Smart Computing and Systems Engineering - 2020 (SCSE 2020)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/23064

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Contribution of reflection in language emergence with an under-restricted situation
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Todo, Kense; Yamamura, Masayuki
Owing to language emergence, human beings have been able to understand the intentions of others, generate common concepts, and extend new concepts. Artificial intelligence researchers have not only predicted words and sentences statistically in machine learning but also created a language system by communicating with the machine itself. However, strong constraints are exhibited in current studies. Models dependent on task settings, or supervisor signals and rewards exist, thus hindering the emergence of languages like the real-world. In this study, we improved Batali and Choi et al.’s research and attempted language emergence under conditions of low constraints such as human language generation. We developed a new language emergence agent that combines a language module and a visual module and included the bias that exists in humans as a “reflection function” into the new emergence algorithm. We used the MNIST dataset for language emergence. Irrespective of the function, messages corresponding to the label of MNIST could be generated. However, through qualitative and quantitative analysis, we confirmed that the reflection function caused pattern structuring in the message. This result suggested that the reflection function performed effectively in creating a grounding language from raw images with an under-restricted situation like the human language generation.
A modified cognitive complexity metric to improve the readability of object-oriented software
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayalath, Thilini; Thelijjagoda, Samantha
Complexity of software can be identified as a term which expresses the difficulty level of reading, understanding, maintaining and modifying the software. This helps to the quality improvement of the software and maintenance process of the software through a long time period without any obstacle. Therefore, software complexity metrics have been introduced to calculate the complexity of a software using numerical values. While there are number of metrics which calculate the complexity of object-oriented programs, they only consider one or two object-oriented concepts. As a result of that, there is no single metric which has the capability of measuring the complexity of a program based on multiple object-oriented concepts. This research aims to build a new metric to evaluate the complexity of an object-oriented program in order to improve the readability. The new metric has been built based on the influence of previous objectoriented metrics and some disregarded factors in calculating the complexity. In order to evaluate the new metric, Weyuker’s properties and Briand’s properties are used. The new metric acquires most of the object- oriented concepts in calculating the complexity and helps to improve the readability of the software as well. In fact, it makes it easy to handle the maintainability, reusability, portability and reliability of the software, when readability is high. This will result in increasing the overall software quality.
Impact of metacognition and age group on contemporary video game interface and gameplay design
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Harischandra, Nandunie N.; Jayakody, Lahiru A.; Madusanka, Tiroshan
Metacognition is about “learning about learning” [1]. However, this theory is far more complicated. This allows people to take charge of their own learning. It involves awareness of how they learn, and evaluation of their learning needs, generating strategies to meet these needs and then implementing the strategies. The theory of metacognition can be identified as humans’ survival instinct. Metacognition also thinks about one’s thinking process such as study skills, memory capabilities, and the ability to monitor learning [2], [3]. Metacognition refers to higher-order thinking which involves active control over the cognitive processes engaged in learning. This concept needs to be explicitly taught along with content instruction. Metacognitive knowledge is about our cognitive processes and our understanding of how to regulate those processes to maximize learning. From ancient times humans have developed their metacognitive skills as a survival factor. For the survival in a video game, players need to follow instructions to get an idea about the gameplay [4]. But most of the time people are likely to skip the instructions without going through them even for the first time. This scenario is noticeable in mobile gameplay. This research has done to identify the factors which affect this dilemma. Metacognition level of a person and age are the variables that were considered for this experiment in order to address the following hypotheses; The more metacognitive skills people have, the more they will find it easy to play never-before-played games. Age range affects performance when it comes to playing the never-before-played games without direct instructions. At the end of this experiment, the first hypothesis became correct while the second one wasn’t. Therefore, people were more likely to ignore direct instructions and go through gameplay successfully when they had a higher metacognitive level, and the age group didn’t seem to affect this factor.
