Smart Computing and Systems Engineering - 2023 (SCSE 2023)

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Adoptability of Chaos Engineering with DevOps to Stimulate the Software Delivery Performance
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Arsecularatne, Merishani; Wickramarachchi, Ruwan
The efficiency of the business processes has a major impact on improving the productivity of organisations. Many organisations use IT-related tools, primarily software, to enhance the efficiency of their business processes. Therefore, timely and reliable delivery of software products has become a top priority. As a result, advancing the concept of “Agility”, organisations implement DevOps practices. However, maintaining the quality of the software delivery service has become an issue due to several challenges related to the implementation of DevOps. Hence, this study was conducted with the aim of understanding the DevOps-related challenges and how “chaos engineering” can be applied along with DevOps to address those challenges. The practice of "chaos engineering" contributes to the reduction of chaos. A systematic literature review was conducted to investigate the concept of “chaos engineering” and the challenges that DevOps-implemented organisations face. Later, a qualitative study was conducted to see how chaos engineering practices can be used to address the identified DevOps challenges. Based on the thoughts and views of the industry experts who participated in this study, it was revealed that implementing chaos engineering with DevOps helps organisations address most of the DevOps challenges both directly and indirectly. Also, the study suggests a methodology to implement chaos engineering with DevOps within organisations to successfully overcome DevOps-related challenges.
Alapana Generation Using Finite State Machines and Generative Adversarial Networks
(Jayatharan Vithushigan; Alwis Dileeka (2023), Alapana Generation Using Finite State Machines and Generative Adversarial Networks, International Research Conference on Smart Computing and Systems Engineering (SCSE 2023), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. Page 6, 2023) Jayatharan, Vithushigan; Alwis, Dileeka
The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation.
An Automatic Density Cluster Generation Method to Identify the Amount of Tool Flank Wear via Tool Vibration
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Adikaram, K.K.L.B.; Furukawa, Y.; Herwan, J.; Komoto4, H.
Determining the amount of tool flank wear (TFW) of a tool during operation is an important and cost-sensitive factor for maintaining the efficiency of the machine and product standards in Industry 4.0. Therefore, a variety of predictive analysis tools have been developed in this regard, with the objective of taking corrective action quickly and efficiently. In this paper, we present a TFW amount estimating method via plotting vibration generated during the cutting process on big data visualization and density cluster generation method known as Graphical Knowledge Unit (GKU). GKU generates density clusters by incrementing the RGB color values in the intersected markers due to data overlapping. In our previous work, the TFW amount of a cutting tool attached to a Computer Numerical Control (CNC) turning machine was checked. A workpiece of grey cast iron with an initial outer diameter of 110 mm was cut until it reached 60 mm. This process was repeated until the TFW amount, which was measured according to ISO 4288, met the recommended value range (0.3 ± 0.005 mm). After each cut, TFW amount and the surface roughness were measured following ISO 4288. Vibration was recorded using a triaxial accelerometer attached to the tool shank of the turning machine. In the present work, out of 29 cutting circles, vibration along the x-axis against vibration along the y-axis of selected cuttings were plotted using GKU. The density of the center of the plot (fixed point, FP) and the density of the highest density (dynamic point, DP) were measured using the color values of pixels as an index. The results showed a very strong linear correlation (0.95) between the TFW amount and vibration data density projected via pixel color values at FP. This shows that processing of vibration with GKU is a promising method to estimate TFW amount.
A Bayesian Approach for Raisin Data Classification
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Kumari, H.M.N.S.; Nawarathne, U.M.M.P.K.
Raisin performs a decisive role in the commodity economy. Recently, low-quality raisin products have been introduced to agricultural markets worldwide. Therefore, it is crucial to identify a suitable classification method to distinguish between varieties of raisins. Previous research has employed various traditional machine learning methods to classify commodities. However, it is challenging to quantify uncertainties through traditional machine learning models. Therefore, this study employed a Bayesian Logistic Regression (BLR) model using seven morphological features of two varieties of raisins grown in Turkey. Initially, different machine learning techniques were employed on data. After that, four priors, such as Jefferys, Laplace, Cauchy, and Gaussian, were considered, and hyperparameters were tuned using the empirical Bayes method. Marginal posterior distributions of the model parameters were estimated, and the convergence of the models was checked. Then, evaluation metrics of the BLR model with different priors were compared to those of machine learning models. According to the results, the BLR model with Gaussian prior produced the highest accuracy of 93%. Finally, it can be concluded that the BLR model with Gaussian prior provides substantially better results when classifying raisin data.
