Smart Computing and Systems Engineering - 2021 (SCSE 2021)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/25343

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Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Thalagala, Shiron; Walgampay, Chamila
Automated inspection of surface defects is beneficial for casting product manufacturers in terms of inspection cost and time, which ultimately affect overall business performance. Intelligent systems that are capable of image classification are widely applied in visual inspection as a major component of modern smart manufacturing. Image classification tasks performed by Convolutional Neural Networks (CNNs) have recently shown significant performance over the conventional machine learning techniques. Particularly, AlexNet CNN architecture, which was proposed at the early stages of the development of CNN architectures, shows outstanding performance. In this paper, we investigate the application of AlexNet CNN architecture-based transfer learning for the classification of casting surface defects. We used a dataset containing casting surface defect images of a pump impeller for testing the performance. We examined four experimental schemes where the degree of the knowledge obtained from the pre-trained model is varied in each experiment. Furthermore, using a simple grid search method we explored the best overall setting for two crucial hyperparameters. Our results show that despite the simple architecture, AlexNet with transfer learning can be successfully applied for the recognition of casting surface defects of the pump impeller.
Application of Game Theory on financial benefits and employee satisfaction: Case study of a state bank of Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Jayasekara, D. D. G. T.; Wijayanayake, A. N.; Dissanayake, A. R.
The principal agent problem revolves around the competing interest between shareholders and the employees. The organization focus is on maximizing shareholder wealth, while employees try to obtain the maximum benefits for themselves. As per the motivational theories, people have different types of needs. Therefore, management should focus on a wide range of factors to motivate the employees to work to their full potential in the interest of the organization. The study focuses on both employee and the management of a state bank. The organization is always eager to minimize the cost and maximize the profit. Game Theory was used to provide a mathematical framework for understanding the optimal outcome and what the tradeoffs are to achieve that outcome. The objective is to find the right balance between financial gains and employee satisfaction. To fulfill that objective, one needs to evaluate the benefits given to employees, the effectiveness of those benefits on employees and finally recommend an effective benefits allocation mix to the organization, which will address both employee and the top management of the bank.
Architectural framework for an interactive learning toolkit
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Jayasiriwardene, Shakyani; Meedeniya, Dulani
At present, a significant demand has emerged for online educational tools that can be used as replacement for classroom education. Due to the ease of access, the preference of many users is focused on m-learning applications. This paper presents an architectural framework for an interactive mobile learning toolkit. This study explores different software design patterns and presents the implementation details of the prototype. As a case study, the application is applied for the primary education sector in Sri Lanka, as there is a lack of adaptive learning mobile toolkits that allow teachers and students to interact effectively. The study is concluded to be user-friendly, understandable, useful, and efficient through a System Usability Study.
Autism spectrum disorder diagnosis support model using InceptionV3
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Lakmini, Herath; Marasingha, M. A. J. C.; Meedeniya, Dulani; Weerasinghe, Vajira
Autism spectrum disorder (ASD) is one of the most common neurodevelopment disorders that severely affect patients in performing their day-to-day activities and social interactions. Early and accurate diagnosis can help decide the correct therapeutic adaptations for the patients to lead an almost normal life. The present practices of diagnosis of ASD are highly subjective and time-consuming. Today, as a popular solution, understanding abnormalities in brain functions using brain imagery such as functional magnetic resonance imaging (fMRI), is being performed using machine learning. This study presents a transfer learning-based approach using Inception v3 for ASD classification with fMRI data. The approach transforms the raw 4D fMRI dataset to 2D epi, stat map, and glass brain images. The classification results show higher accuracy values with pre-trained weights. Thus, the pre-trained ImageNet models with transfer learning provides a viable solution for diagnosing ASD from fMRI images.
Automatic road traffic signs detection and recognition using ‘You Only Look Once’ version 4 (YOLOv4)
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Fernando, W. H. D.; Sotheeswaran, S.
Using scenario transformation methodology, we identified four scenarios that indicated a lack of trusted parties to sell harvest has forced smallholder farmers to sell the harvest to brokers who often collect the harvest at the farm gate at the lowest possible prices and sell in the market for large profits. As blockchain smart contracts provide a mechanism to reduce risk and establish trust between unknown trading partners, we transformed these into a scenario that establishes trust between farmer and unknown broker using smart contracts, generating a trust-enabled market. This scenario enables farmers to search for the optimum farm-gate price without relying on known brokers. The scenario is further enhanced to enable a Many-one-Many market linkage, facilitating automatic aggregated marketing. The paper presents the functional prototype of the scenario, explaining the functionality of the transformed system.