Detecting human emotions on Facebook comments
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Chathumali, E.J.A.P.C.; Thelijjagoda, Samantha
Human emotion detection plays a vital role in interpersonal relationships. From the early eras, automatic recognition of emotions has been an active research topic. Today, sharing emotions on social media is one of the most popular activities among internet users. However, when it comes to a specific domain like emotion detection in social media, it is still on a research-level. There are less number of applications have been developed to detect emotions online, using online comments and user comments. The aim of this research is to develop a system that identifies human emotions on Facebook comments. Among the different social media platforms, this research specifically focuses on Facebook comments written in the English language to narrow down the problem. The research is based on Semantic analysis, which comes under Natural Language Processing (NLP) and the system development consists of four major steps, including the extraction of Facebook comments via Graph API, preprocessing, classification and emotion detection. To classify the emotions, a classification model was created by using Naïve Bayes Algorithm. When it comes to marketing, emotions are what lead your onlookers to purchase. By using the detected emotions, marketers can promote their campaigns by changing online advertisements dynamically. The results obtained through testing the system show that it is capable of accurately identifying human emotions hidden in Facebook comments with an accuracy level of 80%, making it highly useful for marketing purposes.
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, Samantha
The 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.
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.
Keyword extraction from Tweets using NLP tools for collecting relevant news
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayasiriwardene, Thiruni D.; Ganegoda, Gamage Upeksha
Keywords play a major role in representing the gist of a document. Therefore, a lot of Natural Language processing tools have been implemented to identify keywords in both structured and unstructured texts. Text that appears in social media platforms such as twitter is mostly unstructured because of the character limitation. Consequently, a lot of short terms and symbols such as emoticons and URLs are included in tweets. Keyword extraction from grammatically ambiguous text is not easy compared to structured text since it is hard to rely on the linguistic features in unstructured texts. But when it comes to news on twitter, it may contain somewhat structured text than informal text does but it depends on the tweeter, the person who posts the tweet. In this paper, a methodology is proposed to extract keywords from a given tweet to retrieve relevant news that has been posted on twitter, for fake news detection. The intention of extracting keywords is to find more related news efficiently and effectively. For this approach, a corpus that contains tweet texts from different domains is built in order to make this approach more generic instead of making it a domainspecific approach. In fact, the Stanford Core NLP tool kit, Wordnet linguistic database and statistical method are used for extracting keywords from a tweet. For the system evaluation, the Turing test which has human intervention is used. The system was able to acquire an accuracy of 67.6% according to the evaluation conducted.
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, Samantha
Speech 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.
Vision-based automatic warning system to prevent dangerous and illegal vehicle overtaking
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Athree, Mahinsa; Jayasiri, Anusha
The purpose of this research is to implement a system that can be a help for drivers to drive vehicles safely and reducing accidents. Most of the accidents have occurred when overtaking. The driver has to consider the distance between two vehicles and the speed of the front vehicle. If there is another vehicle moving the same or opposite direction, it must be considered as well. Also, traffic signs, solid line, and a broken line must be considered. Reducing the errors which can be happened by the driver when deciding all these stuff in instinct is the goal of the “Automated Vehicle Overtaking System”. This research has been used three pipelines, such as lane line detection, traffic sign detection, and vehicle detection. The input video stream from the front view camera of the vehicle is transferred through the above pipelines. The system gives the final output frame with confirmation that ‘is it safe to overtake now’. The driver can recognize the danger by alarm sound from the system. This system has a Python Application Programming Interface (API). Using that API, any device or application can access this system. Finally, the main goal of this research is to implement a system that can predict dangers in overtaking and saves the lives of passengers and vehicles.
Dengue mosquito larvae identification using digital images
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) De Silva, W.D.M.; Jayalal, S.
Dengue is one of the highest spreading mosquitoborne diseases in tropical and subtropical regions all over the world. This disease is mainly spread by the mosquito vector called ‘Aedes’. In Sri Lanka, the number of infected patients reported is increasing, and it has become a public health problem. Health Inspectors are using different methods to reduce the spread of this viral disease and one of the main methods used is the fumigation by identifying the Aedes Larvae breeding locations. Currently, this identification is done manually by the specialized health inspectors and it is totally observer-biased and consumes a considerable amount of time, which could lead to false decision making and inefficient identification. The purpose of this research is to build an automated computational model to identify Aedes Larvae in real-time with more accuracy and convenience. Even though there are good results in previous researches done in Convolutional Neural Networks (CNN) on Aedes Larvae identification, the method of capturing Larvae Images is a bit complicated since they have used a Microscope lens of amplification capacity 60-100 times to get the magnified images. In this research, we propose the method of identifying Aedes mosquito larvae with a digital amplification of 8-12 times without using any microscope lenses attached, using ResNet50 CNN. The proposed model will identity the mosquito larvae by their genus ‘Aedes’ or ‘non- Aedes’ using a digital photo taken by a smartphone or camera in the upside of the larvae body. Hence it would help Health Inspectors, even the general public on identifying Aedes Larvae more efficiently, accurately and conveniently than the traditional method. This study shows that the trained model can identify images of Aedes and Non-Aedes Larvae separately with an accuracy of 86.65%. Furthermore, with using pre-processing techniques, the accuracy level can be enhanced to 98.76% for magnified images.
