Smart Computing and Systems Engineering - 2020 (SCSE 2020)
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/23064
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Item Demystifying the concept of IoT enabled gamification in retail marketing: An exploratory study(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayasooriya, Shalini; Alles, Tharindhie; Thelijjagoda, SamanthaThe retail landscape is evolving rapidly as firms embrace innovative technologies in an attempt to stay ahead of the aggressive competition prevalent within the industry. Gamification is one such innovative technology that has been gaining popularity in recent times. This study aims to explore the application of Gamification in the context of Retail Marketing in Sri Lanka and ultimately propose a concept for a Gamified application that can be used by customers of moderntrade retailers. The study took an exploratory qualitative approach where intensive surveys of literature and in-depth interviews with a judgmental purposive sample of seven marketing professionals in the modern-trade retail industry were conducted to determine the current play of technology in retail marketing as well as the drivers & challenges of Gamification adoption. Further, in-depth interviews with the customers of such organizations were conducted in gathering user preferences and design recommendations for a Gamified app. Thematic analysis was carried out in deriving insights. Findings show that the retail firms currently employ several technologies in line with those discussed in existing literature such as loyalty card systems, digital signage, VR technologies, online Gamification amidst others in carrying out their marketing efforts. Gamification is predominantly applied in the online context as opposed to the offline (in-store) context. Furthermore, the key drivers that propel firms to implement novel technology like Gamification are to generate customer insights, enhance customer experience and achieve marketing related KPI targets. Conversely, inadequate technology infrastructure, justifying the focus on a niche crowd of techoriented customers and slow ROI pose as challenges in the process of Gamification adoption. Three main themes emerged upon exploring user preferences and design recommendations for a Gamified app and are identified as information at the touch of a fingerprint, automation & integration and use of game mechanics. Ultimately by incorporating these insights gathered, a concept for a Gamified app was proposed.Item Aspect-based sentiment analysis on hair care product reviews(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kothalawala, Malki; Thelijjagoda, SamanthaNowadays, with almost everything being shared online, people are more verbal about their consumer experiences with products via reviews. Reviews can be vital for manufacturers to get insights into consumer opinions and consumers in their purchase decisions. Sentiment analysis, referring to the extraction of subjective opinions on a particular subject within a text, is a field within Natural Language Processing, that can convert this unstructured information hidden within reviews into structured information expressing public opinion. In regards to a specific product group like hair care products, certain brands are rising in the market due to their positive public opinion on particular aspects. While ecommerce websites facilitate users to view the reviews, they do not display which reviews contain which type of opinion on which aspect at a glance. This research aims to introduce an automated process that focuses on determining the polarity of online consumer reviews on different aspects of hair care products by using Aspect-based Sentiment Analysis. The system consists of processes like data gathering, pre-processing, aspect extraction and polarity detection and follows a sequential approach to achieve the intended goal. Consequently, by deciphering the aspect-wise polarity of the reviews, the implemented system demonstrates an accuracy of 85% from the test data for overall aspects, enabling consumers to get an at a glance idea about the public opinion and manufacturers to identify their strong and weak points.Item Relationships between climatic factors to the paddy yield in the North-Western Province of Sri Lanka(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Wickramasinghe, Lasini; Jayasinghe, Jeevani; Rathnayake, UpakaClimate variation is one of the major impacting issues for paddy cultivation. It also highly impacts the harvest. Therefore, many researchers try to understand the relationships between climatic factors and harvest using numerous methods. Sri Lanka is still titled as a country with an agricultural-based economy and thus identifying the impact of climate variability on agriculture is very important. However, previous studies reveal a little information in the context of Sri Lanka on the impact of climate variabilities on agriculture. Therefore, this study showcases an artificial neural network (ANN) framework; that is an ordinary machine learning algorithm based on the model of the human neuron system, to evaluate the relationships among the climatic components and the paddy harvest in the North-Western province of Sri Lanka. This on-going study helps to analyze the relationships between the paddy harvest of the North-Western province and climate, including rainfall minimum atmospheric temperature and maximum atmospheric temperature. Correlation coefficient (R) and mean squared error (MSE) are used to test the performance of the ANN model. The results obtained from the analysis revealed that the predicted and real paddy yields have a significant correlation with rainfall, maximum temperature and minimum temperature.Item Novel computational approaches for border irregularity prediction to detect melanoma in skin lesions(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Abeysinghe, D.V.D.S.; Sotheeswaran, S.Medical image detection has