Smart Computing and Systems Engineering - 2022 (SCSE 2022)

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

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Adding Commonsense to Robotic Application Using Ontology-Based Model Retraining
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Pradeepani, M. K. T.; Jayawardena, C.; Rajapaksha, U. U. S.
In terms of the level of technological capability in the world today, the use of automated robotics is common in various fields. There are large projects going on in many industries that collaborate between robots and other robots, as well as humans and robots. In hospital environments, care for people with medical needs and their needs and used to make appropriate suggestions to their problems. Robots can also be found in certain areas that can respond quickly as an emergency rescue agent. Furthermore, robots, which can be seen in the hotel industry as waiters and as farm assistants in agriculture, have a great tendency to be used as multi-tasking agents in many fields. In each of these areas, robots must co-operate with humans. In that situation, the importance of the exchange of mutual knowledge between robots-robots and between humans-robots comes into the picture. What matters here is not only the quantitative vastness of knowledge but also the ability to understand each other in the same medium. Although the common sense that people need in their day-to-day work is completely obvious to humans, the commonsense knowledge domain needs to be implanted in robots. Whatever concept is defined for adding commonsense to robotics, it should be a consistent concept that can be logically constructed so that it can be understood by a machine. As will be discussed later in the paper, different methods have been used in various related works to add a different kind of domain knowledge to robotics. The objective of this paper is to provide an improved retrained model for robotics in order to give them the ability to act more human-like when performing tasks. By using the proposed model robots are able to answer the incomplete command or inquiries related to a given context. One of the objectives of this work is to use the ontology-based, commonsense-support existing knowledge base as a mechanism to retrain and build a new model.
Analyzing Factors that Impact on Performance of Pickers in Third-Party Logistics Warehouses in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Prasadika, A. P. K. J.; Wijayanayake, A. N.; Niwunhella, D. H. H.
Order picking is the most crucial and expensive operation in a warehouse which affects customer satisfaction and the profitability of the warehouse. Picker is the employee who is responsible for the order picking process. So, picker performance is very important in improving the overall performance of the warehouse. Therefore, identifying the factors that have an impact on the performance of the pickers is advantageous. The main objective of this research is to identify the relationship between factors that has an impact on picker and picker performance through the Partial Linear Square – Structural Equation Modelling (PLS-SEM) technique using SmartPLS software. Initially, the most important twelve factors were identified by reviewing the past literature and industry experts’ opinions. They were divided into three main categories based on the characteristics and to reduce the complexity of the model which are picker-related factors, management-related factors, and warehouse-related factors. The data analysis was done in two steps to discover direct and moderator relationships, separately. The product type that the pickers handle is the moderator used in this study. The results of the PLS-SEM analysis show that picker-related factors and warehouse-related factors have a significant impact on picker performance at the significance level of 0.05, while management-related factors have a significant effect on picker performance at the significance level of 0.10. Further, the product type moderates all three relationships. The outcomes of the study help the managers of the warehouses to improve the performance of the pickers so that the overall performance of the warehouse can be improved.
Automated Spelling Checker And Grammatical Error Detection And Correction Model for Sinhala Language
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Goonawardena, Mithma; Kulatunga, Ashini; Wickramasinghe, Raveena; Weerasekara, Thisuraka; De Silva, Hansi; Thelijjagoda, Samantha
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.
A Blockchain-Based Decentralized Insurance Platform
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Alwis, Santhusha; Jinasena, T. M. K. K.
Blockchain technology is considered as revolutionary for its potential to revolutionize many sectors by addressing existing issues in the traditional systems. A blockchain is a digital ledger consisting of transaction records that is duplicated and distributed to every participant computer in the network. Insurance industry is one of the most significant sectors where large institutes dominate the market. The traditional insurance process is highly centralized and cost-intensive to both insurance companies and customers. The process involves a third party to handle several processes among multiple parties manually in the form of paperwork. Inefficiency in centralized, manual processes lead to large frictional costs being borne by the customers. Also, conflicts of interest between insurance companies and policyholders are very common due lack of trust, lack of transparency, and ambiguity in policy terms. To provide a solution for these issues in the traditional centralized systems, this research proposes a blockchain-based, decentralized platform for insurance. The platform aims to replace the conventional insurance companies. Using blockchain technology, the platform allows users to transact directly with each other, eliminating the need for an intermediary third party.
