2nd International Conference on Advancements in Computing [ICAC] 2020
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Publication Embargo Secure Communication Using Steganography in IoT Environment(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-11-10) Amjath, M.I.M.; Senthooran, V.IoT is an emerging technology in modern world of communication. As the usage of IoT devices is increasing in day to day life, the secure data communication in IoT environment is the major challenge. Especially, small sized Single-Board Computers (SBCs) or Microcontrollers devices are widely used to transfer data with another in IoT. Due to the less processing power and storage capabilities, the data acquired from these devices must be transferred very securely in order to avoid some ethical issues. There are many cryptography approaches are applied to transfer data between IoT devices, but there are obvious chances to suspect encrypted messages by eavesdroppers. To add more secure data transfer, steganography mechanism is used to avoid the chances of suspicion as another layer of security. Based on the capabilities of IoT devices, low complexity images are used to hide the data with different hiding algorithms. In this research study, the secret data is encoded through QR code and embedded in low complexity cover images by applying image to image hiding fashion. The encoded image is sent to the receiving device via the network. The receiving device extracts the QR code from image using secret key then decoded the original data. The performance measure of the system is evaluated by the image quality parameters mainly Peak Signal to Noise Ratio (PSNR), Normalized Coefficient (NC) and Security with maintaining the quality of contemporary IoT system. Thus, the proposed method hides the precious information within an image using the properties of QR code and sending it without any suspicion to attacker and competes with the existing methods in terms of providing more secure communication between Microcontroller devices in IoT environment.Publication Embargo VirtualPT: Virtual Reality based Home Care Physiotherapy Rehabilitation for Elderly(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Heiyanthuduwa, T.A.; Amarapala, K.W.N.U.; Gunathilaka, K.D.V.B.; Ravindu, K.S.; Wickramarathne, J.; Kasthurirathna, D.This paper describes the development of Personal computer based Virtual Reality home-care Physiotherapy system aimed for rehabilitating full body function in elders. VirtualPT is a true virtual reality platform where the environment is completely replaced by a virtual reality platform based on the mental condition of the person at the time. While doing the home-based prescribed physiotherapy exercises, the key health metrics are continuously monitored and tracked by combining the immersive Virtual Reality with the wearable VirtualPT Sensor kit. Virtual Reality combined with 3D motion capture lets real time movements to be accurately translated onto the virtual reality avatar that can be viewed in a virtual environment to assist physiotherapist to add exercises to the system easily. This ultimate virtual reality Physiotherapy assistant avatar is used to provide guidance to elders at home, to demonstrate and assist elders in adhering to the prescribed exercises. As a significant aspect of social interactions, mirroring of movements has been added to focus on whether the elder is able to accurately follow the movements of avatar. Furthermore, the insightful dashboard offers the elders and physiotherapists an interactive platform through virtual reality capabilities. VirtualPT physiotherapy system is cost effective and makes recovery and more convenient to elders at home while the participatory and immersive nature of Virtual Reality offers a unique realistic quality that is not generally existing in clinical-based physiotherapy. When looking at the broader concept of VirtualPT; continuity of care, integration of services, quality of life and access are equally important criteria which add more value.Publication Embargo AI - Driven Smart Bin for Waste Management(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Abeygunawardhana, A. G. D. T.; Shalinda, R. M. M. M.; Bandara, W. H. M. D.; W. D. S. Anesta, D.; Kasthurirathna; Abeysiri, L.With increasing urbanization, waste has become a major problem in the present world. Therefore, proper waste management is a must for a healthy and clean environment. Though government authorities in most countries provide various solutions for waste management, solid waste tends to make a significant impact on the environment as they do not decompose easily. This research focuses on AI (Artificial Intelligence)-driven smart waste bin that can classify the most widely available solid waste materials namely Metal, Glass, and Plastic. The smart waste bin performs the separation of waste using image processing and machine learning algorithms. The system also performs the continuous monitoring of the collected waste level by using ultrasonic sensors. A dedicated mobile application will