been a rapidly growing field of study during the last few years. There are different challenges associated with it. Many works have been done in order to provide solutions for key challenges. This study of work is focused on melanoma detection by using Asymmetry, Border irregularity, Colour textures, and Diameter (ABCD) feature along with proposing two new approaches for border irregularity detection. The proposed two new approaches are distance difference method and gradient method, which follows the main concept as traversing along the continuous borderline of the lesion. Further, this study varies from the existing studies, since it has been taken counts of distances from the centroid to the borderline without considering the distance from the image border to the borderline of the lesion. It was able to achieve a classification rate of 79% and 78.5% using distance difference method and gradient method, respectively whereas the classification without the border irregularity feature achieved 78% of accuracy performing on PH2 dataset. Further, this study can be stated as most appropriate to classify non-melanoma rather than melanoma. It is contributed by generating simple computer science-based approaches rather than complex mathematical methods to detect border irregularity and makes the medical image detection easy.Item Intelligent changeover solution for a domestic hybrid power system(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Herath, H.M.R.M.; Tharindu, K.H.S.; Fernando, K.D.M.; Alahakonn, P.M.K.; Hirshan, R.Electricity plays a major role in the modern world as almost all the equipment used is operated using electricity. Electricity demand of the world is increasing day by day and there should be a proper mechanism to meet the growing demand and to improve the efficiency of the power systems while continuously providing power with less environmental effects. This paper presents a changeover solution for a solargrid hybrid power system that directly focuses on efficiently utilize the power sources by automatically selecting the power source according to the required power demand. Already available automatic power changeover switches in the market are only capable of selecting one source when the other source is not available. They cannot switch the power source considering the power demand. The novelty of this solution is, it can efficiently select the power source considering the apparent power demand of the house.Item Predicting examination performance using machine learning approach: A case study of the Grade 5 scholarship examination in Sri Lanka(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Ranawaka, U.M.; Rajapakse, ChathuraUniversal primary school education is a must requirement and one of the criteria that should be fulfilled by the developing countries according to the International development goals which are also recognized as “Eight Millennium Development Goals”. In the context of Sri Lanka, Government is mostly involved in primary education through government-controlled schools. The success of primary education is measured by conducting a scholarship examination. Those who are getting higher results are given opportunities to attend well-facilitated schools for secondary education. Due to that case, there is a massive competition for passing the examination. Limitless pressure for examination provides lots of issues to students. This paper uses data to investigate a model of academic performance as measured by past results of school tests of Grade 4 and Grade 5. 500 students from eight primary schools in the Gampaha district have been selected for collecting data. The Data on the above-mentioned students have been collected by conducting questionnaires to the teachers who incharged the classes. Then the Logistic Regression model and Multiple Linear Regression model have been applied to predict students’ performances at the examination. The model depicts the likelihood of a student passing or failing the grade 5 scholarship examination and predicts the range of results that students will obtain in the examination. The accuracy of predictive models is measured using the results of students who have already faced the Grade 5 examination. Revealing the potential of students at the grade 5 examination is heavily benefited by teachers because they can provide personalized education for talented students and provide opportunities to other students to improve their talents. The initial architecture of the Grade 5 examination results’ predictive model is being discussed in this paper.Item A survey on applying machine learning to enhance trust in mobile adhoc networks(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jinarajadasa, G.M.; Liyange, S.R.Mobile ad-hoc networks (MANETs) play a vital role in the increasingly networked world where it has many applications in plenty of important fields including military sector, business applications, social networks such as Vehicular Networks (VANETs), and other intelligent systems. Because of the dynamic nature of mobile ad-hoc networks, they are more tent to be objected to the various malicious attacks. Over the recent past decades, a certain amount of researches has been done to increase reliable and trustworthy communications in a MANET environment. Over the proposed solutions, Machine learning applications have significant results. Hence based on those, a critical analysis of existing machine learning-based trust approaches for mobile ad-hoc networks are presented here. The focus of this survey is to classify and evaluate the existing trust mechanisms and to provide guidance for future research work in the area.Item Extending use-case point-based software effort estimation for Open Source freelance software development(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Senevirathne, Dharshitha Srimal; Wijayasiriwardhane, Thareendhra KeerthiAccurately predicting the software development effort is very crucial when delivering