Common Object Request Broker-based Publisher-Subscriber Middleware for Internet of Things - Edge Computing
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Perera, Hansani; Jayakody, Anuradha
The edge computing layer in IoT reduces the flow of a massive amount of data directly to the cloud by processing some data in the local network. The middleware in the layer enables this processing of data and the communication between heterogeneous devices and services in the nearby layers. CORBA, which uses as a powerful middleware technology in developing middleware solutions in enterprise-level distributed applications, has been abandoned in the current generation. The paper presents the design, and the performance evaluation of a publisher-subscriber middleware implemented using CORBA that was studied when exploring the applicability of CORBA as an IoT edge computing middleware. The evaluation was continued in two steps to analyse several parallel connections (Load test) and handle requests in a unit time (burst test) via simulating an IoT environment in a cloud environment.
A Comparative Study of Clustering English News Articles Using Clustering Algorithms
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Disayiram, N.; Rupasingha, R. A. H. M.
The news informs us of what is going on in the world. People nowadays read their interesting news on news websites. There are numerous categories of news. Each newsreader has a different preference for news categories. Sportspeople prioritize sports news, whereas technology fans pay attention to the technology segment of the news. At the end of the day, each news category is important. Every day, a large amount of information is released on news websites. News sites usually categorize the news however, not all of the categories are published on those sites. Some categories are given higher attention by news outlets, while others receive less coverage. As a result, finding an appropriate category of news is tough. These issues make it difficult for newsreaders and content seekers to find relevant sections on news websites. The clustering of English news articles by relative category provides solutions to these issues. This research aims to use clustering algorithms to cluster news articles depending on the relevant domain/cluster. We consider five news categories: politics, sports, health, technology, and business. The data collected online was converted into a vector format using the term frequency-inverse document frequency (TF-IDF) vectorization. Then, on the body of the news and the news heading, the three clustering algorithms: Expectation-Maximization (EM), Simple K-means, and Hierarchical Clustering based on an agglomerative approach were applied individually. The Waikato Environment for Knowledge Analysis (WEKA) tool's classes to clusters evaluation model are used to calculate the accuracy. The EM method had the maximum accuracy of 88.5% with the best results in terms of correctly clustered instances. The comparison between the heading of news and the body of news demonstrates that the body of news clustered the news items better than the heading of news.
A Comprehensive Review on Vision-based Sign Language Detection and Recognition
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Weerasinghe, R. L.; Ganegoda, G. U.
Deaf or hard-hearing people's primary mode of communication is sign language. Communication between hard hearing and hearing people is greatly aided by sign language recognition technologies. With the advent of technology, many approaches proposed for sign language recognition. Among them, vision-based approaches are more convenient than sensor-based approaches. Vision-based approaches are involved five different stages where various techniques and algorithms are utilized in various approaches. The accuracy of the recognition is based on the techniques used and the quality of the input. Background invariance and lighting conditions highly affect the accuracy of the result. Simply by increasing the quality of the input, each and every method can approach a high accuracy rate. This paper provides a comprehensive introduction and comparison of the existing vision-based sign language detection and recognition approaches.
Creating a Sri Lankan Micro-Emotion Dataset for a Robust Micro-Expression Recognition System
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayakodi, J. A. L. P.; Jayamali, G. G. S. D.; Hirshan, R.; Aashiq, M. N. M.; Kumara, W. G. C. W.
In interpersonal communication, the human face provides important signals of a person’s emotional states and intentions. Furthermore, micro-emotions play a major role in understanding hidden intentions. In psychological aspects, detecting micro-emotions play a major role. In addition, lie detection, criminal identification, and security systems are other applications, where detecting micro-emotion accurately is essential. Revealing a micro-expression is quite difficult for humans because people tend to conceal their subtle emotions. As a result, training a human is expensive and time-consuming. Therefore, it is important to develop robust computer vision and machine learning methods to detect micro-emotions. Convolutional Neural Network (CNN) is the most used deep learning-based method in recent years. This research focuses on developing a 3D-CNN model to detect and classify Micro-emotions and creating a local Micro-emotion database. From the related research work we have considered this is the first attempt made at creating a Sri Lankan micro-emotion dataset. Having a local micro-emotion dataset is essential in formulating more accurate real-time applications focused on deep learning methods. Therefore, in this research, our main objective is to create a Sri Lankan micro-emotion database for future micro-emotion recognition and detection research.