Challenges for adopting DevOps in information technology projects
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Jayakody, J. A. V. M. K.; Wijayanayake, W. M. J. I.
An Information Technology (IT) project deals with IT infrastructure, information systems, or computers for delivering an IT product within a temporary period. Proper application of software development methodologies assists software designers to run IT projects to the success of achieving the satisfaction of project stakeholders. Because of the issues raised by traditional software development methodologies such as the Waterfall model, the IT industry began to employ Agile methodology for IT project management. However, due to the separation of software development and operation teams, Agile methodology also caused problems. DevOps is a new approach adapted to the Agile methodology that collaborates the software development and operation teams in order to provide continuous development of high-quality software in a short period of time. However, there are practical issues reported since DevOps approach is still in its infancy in the IT industry. The purpose of this research is to analyze the use of the DevOps concept in IT Projects by evaluating the challenges and mitigating strategies practiced by software development firms in order to ensure the success of IT projects. This purpose was achieved by performing a literature study and soliciting recommendations from industry professionals using a questionnaire survey. The findings reveal the critical challenges and prioritization of challenges experienced by software firms while adopting DevOps, as well as their practices for overcoming those challenges. The research findings will help IT project development teams and future researchers to develop strategies for making the success of DevOps adoption with Agile methodology in the IT industry.
A community-based hybrid blockchain architecture for the organic food supply chain
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Thanujan, Thanushya; Rajapakse, Chathura; Wickramaarachchi, Dilani
This paper presents a novel blockchain architecture to incorporate community-level trust into the organic food supply chain by hybridizing Proof of Authority (PoA) and Federated Byzantine Agreement (FBA) consensus protocols. Community-level trust is an important aspect in the organic agriculture industry. Organic farming, in most parts of the world, happens in small scale farms where the farmers represent rural and less-privileged communities. Even though third-party certification systems exist for quality assurance in organic farming, due to many socio-economic reasons, participatory guarantee systems (PGS) have become a popular alternative among organic farmers and consumers. However, such participatory guarantee systems are still prone to frauds and have limitations in scalability as well. With the recent rise of blockchain technology, there is an emerging trend to adopt blockchain technology to enhance the credibility of organic food supply chains and mitigate the risk of fraudulent transactions. However, despite the popularity of participatory guarantee systems among organic farmer communities, the blockchain researchers have paid little attention to develop blockchain architectures by adopting the community-level trust into their consensus protocols. The hybrid consensus mechanism presented in this paper addresses that gap in existing blockchain research. Apart from discussing the details of the proposed blockchain architecture and the underlying consensus protocol, this paper also presents a qualitative analysis on the proposed architecture based on expert opinions.
Comparison of supervised learning-based indoor localization techniques for smart building applications
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Maduraga, M. W. P.; Abeysekara, Ruvan
Smart buildings involve modern applications of the Internet of Things (IoT). Intelligent buildings could include applications based on indoor localization, such as tracking the real-time location of humans inside the building using sensors. Mobile sensor nodes can emit electromagnetic signals in an ambient sensor network, and fixed sensors in the same network can detect the Received Signal Strength (RSS) from its mobile sensor nodes. However, many works exist for RSS-based indoor localization that use deterministic algorithms. It's complicated to suggest a generated mechanism for any indoor localization application due to the fluctuation of RSSI values. This paper has investigated supervised machine learning algorithms to obtain the accurate location of an object with the aid of Received Signal Strengths Indicator (RSSI) values measured through sensors. An available RSSI data set was trained using multiple supervised learning algorithms to predict the location and their average algorithm errors were compared.
A decentralized social network architecture
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Sarathchandra, Tharuka; Jayawikrama, Damith
Billions of people use social networks, and they play a significant role in people's lifestyles in the current world. At the same time, due to globalization and other factors, the use of these social platforms is expanding daily, and a variety of activities take place inside these platforms. These networks are centralized, allowing social network-owned companies to track and observe the activities of their users. Therefore, this has been challenged to the privacy of the data of users. Also, these companies tend to sell them to third parties keeping huge profits without users' permission. Since data is the most valuable asset in today's and tomorrow's world, many have pointed out this issue. Even though decentralized, community-driven applications have come to play as a solution to this problem, there is still no successful application that competes with centralized social network platforms. Therefore, this study attempted to develop a decentralized social network architecture with the basic functionalities of a social media platform to assure the privacy of the users' data.
