3rd International Conference on Advancements in Computing [ICAC] 2021
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/947
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Publication Embargo Permissioned Blockchain Platform to Enhance Scalability, Security and Performance Issues in Livestock Farms in Sri Lanka(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-11-09) Rathnayka, W.A.C.L.; Jayasena, K.P.N.The blockchain is being applied in the food supply chain, livestock, and farm management to enhance food quality, management activities, and animal health and build trustworthy transactions. The present paper provides an overview of blockchain in livestock farming. This paper identifies the current research topics, their contribution, and the benefits o f applying blockchain in livestock farming. Blockchain applications in the livestock sector are increasing worldwide, and most of them are proposed for security and trust issues. This research study implements a permission-based blockchain platform to enhance the livestock sector’s scalability, security, and performance issues. The designated Platform aims to provide distributed digital data storage with animal disease tracking service and access control security mechanism that cannot be tampered with by an unauthorized person. The performance of the proposed blockchain is evaluated through a series of experiments using a different kinds of metrics.Publication Embargo Crime Analysis, Prediction and Simulation Platform Based on Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12) Herath, I.S.; Dinalankara, R.; Wijenayake, U.As a global social-economical problem, crime has shown complex correlations with spatial-temporal, socio-economical, and environmental factors. Understanding patterns and interactions in the crimes is essential to prepare better to respond to those criminal activities. This study is focused on research and development of crime analysis, prediction and simulation platform that provides descriptive analysis, predictive crime analysis, Reinforcement learning based crime entity simulations and safest route navigation services based on crime data from the city of San Francisco. Ultimately, the proposed crime analysis, prediction and simulation platform provides critical information on root causes and statistical patterns of crime and future crime predictions for the policymakers and security officials to create strategies to minimise the crimes.Publication Embargo AI Based Monitoring System for Social Engineering(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, K.; Udara, S.W.I.; Wijayawardane, U.P.B.; Kularatne, K.N.P.; Navaratne, N.M.P.P.; Dharmaphriya, W.G.V.USocial media is one of the most predominantly used online platforms by individuals across the world. However, very few of these social media users are educated about the adverse effects of obliviously using social media. Therefore, this research project, is to develop an advisory system for the benefit of the general public who are victimized by the adverse impacts of their ignorant and oblivious behavior on social media. The system was implemented using a decision tree model with the use of customized datasets; and for the proceeding operational implementations, Python programming language, Pandas, Natural Language Processing and TensorFlow were used. This advisory system can monitor user behaviors and generate customized awareness reports for the users based on category and level of their behaviors on social media. Furthermore, the system is also capable of generating graph reports of the use behavior fluctuations for the reference of the user. With the help of these customized awareness reports and the graph reports, the users can identify their potential vulnerabilities and improve their social media habits.Publication Embargo Machine Learning-based Prediction Model for Academic Performance(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Tharsha, S.; Dilogera, J.; Mohanashiyaam, B.; Kirushan, S.; Chathurika, K.B.A.B.; Swarnakantha, N.H.P.R.S.This paper represents the work of a new integrated and collaborative Smart application for managing students online through data mining techniques. Nowadays especially in this pandemic situation, there is a necessity for academic management to incorporate and change all study methods online. By considering all these conditions this research is focused to discuss the solution to manage and engage students smartly and easily. Thou technology advancements have a serious impact on the day-to-day life people face troubles when using complex applications, this implemented Smart application is simple to use and a great tool for Student Management systems. The survey feedback from students, academic staff, and the public illustrate that this project helps to improve the effectiveness and efficiency of learning capability among the targeted group. The main objective of this project is to build up a smart model using Machine Learning, Deep Learning, and Artificial Intelligence to overcome generic learning problems. Therefore, this paper aims to present the concept behind the development and implementation of the Smart Study Application for Student Management System.Publication Embargo Early Warning for Pre and Post Flood Risk Management by Using IoT and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Ilukkumbure, S.P.M.K.W.; Samarasiri, V.Y.; Mohamed, M.F.; Selvaratnam, V.; Rajapaksha, U.U.S.Flooding