the software systems on time, within the budget and with the required functionality. Overestimation of the software development effort can lead to losing the projects whereas underestimation can cause budget and schedule overruns. The development effort of a software project depends on various factors and these effort factors associated with the freelance software development are different from those of traditional software development. Software development companies employ various proprietary tools in their projects for their planning, development, testing, etc. However, freelance software developers functioning under tight budgetary constraints are not in a position to afford them. As a result, they tend to use free and open-source tools for their software developments. There are various types of software effort estimation models proposed, published and practiced in the industry. However, there is no such software effort estimation model specifically proposed to estimate the effort of freelance software development. The main objective of this paper is to extend Use Case Point-based software effort estimation for the open-source freelance software development. Initially, details of open source software projects were collected from several freelance software developers. Based on the use case diagrams, Use Case Points counts are then calculated for each project. Taking other effort drivers associated with open source freelance software development also into account, we then estimate the effort of each software development. Our aim is to explore the viability of using Use Case Points as the main effort driver in estimating the effort of open source freelance software development.Item A solution to overcome speech disorder of patients using Brain Neuron EEG Signals(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayawickrama, J.A.D.T.; Thelijjagoda, SamanthaSpeech disorders are neurodevelopmental disorders such as Stuttering, Dysarthria, Dysphonia and Aphasia associated with left inferior frontal structural anomalies that involve repeating or prolonging a word, syllable or phrase, or stopping during speech and making no sound for certain syllables. Most of the people who are suffering from speech disorders encounter difficulties in professional communication. Since people are busy with their day to day life, it is not practical to spend more time in consulting a doctor or do speech therapies for their medical issues. The speech therapist generally charges a significantly much higher rate for a single speech therapy practice, which the patient needs to practice at least twice or more for a week to get a better result. In an economy like Sri Lanka, people with average income cannot afford such an amount of money. Therefore, an innovative desktop application for speech disorder patients to overcome this problem has arisen. The main aim of this application is to reduce the speech imperative percentage of speech disorder patients via capturing the electroencephalogram feed of speech motor (Broca's area) using brain neuron O1, O2, C3, C4, F3, F4, F7, F8 electrodes and analyzing it to identify speech imperative issues. This system identifies the current impact on the left hemisphere of the brain (Broca’s area) using EEG neurofeedback. Using speech voice analysis, the system provides the user to measure the articulation interference of the speech process. Self-Learning video tutorials are available for the clinical practices and treatments are available as prolong, relaxing, and humming exercises. Patients can track down the improvements daily or monthly by the rating system which makes the system unique among all other systems and the result can be directly sent to the desired consultant/neurophysiologist by the system itself. Patients can save time and the total cost of a therapy fee by using this system.Item Industry 4.0 readiness assessment for apparel industry: A study in the Sri Lankan context(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Lakmali, Erandika; Vidanagamachchi, Kasuni; Nanayakkara, JulianSri Lankan apparel industry is the most significant and driving contributor to the country’s economy by constituting a large portion of GDP. In the highly competitive apparel world, manufacturers search solutions for problems such as worker inadequacy while minimizing the human impact. Therefore, there is a need for apparel manufacturers to enhance value chain processes with the latest technologies. Industry 4.0 is the fourth industrial revolution that transforms the physical production into a combined cyber-physical production environment with IoT and decentralized intelligence. It enhances the process functions from new product development to logistics by providing real-time visibility of the production flow. Existing literature mentions the applications of Industry 4.0 in the apparel industry, but these have not addressed the issue of assessing the readiness for its adaptation in the apparel value chain process. Hence this scrutiny proposes a model to assess the current level of readiness of the Sri Lankan apparel industry to adapt Industry 4.0 technologies and practices. The model was developed based on a systematic review of literature with the industry experts' guidance. The factors that determine the readiness for Industry 4.0 within an organizational context were classified under four categories; People, Process, Technology and Data which were defined as readiness dimensions. The proposed model consists of five readiness levels from 0 to 4 namely: Stranger, Beginner, Intermediate, Advanced and Elite. This model enables managers to measure the readiness for adapting of Industry 4.0 in selected apparel value chain processes by using the specified minimum requirements under each dimension and level. The outcome of this study indicates that Sri Lankan apparel industry is in "Intermediate" level in terms of overall readiness with a value of 1.91 in the predefined readiness scale from 0 to 4.