Deep Hybrid Learning Framework for Plant Disease Recognition
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Hewarathna, Ashen Iranga; Palanisamy, Vigneshwaran; Charles, Joseph; Thuseethan, Selvarajah
Following better agricultural practices is the key to catering for the ever-increasing food demand. While new technologies have been adapted over the years, there is still a need for effective plant disease recognition systems because of the existence of harmful plant diseases that can spread rapidly. Effective and early recognition of plant diseases is vital to minimize the damage to crops and hence can save the farmers from potential loss. It is also important for many countries to maintain economic stability, especially for the countries that completely rely on agriculture. In the past, many traditional and deep learning-based approaches have been proposed for plant disease recognition. While traditional approaches need insightful domain expertise, deep learning-based approaches require large sets of labeled data. Further, most of the existing methods fail to meet benchmark performances in terms of recognition accuracy. Therefore, in this study, a novel deep hybrid architecture is proposed to perform plant disease recognition from plant leave images. The Google Inception and ResNet architectures are utilized as the core networks to construct the proposed network. The proposed framework is evaluated on a newly constructed dataset with large sample size. The comparative analysis reveals that the proposed approach can outperform other state-of-the-art deep networks.
Deep Neural Architectures for Ethnicity Classification in Face Images
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Jayasekara, Buddhi G.; Hevapathige, Asela
Ethnicity is a key metric of an individual’s identity, social cluster, physical behaviour and cultural association. Accurate ethnicity identification of humans is required in numerous fields like security, legislation, social analysis and psychology. Ethnicity classification using machine learning is a complex, non-trivial and multi-dimensional research problem due to the feature complexity, class imbalance and the absence of rich data sets. In this research study, we have trained and compared four state-of-the-art deep neural models and their ensemble architectures on the problem of ethnicity classification in large-scale image data. The empirical results demonstrate that these end-to-end deep learning models and their ensemble architectures perform well in learning complex ethnic features in facial images and classifying them. From the evaluated models, Ensemble Convolutional Neural network provided the highest classification performance with 78.9% accuracy. Also, we have tested six prominent pre-trained models using transfer learning for ethnicity classification while being able to achieve comparable results.
Design of an Online Platform for the Agriculture Community to Localise Scientific Knowledge and Foster Sustainability
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Pathirage, Samya; Ginige, Athula
Sustainable agricultural practices are critical to emerging global hunger and environmental issues. Knowledge is the key to improving such practices. In agriculture, knowledge is categorized as local and scientific, where both have their potential. Within the agriculture community with respect to knowledge, there are creators and consumers. Scientists and extension officers who disseminate scientific information can be seen as knowledge creators and farmers as knowledge consumers. This separation leads to a mismatch between the creators' and consumers' contexts, leading to scientific knowledge not applying to the local context. Further, such knowledge disregards practical local knowledge. The challenge is to bring both contexts together and enable knowledge co-creation in a scalable manner to generate context-specific crop recommendations. We designed an online community platform to combine knowledge creation and consumption to enable knowledge co-creation. This will help to generate context-specific crop recommendations while overcoming the tyranny of space and time. First, we identified characteristics of a conducive knowledge creation space through a literature review and designed an enabling space for the agriculture community to develop farming practices. Then we identified user stories for community members. Next, we designed a prototype where scientists, extension officers, and farmers could develop practice packages (PoP). The community knowledge creation process can be initiated with published farming practices. Then, based on the context, the agriculture community can build dialogue to find unavailable information, verify available information based on practicality and finalize the PoP. This intervention will bring knowledge creation contexts closer to where knowledge is put into action and facilitate the agriculture community to harness the power of both local and scientific knowledge to perform farming practices better.
