4th International Conference on Advancements in Computing [ICAC] 2022
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/3384
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Publication Embargo AI-Based Child Care Parental Control System(IEEE, 2022-12-09) Jayasekara, U; Maniyangama, H; Vithana, K; Weerasinghe, T; Wijekoon, J; Panchendrarajan, RDue to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.Publication Embargo Smart UML - Assignment Management Tool for UML Diagrams(IEEE, 2022-12-09) Weerasinghe, D.N.H; Thiwanka, K.A.T; Jayasith, H.B.C; Onella Natalie, P.A.D; Rajapaksha, U.U.S; Karunasena, AThe great majority of manual processes have been automated in the current day. Nonetheless, there is no reliable approach for evaluating UML diagrams for plagiarism and correctness. Unified Modeling Language (UML) provides the basis for UML diagrams. UML Diagrams are typically used to graphically express a system’s stakeholders, activities, objects, and classes to get a deep knowledge of a particular system. UML diagrams are widely used by industry experts as well as beginners. Class diagrams and use case diagrams are the most commonly used diagrams in UML. Though most Computer science-related students get assessed in UML modelling, diagram plagiarism isn’t considered in most exams since it’s a tedious task to perform. The proposed system automates capturing details in UML diagram components and generating a model answer from the given question scenario to compare the diagrams and calculate the plagiarism percentage with the correction percentage.Publication Embargo Success Factors of Requirement Elicitation in the Field of Software Engineering(IEEE, 2022-12-09) Attanayaka, B; Nawinna, D; Manathunga, K; Abeygunawardhana, P. K. WRequirement elicitation (RE) is a cognitively challenging and time-consuming task in software development due to the numerous challenges associated with it including conflicting requirements, unspoken, or assumed requirements, difficulty meeting with relevant stakeholders, stakeholder resistance to change, and not enough time set aside for meetings with all stakeholders. The prime causes of software implementation failure have been identified as inadequacies in the treatment of requirements. Without collecting the quality requirement, cannot achieve the goal of a quality software product. Through identifying the success factors affecting requirement elicitation, the paths to the quality requirements can be identified. The success factors identify through this research are experience, business analyst skills, stakeholder relationship, organizational elicitation process. This study aims to identify the factors affecting requirement elicitation based on the data collected from business analysts and similar positions in the software industry through a survey, interviews, and analyzed data to provide the initial validation for the identified factors. Through the analysis, we identified the main factors affecting successful requirement elicitation with a perfect significance value of less than 0.05 for all factors.Publication Embargo ELIZA: Smart Monitoring and Reporting Toast Master System(IEEE, 2022-12-09) Nizer, F.S.A; Iksudha Bhargavi, R; Agalyah, P; Raveendran, M; Kuruppu, A; Rupasinghe, SPublic speaking is the most common form of fear, and everyone feels uneasy with it. Fear of speaking in public is commonly called “glossophobia,” where people are discouraged from speaking in front of people due to embarrassment and rejection. Public speaking anxiety (PSA) is one of the most universal subtypes of anxiety where people fear, lose their confidence, and become uncomfortable physically and mentally. But public speaking is considered important in the educational sector and workplaces, where people get higher opportunities. Therefore, clubs like Toastmasters help people overcome their fear of public speaking and improve their confidence. We are launching the idea of a Smart Monitoring and Reporting Toastmasters System for people to improve their public speaking so they do not need a supervisor or mentor to train them. This smart monitoring system recognizes the candidate through image processing and deep learning. Moreover, this will analyze some features from the candidates’ speeches, such as facial emotion recognition, speech recognition, hand and body gesture recognition, and the candidates’ attire and appearance separately. This system will identify their mistakes and flaws and provide overall feedback to the users on the speech provided by the candidate. By implementing this web application, users can train themselves without a supervisor, and they can improve themselves and gain the confidence to participate in a Toastmasters competition as perfect candidates.Publication Embargo Real-time Smart Navigation System for Visually Impaired People(IEEE, 2022-12-09) Sudaraka Keshara, S.R.D.; Weragoda, W.R.J.M.; Chandrasiri, S; Ellankovan, J; Madushan, W.AVisual sense plays a primary role in guiding sighted