generate the optimal routes for the available waste collectors to collect the filled bins. Moreover, with this smart bin, the challenge of recognizing each waste item is overcome by using visual data as the source. Therefore, the usage of expensive sensor devices and filtration techniques to determine the category is disregarded. The smart bin can recognize the category of solid waste, collect it to the specified container, and notify the garbage level in each container. So, it is a portable waste management system.Publication Embargo Assist: Rendering, Pipeline Management, and Pipeline Tracking Software Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Salgado, M.V.I; Hettiarachchi, H.A.D.D.; Munasinghe, T.U.; Fernando, K.A.U.; Gamage, I.; Thilakarathna, T.; Cooray, N.C.Video production is one of the most dominant industries in the 21st century, and research into the automation of tasks associated with it has drastically increased. The production of videos take place in three stages: pre-production, production, and post-production. These three stages consist of script writing, scheduling, logistics, and other administration work. There are commercial products to automate these individual tasks. Incorporating all these software into video production can be expensive and difficult to manage. This study proposes the “Assist” software to handle all processes in video production. It has resulted in a product that covers the three main stages featuring scripts, storyboards, inventory management, production progress tracking and management, and rendering. The mentioned features were designed and developed using decision tree algorithm, PyQt5, general decimation algorithm, mesh simplification algorithm, and multi-variable regression.Publication Embargo DNN Based Currency Recognition System for Visually Impaired in Sinhala(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gamage, C.Y.; Bogahawatte, J.R.M.; Prasadika, U.K.T.; Sumathipala, S.Recently researches have been conducted in the domain of currency recognition. The task of recognizing the currency notes has become challenging due to the distortion of the notes over time. Currency recognition systems in Sinhala for visually impaired people are rarely developed. To address this problem a research has been done and a relevant application has been implemented comprising three modules as Speech Recognition module, Currency Recognition module and Text to Speech Module. The major challenge in all three modules is to achieve a better accuracy using deep learning concepts. TensorFlow platform and Keras library were used to build the speech recognition neural network model for Sinhala spoken words. Deep learning neural networks were utilized for the development of currency recognition module and text to speech module.Publication Embargo Aspect Based Sentiment Analysis for Evaluating Movies and TV series Publisher: IEEE Cite This PDF(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Cooray, T.; Perera, G.; Chandrasena, D.; Alosius, J.; Kugathasan, A.Aspect-based sentiment analysis (ABSA) is used in different fields for analyzing customer reviews to project an overall customer opinion on certain products. With the expansion of the internet, people are provided with an inexpensive and time-saving method to express their opinion to a larger audience, while various industries are handed with the opportunity to gather free information from it to obtain market value. The implementation of machine learning methods for the evaluation of aspects related to movies and television series has not been commenced, and it could be a new development for the industry. This study focuses on conducting an ABSA on a movie or a television series based on genre, story as well as cast and crew aspects. The data collected from social media through web scraping is processed to produce adequate results to get a broad understanding on how the popularity of the movie or the television series related to above mentioned aspects. Then, each aspect is further analyzed to gather precise information belonging to each aspect. The accuracy of the results of the proposed system has been achieved over 79%. The results proved that the solution is highly successful than the former works with high business value.Publication Embargo Real-Time Decision Optimization Platform for Airline Operations(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Weerasinghe, P.S.R.; Ranasinghe, R.A.M.D.K.; Mahanthe, M.M.V.R.B.; Samarakoon, P.G.C.B.; Rankothge, W.H.; Kasthurirathna, D.With close to 4 billion origin-destination passenger journeys worldwide, airline operations have become a crucial factor in the global economy. With the increasing number of journeys and passengers, managing the daily operations of airlines have become a complicated task. We have proposed a real-time decision optimization platform for airline operations with the following subsystems: (1) determine the optimum path for a flight, (2) optimum fleet assignment, (3) optimum gate allocation, (4) optimum crew allocation. We have used an