Designing of a Voice-Based Programming IDE for Source Code Generation: A Machine Learning Approach
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Nizzad, A.R. M.; Thelijjagoda, Samantha
Humans are precise in recognizing natural languages and responding contextually unlike machines. However, speech recognition or Automatic speech recognition often refers to converting human speech or voice to textual information with the help of artificial intelligence algorithms. With the advancement of Artificial Intelligence technologies and extensive research being conducted in AI, speech recognition has received much attention and has emerged as a subset of Natural Language Processing where the advancement and accuracy in speech recognition will open many ways to provide a high standard of human-computer interaction. In this study, using the pre-trained transformer model with a transfer learning approach, the English to Python dataset was used to train the transformer model to produce syntactically correct source code in python. Additionally, the Word2Vec model was used to generate voice-to-text as input for the model. For the purpose of demonstration, a custom Python IDE is developed to generate source code from voice input. The results and findings suggest that in the transformer model, with the use of transfer learning, any dataset can be trained to produce syntactically correct source code. The model’s training loss and validation loss were below 5 and 2.1, respectively. Future research can focus on generating valid source code from any human spoken language without restricting it to English only.
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.
Developing A User-Friendly Interface from Robotic Applications Development
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Fernando, W. A. M.; Jayawardena, C.; Rajapaksha, U. U. S.
In this research, we have developed a web-based Robot Operating System (ROS) learning environment with its own set of tools. Our system is a comprehensive learning environment where students can go through the tutorials using the web interface and use our web-based development environment for writing scripts. Furthermore, students can use the web-based Gazebo simulator to visualize the robots. In addition, our learning environment also has its own set of tools that students can utilize for testing and troubleshooting robots. One of the other benefits of our system is that it is platform independent, and hence it can be accessed from either computer, mobile phone or tablet. Our system also has a dropdown for selecting commands. In this, all the descriptions and syntaxes of the commands are predefined and populated whenever a command is added from the dropdown. In addition, we have developed multiple other features that make this system much easier to use and user-friendly. In order to verify the usability of the system, we have performed a heuristic evaluation, and our findings show that the system complies with nine of the ten heuristics in Nielsen's framework. In addition, our system complies with twelve of the fourteen heuristics in Zhang's framework. We performed a performance evaluation as well. In this, we compared the performance of simulating our web-based system against running the same simulation directly from a Linux-based ROS server using the Gazebo client. The results showed that our system was faster by a small margin.
An Effective Lateral Transhipment Model for A Multi-Location Inventory Setting to Minimize
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Kumari, A.G.K.C.; Wijayanayake, A. N.; Niwunhella, D. H. H.
Managing inventory levels to ensure on-shelf availability of products is a challenge that retailers face on a daily basis. Even though it is desirable to have additional inventory to ensure the availability of products, it increases the inventory holding cost. Hence, retailers use lateral transhipment as a method to redistribute inventory from a location which has excess inventory to another outlet which faces / will face stockouts. This paper proposes a mathematical model to minimize the total cost through proactive lateral transhipment while reducing the stockouts, significantly. A multi-item, multi-location inventory system was considered, and a cost minimization model was developed based on the tradeoff between the potential gain and the transhipment cost. The model was implemented using Python programming language and validated using a real-world data set from one of the leading supermarket chains. The results from the model have shown that it can reduce the total cost and stockout occurrences significantly.
Effectiveness of a VR-based Solution to Improve Practical Skills of Trainee Nurses in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Aluthge, C. L. P.; Imeshika, K. A. S.; Weerasinghe, T. A.; Sandaruwan, K. D.
This study describes educational design research conducted to determine the usability of a Virtual Reality (VR) based learning tool to practice nursing skills. With the advancement of technology, new technological solutions have become a part of nursing education. Several technologies such as Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR) can be used to overcome these problems. VR technology can be used to visualize the clinical environment at anytime and anywhere. Also, it has been identified as one of the most promising technologies to support clinical education. Therefore, a VR-based application for practicing nasogastric intubation was developed with the support and advice of a group of nursing lecturers. The application was qualitatively evaluated by nursing lecturers and quantitatively evaluated by a group of trainee nurses. Most of them had a positive opinion about embracing the new experience. In the analysis of overall satisfaction, the developed solution was found to be effective and supportive of reducing the clinical training time in the physical environment. It will quickly familiarise the trainee nurses with the clinical setting and develop the fundamental nursing skills in Sri Lanka.
An Emotion Classification Model for Driver Emotion Recognition Using Electroencephalography (EEG)
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Gamage, T. A.; Kalansooriya, L. P.; Sandamali, E. R. C.