has been a very treacherous situation in Sri Lanka. Therefore, developing a structure to forecast risky weather conditions will be a great aid for citizens who are affected from flood d isasters. I n t his s tudy, t he a uthors explore the use of Machine Learning (ML), Deep Learning (DL), Internet of Things (IoT), and crowdsourcing to provide insights into the development of the pre and post flood r isk management system as a solution to manage and mitigate potential flood risks. Machine learning and deep learning algorithms are used to predict upcoming flooding s ituations and r ainfall occurrences by using predicted weather information and historical data set of flood a nd r ainfall. Crowdsourcing i s u sed a s a n ovel method for identifying flood t hreatening a reas. Weather i nformation is gathered from citizens and it will help to build a procedure to notify the public and authorities of imminent flood risks. The IoT device tracks the real-time meteorological conditions and monitors continuously. The overall outcome showcases that machine learning models, deep learning algorithms, IoT and crowdsourcing information are equally contributing to predict and forecast risky weather conditions. The integration of the above components with machine learning techniques, together with the availability of historical data set, can forecast flood occurrences and disastrous weather conditions with above 0.70 accuracy in specific areas of Sri Lanka.Publication Embargo Personalized Assistive Learning System for Primary Education(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Yapa, Y.M.T.S.; Fernando, W.S.I.; Sampath, W.H.M.K.; Kodithuwakku, K.D.D.I.; Samaratunge, U.S.S.; Lunugalage, D.Due to the COVID 19 pandemic, almost all educational institutions, including schools, remain closed. This caused a dramatic change in the educational systems. The sudden shift away from the classroom made the profound transformation of the teacher-centered education system prevail so far; consequently, the education of primary level students has been collapsed. Therefore, the primary students from grades 1 to 3 cannot acquire the primary education given by the school. This research proposes a personalized assistive learning system for primary education from grade 1 to 3 students, aiming to improve their learning skills. The proposed system aims to increase the automation and self-learning of students. A novel method is proposed to acquire personalized course materials for students of grades 1 to 3 according to their knowledge level. The system is founded on a solid theoretical foundation and enables children to grow cognitive and psycho-therapeutic skills such as drawing, writing, recognizing numbers, enabling self-learning, and focusing on measuring the progress of the students and reporting it to parents. CNN is the primary classifier used in image recognition and classification tasks in computer vision. The components' median accuracy is 94.74%.Publication Embargo Indoor Autonomous Multi-Robot Communication System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Gunawardhana, K.D.W.; Kularathana, D.G.D.P.; Welagedara, W.H.; Palihakkara, H.E.; Abeygunawardhana, P.K.W.; Wellalage, S.Robotics, and automation systems are a hot issue right now. Controlling multiple robots at the same time has become very popular. On paper, we propose that a wireless robot-to-robot communication infrastructure be implemented to accomplish some specific tasks. The major goal of this proposed project is to showcase communication infrastructure, a dual manipulator system and a mobile charging dock robot have been designed to achieve this. Special topics and services were employed for communication infrastructure. It is more precise than current communication systems. Existing manipulation situations are limited to a single manipulator task; however, in this case, a dual manipulator task has been designed to work corporately. The charging docking stations are the only places where mobile robots may recharge. We presented a Mobile Charging Dock for recharging mobile robots in this project. This proposed project is introducing a secure communication strategy which uses a ROS topic filtering mechanism.Publication Embargo SalFix: Solutions for Small Businesses Using Artificial Intelligence and Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Perera, T.; Kuganandamurthy, L.; Ameen, T.; Dassanayake, T.; Ganegoda, D.Every large organization was a small business before. There are many businesses starting every day. Most of them are small businesses. Managing a business is always a challenge. The owners face lots of challenges when they engage with a business. Small business owners do not have enough knowledge about advertising or promoting a product. New owners do not know the trendiest product at present, and they need to know to sell which product to be profitable. L ack o f communication with the customers will impact the customer base. These are the main problems that owners face. By introducing SalFix, these challenges can be conquered. SalFix is a web application that is suitable for current owners and new owners. SalFix uses Artificial I ntelligence t o g enerate a utomated a d i mages, predict what will happen to the business next year, predict which product is the trendiest. To improve