Profiling purchasing behavior of Generation Z
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kahawandala, Nadeesha; Peter, Suren; Niwunhella, Hiruni
Generation Z has emerged as one of the most mystifying consumers as they are tech-savvy, digitally connected and educated users of technologies in the marketplace. These digital natives are predicted to account for around 40% of all consumer shopping by 2020. Their cognitive power and social media networking have made them the market mavens who possess a wide range of information and consumer knowledge about many dimensions of the markets. To be the leader in the marketplace where so many options are available due to the free trade economy, marketers have to escalate their knowhow about their customers if they want to capture the attention of this segment of the market. The chief objective of the study is to find the purchasing habits of Generation Z, specifically from the angle of social influence and technology. A sample of 42 respondents was approached for a survey using a structured questionnaire. The results of this initial study indicate that Generation Z shoppers are coming up with unprecedented shopping habits and preferences. Using cross-tabulation analysis, results showed that some of the most classical influential factors such as product features, price consciousness and family recommendations have a significant effect on the purchasing habits of this generation. However, the results indicate the influence of computer literacy, peer and social influence and social media identity on the purchasing decision of Generation Z. The results of this study can be utilized to assist in predicting potential consumer adoption behavior and in designing favourable shopping environments that are compatible with these specific consumer traits.
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, Samantha
Nowadays, 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.
Smart electricity monitoring and analysing an IoT system with a mobile application
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Fernando, A.I.R .; Perera, M.D.R.
Now, the demand for electricity has increased in the World. This demand was increased concern to the raises of many developed and developing nations in the world. In Sri Lanka, the energy was provided by only less electricity power stations. So, the scarcity of electricity has occurred eventually. Thus, reducing and controlling power consumption will be responsible for all consumers. Furthermore, consuming data should be tracked by the consumer is essential now. But recently, consumers are using traditional meters in each home. It has failed to provide these facilities to the user. Moreover, Digital meter is trying to reduce these limitations. This research study focuses on a Smart Electricity Monitoring System using a mobile application. This is an IoT based project. The electricity consumption can be observed by the user through a userfriendly mobile app. And also, the monthly electricity bill has automated. This smart electricity meter system can be separated into three divisions, and the first system is the hardware setup using Arduino to measure electricity consumption at home. The real-time Alternate current and Alternate voltage through the hardware setup were measured through this system. By considering these values, the Alternate power will be generated. Then the real-time values were converted to units(kW/h) and sent to the database through Wi-Fi. The real-time database in Firebase was the other system. Storing real-time data and permitting them to retrieve them through the mobile application was the main function here. The final system is the user-friendly android application. This system aims to get more involvement of the consumer to their electricity consumption and reduce global electricity consumption. As the results can be observed through the mobile application, the user can get some idea of saving and reducing electricity than earlier.
Blockchain-based distributed reputation model for ensuring trust in mobile adhoc networks
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Peiris, P.P.C.; Rajapakse, Chathura; Jayawardena, B.