Road accidents have been a critical issue that has resulted in fatal injuries, disabilities, and deaths for many individuals worldwide. The notion of Human-Computer Interaction (HCI) is widely considered in monitoring drivers to safeguard their lives on roads. As a solution to the issue of the higher rate of road accidents, driver emotion recognition approaches have gained much attention, and the involvement of biological signals in detecting the emotional states of drivers is also significant. The authors have conducted a comprehensive literature review that concerns contemporary literature on the driver emotion recognition paradigm and comes up with four emotional states in this research to monitor the drivers' affective states. This paper presents a novel approach to detecting sad, angry, fearful, and calm emotional states of drivers with an emotion classification model using Electroencephalography (EEG) signals where the EEG data acquisition for the research is done using the Emotiv EPOC X device. The collected EEG data are preprocessed using the EEGLAB toolbox in Matlab, and feature extraction, selection, and emotion classification model training are done using Matlab. EEG acquisition and preprocessing have already been achieved, and as further work, the authors are to train the proposed emotion classification model as laid out in this paper. The findings of this research encourage the authors to continue towards the completion and provide further insights into enhancing research in the driver emotion recognition paradigm.
Ensemble Deep Learning for Automated Dust Storm Detection Using Satellite Images
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Bandara, N. S.
Dust storms are considered a severe meteorological disaster, especially in arid and semi-arid regions, which are characterized by dust aerosol-filled air and strong winds across an extensive area. Every year, a large number of aerosols are released from dust storms into the atmosphere, manipulating a deleterious impact both on the environment and human lives. Even if an increasing emphasis is being placed on dust storms due to the rapid change in global climate in the last fifty years by utilizing the measurements from the moderate-resolution imaging spectroradiometer (MODIS), the possibility of utilizing MODIS true-color composite images for the task has not been sufficiently discussed yet. Here, a supervised ensemble learning approach comprising three state-of-the-art models is proposed to detect dust aerosols in the atmosphere of the Earth to test the above hypothesis. The proposed method incorporates a linear combination of U-Net, DeepLabv3+ and Swin U-Net as the deep learning architecture which quantifies a binary semantic segmentation problem of distinguishing dust-susceptible and non- susceptible regions in each unconstrained MODIS image. The framework is tested upon a custom-developed dataset from MODIS measurements which contains 1020 true-colour images, over land and ocean. The ground truth label for each image is manipulated through handcrafted binary segmentation which results in a binary ground truth image for each true-colour MODIS image. The performance of the proposed framework is evaluated using mean intersection-over-union (mIoU) and average pixel accuracy (ACC) in terms of the semantic detection of dust aerosols. In the final testing, the framework achieved its highest Acc of 87.3% with a mIoU of 75.2%. The obtained performance of the framework surpasses the single state-of-the-art modalities and thus, aggregates to a more accurate implementation in the domain of dust aerosol detection.
An Ensemble Machine Learning Approach for Stroke Prediction
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2022) Premisha, P.; Prasanth, Senthan; Kanagarathnam, Mauran; Banujan, Kuhaneswaran
Nowadays, one out of four people above 25 will suffer from a stroke. Especially this year, with the highest count of around 13.7 million people discovered with stroke for the first time. Out of 13.7 million, 5.5 million were fatalities. This was stated in a recent WHO study. It is estimated that if no action is taken, the number of fatalities will rise to 6.7 million yearly. The pandemic situation of COVID-19 will play a significant cause in the expanded death rate of stroke. Even for adults and patients with minor risk factors affected by stroke rather than in previous years. This study predicts the impact level of stroke with the development of an ensemble model by combining the various classifiers performed well in isolation. Predicting the stroke status in patients would help the physicians determine the prognosis and assist them in providing the targeted therapy in a limited time. During this study, an ensemble model was built by considering the base, bagging, and boosting classifiers: Support Vector Machine, Naïve Bayes, Decision Tree, Logistic Regression, Artificial Neural Network, Random Forest, XGBoost, LightGBM, and CatBoost. The dataset consists of 5110 patient details, along with 12 attributes that were analyzed in this research. The final ensemble model was developed by carrying out the methodology in two phases. During the first and second phases, the classifiers mentioned above were trained without hyper-parameter tuning and with hyperparameter tuning and tested against the fundamental evaluation matrices. During each phase, the classifier that produces the highest classification accuracy is discovered from the base, bagging, and boosting categories. From the results obtained, the final ensemble model was constructed using the Max Voting approach, which yielded an accuracy of 95.76%.
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.