customer communication, SalFix is embedded with a chatbot plugin that can be integrated into the small business’s website. SalFix can perform a SWOT analysis as well. Owners can use SalFix to fix t heir s ales and boost their income. SalFix is a yearly subscription service and will provide more accurate results.Publication Embargo E-Learn Detector: Smart Behaviour MonitoringSystem to Analyze Student Behaviours DuringOnline Educational Activities(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Bamunuge, H.K.T.; Perera, H.M.; Kumarage, S.; Savindri, P.A.P.; Kasthurirathna, D.; Kugathasan, A.With the rise of online education more attention is being paid to the deficiencies in online learning platforms. Online Learning environments aim to deliver efficacious instructions, but rarely take providing a conventional classroom experience to the students into consideration. Efficient detection of students’ learning situations can provide information to teachers to help them identify students having trouble in real-time. This idea has been exploited several times for Intelligent Tutoring Systems, but not yet in other types of learning environments that are less structured. “E-Learn Detector is a web application solution to these existing issues in online learning which consists of unique features such as verifying the user during logging procedure and throughout an examination, detecting suspicious behaviors and presence of multiple users during online examinations and detecting low engagement levels of students during online lectures. “E-Learn Detector” is developed with the aim to provide guidance to students to improve their academic performance and behavior during classroom activities and to induce the best out of the educational activities.Publication Embargo Event-Driven Malicious URL Extractor(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jonathan, S.W.S.; Arunaasalam, R.H.; Senarathne, A. N.; Wishvajith, V.; Ramanayaka, A.M.; Yapa, K.Cyber-attacks are attacks that are commonly carried out in order to obtain sensitive information or disrupt internet-based services. Recent occurrences, both internationally and locally, have shown an influx of these attacks expanding rapidly through the use of malicious URLs (Uniform Resource Locators). Traditional measures, including such blacklisting malicious URLs, make it extremely difficult to respond to such attacks in a timely and efficient manner. Most existing solutions remain restricted in terms of scalability and proactive user safeguarding in situations when freshly formed URLs are correlated with a recent event, such as Covid-19 related frauds. The proposed solution is presented with the primary aim of addressing traditional system limitations and offering an interface for users to protect themselves by detecting phishing/malicious URLs in real time. In this research, we will examine extracting user-input eventrelated keywords and leveraging NLP (Natural Language Processing) algorithms to match them with the accompanying URL (Uniform Resource Locator) token data to determine whether the URLs are malicious or benign.Publication Embargo Guided Vision: A High Efficient And Low Latent Mobile App For Visually Impaired(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rizan, T.; Siriwardena, V.; Raleen, M.; Perera, L.; Kasthurirathna, D.This paper presents a novel solution for visually impaired individuals. A mobile app is connected to an ESP32CAM and a remote server to help visually impaired individuals to navigate around their environment safely. A deep learning model is deployed in the mobile app to detect obstacles in real-time without connecting to the internet. Other tasks such as reading texts, recognizing people, and describing objects are done in the remote server. We managed to connect the mobile app to the ESP32CAM and the remote server simultaneously. This was possible because the ESP32CAM is connected to the mobile app through Bluetooth. This gave the mobile the ability to connect to the remote server via the internet. To the best of our knowledge, no research has been done using Bluetooth to stream images to do object detection in a mobile app locally. Hence, our solution can detect obstacles locally and do other tasks mentioned previously in the remote server. This paper discusses how the ESP32CAM, obstacle detection module, face recognition module, text reading module, and object description module was implemented such that a low latent and highly efficient mobile app is created using minimal resources.Publication Embargo Modelling Wikipedia’s Information Quality using Informativeness, Reliability and Authority(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Sugandhika, C.; Ahangama, S.; Ahangama, S.Wikipedia is the largest collaborative encyclopedia published on the internet. Due to its ‘open source' model, Wikipedia faces many issues regarding its Information Quality (IQ). Due to this reason, Wikipedia is generally not recommended for academic and research activities. However, hybrid approach which utilizes both content and metadata statistics of Wikipedia articles provide good insights in measuring the underlying IQ. Therefore, aligning with this hybrid approach, this study presents a simple yet precise model to assess the IQ of Wikipedia. The model comprises three IQ dimensions (1) Informativeness, (2) Reliability and (3) Authority, and 23 