Smart Computing and Systems Engineering - 2021 (SCSE 2021)

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

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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.
Vibration analysis to detect and locate engine misfires
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Jayasooriya, Prathap V.; Siriwardana, Geethal C.; Bandara, Tharaka R.
Vibration analysis is used to detect faults and anomalies in machinery and other mechanical systems that produce vibrations during operation. The study aimed to develop an algorithm that can detect and locate engine faults in automobiles by analyzing vibrational data produced during engine operation. Analysis was done on one type of engine fault – Spark Ignition Engine misfire. To detect anomalies in the vibrational pattern (waveform), analysis was carried out in both time and frequency domains. To obtain vibrational data an AVR – 32 (Arduino) based data acquisition device was built, and analysis was carried out in MATLAB using scripts and functions. The developed algorithm isolates frequency components in the waveform that corresponds to engine faults and converts them into numerical quantities that are then compared with computed ranges. The algorithm was able to identify the presence of a misfire in the engine and could locate the cylinder in which the misfire occurs with significant accuracy.
Student concentration level monitoring system based on deep convolutional neural Network
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Shamika, U. B. P.; Panduwawala, P. K. P.G.; Weerakoon, W. A. C.; Dilanka, K. A. P.
As synchronous online classrooms have grown more common in recent years, evaluating a student's attention level has become increasingly important in verifying every student's progress in an online classroom setting. This paper describes a study that used machine learning models to monitor student attentiveness to distinct gradients of engagement level. Initially, the experiments were conducted using a deep convolutional neural network of student attention and emotions exploiting Keras library. The model showed a 90% accuracy in predicting attention level of the student. This deep convolutional neural network analysis aids in identifying crucial emotions that are important in determining various levels of involvement. This study discovered that emotions such as calm, happiness, surprise, and fear are important in determining a student's attention level. These findings aided in the earlier discovery of students with poor attention levels, allowing instructors to focus their assistance and advice on the students who require it, resulting in a better online learning environment.
A tree structure-based classification of diabetic retinopathy stages using convolutional neural network
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Peiris, M. S. H.; Sotheeswaran, S.
Detection, and classification of medical images have become a trending field of study during the last few decades. There is a considerable amount of vital challenges to be overcome. Ample work has been carried out to provide proper solutions for those key challenges. This study was carried out to extend one such medical image classification process to classify the stages of Diabetic Retinopathy (DR) images from colour fundus images. The study proposes a novel Convolutional Neural Network (CNN) architecture which is considered to be one of the most trending and efficient forms of classification of DR stages. Initially, the pre-processing techniques were employed to the DR fundus images with Green channel extraction and Contrast Limited Adaptive Histogram Equalization (CLAHE). The data augmentation strategy was utilised to increase training images from the DR images. Finally, Feature extraction and classification were carried out by using the proposed CNN architecture. It consists of a 14 layered CNN model, which continues three main classifications. In this proposed classification, the images were classified into a tree structure based binary classification as No_DR and DR at the beginning, and then the DR images were again classified into two classes, namely Pre_Intermediate and Post_Intermediate. Moreover, those two classes were again separately classified into Mild, Moderate, and Proliferate_DR, Severe, respectively. The Kaggle is one of the benchmark dataset repositories which was used in this study. The proposed model was able to achieve accuracies of 81%, 96%, 84%, and 97% for the above-mentioned classifications, respectively.
A novel approach for weather prediction for agriculture in Sri Lanka using Machine Learning techniques
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Premachandra, J. S. A. N. W.; Kumara, P. P. N. V.