approximation (heuristics) based optimization approach: Genetic Programming (GP) to implement the modules. The results of our proposed platform illustrate that, the decision-making process of Airline Operations Control Center (AOCC) can be optimized, and dynamic change requirements can be accommodated.Publication Embargo Evaluating Optimal Lockdown and Testing Strategies for COVID-19 using Multi-Agent Social Simulation(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Dunuwila, P.M.; Rajapakse, R.A.C.P.COVID-19 pandemic has become a major concern due to its rapid spread throughout the world. We can observe some countries are successful in formulating effective strategies for managing the pandemic, while some are struggling. The research is based on the question of formulating effective policies for COVID-19 to reduce community transmission. While many countries are suffering from the pandemic, it is a critical issue that the policymakers should be concerned with formulating effective policies to address the problem. We use computational methods to foresee the future by creating a simulation model based on multi-agent and simulation methodology because it is not always possible to predict the future state of a complex adaptive system. The data are collected through a survey and the literature to calibrate the model parameters to build a constructive and realistic model. Once the model is constructed, the simulation results are compared with the real-world observations to validate the model. The implementation of the model follows an iterative process for improving the validity of the model. This paper presents the conceptual model of the system being investigated and its initial implementation, which needs to be calibrated further with empirical data before using it as a decision support tool.Publication Embargo Real-Time Location based Augmented Reality Advertising Platform(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Batuwanthudawa, B.I.; Jayasen, K.P.N.Augmented Reality (AR) is growing rapidly and is becoming more mature and robust technology, combining virtual information with a real-time performance environment. Most of the Augmented reality applications available today & popular because of the interactive virtual objects placed in the real environment. For education, navigation, tourism & many sectors use this technology due to clear understand of real objects appear as it is as virtual objects, in front of you. Like that, the Marketing sector also uses AR technology to brand themselves interactively. Most of them are marker-based AR applications which the virtual contents are showing when the AR camera directs to a target such as paper advertisement. On other hand marker-less AR advertising applications are developed for individual businesses from AR supported plugins, apps rare to see & as unique published app. From this research, I proposed a real-time marker-less augmented reality platform, streaming & showcasing virtual marketing assets in front of shops for common business use. The main objective of this research is to develop a real-time location-based Augmented Reality platform to improve marketing & sales aspects of businesses. The users can easily find the exact location of the shop though AR objects. This novel marketing concept engages more customers to business and enhances the usability of AR application among users though easy to access on their selling products. The users can use app in native platforms(both android & IOS) and ready to access interactive virtual 3D objects with animation as marketing materials placed in front of shops. This platform solved the existing problems of location-based AR application which are interactivity of AR & adequately perform as real-time platform extract data from a live real-time server & show the locations through AR camera.Publication Embargo WANHEDA: A Machine Learning Based DDoS Detection System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sudugala, A.U.; Chanuka, W.H.; Eshan, A.M.N.; Bandara, U.C.S.; Abeywardena, K.Y.- In today’s world computer communication is used almost everywhere and majority of them are connected to the world’s largest network, the Internet. There is danger in using internet due to numerous cyber-attacks which are designed to attack Confidentiality, Integrity and Availability of systems connected to the internet. One of the most prominent threats to computer networking is Distributed Denial of Service (DDoS) Attack. They are designed to attack availability of the systems. Many users and ISPs are targeted and affected regularly by these attacks. Even though new protection technologies are continuously proposed, this immense threat continues to grow rapidly. Most of the DDoS attacks are undetectable because they act as legitimate traffic. This situation can be partially overcome by using Intrusion Detection Systems (IDSs). There are advanced attacks where there is no proper documented way to detect. In this paper authors present a Machine Learning (ML) based DDoS detection mechanism with improved accuracy