept over the same configurations on the cloud service. With the usage of encapsulated components container approach, all internal data was secured on top of the host operating system. Due to the portability of Docker containers, it was easy to migrate the monolithic computer system to microservices architecture. By using fast Docker containers, it was facilitated to DevOps engineers on the engine to improve the scalability and security across the system infrastructure.
Architecting advanced devops engine with docker by using microservices for enterprise software applications
(EdHat International, UK, 2019) Kithulwatta., W.M.C.J.T.; Jayawickrama., D.
In the industry approach, bare-metal hardware, virtual machines (VMs) or cloud infrastructure are using to launch enterprise-ready applications. At the time of using those platforms, the main problems that occur are the difficulty of scaling the infrastructure and maintain the infrastructure, long-time data persisting issues, the difficulty in archiving the systems and large payments on cloud services. To overcome the identified problems, an advanced DevOps engine was developed on the Docker container management system with microservices applications. The objectives of this study are to develop conceptual and technical DevOps engine module, to apply the containerization platform on Docker engine for enterprise-ready microservices applications and to make an agile DevOps platform. The proposed system was analyzed over the cloud infrastructure with the same configurations. Jha, et al. (2018) describes that Docker is a better approach for microservices applications but there are no existing researches for microservices architecture for the enterprise-ready platform with Docker
Change detection in noisy dynamic networks: a spectral embedding approach
(2020) Hewapathirana, I.U.; Lee, Dominic; Moltchanova, Elena; McLeod, Jeanette
Change detection in dynamic networks is an important problem in many areas, such as fraud detection, cyber intrusion detection and healthcare monitoring. It is a challenging problem because it involves a time sequence of graphs, each of which is usually very large and sparse with heterogeneous vertex degrees, resulting in a complex, high-dimensional mathematical object. Spectral embedding methods provide an effective way to transform a graph to a lower dimensional latent Euclidean space that preserves the underlying structure of the network. Although change detection methods that use spectral embedding are available, they do not address sparsity and degree heterogeneity that usually occur in noisy real-world graphs and a majority of these methods focus on changes in the behaviour of the overall network. In this paper, we adapt previously developed techniques in spectral graph theory and propose a novel concept of applying Procrustes techniques to embedded points for vertices in a graph to detect changes in entity behaviour. Our spectral embedding approach not only addresses sparsity and degree heterogeneity issues, but also obtains an estimate of the appropriate embedding dimension. We call this method CDP (change detection using Procrustes analysis). We demonstrate the performance of CDP through extensive simulation experiments and a real-world application. CDP successfully detects various types of vertex-based changes including (1) changes in vertex degree, (2) changes in community membership of vertices, and (3) unusual increase or decrease in edge weights between vertices. The change detection performance of CDP is compared with two other baseline methods that employ alternative spectral embedding approaches. In both cases, CDP generally shows superior performance.
A Case Study in Financial Fraud Detection using Big Data Analytics
(2021) Boteju, W. P. A.; Hewapathirana, I. U.
The financial industry is currently undergoing digital transformations across products, services and business models. This digitization is aimed at automating most of the manual financial transactions and other relevant services. Therefore, spotting fraud in financial transactions has become an important priority for all financial institutes. With the advances in modern technology and global communication, fraud has increased significantly, causing great damages. The focus of this paper is to experiment different approaches for detecting fraudulent activities in a real-world dataset of financial payment transactions. The dataset is obtained from Kaggle and consists of 6 million transaction records and 10 features with the transaction label as ‘fraudulent’ or ‘non-fraudulent’. These features are investigated using exploratory data analysis and only 6 are retained for the experiment such as payment-type, account-balance, transaction-amount etc. Two supervised machine learning algorithms, the random forest and the support vector classifier are employed for detecting fraudulent transactions. The dataset is large and requires high computational power to process and train machine learning algorithms. Furthermore, another challenge is the highly imbalanced distribution between fraudulent (0.1%) and the non-fraudulent (99.9%) classes. The goal of this research is to solve both these issues. In order to handle class imbalance, the effect of oversampling the minority class data using the synthetic minority oversampling technique (SMOTE), and undersampling the majority class using random undersampling are investigated. Computational efficiency is achieved through the Apache Spark implementation, which provides distributed processing for big data workloads. The best performance is obtained using the random forest algorithm on the oversampled dataset with an accuracy of 99.95%, F1-score of 0.9994, recall of 0.9994, Geometric mean of 99.94% and a model training time of 13.9 minutes. This paper provides valuable insights on dealing with large scaled highly imbalanced big datasets for predicting financial frauds and generating alerts.