Decision-making models for a resilient supply chain in FMCG companies during a pandemic: A systematic literature review
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Madhavi, B. R. H.; Wickramarachchi, Ruwan
Decision-making during a crisis impacts the performance of an entire organization. Due to the COVID-19 pandemic, many organizations had undergone supply chain disruptions due to the forward and backward propagation of disruptions in the global supply chain networks, implying the importance of building up resilience in the supply chain networks. This study intends to systematically review the existing literature to determine the impact of optimal decision-making during crises to build up supply chain resilience. The paper has focused on the need for evaluating the impact of the COVID- 19 pandemic on the FMCG industry and how supply chain resilience would improve in performance during such crises. The study also assessed the existing decision support systems for resilience in a supply chain network and their applicability during a crisis. Some of these models could be used to facilitate decision-making during an epidemic as well. Precisely determining resilience factors affected during an unexpected circumstance would enhance the value of the decision support system in use. Furthermore, it was concluded that the use of quantitative models should be further investigated, as most published work focuses on the conceptualization of a restricted number of resilience factors instead of the development of integrated, comprehensive approaches.
Deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Sangeevan, Siventhirarajah
The study proposes a deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka. It is an intelligent system to get suitable pesticides prescriptions for plant leaf diseases. Home gardening has become popular and is rapid because of the current pandemic situation. However, plant diseases are a major problem in gardening activities, even in a home garden or in a commercial garden. Identifying and finding a solution for the plant disease is a big challenge for home gardeners rather than commercial farmers. The proposed system of deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka will be the best solution for identifying and finding a solution to the plant diseases. The system is using a trained model for prescribing pesticides. The model was built using the deep learning method and trained in the supervised learning process. The convolutional neural network algorithm was used in the model. Transfer learning with AlexNet pre-trained model was used to increase the performance in the proposed solution and the best accuracy of 88.64% was achieved in the experiments.
Design and development of pump based chocolate 3D printer
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Rajapaksha, R. R. A. K. N.; Thilakarathne, B. L. S.; Kondarage, Yashodha G.; De Silva, Rajitha
The use of 3-Dimensional (3D) printing, known as Digital fabrication (DF) or additive manufacturing (AM), technology in the food sector has countless potential to fabricate 3D constructs with complex geometries, customization, and on-demand production. For this reason, 3D technology is driving major innovations in the food industry. This paper presents the construction of a chocolate 3D printer by applying the pressure pump technique using chocolate as a printing material. Here the conventional 3D printer’s design was developed as a chocolate 3D printer. As an improvement, a new extruder mechanism was introduced. The extruder was developed to print the chocolate materials. In the working mechanism, the 3D printer reads the design instruction and chocolate material is extruding accordingly, through the nozzle of the pump to the bed of the 3D printer followed by the design (layer by layer). The special part of this chocolate 3D printer is the pressure pump in the extruder part. That pressure pump provides pressure on melted chocolate from the chocolate container to the nozzle point. The usability and efficiency of the 3D printer were tested with sample designs. The obtained results were presented and discussed. Together with these advances this 3D printer can be used to produce complex food models and design unique patterns in chocolate-based sweets by satisfying customers.
Docker incorporation is different from other computer system infrastructures: A review
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Kithulwatta, W. M. C. J. T.; Jayasena, K. P. N.; Kumara, B. T. G. S.; Rathnayaka, R. M. K. T.
Currently the computing world is getting complex, innovating and maturing with modern technologies. Virtualization is one of the old concepts and currently containerization has arrived as an alternative and innovative technology. Docker is the most famous and trending container management technology. Different other container management technologies and virtualization technologies are respective other corresponding technologies and mechanisms for Docker containerization. This research study aims to identify how Docker incorporation is different from other computer system infrastructure technologies in the perspective of architecture, features and qualities. By considering forty-five existing literatures, this research study was conducted. To deliver a structured review process, a thorough review protocol was conducted. By considering four main research questions, the research study was lined up. Ultimately, Docker architecture and Docker components, Docker features, Docker integration with other computing domains and Docker & other computing infrastructures were studied. After synthesizing all the selected research studies, the cream was obtained with plenty of knowledge contribution to the field of computer application deployment and infrastructure.