Canine Sleeping Posture Identification using Transfer Learning
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Nisansala, Achini; Puvanendran, Rukshani
The ability to recognize different postures of any living creature is a prerequisite for getting an accurate idea about their mental and physical well-being. Dogs are the most friendly and social canine breeds that provide love and security for human companions being their best friend at all times. The present study aimed at paying the initiatives at exploring important information about the wellbeing of the dogs with their sleeping postures. The paper studies and compared the classification performance of three deep transfer learning algorithms: VGG16, xception, and ResNet50, and Convolutional Neural Network on a manually collected and augmented dataset of nearly 4000 images consisting of four different sleeping postures of dogs. Our model reveals that ResNet50 outperforms all other algorithms and achieved the highest accuracy of 87.35%. Overall, our finding would help disabled and special requirement dogs and their owners to identify canine's health conditions and requirements using the sleeping postures and provide a more comfortable and better life for them.
A Comparative Evaluation of PDF-to-HTML Conversion Tools
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Pathirana, Pramodya; Silva, Asini; Lawrence, Thenuka; Weerasinghe, Thushani; Abeyweera, Roshan
PDF (Portable Document Format) is a popular file format used for sharing and storing documents across different platforms. However, there are occasions when the content of a PDF document needs to be re-purposed for online use. PDF-to-HTML conversion is a common method used to achieve this goal. This research paper presents a comparative evaluation of existing PDF-to-HTML conversion tools for their suitability in extracting text and images. These tools were tested using school textbooks in Sri Lanka, which contain complex text formatting and non-textual elements. The evaluation was based on various criteria, such as the accuracy of the output, handling of complex text formatting, and non-textual elements. Comparisons were drawn based on the performance of each of these tools with respect to the criteria. The study provides useful insights for individuals and organizations looking to re-purpose PDF content for online use in the HTML format, particularly in the education sector.
A Comparative Study of Three User Experience Frameworks for Enhancing Health Mobile Applications
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Goonatillaka, W.A.D.B.C.; Kodithuwakku, C.K.; Sandaruwan, B.W.G.A.; Bandara, H.M.V.T.W.; Wickramarathne, Jagath
Apps for mobile health (mHealth) have proliferated and offer a variety of features to help users achieve better health outcomes. Thousands of mHealth apps are giving many great options for end users and they also introduce different options for different requirements. In this study the focus is specifically on the fitness mobile health apps. There are a variety of UX evaluation frameworks that are being used for the UX evaluation of those apps. However, not much research work is available in evaluating the UX frameworks relevant to mHealth mobile apps. The three frameworks evaluated in this study are the hook model, the mental model, and the double diamond model as those models have shown considerable success in this context. Five main user case studies are used in the user testing relevant to the UX of the selected mHealth app. At least three casual interviews together with three observation sessions are conducted per respondent to gather feedback on the usability, accessibility, and the effectiveness of the three frameworks. Thereby, the three frameworks are compared for their suitability and recommendations are tendered in suggesting a better suited framework for the UX evaluation purposes for mHealth apps. The Double Diamond Hook Mental (DDHM) hybrid model is proposed as the main outcome of this study to overcome the inherent drawbacks of each framework if used individually. After usability testing, it has proven that this proposed model enables to guide improved UX of mHealth apps.
A Comprehensive Approach to Evaluating Software Code Quality Through a Flexible Quality Model
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Shyamal, D.K.K.; Asanka, P.P.G.D.; Wickramaarachchi, D.