Utilizing Prediction Intervals for Unsupervised Detection of Fraudulent Transactions: A Case Study
(2022) Hewapathirana, I.U.
Money laundering operations have a high negative impact on the growth of a country’s national economy. As all financial sectors are increasingly being integrated, it is vital to implement effective technological measures to address these fraudulent operations. Machine learning methods are widely used to classify an incoming transaction as fraudulent or non-fraudulent by analyzing the behaviour of past transactions. Unsupervised machine learning methods do not require label information on past transactions, and a classification is made solely based on the distribution of the transaction. This research presents three unsupervised classification methods: ordinary least squares regression-based (OLS) fraud detection, random forest-based (RF) fraud detection and dropout neural network-based (DNN) fraud detection. For each method, the goal is to classify an incoming transaction amount as fraudulent or non-fraudulent. The novelty in the proposed approach is the application of prediction interval calculation for automatically validating incoming transactions. The three methods are applied to a real-world dataset of credit card transactions. The fraud labels available for the dataset are removed during the model training phase but are later used to evaluate the performance of the final predictions. The performance of the proposed methods is further compared with two other unsupervised state-of-the-art methods. Based on the experimental results, the OLS and RF methods show the best performance in predicting the correct label of a transaction, while the DNN method is the most robust method for detecting fraudulent transactions. This novel concept of calculating prediction intervals for validating an incoming transaction introduces a new direction for unsupervised fraud detection. Since fraud labels on past transactions are not required for training, the proposed methods can be applied in an online setting to different areas, such as detecting money laundering activities, telecommunication fraud and intrusion detection.
Combining Information from Multiple Views for Vertex-Based Change Detection in Dynamic Networks: A Comparative Study
(2022) Hewapathirana, I.U.
In the majority of previous network-based change detection methods, entities are often represented as vertices in an undirected graph with weighted edges describing the relationships between entities. However, in most real-world applications, entities share different types of relationships forming multi-view networks that can be well represented by multiple undirected graphs over the same set of vertices. Integrating information across multiple undirected graphs for change detection in noisy dynamic networks is a crucial but challenging issue. In this paper, a multi-view dynamic network is represented as a time sequence of tensors by encoding multi-graph information into the slices of the tensor. We employ spectral embedding together with Procrustes analysis to detect changes due to vertex behavior, that is, vertex-based changes. Through extensive simulation experiments, we demonstrate the performance of several strategies to combine the information from different slices of the tensor, and obtain a single embedding of the multi-view network. In all experiments, we compare the performance of these methods over a variety of changes ranging from easy to difficult for the purpose of detecting vertices that undergo change. We show that two strategies, MCDP-I and MCDP-II, successfully detect all types of vertex-based changes that were considered in the experiments and show better performance compared to the others when the connectivity structure varied considerably across the slices of the tensor. MCDP-I employs a higher-order singular value decomposition to factorize the tensor at each time instant, and MCDP-II, initially embeds each slice of the tensor separately using matrix factorization and then applies generalized Procrustes analysis techniques on the resulting set of embeddings to obtain a combined embedding. Finally, we also illustrate the performance of MCDP-I and MCDP-II for a real-data application.