Estimation of the incubation period of COVID-19 using boosted random forest algorithm
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Rathnayake, P. P. P. M. T. D.; Senanayake, Janaka; Wickramaarachchi, Dilani
Coronavirus disease was first discovered in December 2019. As of July 2021, within nineteen months since this infectious disease started, more than one hundred and eighty million cases have been reported. The incubation period of the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), can be defined as the period between exposure to the virus and symptom onset. Most of the affected cases are asymptomatic during this period, but they can transmit the virus to others. The incubation period is an important factor in deciding quarantine or isolation periods. According to current studies, the incubation period of SARS-CoV-2 ranges from2 to 14 days. Since there is a range, it is difficult to identify a specific incubation period for suspected cases. Therefore, all suspected cases should undergo an isolation period of 14 days, and it may lead to unnecessarily allocation of resources. The main objective of this research is to develop a classification model to classify the incubation period using machine learning techniques after identifying the factors affecting the incubation period. Patient records within the age group 5-80 years were used in this study. The dataset consists of 500 patient records from various countries such as China, Japan, South Korea and the USA. This study identified that the patients' age, immunocompetent state, gender, direct/indirect contact with the affected patients and the residing location affect the incubation period. Several supervised learning classification algorithms were compared in this study to find the best performing algorithm to classify the incubation classes. The weighted average of each incubation class was used to evaluate the overall model performance. The random forest algorithm outperformed other algorithms achieving 0.78 precision, 0.84 recall, and 0.80 F1-score in classifying the incubation classes. To fine-tune the model AdaBoost algorithm was used.
Exploiting optimum acoustic features in COVID-19 individual’s breathing sounds
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Milani, M. G. Manisha; Ramashini, Murugaiya; Murugiah, Krishani; Chamal, Lanka Geeganage Shamaan
The world is facing an extreme crisis due to the COVID-19 pandemic. The COVID-19 virus interrupts the world’s economy and social factors; thus, many countries fall into poverty. Also, they lack expertise in this field and could not make an effort to perform the necessary polymerase chain reaction (PCR) or other expensive laboratory tests. Therefore, it is important to find an alternative solution to the early prediction of COVID-19 infected persons with a low-cost method. The objective of this study is to detect COVID-19 infected individuals through their breathing sounds. To perform this task, twenty-two (22) acoustic features are extracted. The optimum features in each COVID-19 infected breathing sound is identified among these features through a feature engineering method. This proposed feature engineering method is a hybrid model that includes; statistical feature evaluation, PCA, and k-mean clustering techniques. The final results of this proposed Optimum Acoustic Feature Engineering (OAFE) model show that breathing sound signals' Kurtosis feature is more effective in distinguishing COVID-19 infected individuals from healthy individuals.
An exploratory evaluation of replacing ESB with microservices in service-oriented architecture
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Weerasinghe, L. D. S. B.; Perera, Indika
With the continuous progress in technology during the past few decades, cloud computing has become a fast-growing technology in the world, making computerized systems widespread. The emergence of Cloud Computing has evolved towards microservice concepts, which are highly demanded by corporates for enterprise application level. Most enterprise applications have moved away from traditional unified models of software programs like monolithic architecture and traditional SOA architecture to microservice architecture to ensure better scalability, lesser investment in hardware, and high performance. The monolithic architecture is designed in a manner that all the components and the modules are packed together and deployed on a single binary. However, in the microservice architecture, components are developed as small services so that horizontally and vertically scaling is made easier in comparison to monolith or SOA architecture. SOA and monolithic architecture are at a disadvantage compared to Microservice architecture, as they require colossal hardware specifications to scale the software. In general terms, the system performance of these architectures can be measured considering different aspects such as system capacity, throughput, and latency. This research focuses on how scalability and performance software quality attributes behave when converting the SOA system to microservice architecture. Experimental results have shown that microservice architecture can bring more scalability with a minimum cost generation. Nevertheless, specific gaps in performance are identified in the perspective of the final user experiences due to the interservice communication in the microservice architecture in a distributed environment.