The rapid growth of the software engineering sector has led to a detrimental effect on the quality of software being developed. Code quality is crucial in determining the overall quality of software however, it is often observed that quality management programs primarily focus on internal processes within organizations, while the importance of code quality lacks proper attention despite the existence of quality standards for software products and processes. Due to its dynamic nature, the concept of quality poses a challenge in terms of precise definition, however, this paper addresses this issue by providing a comprehensive definition for code quality that considers all its dimensions, thus laying the foundation for conducting research related to quality. Code quality encompasses factors such as readability, scalability, performance, and adherence to industry standards. High-quality code is easy to understand, modify, and test, making it more reliable and less prone to bugs. By considering the multitude of challenges that currently exist and acknowledging the criticality of code quality, this study proposes an approach for assessing code quality, and a comprehensive quality model that considers the most critical code quality attributes and their relevant metrics along with corresponding threshold values specifically use in the contemporary software industry.
Computer Laboratory Management System for Government Schools in Sri Lanka: Design Science Approach
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Damayanthi, Thamara; Ahangama, Supunmali
Due to globalization, Sri Lanka's education system is experiencing major difficulty in maintaining educational quality. It is imperative to adopt the latest educational technology to meet global education standards. Information Technology (IT) tools can be used as creative teaching aids to increase the quality of teaching and learning. Computer laboratory in-charge teachers will have to share scarce IT resources among the school community. This study proposes a new methodology for sharing IT resources and it will facilitate the implementation of a computer laboratory management system (CLMS). The study was conducted using the Information System (IS) Design Science approach to create a usable IT artifact to solve this foreseen problem in government schools. The pre and post- evaluations were done with research rigor based on Delone and McLean's IS success model in multiple iterations to allow users to determine whether their expectations are achieved by the system. 59 computer laboratory in-charge teachers participated in the evaluation process of the existing system and the new system. The result shows that the new CLMS will benefit the target community with some improvements to increase the service quality of the IS.
Consumers’ Acceptance of the Retail Service Robots: A Humanoid Perspective
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Arachchi, H.A. Dimuthu Maduranga; Samarasinghe, G. Dinesh
This study focused on explaining the influence of retail service robot (RSR) personality traits on consumers’ acceptance of retail service robot (RSR); this study also examined moderating impact of anxiety toward robots. Based on the extensive literature review, this study formulated eight hypotheses to support the arguments. Quantitative methodology with a survey strategy was undertaken, which had an effective sample size of 259 young consumers. Furthermore, analysis was carried out using Smart partial least squares (PLS)-structural equation modelling. The study finds a significant direct relationship between the RSR personality traits (intelligence, sincere, and creative) and consumers’ perceived control. Other than that, it also finds significant relationships between consumers’ perceived control, anticipated service quality and RSR acceptance. It was further revealed that anxiety toward Robots significantly moderates impact on RSR personality traits on consumers’ perceived control. The findings shed the light on improving retail service quality with humanoid based applications.
Containerized Cargo Transport Network to Connect the Port of Colombo and Free Trade Zones in Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Rathnayake, P.G.B.P.S.; Kavirathna, C.A.
Currently, in Sri Lanka, the road (truck) has always been the dominant transport mode for moving import/export containerized cargo to/from Colombo port. According to the past literature, there are a lot of road-based containerized cargo transportation issues in Sri Lanka. This study introduces a dry port-based containerized import/export cargo transportation method using a railway network to connect the port of Colombo and the Board of Investment (BOI) Export Processing Zones (EPZ). A dry port is an inland intermodal terminal, directly connected by a railway line to the Colombo port. The study mainly focuses on two dry port-based networks under several alternative network configurations. The locations of the proposed dry ports are Orugodawatta's current customs clearing yard and cargo inspection center in Kerawalapitiya proposed by the Asian Development Bank (ADB). In this study, mathematical models were developed to analyze and compare the cost, environmental, and time benefits of the proposed networks between major BOI EPZs and Colombo port considering current freight demand. The results highlighted the advantages of the proposed network under several scenarios from economic and environmental and travel time perspectives. Then the study estimates the import/export BOI freight demand for 2050 and analyzes the potential of the proposed railway- based cargo transport system for 2050. These figures can be further reduced by optimizing the dry port location. Therefore, a simulation- based approach was considered to optimize the dry port location by Greenfield analysis method with “Supply Chain Guru” software. Through simulation results, the study shows the new dry-port location compatibility for the proposed system. The findings of the study have demonstrated a systematic approach to decision-making by optimizing the local cargo handling process. By adopting this system, Sri Lankan inland logistic operations will become more efficient and the total transportation costs, environmental pollution, and transportation time will decrease significantly.