Climate variability in recent years has critically affected the usual aspects of human lives, where the agriculture sector can be considered as one of the most vulnerable. Sri Lanka is also facing these climate changes over the past few decades. It has resulted in rainfall pattern changes where the expected rain may not occur during the expected time and amount. The mismatch between the rainfall pattern and traditional seasonal cultivation schedule has critically affected the agricultural sustainability. Even with the current technological advancements, weather prediction is one of the most technically and scientifically challenging tasks. This paper presents a novel machine learning-based approach for predicting rainfall for precision agriculture in Sri Lanka and it can be recognized as the first attempt to validate machine learning models to predict the weather in Sri Lankan context for precision agriculture. By analyzing the nature of the weather in Sri Lanka, the relationship of weather attributes with agriculture, availability, and accessibility, seven attributes are selected including rain gauge, relative humidity, average temperature, wind speed, wind direction where solar radiation and ozone concentration are uniquely selected for Sri Lankan context. For the prediction model, cross-validated data are trained and tested with four machine learning algorithms: Multiple Linear Regression, K-Nearest Neighbors, Support Vector Machine, and Random Forest. Currently, Support Vector Machine, K-Nearest Neighbors models have achieved accuracies of 88.57%, 88.66%. Random Forest has been recognized as the best-fitted model with 89.16% accuracy. The results depict a significant accuracy in this novel approach for Sri Lankan weather prediction.
Simulation analysis of an expressway toll plaza
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Grabau, Shehara; Hewapathirana, Isuru
Since the early civilizations, transportation has played a significant role, from fulfilling basic human needs to contributing towards major economic growths all over the world. With the advancement in technology, the demand for smooth and hassle-free transportation increased and it is particularly true for road transportation in Sri Lanka as well. As a result, the expressway road network was introduced to Sri Lanka in 2011. Although a toll is payable for the use of expressways, many vehicle users prefer to utilize the expressway due to the extensive amount of time saved. Time is of utmost importance for expressway users. Hence, long queues and waiting time at toll plazas where the toll payment is made should be minimized. This study is aimed at analyzing the performance at the Peliyagoda toll plaza of the Colombo-Katunayake expressway where the formation of long queues and long waiting time in queues can be observed during peak hours. Due to the high complexity of using the analytical approach in obtaining the performance measures, a simulation approach was used with Arena Simulation Software. Few setup improvements were identified, and each of the setups were simulated to obtain the performance measures. Based on the comparison of the results, recommendations and suggestions to improve the efficiency of the operations at the Peliyagoda toll plaza have been outlined.
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.
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.
Novel deep learning approaches for crop leaf disease classification: A review
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Ekanayake, E. M. T. Y. K.; Nawarathna, R. D.
To encourage sustainable progress, it is suggested that in a world connected by virtual platforms, modern society should merge big data, artificial intelligence, machine learning, information and communication technology (ICT), as well as the “Internet of Things” (IoT). When real-life problems are considered, the above technology processes are essential in solving the issues. Food is an essential need of human beings. Food supply has become crucial, and it is very important to increase the adequate cultivation of plants for large populations due to huge population growth. At the same time, farmers are struggling with a variety of food plant diseases that significantly affect the harvesting and production in agricultural fields. Nevertheless, the agricultural productivity of rural areas is directly involved with the increase in the economic growth of developing countries such as Sri Lanka, India, Myanmar and Indonesia. Early identification of crop disease, using a well-established modern technique, is vital. It necessitates a number of processes observing large-scale agricultural fields as a disease can infect different parts of the plant such as leaf, roots, stem and fruit. Most diseases appear in plant leaves and have the potential to spread them all over the field within a very short time. This paper reviews several state-of-the-art methods that can be used for plant leaf disease recognition with a special reference to deep learning-based methods.
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.
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.
Theoretical framework to address the challenges in Microservice Architecture
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Premarathna, Dewmini; Pathirana, Asanka
Microservice Architecture (MSA) is a recommended way to introduce the application software in a modularized manner instead of the traditional Monolithic Architecture (MA) approach due to the inherent advantages. The MSA is very much effective considering the true benefits of scalability, flexibility, cost-effectiveness, etc. However, there are significant challenges in the use of MSA as well in the viewpoint of the seniors in the field of Software Engineering (SE). So, the objective of this research is to introduce a theoretical framework to be followed by the SE industries to address the challenges they face in providing MSA-based software solutions. In this research, the literature of MSA is evaluated in detail to understand the influencing factors to cater to the requirements of the software developments. In methodology, two research questions are derived based on the hypothesis of not getting adequate benefit in the process of adopting MSA for software application development; 1. What are the challenges to implementing applications incorporating MSA? 2. How to achieve the exact needs of the clients via MSA? For this study, based on purposive sampling the five SE professionals are selected for interviews to understand the true impact on identified factors through literature for development challenges and client satisfaction. Further, thematic analysis is conducted for evaluating those extracts of the interview qualitatively. Nevertheless, the online questionnaire is distributed among a wide range of SE professionals in the domain of MSA implementation for overall understanding about significant factors filtered out through the literature and the interviews, and those were analyzed descriptively. Based on the findings, a theoretical framework is introduced for successful implementation of MSA assuring the clients’ requirements. Eventually, this study confirms how MSA adaptation with the theoretical framework is effective for both organizations and clients.