people through an unfamiliar environment and assisting them to reach their destination safely. Visual impairment describes the actual damage that makes it difficult to accomplish visual tasks because it makes it difficult to see clearly. This paper proposes an approach to overcome the challenges faced by visually impaired people with the help of machine learning. This proposed system combines a smart cane and a wearable smart glass. The detection of obstacles and potholes helps to increase the safety and comfort of visually impaired users by detecting and displaying obstacles, and the Smart Walk-lane Navigation assists in navigating through the walk-lanes without letting them enter the main roads and helps to prevent accidents. Road sign detection allows users to follow road signs and cross the roads safely, while face and emotion detection allows users to recognize well-known people and their emotions.Publication Embargo Solution to Measure Employee Productivity with Employee Emotion Detection(IEEE, 2022-12-09) De Silva, T.R.S.; Dayananda, K.Y.; Galagama Arachchi, R.C.; Amerasekara, M.K.S.B.; Silva, S; Gamage, NHealth and safety of workers has become a top priority in modern businesses. The reason is that it will have an impact on both individual and team output. In the last few decades, automatic facial expression analysis using machine learning has emerged as a promising and bustling field of study. In this study, the system primarily evaluates the efficiency of workers and, through the detection of their emotional states, determines their levels of motivation. The task completion rate of employees is measured by the system in the first component, and the system predicts the level of satisfaction that the employees will have. In place of linear regression, this component makes use of random forest regression, which boasts a higher degree of precision than its counterpart. The performance of workers on their tasks will be evaluated periodically, about once every fifteen minutes, and the results will be shown on a dashboard. The system will pick up on the emotions of the staff members throughout the second phase of the process. These characteristics will be used to assess the level of motivation inside the organization, with the end goal of increasing overall productivity. The accuracy of this emotion detection will also be checked periodically, namely once every fifteen minutes. The following part of the process monitors the use of the PC and calculates the level of productivity. It will be possible to get an increase in productivity if one monitors and keeps track of the application usage of each employee. The final components monitor the websites that employees visit and how they use the network. This component makes it easier to generate reports based on the utilization of the internet and the network, as well as information on performance and reports that summarize website traffic. When it is fully operational as an integrated system, most businesses will rely on this system as their primary driver of success.Publication Embargo Sinhala Named Entity Recognition Model: Domain-Specific Classes in Sports(IEEE, 2022-12-09) .Wijesinghe, W.M.S.K; Tissera, MNamed Entity Recognition (NER) is one of the crucial and vital subtasks that must be solved in most Natural Language Processing (NLP) tasks. However, constructing a NER system for the Sinhala Language is challenging. Because it comes under the category of low-resource languages. Therefore, the proposed approach attempted designing a mechanism to identify specific named entities in the sports domain. Firstly, a domain-specific corpus was built using Sinhala sport e-News articles. Then a semi-automated, rule-based component named as “Class_Label_Suggester” was built to annotate pre-defined named entities. After auto annotation, the outcome was further validated manually with a little effort. Finally, it was trained using the annotated data. Linear Perceptron, Stochastic Gradient Descent (SGD), Multinomial Naive Bayes (MNB), and Passive Aggressive classifiers were used to train the NER model. Though, the above Machine Learning (ML) algorithms showed approximately 98% accuracy, the MNB model demonstrated highest accuracy for the identified class labels of which, 99.76% for ‘Ground’, 99.53% for ‘School’, 98.55% for ‘Tournament’, and 97.87% for ‘Other’ classes. Additionally, high precision values of the above classes were 81%, 72%, 62%, and 98% respectively. An accurately annotated Sinhala dataset and the trained Sinhala NER model are main contributions of the study.Publication Embargo Depression Detection System Using Real-Time and Social Media Data(IEEE, 2022-12-09) Jayasinghe, G.C.J.; Shamika, I.P.M.A.; Dissanayake, G.A.I.P; Ranaweera, R.M.I.A; Bandara, P.SThe main objective of this study is to measure the depression level of the participants. The guidance will be provided by the psychiatrist to understand the parameters. The end system has been implemented to measure it with a live