Critical Success Factors Affecting the Successful Implementation of Industry 4.0 in the Sri Lankan Apparel Manufacturing Industry
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Silva, A.M.H.; Withanaarachchi, A.S.
The Sri Lankan apparel manufacturing business, a major contributor to the country's export revenue, has been attempting to adopt industry 4.0. By implementing Industry 4.0, they intend to increase the productivity and efficiency of the shop floor and obtain a competitive edge. Only a few developing nations have been able to capture the maximum benefits of the fourth industrial revolution. The purpose of this study is to identify the critical factors that must be considered for the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector. Throughout the research, a quantitative approach was used. Initially, the six most significant critical factors and two moderating variables were determined by a review of prior research and the opinions of industry professionals. Partial Least Square – Structural Equation Modelling (PLS- SEM) was used to analyze the relationship between the factors. Greater financial investments, organizational strategy, workforce, a dynamic organizational culture, the involvement of top management, and the availability of IT infrastructure have a significant positive impact on the successful implementation of industry 4.0 in the Sri Lankan apparel manufacturing sector, as determined by the final findings of the data analysis. In addition, the availability and accessibility of support services have a significant positive moderating effect on financial investments, when successfully implementing industry 4.0 in the Sri Lankan apparel industry. In addition, the advancement of digital technologies has a significant positive moderating effect on financial investments and, a significant negative effect on organizational strategy and the involvement of top management when successfully implementing industry 4.0 in the Sri Lankan apparel industry. The outcomes of this study assist the managers of the Sri Lankan clothing manufacturing sector in comprehending the critical factors that must be considered when successfully implementing industry 4.0 technologies.
Customer Satisfaction Analysis Based on Delivery Logistics Factors in Sri Lankan E-Commerce
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Damruwan, M.V. Thathsara; Jayasinghe, Shan; Wijayanayaka, W. M. J. I.
The rapid growth of e-commerce in Sri Lanka has resulted in an increase in the number of e- customers and e-retailers. To sustain this growth, e-commerce players must differentiate their offerings and operations to meet the evolving needs of customers, with customer satisfaction being a crucial factor in achieving a competitive advantage. Delivery logistics plays a critical role in ensuring customer satisfaction. A systematic literature review, following the PRISMA framework, identified the most impactful delivery logistics factors on customer satisfaction as delivery time, cost, and quality. Building upon this, the study utilized the mental accounting theory (MAT) to develop a conceptual framework. The objective of this study was to examine the relationship between delivery logistics factors and customer satisfaction and to explore the moderating effect of geographical variations and product categories on this relationship. Data was collected from a sample of 272 respondents living in rural and urban areas, using a structured questionnaire. The data were analyzed using partial least squares structural equation modelling (PLS-SEM). The findings suggest that delivery logistics factors have a positive impact on customer satisfaction and that the geographical location of customers, and the product category moderate this relationship. Specifically, for econsumers from rural areas, delivery cost was found to be a significant predictor of customer satisfaction. Furthermore, delivery logistics factors positively influenced customer satisfaction for shopping and special goods, but not for convenience goods. Overall, this study emphasizes the importance of delivery logistics in e-commerce, particularly in a developing country like Sri Lanka. It provides valuable insights for e-commerce players to enhance their operations and offerings, meet customers' needs, and improve their competitiveness.
