4th International Conference on Advancements in Computing [ICAC] 2022

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/3384

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AI-Based Child Care Parental Control System
(IEEE, 2022-12-09) Jayasekara, U; Maniyangama, H; Vithana, K; Weerasinghe, T; Wijekoon, J; Panchendrarajan, R
Due 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.
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Anomaly Detection in Microservice Systems Using Autoencoders
(IEEE, 2022-12-09) de Silva, M; Daniel, S; Kumarapeli, M; Mahadura, S; Rupasinghe, L; Liyanapathirana, C
The adaptation of microservice architecture has increased massively during the last few years with the emergence of the cloud. Containers have become a common choice for microservices architecture instead of VMs (Virtual Machines) due to their portability and optimized resource usage characteristics. Along with the containers, container-orchestration platforms are also becoming an integral part of microservice-based systems, considering the flexibility and scalability offered by the container-orchestration media. With the virtualized implementation and the dynamic attribute of modern microservice architecture, it has been a cumbersome task to implement a proper observability mechanism to detect abnormal behaviour using conventional monitoring tools, which are most suitable for static infrastructures. We present a system that will collect required data with the understanding of the dynamic attribute of the system and identify anomalies with efficient data analysis methods.
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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, S
As 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.
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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, W
Recruitment 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.
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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, D
Banana 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.
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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, U
Sri 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.
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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.S
The 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.
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DevFlair: A Framework to Automate the Pre-screening Process of Software Engineering Job Candidates
(IEEE, 2022-12-09) Jayasekara, R.T.R; Kudarachchi, K.A.N.D; Kariyawasam, K.G.S.S.K; Rajapaksha, D; Jayasinghe, S.L; Thelijjagoda, S
The HR department of a technology company receives hundreds of job applications for each Software Engineering related vacancy. Evaluating a candidate by looking at the curriculum vitae may appear to be easy during the pre-screening process. However, an automated pre-screening process using Natural Language Processing and Machine Learning methodologies would help the recruiter to obtain a more accurate and deeper understanding of the candidate. In this paper we propose “DevFlair”, a framework to automate pre-screening Software Engineering job candidates. DevFlair uses data from social media, GitHub, and open-ended questionnaires to predict the Big-Five personality traits, analyze technical skill expertise, and analyze the experience in using industry-related online platforms. After analysis, the candidates are ranked according to their personality and technical skill levels. We conduct the personality prediction experiments using a social media posts dataset annotated with gold-standard Big-Five personality labels. We train FastText classification models and compare their accuracy against other state of the art classification models. The comparisons conclude that the FastText classification models substantially outperform the state of the art classification models when predicting Openness, Conscientiousness, and Agreeableness personality traits.
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E-Learning Assistive System for Deaf and Mute Students
(IEEE, 2022-12-09) Ranasinghe, P; Akash, K; Nanayakkara, L; Perera, H; Chandrasiri, S; Kumari, S
E-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.
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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, T
With 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.
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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, S
Public 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.
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Energy and Operations Optimization for Effective Greenhouse Management
(IEEE, 2022-12-09) Prihan Nimsara, K. I.; Bodaragama, J.; Roshan Maduwantha, K. A.; Fernando, S. D.
IoT technology-based process automation that can be applied to a greenhouse leads to making condition management and status monitoring more robust while leading to saving energy and resources. The proposed system which is based on IoT technology and MQTT protocol can set optimal growth conditions for plant and seed growth within the greenhouse. The sensor-based inputs are to be transformed into the processed values based on the defined logic and the standard benchmarks gathered from the local agricultural authorities. The key areas of condition monitoring to be done via temperature, humidity, soil moisture, and lighting can ultimately yield an increased harvest having supported both the plant and seeds-based implementations for multiple types of plants. One of the most important factors to consider is that the farmers can have energy savings through the proposed solution by controlling the actuators in an optimal manner and reducing manual intervention by a considerable amount. The excess usage of electricity by lights and cooling fan usage in the greenhouse can be controlled with real-time data tracking and better analytics. The use of water can be properly maintained for the plants by putting only the required amount will make the soil wet and spraying the required amount to air will make better humidity control. Thus, the real-time condition-based controlling of the actuators leads to making the greenhouse operations more optimal and better utilization of resources and energy which ultimately results in financial benefits for the greenhouse owner. Based on the evaluated power consumption of the greenhouse power usage before and after the system was installed, the newly introduced system can save energy by having optimal control of actuators by performing algorithmic calculations to meet only the required level of weather conditions. This is to be proven experimentally by implementing the proposed system for a defined period of time under the monitoring of energy usage.