session with pre-designed questionnaire set. During the session time, the behavior of the participant has been captured through audio and video method. The long-term depression level measurement will be analyzing the social media behavior of the participant within a month. The Convolution Neural Network (CNN) and Natural Language Processing (NLP) are using to analyze the video, audio and text data. To analyze the results; The Beck Depression Inventory (BDI II) scale will be utilized. The accuracy of the output results measured as high as it has been individually analyzed the subcomponents and then predict to a one result.Publication Embargo Human Behavior Analysis for Psychological Healthcare Sector (Project SERENITY)(IEEE, 2022-12-09) Dassanayake, D.M.H.; Wanigathunga, C.V.; Meeriyagalla, P.Y.; Yapa, K; Wickramarathne, K.A.P.P.; Rukgahakotuwa, LMental health is a key area of the healthcare sector. While taking care of the physical health of the human body, it is important to pay attention to mental health as well. This project is done to help people maintain their mental health. ‘ SERENITY’ is a web application designed not only for patients but also for doctors. This app works as a virtual assistant for a doctor, and this app helps doctors constantly monitor their patients’ behaviour, as well as SERENITY, which will be able to analyze the emotions of patients individually.Publication Embargo E-Learning Assistive System for Deaf and Mute Students(IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, SE-learning has become a popular digital platform among both students and teachers. When using an e-learning system, deaf-mute students can get significant benefits. It allows students to better grasp their studies by providing additional details. The major problem that the deaf and mute community encounters in the e-learning environment is that they are no longer attempting to enroll in normal institutions, which do not provide many facilities for them due to a lack of resources, a lack of learning facilities, and some social disturbances. To achieve that problem this system will translate the lecturer’s voice into text, map words with pre-created sign language animations, generate subtitles for lecture videos, clearly identify the face position of the lecturer, detect difficult words, track the hand gestures, and practice sign language so that it will increase learning resources, facilities, usability and help teachers to execute their teaching process through this platform. Therefore, normal institutions can use this system as their learning management system. It will approach deaf and mute students to enroll in normal institutions and do their studies as typical students.Publication Embargo Assistant Zone – Homeschooling Assistance System based on Natural Language Processing(IEEE, 2022-12-09) Premendran, K; Bopearachchi, S.B.D.D.; Senevirathna, S.D.M.; Giridaran, S; Archchana, K; Ganegoda, D; Thelijjagoda, SAs a developing country, most people give their highest priority to education. When focusing on building an e-learning platform to improve the knowledge of students and teacher-student interactivity, the pandemic season can be mentioned as the main blocker which highly impacted the education field. Not only by considering the pandemic situation but also by addressing the concerns when it comes to teacher and student evaluation and psychological levels of students who are undergoing different difficulties, the “Home Schooling Assistance System” (Assistant Zone) has been introduced as a solution. The Assistant Zone has been initiated with three unique features which are valuable for both students and teachers. This system analyzes the strengths, weaknesses and evaluates the student performance, suggests study materials to improve themselves, provides solutions to the problems faced by the students, teachers, and parents and measures the performance of teachers based on their students, and recommends learning materials for the low-performing teachers. The Assistant Zone fulfills the targeted problems and introduces the above-mentioned three unique features with the use of Natural Language Processing (NLP) such as the BERT algorithm and Machine Learning models such as the Recurrent Neural Network, Forward Neural Network, and Gaussian Model.Publication Embargo LAWSUP - A Smart Platform to Assist Stakeholders of Business Law(IEEE, 2022-12-09) Sulakshi, U L H; Opatha, S D; De Silva, K S D; Sandeepa, M M A D N; Nawinna, D; Harasgama, H; Gamage, NCorporate law, sometimes known as business law, is the body of law that governs the rights, relationships, and behavior of persons, corporations, organizations, and businesses. Business Organizations, employees/laborers, and the public are involved in this area of the law accompanying lawyers, and legal advisors. Business organizations need legal advice. Employees face many difficulties and injustices at their workplaces. People who wish to start a new business, search for legal guidance. When