A Systematic Investigation on the Effectiveness of the Tabbert Model for Credit Card Fraud Detection
(2022) Hewapathirana, Isuru Udayangani; Kekayan, Nanthakumar; Diyasena, Deshanjali
As a result of rapid digitisation, online transactions using credit cards have become popular. With this, fraudulent activities have also increased considerably. Although many supervised and unsupervised machine learning techniques were proposed in past research for identifying fraudulent transactions, they do not fully utilize the tabular and hierarchical structure present in transaction datasets. Recently, the TabBERT neural network model was proposed to calculate row-wise embeddings that capture both inter and intra dependencies between transactions in tabular time series data. In this research, we present a systematic experimental framework to assess the effectiveness of applying the embeddings calculated using the TabBERT model for credit card fraud detection. We employ the calculated row embeddings for fraud detection using three unsupervised machine learning algorithms and two supervised machine learning algorithms. We perform our experiments on a synthetic dataset that has been generated using the TabGPT model. Overall, TabBERT-based embeddings increase the performance of the supervised learning models with the extreme gradient boosting model achieving a precision of 99% and an F1 score of 98%, and the multilayer neural network model achieving a precision of 97% and an F1 score of 95%. For unsupervised learning, the use of TabBERT embeddings increases the recall rate of K-means clustering algorithm by 0.19%.
A Review on Current Trends and Applications of Social Media Research in Sri Lanka
(2023) Hewapathirana, I.U.
Standard research on social media and its applications has been widely disseminated in developed nations. But in Sri Lanka, research in this area has been released far less frequently. However, social media usage in the country is evolving regardless of age, sex, education level, or other limitations. This study aims to fill the gap by conducting a comprehensive review of social media-based research conducted in Sri Lanka between 2012 and 2022. A systematic search of reputable databases, including IEEE Xplore, ScienceDirect, Emerald Insight, Google Scholar, and Springer Link, identified 57 relevant papers for analysis. The review highlights the diversity of application areas where social media research has been employed in Sri Lanka, including disaster management, public health, marketing, education, and more. Additionally, the analysis highlights the methodological approaches employed in social media analytics and the specific social media platforms utilized by researchers in Sri Lanka. The results of the current study serve as a timely resource, enabling policymakers and decision-makers to identify the potential avenues of social media research in Sri Lanka. By understanding the existing trends and implications, stakeholders can harness the power of social media data to make informed policy decisions, develop effective marketing strategies, enhance public health initiatives, and revolutionize educational practices.
Advancing tourism demand forecasting in Sri Lanka: evaluating the performance of machine learning models and the impact of social media data integration
(Emerald Publishing Limited, 2023) Hewapathirana, Isuru Udayangani
Purpose – This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka. Design/methodology/approach – Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated. Findings – The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends. Practical implications – The findings offer substantial implications for the industry’s growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka’s tourism sector. Originality/value – This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
Analysis of Employee Satisfaction using Artificial Neural Networks: A Case Study in the Information Technology Industry in Sri Lanka
(2023) Hewapathirana, I.U.; Thilakarathna, W.A.S.M.S.
Job satisfaction is vital to the prosperity of all industries, including the information technology (IT) sector. This research represents the pioneering attempt to employ Artificial Neural Networks (ANNs) for the purpose of examining the factors that affect job satisfaction among IT professionals in Sri Lanka. Data was gathered from a survey of 156 IT professionals and was analyzed statistically to identify seven factors that influence job satisfaction. An ANN was trained to predict job satisfaction using the extracted factors as inputs. The accuracy of the model in predicting job satisfaction was 94.64%. This suggests that ANNs have the potential to identify critical factors for IT employees and target interventions to increase their satisfaction. Additional research on the fitted ANN revealed that working conditions, family-friendly policies, and level of autonomy at work are crucial factors in determining a person's job satisfaction. Employers can use our findings to increase employee satisfaction by implementing appropriate policies. In addition, our ANN model can used to identify employees who are likely to leave and to provide them with customized interventions.
A Review of Recent Trends in Sri Lankan Social Media Analytics Research
(2023) Hewapathirana, Isuru Udayangani; Sandaruwani, M. D.