and low false positive rates. The proposed approach gives inductions based on signatures previously extracted from samples of network traffic. Authors perform the experiments using four distinct benchmark datasets, four machine learning algorithms to address four of the most harmful DDoS attack vectors. Authors achieved maximum accuracy and compared the results with other applicable machine learning algorithms.Publication Embargo NoFish; Total Anti-Phishing Protection System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Atimorathanna, D.N.; Ranaweera, T.S.; Pabasara, R.A.H.D.; Perera, J.R.; Abeywardena, K.Y.Phishing attacks have been identified by researchers as one of the major cyber-attack vectors which the general public has to face today. Although many vendors constantly launch new anti-phishing products, these products cannot prevent all the phishing attacks. The proposed solution, “NoFish” is a total anti-phishing protection system created especially for end-users as well as for organizations. This paper proposes a machine learning & computer vision-based approach for intelligent phishing detection. In this paper, a realtime anti-phishing system, which has been implemented using four main phishing detection mechanisms, is proposed. The system has the following distinguishing properties from related studies in the literature: language independence, use of a considerable amount of phishing and legitimate data, real-time execution, detection of new websites, detecting zero hour phishing attacks and use of feature-rich classifiers, visual image comparison, DNS phishing detection, email client plugin and especially the overall system is designed using a level-based security architecture to reduce the time-consumption. Users can simply download the NoFish browser extension and email plugin to protect themselves, establishing a relatively secure browsing environment. Users are more secure in cyberspace with NoFish which depicts a 97% accuracy level.Publication Embargo Smart Intelligent Troubleshooter to Solve Windows Operating System Specific Issues(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Rajapakshe, D.I.K.; Shamil, M.P.P.; Paththinisekara, P.M.C.P.; Liyanage, S.K.; Samaratunge Arachchillage, U.S.S.; Kuruppu, A.While working on computers, people frequently confront with various kinds of problems, those beyond their extensive expertise. Microsoft Windows is the widely used Operating System running on numerous personal computers and the reason which gives more irritating problems that require to be addressed. Currently, troubleshooting is considered as a costly and time-consuming approach. The SAITA is an Artificial Intelligent Troubleshooting Agent that utilizes natural language generation, machine learning, and dependency resolving and ontology-based methodologies for solving most common Windows-specific issues within a short period of time than the traditional approach. The assistant learns from the accessible data and accomplishes the task for users as performed by human experts. The main objective of this exploration venture is to distinguish the constraints of existing troubleshooting software and create an AI troubleshooting assistant to provide solutions to fix the identified user issues. The use of this assistant would be economical as an IT help desk alternative in the industry. SAITA is developed to serve as a representative troubleshooter for fundamental user issues, service issues, application issues, and perform environment setup by analyzing software. This system will be able to solve the common Windows user’s issues as same as a human with less time.Publication Embargo Plant Leaf Recognition: Comparing Contour-Based and Region-Based Feature Extraction(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Donesh, S.; Ishanka, U.A.P.Plants play a vital role in the environment. Identifying them and classifying them is an important task for botanists. This study briefly points out- how to recognize plant species using image processing techniques that can help botanists and scientists, the appropriate features for plant species recognition in feature extraction, how can a classification help to increase the accuracy of the plant leaf classification. There are four major phases used in here for the recognition, and they are image input, image pre-processing, feature extraction, and SVM classification. This automatic recognition system is developed using python with Jupyter Notebook environment gives higher accuracy for the plant recognition for the botanists and comparing the feature extractions such as Contour-based and Region-based to get down more accurate results than previous researches is the main purpose of the proposed study. Contour-based and Region-based features were calculated through equations. SVM classification is used for both feature extraction methods. For individual feature extraction the Contour-based feature extraction is more efficient with 72.25% accuracy than Region-based feature extraction with 70.41% accuracy, and for combining both feature