Model to optimize the quantities of delivery products prioritizing the sustainability performance
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Prabodhika, A. P. K. J.; Niwunhella, D. H. H.; Wijayanayake, A. N.
Many manufacturers and retailers often outsource their logistics functions to Logistics Service Providers (LSPs) to focus more on their core business process. Due to the competitiveness and the popularity of the sustainability concept, those organizations evaluate their prospective LSPs not only based on economic aspects like cost, service quality but also on social and environmental aspects as well when selecting LSPs. This paper proposes a methodology that can be used by organizations when evaluating and selecting LSPs based on their sustainability performance. Analytic Network Process (ANP) is used in evaluating the LSPs’ sustainable performance since multiple dimensions and indicators need to be incorporated when measuring the sustainability performance. A Linear Programming Problem (LPP) model was proposed which allows the organizations to decide both desired number of LSPs and the volume to be allocated for those selected LSPs. The proposed methodology is flexible as it depends on the sustainability requirements of the organization when selecting LSPs. Both the indicators and their relative importance are up to the organization to decide.
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.
Reduce food crop wastage with hyperledger fabric-based food supply chain
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Premarathna, Dewmini
Food is the utmost important thing for every living being. The quality and safety of food has become a crucial factor in the food industry. Most of the customers tend to pay more attention to food safety and seek to get food from verifiable resources. To improve this trustworthiness Distributed Ledger Technology (DLT) - based Food Supply Chain (FSC) plays a vital role because of its traceability. There are multiple actors involved throughout the journey of FSC and with the high visibility of data in DLT, everyone can ensure trust. The transparency of data itself is a reason for some to opt-out because some of their private data can be exposed to others. Hyperledger Fabric (HF) based FSC can address that matter as it supports permissioned network solutions. Though there are a lot of solutions available in a similar kind of approach, whether the crops take their journey throughout the FSC without any wastage, is still questionable. This study focuses on reducing wastage of food crops as they take a long journey in their raw state and possible hazards are high. It discusses farmers' behavior based on the Sri Lankan context and how it accompanies food crop wastage. Further, this paper ruminates the other possible crop wastage that can take place in FSC and how to eliminate it with the proper involvement of knowledgeable and authorized parties. Then, the study explores how all the parties can collaboratively join the FSC based on HF so that everyone can benefit. Finally, it concludes on how such design is effectively contributing to reducing food crop wastage in Sri Lanka (SL).
Thought identification through visual stimuli presentation from a commercially available EEG device
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2021) Gunawardhana, M. P. A. V.; Jayatissa, C. A. N. W. K.; Seneviratne, J. A.
Thought identification has been the ultimate goal of brain-computer interface systems. However, due to the complex nature of brain signals, classification is difficult. But recent developments in deep learning have made the classification of multivariate time series data relatively easy. Studies have been carried out in the recent past to classify thoughts based on signals from medical-grade EEG devices. This study explores the possibility of thought identification using a commercially available EEG device using deep learning techniques. The crucial part of any EEG experiment is contamination-free data collection. Keeping the subject’s mind concentrated only in the decided state is important, yet challenging. To address this issue, we have developed a graphical user interface (GUI) based program that allows stimulus controlling and data recording. With the use of the low-cost commercially available EEG device, accuracies up to 89% were achieved for the classification of high contrast signals. However, tests on complex thought identification did not produce statistically significant results over the chance accuracy.
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.
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.
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.
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.