3rd International Conference on Advancements in Computing [ICAC] 2021
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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 Machine Learning-Based Smart Shopping for Visually Impaired(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Nagenthiran., N.; Priyanha, P.; Nirosha, S.; Vivek., J.; De Silva, H.; Sriyaratna, D.There are diverse applications built for the e- commerce platform, each with its own set of advantages. All goods should be relevant to all members of society around the world, but there are people with unique needs who should be considered when technology advancements are made for the common good. People who are blind require particular attention since they require assistance from others. For this research, we referred to several studies with comparable goals and approaches, which our group closely examined to enhance our study and outcomes, and which were highlighted when relevant work was discussed. The purpose of this study is to provide accessibility for all members of society, including the blind, as well as location-based solutions for consumers. These include voice navigation through the app, product suggestions, offering quick paths to the shop location by comparing existing algorithms for identifying short paths and providing a function to give voice feedback. This paper thoroughly examined the findings and provided appropriate evidence to support the answer to the challenge mentioned above. This sort of research will have a beneficial influence on our IT companies' consideration for those who live with specific needs that technology may help them meet.Publication Embargo Application of RFID and IoT technology into specimen logistic system in the healthcare sector(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Thwe Chit, M.M; Srisiri, W.; Siritantikorn, A.; Kongruttanachok, N.; Benjapolakul, W.The invention and innovation of RFID technology changed the world and many sectors (such as logistics, railways, healthcare, and so on) are now deployed with RFID technology instead of using barcode systems. With the numerous advantages, Radio Frequency Identification (RFID) got many expectations in the healthcare sector. The main objective of this research work is to implement the RFID technology in Specimen collection in the healthcare sector and the IoT (Internet of Things) network supports the transaction while the specimen test box is being delivered. The system uses a Sparkfun RFID reader to read/write patient information to the Gen2 RFID tag, which is attached to the test tube collected from the patients. When the test box is delivered to another laboratory, we develop an IoT network to know the box’s temperature, humidity, and GPS location instantly, with the help of an NB-IoT shield. The major advantage of the combination between IoT technology and RFID is that the management of test box overall condition becomes much easier. To summarize, this method is highly competent in identifying the location of medical devices in real-time and reduces the time-consuming of data logging than the barcode system.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.Publication Embargo EduHelp – An Online Tutoring Application(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Fernandopulle, S.R.; Warnasooriya, W.M.C.D.B.; Jayasinghe, J.M.H.N.; Theeraj, S.M.D.; Samarakoon, U.; Kumari, S.E-learning via electronic mail, the Internet, the Worldwide Web (WWW), and multimedia has emerged as a result of the convergence of digital technologies and growing interest in the computerized delivery of higher education. The rapid emergence of Covid-19, a lethal disease caused by the Corona Virus, shocked the entire world. It was labeled a pandemic by the World Health Organization. This has posed a challenge to the global education system, forcing instructors to switch to an online form of instruction overnight. Many academic institutions that had previously been hesitant to adapt their traditional pedagogical method now have no choice but to fully embrace online teaching-learning. Because the platform is new to students and teachers, it appears that they are having some difficulties conducting their classes. Hence in this paper, we proposed an E-learning Application called EduHelp to design and build a tutoring platform that includes payment validation for each student, lesson summaries with a video summary note, and more. Upload relevant files and papers by automatically identifying and directing them, as well as creating automated questions and monitoring student attention.Publication Embargo Sensor-Based Emotion Tracking System for Computer Games(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Wickramasinghe, W.R.M.G.K.; Devduni, R.M.B.; Dasanayaka, D.T.C.B.; Mohomed, M.N.N.; Kumari, S.; Dassanayake, T.The game development industry is among the leading industries globally, and in 2020, gaming emerged as a popular entertainment activity upon the COVID-19 outbreak. Thus, competition among gaming companies is high. Hence, they try to adopt new technologies often. Gaming brings multiple feelings for the gamer. At times, the conditions may get even worse from the game’s end where the gamer may end up venting out his rage and annoyance. Hence, there is a massive possibility for the gamer to switch to another game which may result in the company to lose its customers. In that scenario, this system can monitor the emotional states of the gamer while playing and manipulate the gaming environment, sound environment, enemy behavior, and gamer mechanism according to the emotional state of the gamer. The sensor-based emotion tracking system identifies the gamer's emotional state using facial emotions, detected through a webcam and heart rate, detected through sensors. The development was carried out through the machine learning models, open cv, Arduino techniques, and reactive programming. The emotional state and facial emotions that will be tracked will count to an accuracy of above 95%. Through that, the target will be to make the gamer satisfied by building appreciation for the services given and by improving the gamer's gaming experience and retain the gamer with the game provider.Publication