Deep Learning-Based E-Learning Solution for Identifying and Bridging the Knowledge Gap in Primary Education
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Arunoda, D.P.H.; Walpola, S.R.; Piumira, S.M.I.; Athukorala, A.D.M.P.; Thilakarathna, Thusithanjana; Chandrasiri, Sanjeevi
Educational teaching apps are primarily available in app stores to educate students in various contexts. Lack of educational resources, physical and mental health conditions, and poverty cause some students to skip school and move on to the next school grade without completing the course content of the previous grade. Most of the available apps focus on specific content to cover. The Smart Primary Education Tutor (SPET) teaching app specifically focuses on the missed content by analyzing their knowledge gap and providing lessons to cover the missed content. The main objective of SPET is to develop a methodology to identify the gap in student knowledge and fill the knowledge gap by teaching using smart techniques. SPET is determined to identify students' interactions (attention, emotions) with the system to identify students' ability to use the learning tool, identifying gaps in students' knowledge levels compared to their actual grades using activities and voice-based technologies, teaching to cover the knowledge gap by providing engaging activities and lessons and evaluating students by conducting a final assessment and analyze students' knowledge and performance obtained through the system. Students between the ages of 5 and 8 are targeted in the community to apply. The solution embeds deep learning-based models including attention classification models using head posture estimation, facial expression recognition, and eye gaze estimation, speech recognition models to identify provided verbal answers, handwriting recognition models to evaluate student performance, and smart teaching. The child emotion recognition model achieved 93% accuracy. The Attention span evaluation model achieved 85% accuracy. The handwritten numerical and English character data recognition model which detects answers for the final assessment paper achieved 85% percent of accuracy.
Defaulter Prediction in the Fixed-line Telecommunication Sector Using Machine Learning
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Ginige, Sachini; Rajapakse, Chathura; Asanka, Dinesh; Mahanama, Thilini
In the modern connected era, the telecommunications sector plays a critical role in enabling efficient business operations across all industries. However, defaulting customers who fail to pay their dues after consuming services remain a significant challenge in the industry. Defaulters pose a risk to service providers, calling for measures to lessen both the probability of occurrence as well as its impact. Early identification of defaulters through prediction is a possible solution that enables proactive measures to mitigate the risk. However, the nature of the fixed-line product segment poses additional constraints in identifying defaulters, highlighting an existing knowledge gap. The research aims to evaluate the effectiveness of machine learning as a technique for the prediction of defaulters in the fixed-line telecommunication sector, and to develop an effective predictive model for the purpose. The success of machine learning techniques in analysis and prediction over traditional methods prompted its use in this study. The study followed the design science research methodology. An analysis was conducted based on past transaction data. Special consideration was given to the scenario of customers with little to no transaction history. Based on the analysis, a feature list for identifying defaulters was compiled, and multiple predictive models were developed and evaluated in comparison. The resulting predictive model, which uses the Random Forest technique, shows high performance in all considered aspects. The findings of the study demonstrate that machine learning techniques can effectively predict defaulters in the fixed-line telecommunication sector, with significant implications for mitigating the risk associated.
Detecting Click Fraud Using an Improved Lenet-5 Convolution Neural Network
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Fernando, C.D.; Walgampaya, C.K.
Online advertising has grown drastically over the last couple of decades by making billions worth of business markets all over the world. Click Fraud can be identified as one of the common malpractices when it comes to digital platforms. This leads to an increase in the revenue of the Ad publishers and huge losses for the advertisers. Hence the need of detecting click fraud has become a major concern in online marketing. Recent studies have proposed different kinds of machine learning based approaches to detect these fraud activities. In this study, we propose an improved Lenet-5 Convolution Neural Network to identify click fraud. This proposed novel deep learning algorithm was able to achieve an accuracy of 99.09% by using deep features of the proposed Lenet-5 based Convolution Neural Network.
Determinants that Drive the Behavioural Intention of Employees in the IT industry to Use CI/CD Framework: A Study based on Sri Lankan IT Companies
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Karunarathna, Chamindu; Jayasinghe, Shan; Wijayanayaka, W. M. J. I.
Continuous Integration and Continuous Delivery (CI/CD) is an Agile-based software development methodology becoming increasingly popular in the software development industry due to its ability to automate the software delivery process, reduce the time to market, and enhance software quality. However, despite the growing interest in CI/CD adoption, many organizations have not achieved full success in implementing and utilizing the CI/CD workflow. To address this gap, this study aimed to identify the factors that drive the behavioural intention of IT employees to use the CI/CD workflow: based on the Sri Lankan context. A systematic literature review using the PRISMA framework identified the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology models as the most widely used and accepted models for understanding technology adoption. Therefore, TAM, UTAUT and past literature were used to develop the conceptual framework. The variables in this research model were measured through questionnaires with nominal and five- point Likert scales and close-ended questions, which were completed by the IT employees in Sri Lanka. Data cleaning and demographic data analysis were conducted using IBM SPSS 21, and preliminary data analysis was performed using PLS-SEM (SmartPLS 4). The study found that Performance expectancy is the most significant factor determining IT employees' behavioural intention to use CI/CD workflow. Therefore, the study concluded that organizations and management should focus more on enhancing employees' performance expectancy to adopt CI/CD workflow successfully.