Mobile ad-hoc networks also known as MANETs have been in global use for numerous applications which are not possible with fixed network topologies. The distributed operation and dynamic topology have encouraged MANETs to be applied for establishing communication in unstable environments. MANET's dynamic topology and mobility have been very advantageous in the fields of military and disaster management. These dynamic characteristics of a MANET also create a major challenge in managing trust between the mobile nodes. Managing the trustworthiness of information that a node provides to the rest of the MANET is very crucial as misinformation spread can lead to erroneous decision making. Although previous studies have been carried out on trust management in MANETs using price-based and reputation systems, the potential of a globally distributed system has not been utilized practically. Therefore, these systems address the trust management issue within a boundary of a single MANET. Above mentioned systems should be re-evaluated when a node from another MANET joins a new MANET as the reputations of the node in the previous MANET cannot be imported to the new MANET. Lack of a possible solution for this gap may result in misinformation spreading by a malicious node before other nodes determine its reputation, which could be very dangerous in sensitive environments. Therefore, a globally distributed reputation model is a timely need in mobile ad-hoc networking. Blockchain technology is one of the most suitable technologies in present for its immutable and distributed properties to build robust systems. Blockchain is a distributed ledger, that has the ability to store feedback from mobile nodes about the accuracy of information provided by other nodes. A trust factor for each node can be calculated using these feedbacks. A mobile node can then decide whether to trust information, based on nodes’ trust factors. Adopting a development-oriented research methodology, a blockchain based reputation model prototype has been implemented and validated within the study.
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 Keerthi
Accurately 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.
Affective gaming in real-time emotion detection and music emotion recognition: Implementation approach with electroencephalogram
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kalansooriya, Pradeep; Ganepola, G.A.D. ,; Thalagala, T.S.
Affective Gaming can be considered as the concept of detecting the real-time emotional state of a player during various stages in gameplay and then enhancing the user interactivity accordingly to the emotional state. Based on this conception, this paper presents the research phase of the development of an Affective Car Racing computer game. The designs were created based on the theory of “Affective Loop” in games. Affective Loop consists of Emotion Elicitation, Emotion Detection/Modelling and finally Emotion Expression by Game Engine. This paper considers the second and third subphases of this loop. Designs are done for these two phases based on technologies that are still not been utilized by many game developers when designing a game. Emotion Detection/Modelling phase is introduced with a technique of capturing Electroencephalography (EEG) signals for predicting the real-time emotion of the player while interacting with the game engine. Emotion Expression phase considers the concept of Music Emotion Recognition (MER), which is a novel concept for the Gaming Industry. The authors had trained SVM models for emotion modeling via EEG Signals that will be captured by the Emotiv Epoc 14 channel device. The authors had classified the Rock and Electronic genre of music via Multi-Label RAKEL classification (Precision score of 75%) to play music excerpts based on the effect of the gamer during gameplay.
A modified cognitive complexity metric to improve the readability of object-oriented software
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Jayalath, Thilini; Thelijjagoda, Samantha
Complexity of software can be identified as a term which expresses the difficulty level of reading, understanding, maintaining and modifying the software. This helps to the quality improvement of the software and maintenance process of the software through a long time period without any obstacle. Therefore, software complexity metrics have been introduced to calculate the complexity of a software using numerical values. While there are number of metrics which calculate the complexity of object-oriented programs, they only consider one or two object-oriented concepts. As a result of that, there is no single metric which has the capability of measuring the complexity of a program based on multiple object-oriented concepts. This research aims to build a new metric to evaluate the complexity of an object-oriented program in order to improve the readability. The new metric has been built based on the influence of previous objectoriented metrics and some disregarded factors in calculating the complexity. In order to evaluate the new metric, Weyuker’s properties and Briand’s properties are used. The new metric acquires most of the object- oriented concepts in calculating the complexity and helps to improve the readability of the software as well. In fact, it makes it easy to handle the maintainability, reusability, portability and reliability of the software, when readability is high. This will result in increasing the overall software quality.
Identification of factors and classifying the accident severity in Colombo - Katunayake expressway, Sri Lanka
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Kushan, M.A.K.; Chandrasekara, N.V.
Sri Lanka’s expressway system was launched in 2011 and now owns three major expressways. Many peoples choose expressways rather than normal ways due to the reasons of time, traffic, easy of driving, etc. According to police reports of highway main traffic police branch, in recent years the number of accidents occurring in expressways is increasing drastically. Nowadays, the rate of accident occurrence in Colombo-Katunayake Expressway is high compared to the other two expressways and there was no previous research has been done in Sri Lanka regarding accidents on ColomboKatunayake expressway. Therefore, the objective of the study was to identify the factors contributing to accidents on the Colombo-Katunayake Expressway and to develop appropriate machine learning models to classify the severity of the accidents. In this study, 704 total accident cases were considered during the period 2013-2019. Chi-square test, logistic regression, and Kruskal–Wallis tests were used to identify the association between the accident severity and other influential variables found from the literature. Finally, seven variables: time category, driver’s age category, vehicle type, the reason for the accident, number of vehicles involved, cause for accident and rainfall were identified as influencing variables to accident severity under 5% level of significance. Naïve Bayes classification algorithm and probabilistic neural network (PNN) were used in the study to forecast accident severity. A random under-sampling technique was used to overcome the class imbalanced problem persists in the data set considered in the study. The final models developed using the Naïve Bayes algorithm and PNN exhibit 72.14% and 74.29% overall classification accuracy respectively. Both aforementioned models can be considered as suitable models to forecast accident severity in the Colombo-Katunayake expressway where the PNN model exhibits slightly higher accuracy. The final models developed by this study can be used to implement safety improvements against traffic accidents in expressways of Sri Lanka.