one of these parties needs support, they must seek a lawyer, go to the lawyer, and get legal support. When delivering legal support to clients, lawyers are still going through a manual process. There are very few systems that have been implemented for the law domain so far, and those only search engine types of systems that are unable to support every stakeholder of this domain. There is no common platform for all these stakeholders to find solutions, connect with a good lawyer and get support. We have identified the main issues faced by business organizations, employees that need legal support, the general public, and lawyers, and developed a web solution by implementing Machine Learning, Classification Algorithms, Text mining, Natural Language Processing, and Web Crawlers.Publication Embargo E-tutor: Comprehensive Student Productivity Management System for Education(IEEE, 2022-12-09) Silva, K; Induwara, R; Wimukthi, M; Poornika, S; Samaratunge Arachchillage, U.S.S; Jayalath, TWith the advancement of technology, e-learning has emerged as predominant in the education sector. As students, parents, and educators acknowledged, adopting e-learning can offer several benefits over traditional learning techniques. Since more individuals are becoming acclimated to online learning platforms, these online platforms can provide a simple, instructive, and efficient mode of delivery. This novel approach could be improved with the aid of Artificial Intelligence (AI) to comprehend consumers more thoroughly and provide valuable and better-suited services. Most sectors in education, including universities, swiftly adapted to new educational methodologies because of their flexibility and productivity. Nevertheless, there are some downsides that young demography experiences, such as less instructiveness, distraction due to the absence of teachers, and poor IT literacy. Consequently, these drawbacks would recede the capability of students to assimilate content during the lecture. Therefore, the main objective of this research is to implement an E-learning platform with AI learning analytics to enhance students’ performance regularly while reducing the significant drawbacks of the E-learning platforms. This research consists of students’ focus detection, essay-based answer evaluation, note summarization, mind map generation, and personalized guidance facilities.Publication Embargo Monitoring System for Underage Smart Phone Users(IEEE, 2022-12-09) Jayawardena, M.A.P; Mahadi Hassan, M.H.F.M; Aflal, M.I.A; Weerathunga, W.A.A.S; Harshanath, S. M. B.; Rajapaksha, U. U. SIn today’s world, it is very common among children to use a smartphone or a handheld digital device such as a tablet to entertain themselves and as a medium of socializing with people easily. The COVID-19 pandemic forced many people to stay in their homes and rely on these digital devices to do their day-to-day work and communication. The latter caused the increase in reliance on digital devices to acquire information about the outside world and as a source of entertainment. This new tendency increased the likelihood of children being exposed to pornography, cyberbullying, cyberstalking, excessive gaming, sexting, and behavioral traits related to narcissism. These habits caused many children to develop psychological and physiological illnesses, which affected them in the short term and, for some, which affected them and their families in the long run, such as suicide. Our research proposes to constantly monitor behavioral patterns such as this, notify the relevant individuals, and prevent the children from being prone to such ill fates. According to the findings, using machine learning and natural language processing, sexting, phonographic words, and cyberbullying can all be recognized with pinpoint accuracy. Also, by using two machine learning models, depression and anxiety are detected with an accuracy of 0.84 and 0.86. To prevent and analyze computer vision syndrome caused by improper face-screen distance. An image processing-based algorithm is used to measure the distance from face to screen, and results are narrowed down to an accuracy of 1 inch.Publication Embargo TRIPORA: Intelligent Machine Learning Solution for Sri Lanka Touring Access and Updates(IEEE, 2022-12-09) Legrand, T.R; Bandara, K.M.R.A.I; Stefania Crishani, J.A.D; Uvindu, L.W.P; Amarasena, N; Kasthurirathna, DSri Lanka is one of the top tourist destinations in the world. However, tourists face various inconveniences due to the obsolescence of facilities. There are various tools designed to solve such problems. But they are scattered in different places and users have to use different tools. The biggest issue in the tourist sector is that travelers are unable to get the most out of their tours since there may be days when a large number of people visit the same location, causing the location to become overcrowded, and preventing tourists from enjoying their visit as anticipated. There are seasons when natural