Due to industry demands and massive applications, the social media landscape is rapidly expanding. However, in Sri Lanka, analyzing social media data is still considered a young research topic. This article examines the present status of social media analytics research in Sri Lanka, highlighting selected technologies and applications and discussing their proven and future benefits. The primary goal of this research is to provide information regarding social media analytics usage in Sri Lanka and to identify shortcomings in this area. We select 45 publications published between 2013 and 2022 from the most used web-based databases, including Google Scholar, IEEE Xplore, ScienceDirect, Springer, and ResearchGate. To identify eligible papers for thorough analysis, multi-phase searches and selections are accomplished. The study also includes extensive discussions on social media platforms and the technology, tools, and techniques used in analytics. The review discovered several methodologies and tools that were utilized with social media data. Descriptive analysis, regression analysis, and text analysis were the most commonly used analysis methods, while Facebook, Twitter, YouTube, Instagram, and Viber were the most popular social media networks. Current social media analytics research were noticed in a variety of domains, including marketing, education, politics, health, social, and business.
Labelled Vulnerability Dataset on Android Source Code (LVDAndro) to Develop AI-Based Code Vulnerability Detection Models
(2023) Senanayake, Janaka; Kalutarage, Harsha; Al-Kadri, Mhd Omar; Piras, Luca; Petrovski, Andrei
Ensuring the security of Android applications is a vital and intricate aspect requiring careful consideration during development. Unfortunately, many apps are published without sufficient security measures, possibly due to a lack of early vulnerability identification. One possible solution is to employ machine learning models trained on a labelled dataset, but currently, available datasets are suboptimal. This study creates a sequence of datasets of Android source code vulnerabilities, named LVDAndro, labelled based on Common Weakness Enumeration (CWE). Three datasets were generated through app scanning by altering the number of apps and their sources. The LVDAndro, includes over 2,000,000 unique code samples, obtained by scanning over 15,000 apps. The AutoML technique was then applied to each dataset, as a proof of concept to evaluate the applicability of LVDAndro, in detecting vulnerable source code using machine learning. The AutoML model, trained on the dataset, achieved accuracy of 94% and F1-Score of 0.94 in binary classification, and accuracy of 94% and F1-Score of 0.93 in CWE-based multi-class classification. The LVDAndro dataset is publicly available, and continues to expand as more apps are scanned and added to the dataset regularly. The LVDAndro GitHub Repository also includes the source code for dataset generation, and model training.
Empowering influence discovery: Utilizing machine learning for social media influencer identification
(2024) Devyanjalee, D.D.W.N.; Hewapathirana, I. U.
In today’s dynamic digital landscape, influencer marketing has become a cornerstone of marketing strategies, leveraging social media platforms to engage with audiences. Accurately identifying influencers within social media platforms poses a formidable challenge. Traditional machine learning approaches relying solely on metrics such as network analysis and user profile data, often fall short in capturing the dynamics of influencer impact and resonance with audiences. To address this gap, this study aimed to enhance influencer identification accuracy by leveraging both user profile and engagement metrics alongside text analysis. The methodology adopts a sequential explanatory design, combining quantitative analysis of user profile metrics with qualitative analysis of text-related factors. Data collection from social media platforms, particularly X, comprises user profile and social data. The quantitative phase employs established algorithms like the PageRank algorithm to identify top influencers based on user profile data, while machine learning models, logistic regression, decision trees, and random forest are trained using user profile data to discern influential user profiles. The qualitative phase involves text analysis techniques, including keyword matching and lemmatization, to extract valuable insights from tweets. Machine learning models are then trained using both user profile and social data alongside text analysis data to discern influential user profiles. The models are then compared to assess the impact of incorporating engagement metrics with text analysis. Findings from this study indicate that while user profile metrics alone exhibit high accuracy in influencer identification, with the random forest model achieving an F1 score of 0.90, the incorporation of engagement metrics introduce complexities affecting model performance, resulting in an F1 score of 0.70. The random forest model emerges as the most robust performer, maintaining high accuracy despite these challenges. This research contributes to advancing influencer identification strategies within digital marketing, offering insights into the effectiveness of integrating both user profile and engagement metrics with text analysis for capturing the true essence of influencer influence and resonance with audiences. The findings underscore the challenges of leveraging engagement metrics for influencer identification and highlight the need for further refinement of methodologies to empower marketers in navigating the complexities of the ever-evolving digital landscape.