Towards detecting morning surge from sleep self-evaluations
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Takahashi, Masakazu; Sugahara, Noriyuki; Shibata, Masashi
This paper aims to analyze the blood pressure transition during sleep. Morning surge is a sudden increase in blood pressure from awakening or before awakening. Morning surge is implicated in cardiovascular diseases, such as Stroke, Angina Pectoris, and Myocardial Infarction. Morning surge has been detected mainly by the ABPM (Ambulatory Blood Pressure Monitoring) method, which measures blood pressure for 24-hours. Since the ABPM method cannot distinguish awakening and sleep automatically, their alternative method is forcibly delimiting time or manually processing based on behaviour records. Therefore, it is necessary to capture the blood pressure change under clear sleep separation. This paper employs two sleep criteria for accurate blood pressure during sleep.
Social media has gained impressive popularity all around the world in the last decade. Social networks such as Twitter, Facebook, LinkedIn, and Instagram have acquired their user’s attraction by maintaining their identity with very similar features. With the popularity of these platforms, now a day most of the users tend to rely on the information published on social media. Therefore, the credibility of social media information is playing a major role in the present cyberspace. As an example, the Twitter platform is handling 500 million tweets per day. Most of the twitter messages are truthful, but the twitter platform is also used to spread rumors and misinformation. Truthfulness or reliability is depending on the source's credibility. Twitter profiles can be identified as the information source on the twitter platform. In this paper, a user reputationbased prediction method is proposed to analyze the twitter source credibility. The proposed solution is mainly based on the k-means clustering model. Another two models namely, news category analysis and sentiment analysis are deployed to generate novel features for the clustering method. The objective of this paper is to introduce a credibility rating method to visualize the user credibility of twitter user profiles. So that followers can have an understanding about the trustworthiness of the information published on that profile. Producing the agreement score for a specific twitter user is one of a novel experiment in this research. Achieved accuracy by the system is 0.68 according to the evaluations conducted.
(Department of Industrial Management, Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Fernando, Aneesha; Wijayasiriwardhane, Thareendra Keerthi
Religion is one’s relation to what he or she regards as holy, sacred, spiritual, or worthy of especial reverence. Religious extremism is the advocacy of extreme measures over a religion whereas religious extremists are even willing to murder as they provide sanctions for violence in the service of God. Sri Lanka has a tragic history of religious and ethnic extremism and the Easter Sunday attack coordinated by a radical Islamic group that killed over 300 and injured another several hundred can be identified as the recent climax of these events. In this modern information age, it is evident that these radical extremist groups utilize social media for spreading their extreme ideologies due to its free and unregulated nature. If there were a mechanism to even slightly identify the possibility of tragic incidents like Easter Sunday bombing, the 300 souls who had to sacrifice their lives for an unreasonable cause would be still alive happily. In this research, we propose a predictive methodology for identifying any upcoming religious extremism-based threats in Sri Lanka using social media intelligence. We aim to specifically address Sri Lanka’s multi-lingual culture by analyzing all the bilingual social media posts in Sinhala and Tamil languages. A hybrid sentiment analysis methodology consisting of a Machine Learning model and a sentiment lexicon was trained on carefully chosen labelled social media text data and each text was classified as either religious-extreme or not, using Naïve Bayes, SVM, and Random Forest algorithms. When comparing their results, we were able to achieve the best results with the Naïve Bayes algorithm resulting in an accuracy of 81% for Sinhala tweets while Random Forest algorithm resulted in an accuracy of 73% for Tamil tweets proving that social media intelligence can be used to predict religious extremism-based threats.