disasters occur, as well as human-centered crises. Also, there are situations when travelers feel helpless because they are unable to find the best tour guide for them. We developed a cost-effective, automatic, and efficient Machine Learning-based recommendation system as a result of this research. Based on past data on tourists and data received from the SLTDA, this research can provide the best trip plan with the tour guide and provide destination news alerts on regular basis. Furthermore, in order to achieve the best accuracy through the system, unique machine learning approaches were used in this study.Publication Embargo An Automated System for Employee Recruitment Management(IEEE, 2022-12-09) Silva, G.L.L.I.; Jayasinghe, T.L; Rangalla, R.H.M; Gunarathna, W.K.L; Tissera, WRecruitment of employees is an important process in the human resource management of a company. Currently, most of the recruitment process is done manually in many companies. This manual process may be time-consuming and possibly may be erroneous in employing inappropriate individuals. This may result in the loss of time, money, and efficiency of a company. As a solution to the above problem, we are considering developing an automated process for recruitment. The scope of the system is to cover not only the recruitment process but also to provide job seekers a platform to identify their current skills, help them identify the current skill trends that are required by companies, and provide the ability to automatically generate their resumes through the system. On the other hand, employers will save a lot of time and money since the system will automate the processes such as skill matching of the employee and the company, shortlisting of resumes, and scheduling interviews. The platform involves features such as online mock interview hosting, automated scheduling, and a pre-interview quiz with a monitoring background. To achieve the above components, machine learning algorithms are used along with other technologies such as web scraping.Publication Embargo Ensemble Learning Approach to Human Stress Detection Based on Behaviours During the Sleep(IEEE, 2022-12-09) Jayawickrama, J. G.; Rupasingha, R.A.H.M.Stress is an emotional or mental state caused by inescapable or demanding situations, known as stressors. Because of the high stress level human are addicted to some illegal or unethical activities and also they try to do different activities to reduce their stress level. Because of that, the detection of human stress levels becomes important today. The major goal of this study is to look into how human stress detection is based on the behaviors during sleep using the ensemble learning algorithm. In the first experiment, five Machine Learning (ML) algorithms were used in the classification level, including Random Forest, Support Vector Machine (SVM), Decision Tree (J4S), Logistic regression, and Naive Bayes. In a second experiment, an ensemble learning algorithm was used with an average probability combination method for the above five algorithms. Based on the experiment results, ensemble learning can classify the data with 94.25% highest accuracy, high precision, recall, f-measure values, and the lowest error rate in Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) better than the separate algorithm results.Publication Embargo Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis(IEEE, 2022-12-09) Kothalawala, M.U.; Gaveshith, M.G. K; Tharaka, A.H.D.H.; Punchihewa, I.A; Sriyaratna, DBanana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.Publication Embargo CertiMart: Use Computer Vision to Digitize and Automate Supermarket with Fruit Quality Measuring and Maintaining(IEEE, 2022-12-09) Rathnayake, W.P.D.N. P; Geeth Dulanjana, D; Punchihewa, A.V.B.W. G; Anjana, N.W. G; Suriya Kumari, P. K.; Samarakoon, USri Lanka has a tropical environment, which makes it easy for fruit and vegetable plants to thrive. Vitamins, proteins, and other nutrients are abundant in fruits. However, there is a time when the fruit is considered to be fresh. During this time, many fruit supplier firms continue to supply fruit that is unsafe for ingestion due to inaccuracy in the sorting process when the fruit is taken from the plantation and the introduction of other fruit into an incorrect packing. As a result, detecting food rotting from the point of production to the point of consumption is critical. Inside the market we realize that there is unavailability of sorting of fruits. Just after receiving the fruit into the supermarket, we should have a way to measure freshness of fruit and maintain it. In addition to this ripened method identification and disease identification will be great help to this help.Publication Embargo Face Skin Disease Detection and Community based Doctor Recommendation System(IEEE, 2022-12-09) Udara, M.A.A.; Wimalki Dilshani, D.G.; Mahalekam, M.S.W.; Wickramaarachchi, V.Y.; Krishara, J; Wijendra, DIn our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.