Embargo VOYAGER – Smart Travel Guidance Cross Platform Mobile Application(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Wijesinghe, W.A.K.D.; Amarasinghe, A.G.L.D.P.; Bandara, T.M.U.A.; Gamage, A.I.; Ganegoda, D.The tourism industry is one of the largest industries that contribute to the worldwide economy. Also, it is one of the best income sources in Sri Lanka. It makes a considerable amount of money directly and indirectly through contributing to the government budget, job market and foreign exchange earnings. Same as foreign visitors, a significant amount of domestic travelers contributes to the economy in Sri Lanka. Hence, the tourism industry is popular in Sri Lanka; this research aims to enhance travelers’ travelling experience. When considering today’s travel communities, there are a set of unanswered questions that travelers tend to find answers manually from forums and social media because of the lack of features in the current apps. This paper discusses the development of the “Voyager – Smart travel guidance mobile application” to fulfil the gap between current apps and user requirements. The mobile application mainly focuses on the following features; smart travel plan within the desired travel period, shortest and scenic path options, automatic review categorizing and rating system, place tagging and place feature extracting system, fuel data analyzer, and one-tap emergency guidance feature. Despite the previous mentioned features, the final mobile application includes a friends system between app users, group travel planning, and an in-app chat system. As a result of the research project, this user-friendly mobile application will provide a better user experience when travelling, and it will help to develop the tourism culture in Sri Lanka.Publication Embargo SmartCoach: Comprehensive Tutor Recommender and Student Attentiveness Measuring Platform(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Samarasekara, H. D. K.; Hansaka, H. M. P.; Rajapaksha, S. D. D.; Tharaka, W. A. D. G.; Manathunga, K.; Sriyarathna, D.C.With the pandemic, coaching or tutoring classes became to halt as the underlying system wasn’t facilitating to adapt to emergency teaching mechanisms due to sparse of relevant technologies and tutor unawareness. SmartCoach is an integrated platform that allows students to find suitable tutors based on recommendations and other parameters like proximity, previous track record etc. using sentiment analysis and natural language processing. For tutors, SmartCoach allows publishing learning materials, automatic quiz creation, tracking attendance and, attentiveness during classes using OpenCV library. Moreover tutors get a dashboard featuring critical aspects about their classes and, personal income. This research is aimed to introduce a comprehensive distance learning platform with novel technological approaches that connect both potential students and tutors to a common platform.Publication Embargo Fully Automatic Hydroponic Cultivation Growth System(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Thalwatte, A. M.; Ranasinghe, U. G. K. L.P. S.; Sakee, M. F. A.; Ikram, M. M. M.; Mahaadikara, H.; Wellalage, S.Fully automated hydroponic system with monitoring and controlling components. This technique of developing plants can be used to develop plants in the flats the improvement is focused on the deployment of agricultural greenhouses into small-scale stages reworking it into a smart greenhouse. The identification system successfully identified the stage of plants well into the sprout stage and primary stage. Using an automated system and assembling the sensors and actuators considered about four factors which mainly impact the plant growing, light intensity level measurement, Temperature level, water level land co2 supply Also implement a pre-harvest disease detection using image processing and machine learning and alert the user regarding the prevention methods. The system counts infected disease plant and gets percentage then graphically represent a comparison of the yield production. Then forecast yield production. Another important component is to identify the Leaf Disease That Has Affected the Plant, Design database to record all data and provide a report for each disease.Publication Embargo WoKnack – A Professional Social Media Platform for Women Using Machine Learning Approach(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Shanmugarajah, S.; Praisoody, A.; Rakib Uddin, M.D.Today’s generation is heavily influenced by social media. However, most users decline to post their abilities on these platforms for a variety of reasons, including security, a lack of basic skills, and a lack of knowledge about the various skill sets. It's understandable that women face many security risks on these platforms. WoKnack is a professional social networking platform dedicated to women. This opens opportunities for women to demonstrate their abilities and teach other women. This paper targets onfunctionalities like registration limited to female users, skill categorization, post verification and privacy preservation. Facial image, identification document and Voice related gender verification done using machine learning approaches to identify thegender before registration. Accuracy of 91% gained during the process. Skills have been categorized using Natural language processing and post verification done based on these categories. Usage of the best accurate algorithm gives an accuracy of 94% during this process. In order to preserve the privacy of users Data anonymization, skill and location clustering have been added to the system.Publication Embargo Cricket Shot Image Classification Using Random Forest(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Devanandan, M.; Rasaratnam, V.; Anbalagan, M.K.; Asokan, N.; Panchendrarajan, R.; Tharmaseelan, J.Cricket is one of the top 10 most played sport across the world regardless of age and gender. However, learning cricket has been quite challenging as the majority of the cricket-playing individuals are unable to afford quality infrastructure. While this has opened up many research opportunities to provide solutions to automatically learn cricket, very little