IQ features. The proposed model was tested with 1000 articles extracted from five WikiProjects Medicine, Politics, Sports, History, and Science. A Selenium-based web scraping technique was used to extract the data from articles automatically. The model received a classification accuracy of 79% and a clustering accuracy of 84%. Thus, this extensive experiment validates the effectiveness of the proposed model. Accordingly, the methodology, analysis and results, implications of the findings to theoretical discourse and practical applications, limitations, and futuristic directions are discussed in this paper.Publication Embargo Standalone Application and Chromium Browser Extension-based System for Online Examination Cheating Detection(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kariyawasam, S.; Lakshan, A.; Liyanage, A.; Gimhana, K.; Piyawardana, V.; Mallawarachchi, Y.Educational organizations and institutes that provide services to the public use e-learning frequently than before. The incapacity to evaluate the knowledge acquired is a flaw in education. Due to the current situation, traditional evaluation and examinations are not possible. In a developing country like Sri Lanka, the conduct of online examinations has not been efficient, resulting in cheating at examinations due to vulnerabilities resulting from organizational policies and the difficulty to track down candidates who are prone to cheating, therefore use of facial features for candidate verification and to monitor the background interactions the use of audio and video is taken into consideration with the aid of two cameras; the system mounted camera and a wearable camera containing a microphone allowing audio detection. In this research, we suggest using the training data set generated from individuals to undertake a training approach to improve the robustness for background interactions through audio and video to detect the level of cheating of candidates.Publication Embargo Artificial Intelligence-based Business Strategy for Optimized Advertising(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Kannangara, L.; Harsha, S.; Isuru, T.; Wijesiriwardhane, C.; Wijendra, D.R.; Kishara, J.Television commercials are a passive type of advertising technique that does not consider consumer demographics who are viewing the television at a specific time. As a result, the user sees irrelevant advertisements, which tends to reduce user engagement and sales conversions.As Sales ,which is the expected target of any advertisement campaign, a user-based advertising approach can be considered as a solution to mitigate the negative aspects. A user-based advertisement suggesting system for television, which is extensively utilized in every other digital media, is expected to be given as the solution. For the suggestion process, user attributes such as age, gender, peer group, and the mood identified in which the advertising is shown were taken into consideration. This will result in more relevant commercials for consumers, making television advertisements more user-friendly, resulting in greater sales conversion for the advertising agency.Publication Embargo Dynamic User Interface Personalization Based on Deep Reinforcement Learning(2021-12-09) Silva, K. G. G. H.; Abeyasekare, W. A. P. S.; Dasanayake, D.M. H. E.; Nandisena, T. B.; Kasthurirathna, D.; Kugathasan, A.Personalization is one of the most sought out and popular methods for brand recognition and consumer attraction. The usage of deep reinforcement learning due to its’ ability to learn actions the way humans learn from experience, if utilized and evaluated properly it can result in a revolutionary effect on personalization. The methodology proposed in this research utilizes deep reinforcement learning where an artificial agent may be trained by interacting with its environment. Utilizing the experience gathered, the agent is able optimize in the form of rewards. The approach explained, can be utilized across applications which can be personalized. Several scenarios ranging from changing the layout of webpages, to rearranging icons on mobile home screens are discussed. The main objective is to develop an API for the web developers and smartphone manufacturers to utilize so that depending on the application personalization can be achieved by enhancing saliency, minimizing selection time, increasing engagement, or an arrangement of these. The technique can manage a variety of adaptations, such as how graphical elements are shown and how they behave. An experiment was conducted which showcased improved user experience considering the position change of thePublication Embargo Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Rajapakse, R.M.M.P.K.; Mudalige, J.M.A.I.; Perera, L.A.D.Y.S.; Warakagoda, R.N.A.M.S.C.B.; Siriwardana, S.Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-Publication Embargo Data-driven Business Intelligence Platform for Smart Retail Stores(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Eheliyagoda, D.R.M.R.R.D.R.S.; Liyanage, T.K.G.; Jayasooriya, D.C.; Nilmini, D.P.Y.C.A.; Nawinna, D.; Attanayaka, B.The following research paper presents the design and development of a data-driven decision support platform for the effective management of contemporary retail stores in Sri Lanka. This research has four core components, as a solution to the identified shortcomings. These components are Customer Relationship Management (CRM), Supplier Relationship Management (SRM), Price and Demand estimation, and Branch and Employee Performance Monitoring and Rating. The developed system has features such as product replenishment levels, decrease capital movement, reduced material wastage, better item assortment, provide supplier service efficiency, improve employee and branch-level efficiency, and elevated client delivery. This decision support system used Machine Learning (ML) technologies such as LSTM (Long short-term memory) and ARIMA (Autoregressive integrated moving average) models, Regression, Classification, and Associate Rule Mining Algorithms as key technologies. Data were obtained from websites such as Kaggle and other free platforms for the analysis of datasets. The resulting platform was able to perform with an accuracy of over 90% for all four core components with the tested data sets. The system presented would be particularly beneficial for the top management in retail stores to make effective and efficient decisions based on predictions and analyzes provided by the system.Publication Embargo Symptomatic Analysis Prediction of Kidney Related Diseases using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Lansakara, D.; Gunasekera, T.; Niroshana, C.; Weerasinghe, I.; Bandara, P.; Wijendra, D.Sri Lanka has been witnessing an increase in kidney disease issues for a while. Elderly kidney patients, kidney transplant patients who passed the risk level after the surgery are not treated in the emergency clinic. These patients are handed over to their families to take care of them. In any case, it is impossible to tackle a portion of the issues that emerge regarding the patient at home. It is hoped to enter patient’s data from home every day and to develop a system that can use that entered data to predict whether a patient is in an essential circumstance or not. Additionally, individuals in high-hazard regions cannot know whether they are in danger of creating kidney disappointments or not and individuals in danger of creating kidney sickness because of Diabetes Mellitus. Thus, we desire to emphasize the framework to improve answers for this issue. The research focuses on developing a system that includes early kidney disease prediction models involving machine learning classification algorithms by considering the relevant variables. In predictive analysis, six machine learning methods are used: Support Vector Machine (SVM with kernels), Random Forest (RF), Decision Tree, Logistic Regression, and Multilayer Perceptron. These classification algorithms' performance is evaluated using statistical measures such as sensitivity (recall), precision, accuracy, and F-score. In categorizing, accuracy determines which examples are accurate. The experimental results reveal that Support Vector Machine outperforms other classification algorithms in terms of accuracy.Publication Embargo Performance Evaluation for Relay Selection on Device-to-Device (D2D) Communications in Rayleigh Fading(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Woo, W.H.; Annur, R.; Ponnusamy, V.Relay-aided device-to-device (D2D) is an alternative method to support short-distance direct communication in the facts of poor channel links and limited coverage. In a wireless communication system, failed communication can be caused by many factors, such as a lack of energy to maintain the transmission or the inability to hear the signals due to weak RSS. This paper presents Received Signal Strength (RSS) and battery awareness as the parameters of relay selection to select one or more optimal relays. In order to study the impact of radio propagation on the proposed scheme, the D2D network is implemented in the Rayleigh fading with obstacles environment. Performance is determined by packet drop, throughput, and end-to-end delay. Overall, the proposed scheme performs better than the existing scheme. From the evaluations, the packet drop in the proposed scheme is lesser than the existing scheme, reducing the throughput by 46%. The end-to-end delay in the proposed scheme is lesser than the existing scheme by 0.003s.Publication Embargo Smart Monitoring and Disease Detection for Robotic Harvesting of Tomatoes(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Pasindu, I.; Viraj, S.; Dilshan, R.; Kalhara, A.; Senaweera, O.; de Silva, R.; Jayawardena, C.Tomato is a one of the most popular produced and extensively consumed vegetables in the world. Typical agricultural systems make extensive use of human labor which is more costly and less effective. This research explores the minimization of human labor through automation. The diseases infected by tomato plants are hard to detect. Identifying these diseases in advance would save the cultivation of the disease from spreading, thereby saving the crop.It is also a difficult task to recognize the ripe harvest and experienced labor is required. The efficiency of the harvesting method will be increased by automating the identification process of ripened fruits. Manually picking tomatoes can cause some harm to the fruits during plucking due to inconsistencies in human labor. Such damage will be reduced through a better implemented robotic scheme. This paper presents the development of autonomous system for tomato harvesting and disease detection.
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