Feature selection in automobile price prediction: An integrated approach
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Selvaratnam, Sobana; Yogarajah, B.; Jeyamugan, T.; Ratnarajah, Nagulan
Machine learning models for predictions enable researchers to make effective decisions based on historical data. Automobile price prediction studies have been a most interesting research area in machine learning nowadays. The independent variables to model the price and the price predictions are equally important for automobile consumers and manufacturers. Automobile consulting companies determine how prices vary in relation to the independent variables and they can then adjust the automobile's design, commercial strategy, and other factors to fulfill specified price targets. Furthermore, the model will assist management in comprehending a company's pricing patterns. The ability of machine learning systems to predict outcomes is entirely dependent on the effective selection of features. In this paper, we determine the influencing features on automobile price using an integrated approach of LASSO and stepwise selection regression algorithms. We use multiple linear regression to build the model using the selected features. From the experimental results using the automobile dataset from the UCI machine learning repository, the influencing features on automobile price are width, engine size, city mpg, stroke, make, aspiration, number of doors, body style, and drive wheels. Training data accuracy for predicting price was found to be 92%, and testing data accuracy was found to be 87%. The proposed approach supports selecting the most important characteristics of predicting the price of automobiles efficiently and effectively. This research will aid in the development of a model that uses the selected attributes to predict the price of automobiles using machine learning technologies.
Forecasting foreign exchange rate: Use of FbProphet
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Raheem, Fanoon; Iqbal, Nihla
Foreign exchange rate prediction can be considered crucial in today’s world. The exchange rate of a country plays a vital role in its economic growth. The Central Bank of a country holds the authority in managing the exchange rate and its policies. The study predicts the foreign exchange rate of American Dollar to Sri Lankan Rupee using FbProphet model; a time-series forecasting model developed and introduced by Facebook. The daily exchange rate values for USD/LKR were obtained and the values are predicted for another twenty-four months starting from November 2020. R Squared value is calculated to verify the fitting of the model and the value is 0.98, which indicates that the model for prediction very well fits for the data set used. And further, Mean Squared Error and Mean Absolute Error are calculated to measure the performance of the model. These metric measurements show that the model is appropriate for the data set which has been selected for the research study.
Framework to mitigate supply chain disruptions in the apparel industry during an epidemic outbreak
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Perera, M. A. S. M.; Wijayanayake, A. N.; Peter, Suren
Billions of people use social networks, and they play a significant role in people's lifestyles in the current world. At the same time, due to globalization and other factors, the use of these social platforms is expanding daily, and a variety of activities take place inside these platforms. These networks are centralized, allowing social network-owned companies to track and observe the activities of their users. Therefore, this has been challenged to the privacy of the data of users. Also, these companies tend to sell them to third parties keeping huge profits without users' permission. Since data is the most valuable asset in today's and tomorrow's world, many have pointed out this issue. Even though decentralized, community-driven applications have come to play as a solution to this problem, there is still no successful application that competes with centralized social network platforms. Therefore, this study attempted to develop a decentralized social network architecture with the basic functionalities of a social media platform to assure the privacy of the users' data.
Identifying interrelationships of key success factors of third-party logistics service providers
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Perera, Theruwanda; Wickramarachchi, Ruwan; Wijayanayake, A. N.
To be more cost-effective as well as to maintain a sustainable competitive advantage, many enterprises tend to improve their business practices by having a strong relationship with third-party (3PL) logistics service providers. The main objectives of this paper are to determine the key success factors associated with the Sri Lankan 3PL industry and identify the interrelationships of these key success factors. A systematic literature review and expert opinions were used to identify the key success factors of the 3PL industry in Sri Lanka. In total 21 key success factors were obtained, and those key success factors were grouped into four categories as organization strategy, management, and process, human resources, and customer orientation. Q-sort technique was used to group key success factors into four categories. Decision-making trial and evaluation laboratory (DEMATEL) method was used to capture the interactive relationships among the key success factors of 3PL service providers, and the casual effect map analyzed. Data were collected through questionnaires from middle and senior-level managers of 3PL firms. A total of eleven experts in the 3PL industry participated in the data collection process. The result shows that organization strategy is a core success factor since it has both high prominence and high interrelationship. Management and process were classified as driving factors since they had a low prominence but a high interrelationship. However, human resources and customer orientation had high prominence but low relationship, which are influenced by other factors and cannot be directly improved. The findings may assist managers to formulate long-term flexible decision strategies in their 3PL firms.