Deep learning-based correctness assessment for the Tadasana (Mountain Pose) Yoga Asana
(2024) Gayan, V.G.N.; Hewapathirana, I. U.
Yoga has become increasingly popular worldwide, but practicing without proper guidance can lead to incorrect posture alignment, reducing effectiveness, and increasing injury risk. This research aimed to address this issue by developing a deep learning-based system that relies on the MediaPipe framework to assess the correctness of the Tadasana yoga asana and provide real-time feedback for improvement. A deep learning-based system was selected for the proposed study to implement the MediaPipe framework, for its outstanding real-time performance (75.9% mean average precision on the COCO dataset) and cross-platform efficiency. Using MediaPipe, a custom-developed web app analyzed more than 50 professional yoga instruction videos to extract crucial body angles for each Tadasana step, generating the dataset for the yoga pose angle calculation algorithm. This approach accounts for MediaPipe’s inherent variability in landmark detection, ensuring robust angle calculations. The primary goals of this study were to develop an accurate pose estimation and angle calculation algorithm specifically optimized for Tadasana, as well as a comprehensive, real-time feedback mechanism for pose correction. The proposed system integrated MediaPipe’s pose estimation capabilities with a custom angle calculation algorithm and a rule-based feedback system. An extensive evaluation was conducted using more than 100 images of correct and incorrect poses for each of the three Tadasana steps. The system demonstrated promising results, achieving accuracy scores of 78, 75, and 72% for steps 1, 2, and 3, respectively. It was observed that the system’s performance varied based on factors such as image quality and environmental conditions. This study demonstrates the feasibility and potential of using deep learning and computer vision techniques for precise yoga pose correction. Future work will focus on enhancing the system’s robustness across diverse conditions, expanding its capabilities to encompass a wider range of yoga poses, and implementing real-time video analysis for feedback generation. These advancements could significantly enhance the accessibility and effectiveness of remote yoga instruction, making proper technique more attainable for practitioners.
DevOps Adoption in Software Development Organizations: A Systematic Literature Review
(Institute of Electrical and Electronics Engineers (IEEE), 2024) Karunarathne, M.A.W.; Wijayanayake, W. M. J. I.; Prasadika, A. P. K. J.
In today's IT industry, where many software development organizations work for increased software quality, and improved customer satisfaction, the traditional approaches to software development and operations are becoming ineffective. To address this, an approach known as DevOps has emerged as a solution. DevOps is a development methodology that closes the gap between development and operations teams emphasizing collaboration, automation, and continuous practices. It provides many important benefits including enhanced software delivery performance and increased team engagement. Even though the concept of DevOps has been discussed for a long time it is still challenging to adopt DevOps in software development organizations and many of them are trying to implement DevOps practices but stuck in middle due to various reasons such as cultural barriers, lack of expertise and organizational silos. Also, DevOps adoption is influenced by factors including culture, automation, and people. Best practices such as continuous integration and continuous deployment can be incorporated in companies for a successful DevOps adoption. Therefore, it is important for companies to have a clear understanding about DevOps and know their readiness for adopting DevOps for successful DevOps implementation. For that, this systematic literature review provides the critical components of DevOps adoption with challenges, best practices, and factors affecting DevOps. Through the findings of this study, organizations can identify the possible challenges they may encounter, understand DevOps best practices, and the key factors affecting DevOps success. This review also contributes to the literature in the area of software development by providing extensive knowledge to researchers to develop frameworks for successful DevOps adoption.
TourismXplorer: Interactive Dashboard for Data-Driven Decision-Making in Sri Lanka’s Tourism Industry
(2024) Thilakarathna, W. A. S. M. S.; Hewapathirana, I. U.
