Smart Computing and Systems Engineering - 2022 (SCSE 2022)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25392

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Author: search.filters.author.Rajapakse, ChathuraHas files: Yes

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    Evaluating the Factors that Affect the Adoption of Blockchain Technology in the Pharmaceutical Supply Chain - A Case Study from Sri Lanka
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Paththinige, Pavani; Rajapakse, Chathura
    One of the significant causes of medicine counterfeiting is the pharmaceutical industry's inadequate supply chain system, which makes it hard to keep track of it. This study aims to identify the factors affecting the adoption of Blockchain in the pharmaceutical supply chain in Sri Lanka. The study's conceptual framework is developed through a thorough literature review and structured interviews. Sample data is acquired from supply chain practitioners, pharmaceutical manufacturers, Medical Supply Division, and National Medicine Regulatory Authority to validate the conceptual model. The Partial Least Squares, Structural Equation Modelling (PLS-SEM) technique was used to investigate the effect of factors on the adoption of Blockchain. Based on a thorough examination of the literature, the suggested conceptual model incorporates the complex relationships between eight significant factors, namely1) Relative advantage, 2) Upper management support, 3) Human resources, 4) Compatibility, 5) Cost, 6) Complexity, and 7) Technological Infrastructure and 8) Architecture. Academics can use the proposed framework to design and review blockchain-based research as a starting point for implementing blockchain applications in the pharmaceutical supply chain.
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    Personalized Classification of Non-Spam Emails Using Machine Learning Techniques
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Dinendra, Harsha; Rajapakse, Chathura; Asanka, P. P. G. Dinesh
    With the advent of computer networks and communications, emails have become one of the most widely accepted communication means, which is faster, more reliable, cheaper, and accessible from anywhere. Due to the increased use of email communications, day-to-day computer users; particularly corporate users, find it cumbersome to filter the most important and urgent emails out of the large number of emails they receive on a given business day. Enterprise email systems are able to automatically identify spam emails but still, there are many non-urgent and unimportant emails among such non-spam emails which cannot be filtered by conventional spam filter programs. Though it may be feasible to set up some static rules and categorize some of the e-mails, the practicality and sustainability of such rules are questionable due to the magnitude of such rules, and the validity period as such rules may become redundant after some time. Thus, it is desired to have an email filtering system for non-spam emails to filter unimportant emails, based on the user’s past behaviour. Despite the availability of research on identifying spam e-mails in the area of further classifying the non-spam e-mails, is lacking. The purpose of this research is to provide a machine learning-based solution to classify non-spam e-mails considering the importance of such e-mails. As part of the research, several machine learning models have been developed and trained using non-spam e-mails, based on the personal mailbox of the first author of this research. The results showed a significant accuracy, particularly with a decision tree, random forests and deep neural network algorithms. This paper presents the modelling details and the results obtained accordingly.
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    Detection of IoT Malware Based on Forensic Analysis of Network Traffic Features
    (Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Nimalasingam, Nisais; Senanayake, Janaka; Rajapakse, Chathura
    The usage of Internet of Things (IoT) devices is getting unavoidable lately, from handheld devices to factory automated machines and even IoT embedded automotive vehicles. On average, 100+ devices are connected to the IoT world per second, and the volume of data generated by these devices and added to the space is just too enormous. The value of the data costs more, and sometimes it is invaluable, and it may pull over the cybercriminals and eventually increases the number of cybercrimes. Therefore, the need to identify malware in IoT is a timely requirement. This research work applies Machine Learning (ML) models and yields an efficient lead to identifying the IoT malware using forensic analysis of their network traffic features by selecting the foremost unique features and combining them with the binary features of the malware families. An outsized dataset with many network traffic collections used various network traffic features. Thus, the proposed model's detection accuracy of almost 100% was achieved from the model during the experimental phase of the study, which was a result of the feature extraction process for each malware type. This model can be further improved by considering the fog level implementation of the IoT layer, where the learning will help identify a malicious packet transfer to the network at level zero.