work has been done in this era. In this paper, we focus on the batting skills of cricket players. We develop a Random Forest model to classify the cricket shot images using human body keypoints extracted with MediaPipe. Experiment results show the proposed model achieves an F1-score of 87% and outperforms the existing solution in a 5% margin. Further, we propose a similarity estimation approach to compare the user’s cricket image with popular international cricket players’ cricket shot images of the same type and retrieve the most similar one. The mobile application we developed based on our solution will enable cricket-playing individuals to analyze, improve and track their batting performances without the need of having a coach.Publication Embargo Stress Analysis and Care Prediction System for Online Workers(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Amarasinghe, A.A.S.M.; Malassri, I.M.S.; Weerasinghe, K.C.N.; Jayasingha, I.B.; Abeygunawardhana, P.K.W.; Silva, S.Working from home (WFH) online during the covid-19 pandemic has caused increased stress level. Online workers/students have been affecting by the crisis according to new researches. Natural response of body, to external and internal stimuli is stress. Even though stress is a natural occurrence, prolonged exposure while working Online to stressors can lead to serious health problems if any action will not be applied to control it. Our research has been conducted deeply to identify the best parameters, which have connection with stress level of online workers. As a result of our research, a desktop application has been created to identify the users stress level in real time. According to the results, our overall system was able to provide outputs with more than 70% accuracy. It will give best predictions to avoid the health problems. Our main goal is to provide best solution for the online workers to have healthy lifestyles. Updates for the users will be provided according to the feedback we will have in the future from the users. Our System will be a most valuable application in the future among online workers.Publication Embargo Effectiveness of Stock Index Forecasting using ARIMA model: Evidence from New Zealand(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Dassanayake, W.; Ardekani, I.; Gamage, N.; Jayawardena, C.; Sharifzadeh, H.Time series of stock market indices are dynamic, interdependent, and considered sensitive to many factors. Thus, the prediction of such indexes is always challenging. A comprehensive review carried out by the authors finds that no attempts have yet been carried out to test ARIMA models’ predictive efficacy applied to the New Zealand financial markets. Thus, technical analysis based ARIMA prediction models are developed and empirically tested on the New Zealand stock market (NZX50) index. Daily NZX50 index data are used, and the forecasting precision of the models is assessed based on Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE]. Our study finds that ARIMA (1, 1, 0) plus intercept is the best order forecasting model out of the models we examined. Once a substantiate algorithm training is implemented, formulated ARIMA models could be successfully employed to forecast the time series of other stock market indexes or the same index for varied periods. Future researchers could compare the forecasting efficiencies of ARIMA with a deep-learning model such as long short-term memory (LSTM). The presence of limited published research of ARIMA applied to the financial markets of New Zealand validates the need and the contribution of this paper.Publication Embargo Digital Preservation and Noise Reduction using Machine Learning(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Aravinda, K.P.; Sandeepa, K.G.H.; Sedara, V. V.; Chamodya, A.K.Y.L.; Dharmasena, T.; Abeygunawardhana, P.K.W.This paper proposes a digital preservation solution for Sinhala audios to conserve those as documents with noise reduction. The solution has implemented multiple noise reduction techniques as a pre-processing step to remove unwanted internal and external noises. A two-step, two-way noise reduction process is applied to produce clean audios based on Deep Convolutional Neural Network (DCNN) and adaptive filter-based techniques. This approach implements two separate noise reduction models for internal and external noises. After that, the speech recognition decoder recognizes the speech and converts it to a Unicode document by acoustic, language, and pronunciation models using extracted audio features from the denoised audio. Further, noise reduction models are decoupled from the preservation solution and exposed as a sub solution for multilingualism noise reduction, supporting English and Sinhala audios.Publication Embargo Use of Natural Language Processing and Deep Learning towards Guiding Healthy Cholesterol Free Life(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Sasanka, D.; Malshani, H. K. N.; Wickramaratne, U.I.; Kavindi, Y.; Tissera, M.; Attanayaka, B.High blood cholesterol is a key risk factor for cardiovascular diseases such as coronary heart disease and stroke. This has become a severe health problem, because it causes a considerable amount of deaths annually. The major risk factors that affect a person’s cholesterol level include unawareness of cholesterol risk, unhealthy dietary habits, lack of proper exercises, and high stress conditions. In this research, novel approaches are introduced to provide an automated and personalized guidance to maintain healthy cholesterol level and raise the awareness of each risk factors mentioned above. This research associates with four novel approaches. Natural Language Processing (NLP) based Cholesterol risk analyzer, Fuzzy based Food management with Meal predictor, Machine Learning based Physical exercise planner and Stress controller. Altogether with results, this research will provide a complete and facts-proven solution to reduce and guide people towards a cholesterol-free healthy lifestyle.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 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 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.
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