Developing and Training a Mathematical Model for Optimizing a Given Interior Space of a Supermarket
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Alahakon, Shalitha; Siriwardana, Tharindu; Udupihilla, Deshan; Wickramasinghe, Tharukshi; Rajapaksha, Samantha
Retailers are crucial in supply chains, acting as the bridge between consumers and resources. However, there is limited analytic-based literature on block design in grocery stores. This paper employs an algorithmic approach with optimization techniques to efficiently design the interior space of a provided supermarket. The objective is to create an analytical method for handling design issues without relying on human-centered approaches. Using data from supermarket store arrangements, the paper showcases efficient space utilization by aligning item measurements with customer needs. Decision variables offer decision makers a precise collection of non-dominated designs. Previous studies demonstrate the effectiveness of this approach in analytically designing a data-driven structure for supermarket block layouts. The model identifies layouts that maximize space utilization while meeting industry standards. Although primarily focused on Asian retailers, the approach is generally applicable due to the similarity of grocery store layouts worldwide. The method and results are easily translatable for other retailers.
DrivEmo: A Novel Approach for EEG-Based Emotion Classification for Drivers
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Gamage, T.A.; Sandamali, E.R.C.; Kalansooriya, Pradeep
Electroencephalogram (EEG) based emotion recognition approaches have proven to be successful with the latest technologies, and therefore, driver emotion recognition is also being widely discussed for enhancing road safety. This paper reveals a unique approach to driver emotion recognition for the calm, fear, sad, and anger emotional states where calm is the desired state of mind while driving. Emotiv EPOC X 14 channel EEG headset is utilised for the EEG collection, and ten subjects are involved in the experiment. EEG preprocessing of the collected EEG data is done using the EEGLAB toolbox in Matlab. EEG feature extraction is performed using Matlab, and feature selection and classification model training is done using the Classification Learner app in Matlab. ANOVA and ReliefF are employed as the feature selection algorithms, and Support Vector Machine (SVM) and Naïve Bayes classifiers are utilised for the emotion classification. The outcomes reveal that the highest mean accuracy of 95% is achieved from the Coarse Gaussian SVM classifier, while the lowest mean accuracy of 85% is obtained from the Fine Gaussian SVM classifier detecting the calm, fear, sad, and anger emotional states. In addition, all the other trained classifier models have an accuracy between 85% and 95%. Therefore, the findings suggest that the proposed EEG-based implementation approach of an emotion classification model for drivers is highly successful and can be employed in future research in the paradigm of driver emotion recognition as well. Besides, this research presents a critical literature review concerning critical aspects of EEG- based emotion recognition research.
Effectiveness of Hybrid Teaching Methods: The Perspective of Academics (Special Reference to One of the Leading Private Higher Educational Institutes in Sri Lanka)
(Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka, 2023) Dias, Chethima; Wickramasinghe, Pulsarani; Jayamalaki, Ashely; Sivaguru, Ramesh; Rathnayake, Nilmini; Jayasinghe, Punmadara
Hybrid teaching become a major part of the teaching style for the higher education sector in the Sri Lankan context. Hybrid teaching allows for a part of the academics to go to the course physically and simultaneously permitted the rest to conduct the sessions applying videoconferencing from different locations. The objective of this research study is to explore the effectiveness of the hybrid teaching to enhance academics outcome in the business faculty of one of the leading private higher education institutes in Sri Lanka. The purpose of the study was to explore the effectiveness of hybrid teaching practices. The data for the study was collected through 11 semi-structured interviews and the data were analysed by using the content analysis. The results show that the effectiveness of the hybrid teaching is somewhat higher than traditional techniques from the perspective of the academics. In addition, based on the content analysis researchers have identified variables such as: perceptions of effectiveness, experience in different teaching capacities, instructor attitude and belief and challenges in hybrid teaching methods. The output of this study will be helped to recognize how academics perceive the effectiveness of hybrid teaching with these significant contents in one of the leading private higher education institutes.