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Enhancing Conversational AI Model Performance and Explainability for Sinhala-English Bilingual Speakers
(IEEE, 2022-12-09) Dissanayake, I; Hameed, S; Sakalasooriya, A; Jayasinghe, D; Abeywardhana, L; Wijendra, D
Natural language processing has become essential to modern conversational tools and dialogue engines, including Chatbots. However, applying natural language processing to low-resource languages is challenging due to their lack of digital presence. Sinhala is the native language of approximately nineteen million people in Sri Lanka and is one of many low-resource languages. Moreover, the increase in using code-switching: alternating two or more languages within the same conversation, and code-mixing: the practice of representing words of a language using characters of another language, has become another major issue when processing natural languages. Apart from natural language processing, the explainability of opaque machine learning models utilized in chatbots has become another prominent concern. None of the existing modern chatbot development platforms supports explainability and relies on a performance score such as accuracy or f1-score. This paper proposes a no-code chatbot development platform with a series of built-in novel natural language processing, model evaluation, and explainability tools to tackle the problems of processing Sinhala-English code-switching and code-mixing natural language data and model evaluation in modern chatbot development platforms.
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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.
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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, D
In 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.
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Group Formation and Communication of Multitasking Multi-Robots for Smart City Cleaning Process
(IEEE, 2022-12-09) Dahanaka, D.M.S.J; Wijesooriya, A.I.E; Wickramasinghage, D.S.S; Bhaggya, G.V.C; Harshanath, S.M.B; Rajapaksha, U. U. S
In this research paper, we focus on how multitasking robots team up to clean a city. In particular, we consider how they build their team, how they position themselves in their positions, how they work with teams, how they face obstacles along the way, and how to move groups out of control in an emergency. We use a leader-follower strategy here, and we are also tasked with selecting a leader for each group. The leader finds the shortest route to avoid the obstacle by considering the obstacle details such as obstacle location, obstacle width, and destination. The leader decides the best way for the team to go. If the leader wants to change the group, it gives the message to the relevant member. In the event of meeting an obstacle, it changes its shape and moves. A Robot Operating System (ROS) framework was created to perform real-time experiments with ROS-capable mobile robotic TURTLEBOTs to evaluate this control strategy. Simulations performed on a mobile robot team demonstrate the effectiveness of the proposed approach.
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Guardian - Smart Assistant Tool for Visually Impaired People
(IEEE, 2022-12-09) Amarasinghe, C.K.; Pinto, R.D.S.P.; Sudusinghe, K.N.
At present, with the advancement of technology, various devices and solutions have been found to aid the visually impaired community (VI Community). Even with the countless technological breakthroughs, yet they face many problems performing the most basic functions in daily life. Identifying the objects, they use daily, identifying a person, whether it’s someone they know or not, and their emotions, and reading a text information displayed anywhere, without the assistance from another person are the basic issues we deal with and try to resolve using a tool consisting of a pair of spectacles with an inbuilt camera that is integrated with a mobile application. The inbuilt camera will capture the image of a text containing label, an object, or a person which will be then detected, analyzed, and recognized and will be converted to Speech using Google TTS engine and produced through the headphones giving the output to the user. Tesseract OCR, YOLO algorithm, and TensorFlow models have been used for each feature of the tool respectively. This tool will be very beneficial to a blind person as it mitigates them the frustration caused by being incapable of performing daily activities without any assistance from another person.
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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, L
Mental 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.
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Investigation of Routing Techniques to Develop a Model for Software-Defined Networks using Border Gateway Protocol
(IEEE, 2022-12-09) Dayapala, B; Palanisamy, V; Suthaharan, S
In many cases, children between this age are using smartphones and other technology devices, to play games, watch cartoons, take photos and sometimes the chance is getting higher than we think that children access unnecessary contents due to lack of guidance and unawareness of parents. This interactive mobile application is used as an adaptive learning tool for the primary school students. Utilizing children’s comfort with technology allows for the development of their talents. In math skills development, some attractively designed gamified activities to solve basic math questions are given according to the skill level the child is currently in. The accuracy was much higher in the Convolutional Neural Network approach as it recorded a value of 0.9919. In environmental skills development component, the app will ask child to identify the surroundings according to a flow, starting from the house and towards the garden using object detection and the results were detected with a higher accuracy level around 0.9-0.99 after training the Machine Learning model. And in the language skills development component the child is given activities to develop pronunciation skills using audio processing and finally the verification of online achievements of a child by Non-Fungible Token technology, is fulfilled via the app.
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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, N
Corporate 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.