extraction SVM classification gives 68.58% accuracy. Contour-based feature is the most appropriate feature for a plant species recognition.Publication Embargo IoT Based Sign Language Recognition System(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Punsara, K.K.T.; Premachandra, H.H.R.C.; Chanaka, A.W.A.D.; Wijayawickrama, R.V.; Abhayasinghe, N.; De Silva, R.Sign language is the key communication medium, which deaf and mute people use in their day - to-day life. Talking to disabled people will cause a difficult situation since a non-mute person cannot understand their hand gestures and in many instances, mute people are hearing impaired. Same as Sinhala, Tamil, English, or any other language, sign language also tend to have differences according to the region. This paper is an attempt to assist deaf and mute people to develop an effective communication mechanism with non-mute people. The end product of this project is a combination of a mobile application that can translate the sign language into digital voice and loT-enabled, light-weighted wearable glove, which capable of recognizing twenty-six English alphabet, digits, and words. Better user experience provides with voice-to-text feature in mobile application to reduce the communication gap within mute and non-mute communities. Research findings and results from the current system visualize the output of the product can be optimized up to 25 % -35 % with an enhanced pattern recognition mechanism.Publication Embargo SURAKSHA e-Caretaker: Elders Falling Detection and Alerting System using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mendis, L.; Hathurusinghe, S.; Epa, H.; Edirisinghe, T.; Wickramarathne, J.; Rupasinghe, S.People become unable to perform tasks that were done at the younger ages as they were when the ages pass with time. Falls play a major issue in the lives of elderly people as the physical and mental quality of life is dependable on the effects of falls. This research presents an e-Caretaker SURAKSHA which is an elder falling detection and alerting system based on Machine Learning concepts. Researchers that have been done in this area have produced different solutions to detect only the falls but not to automatically detect and notify them to the caretakers. This solution serves as a smart wearable device that is capable of automatically monitoring real-time postures, detecting sudden falls, possible arrhythmia conditions of the heart of the fallen person, and daily route deviations along with the fallen location which is finally notified to the caretakers through a mobile application. According to the performed studies, python model development was used to implement the system through Machine Learning concepts by referring to the Markov model, Prophet model, and Naïve Bayes algorithms. This solution provides the results of this research with an accuracy of around 89.9% leading to a successful product in the domain. Keywords—Publication Embargo Computational Model for Rating Mobile Applications based on Feature Extraction(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunaratnam, I.; Wickramarachchi, D.N.Google Play Store and App Store allow users to share their opinions and helps to measure users satisfaction level about the app through user comments. However, it's highly time-consuming to process all reviews manually. The usefulness of star ratings is limited for development teams since a rating represents an average of both positive and negative evaluations. Therefore, an automated solution is needed to systematically analyze reviews and other textual forms of data. The main objective of this research is to build a platform that rate apps by feature extraction and sentiment analysis to calculate the functionality index of apps based on metrics obtained by surveying 204 mobile phone users. The 5 topmost metrics obtained from them among the 16 metrics obtained from the literature review are usability, price, and frequency of updates, ad-freeness and battery consuming level. This research focuses on selected apps in music and audio category. To perform app rating indexes calculation of the overall app's reviews; data extraction, data cleaning, POS tagging, feature extraction, feature/feature values pairing, weighted feature rating, overall apps' rating and feature-wise app rating is done on textual data. The accuracy of the created model is measured by the level of satisfaction from users.Publication Embargo Evaluating Teaching Content and Assessments Based on Learning Outcomes(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Pallegama, P.M.O.N.; Kumari, K.A.M.R.; Dissanayaka, D.M.D.P.M.; Ravihansi, A.V.Y.; Karunasenna, A.; Samarakoon, U.A modularized syllabus content assigned to different units of a subject proves very useful to both teachings as well as the student community. In each module, learning outcomes are defined. In each learning outcome, lesson learning outcomes are defined. When the Teaching Content (Lecture content), Learning activities (Labs sheets and Tutorials), Final Question Papers are being