Abstract: The tourism industry is a critical component of Sri Lanka’s economy, necessitating advanced tools for data-driven decision-making to enhance strategic planning and operational efficiency. This study presents the development of a comprehensive tourism dashboard designed specifically for tourism businesses in Sri Lanka. The dashboard offers a holistic view of the tourism landscape by integrating diverse data sources, including annual statistical reports (2018-2023), climate variables from the Sri Lanka Meteorological Department, and TripAdvisor reviews. The novelty of this research lies in its multifaceted data integration, advanced visualization techniques, and predictive analytics capabilities. The dashboard provides stakeholders with real-time and historical insights into tourism dynamics. It includes key performance indicators (KPIs) such as tourist arrivals, revenue, expenditure, accommodation statistics, climate impact, visitor demographics, and sentiment analysis from reviews. Visualizations range from line, pie, and bar charts to shape maps, heat maps, and word clouds, enhancing data accessibility and interpretability. A standout feature of the dashboard is its predictive analytics page, which allows users to forecast tourist arrivals based on selected explanatory variables such as climate data and customer sentiments. This predictive ability enables stakeholders to simulate various scenarios and better prepare for future trends, making the dashboard an invaluable tool for strategic decision-making. The dashboard’s user-friendly interface and customizable filtering options allow users to tailor their analyses based on specific criteria, such as year, region, and visitor attributes. This targeted approach ensures that tourism businesses can leverage the dashboard for practical decision-making, aligning with sustainable tourism development goals by monitoring environmental and social impacts. This research advances the field of tourism analytics and provides a practical tool for enhancing the strategic and operational capabilities of tourism businesses in Sri Lanka. Future enhancements may include the incorporation of more sophisticated predictive models, which would further improve the dashboard’s utility.
The Impact of Teleworking on Sri Lankan University Internship Programs: Systematic Review of Literature
(Institute of Electrical and Electronics Engineers (IEEE), 2024) Abeygunawardhana, D.; Wijayanayake, J.; Prasadika, J.
The popularity of the Work from Home paradigm has seen a significant surge in relevance and importance, due to the COVID-19 pandemic and its associated quarantine measures. This shift towards teleworking has influenced the landscape of internships also. While some aspects of teaching and learning have smoothly transitioned into online environments, Others face several challenges. Identifying the specific strategies, skills, and practices required for effective online learning, especially in the context of an online internship course, remains a less defined domain specially in Sri Lanka. The pandemic-induced transition to remote work and education has been a global phenomenon, yet its impact on internships remains relatively unexplored. Moreover, the existing literature on new Information and Communication Technologies (ICT) and teleworking is frequently outdated due to the rapidly changing technological landscape. This systematic literature review tries to identify the impact of this shift towards teleworking on university internship programs in Sri Lanka. Internships are essential for an undergraduate to find future career opportunities. Also, to strengthen their self-confidence and self-satisfaction in the lifelong learning process. When considering the major drawbacks, there's a big gap in communication between interns and their supervisors, harder to establish and maintain work relationships when interns can't be physically present or meet their supervisors face-to-face. There are identified positive sides also. Big companies and smaller ones alike can benefit from virtual internships. They're good for both students and employers because they're flexible, cost-effective, and easy to access. Plus, virtual internships can be especially helpful for differently abled students. This benefits both the employer and the student. Also mentoring is identified as a key factor for the success of virtual internships.
Leveraging Artificial Intelligence for Ethical Social Media Influencer Communication
(2024) Hewapathirana, I. U.
This chapter explores the connections between artificial intelligence (AI) and the ethical dimensions of influencer communication on social media. The ethical aspects are evaluated according to the criteria outlined in the Professional Code of Ethics of the Public Relations Society of America (PRSA). The study reviews the multiple aspects of influencer communication, including emerging challenges and legal implications resulting from the continued development of AI in social media. Furthermore, a dataset was collected from the social media platform Reddit, and a case study analysis was performed using the NodeXL software. This empirical investigation aims to investigate social media users' perspectives on specific ethical concerns associated with integrating artificial intelligence (AI). The findings presented in this chapter provide scholars with an advanced understanding of AI capabilities, offer industry professionals valuable guidance for ethical decision-making, and offer lawmakers guidance for developing regulatory frameworks.