made the subject learning outcome should be considered and it should be made within the subject learning outcomes. Then the teaching and learning process will be done properly. Nowadays Revised Bloom's Taxonomy standard is used to structure the Teaching Content, Learning Activities, and Final Question paper of a course in the best way. Currently, there is no proper solution to corporate above areas according to the Revised Bloom's Taxonomy. This paper discusses an automated system that provides the features to verify the module and lesson learning outcomes and their levels according to Revised Bloom's taxonomy and to verify that the teaching content and learning activities are within the learning outcomes. Beyond that, this system uses various technologies and algorithms to improve the accuracy and efficiency of this research. This automated system is able to achieve to the final outcome with the best accuracy and efficiency than the manual process.Publication Embargo A Deep Learning Approach to Outbreak related Tweet Detection(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayawardhana, B. A. S. S. B.; Rajapakse, R. A. C. P.Due to the popularity of social media around the world, people use to report and discuss real-world events, personal health complications, and disaster situations through these platforms. These social media data streams can be used to track and detect different types of outbreaks. A mechanism is needed to identify outbreak-related tweets to predict the outbreak in advance. In this paper, we propose a deep learning model that can detect tweets related to different outbreaks Epidemics, Public Disorders, and Disasters. GloVe (Global Vectors for Word Representation) embeddings are used as the feature extraction technique as it can capture the semantic meanings of the tweets. Long Short-term Memory (LSTM) which is a specialized Recurrent Neural Network architecture is used as the classification algorithm. In the process, first, outbreak-related tweets were manually collected and curated. Pretrained GloVe word embeddings of 100 dimensions were then used to represent the words of the tweets. As the next step, a Deep Learning Model was trained by using LSTM technique on the curated dataset. Finally, the performance of the model was evaluated using a different dataset. With the results, it can be concluded that the proposed deep learning model is an accurate approach for outbreak-related tweet detection.Publication Embargo MOOCs Recommender Based on User Preference and Video Quality(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Sankalpa, R.; Sankalpani, T.; Sandeepani, T.; Ransika, N.; Kodagoda, N.; Suriyawansa, K.MOOCs (Massive Open Online Courses) are a new revolution in the field of e-learning. MOOCs are capable of providing several thousands of learners with access to courses over the internet. MOOCs are produced in many different video production styles and these styles play an important role in helping the consumer stay engaged and interested in the courses. MOOCs provide a large number of courses in different domains to a wide range of learners. It has become difficult and a timeconsuming task for a user to find the most suitable courses that suit a learner’s personal preferences. This paper describes how to recommend a course based on the preferred video style of the learner and the basic learning style of the learner which determines the learner’s preferences on other materials in a course. In the course recommendation process, this paper also describes how to classify the course in order to recommend the most appropriate massive open online courses for users according to their most preferred video production style.Publication Embargo Prediction of CKDu using KDQOL score, Ankle Swelling and Risk Factor Analysis using Neural Networks(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Lokuarachchi, D.N.; Manoj, J.V.T.; Weerasooriya, M.N.H.; Waseem, M.N.M.; Aslam, F.; Kumarasinghe, N.; Kasthurirathne, D.Chronic Kidney disease (Chronic Kidney Disease (CKD)) is a type of kidney disease where gradual loss of kidney function occurs over a period of months to years. But, when CKD cannot identify a manner or causation of the disease or set of causes it is known as Chronic Kidney disease with unknown etiology (CKDu). There are several factors to be considered when analyzing the main causes for CKDu such as socio-economic, environmental, meteorological and health aspects in relation to the CKDu in Sri Lanka. In this research work, identification of CKDu has been done using the relationship of the Kidney Disease Quality of Life (KDQOL) score, ankle swelling with the serum creatinine level of blood and considering risk factors. This research has been done using three major branches of Artificial Intelligence namely neural networks, convolutional neural networks and machine learning. The relationship between the mentioned factors and CKDu has been identified. The sensitivity of 77.27% and a specificity of 89.28% have been marked for the detection of CKDu related to ankle swelling.