2nd International Conference on Advancements in Computing [ICAC] 2020
Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1317
Browse
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 An Integrated Framework for Predicting Health Based on Sensor Data Using Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Jayaweera, K.N.; Kallora, K.M.C.; Subasinghe, N.A.C.K.; Rupasinghe, L.; Liyanapathirana, C.According to recent studies, the majority of the world's population shows a lack of concern in their health. As a consequence, the non-communicable disease rate has increased dramatically. Amongst these diseases, heart diseases have caused the most catastrophic situations. Apart from the busy lifestyle, studies also show that stress is another factor that causes these diseases. Therefore, the focus of our research is to provide a user-friendly health monitoring system that causes minimum disturbance to its users. However, many studies have focused on predicting health; very few have focused on its usability. The objective of our research is to predict the possibility of cardiac arrests and the presence of stress in real-time using a wearable device prototype. The system uses biometric signals obtained from the photoplethysmogram sensor embedded in the wearable device to perform real-time predictions. We trained three models using random forest, k-nearest neighbor, and logistic regression classification algorithms to predict sudden cardiac arrests with accuracies 99.93%, 99.10%, and 94.47%, respectively. Further, we trained three additional models to predict stress using the same algorithms with accuracies 99.87%, 96.83%, and 65.00%, respectively. Thus, the results of this study show that an integrated framework, capable of predicting different health-related conditions, through sensor data collected from wearable sensors, is feasible.Publication Embargo Facial Emotion Prediction through Action Units and Deep Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Nadeeshani, M.; Jayaweera, A.; Samarasinghe, P.With the recent advancements in deep learning techniques, attention has been given to training and testing facial emotions through highly complex deep learning systems. In this paper we apply machine learning techniques which require less resources to produce comparable results for emotion prediction. As the underlying technique for the emotion prediction in this research is based on clinically recognized Facial Action Coding System (FACS), a further analysis is given on the contribution of each of the Action Units (AUs) for the predicted emotion. This analysis would complement, strengthen and be a main resource for addressing many different health issues related to facial muscle movements.Publication Embargo Behavior Segmentation based Micro-Segmentation Approach for Health Insurance Industry(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Nandapala, E.Y.L.; Jayasena, K.P.N.; Rathnayaka, R.M.K.T.To manage the company’s future growth, the relationship between companies and customers is important. This can be referred to as Customer Relationship Management (CRM). By applying the micro-segmentation process companies can succeed in this CRM process. Micro-segmentation is a breakdown into micro-segments of the entire data collection. The user can easily be deeply defined with this segmentation process. Demographic segmentation is a breakdown of the dataset based on the consumers’ age, gender, etc. Behavior segmentation is diving the whole dataset based on customers’ behaviors. RFM analysis is a behavioral segmentation process based on consumer’s behaviors. There is no exact way to precisely conduct micro-segmentation. Thus, this study proposed a new micro-segmentation process. That is applying demographic segmentation with the support of the RFM analysis. This method can easily determine the customers’ behaviors accurately and deeply. Insurance companies offer different types of insurance and health insurance is the most critical insurance type for humans. By applying the proposed method in this research, health insurance companies can determine the policyholder’s behaviors, claiming patterns, claiming chargers, and other information precisely. Furthermore, health insurance providers can effectively manage their claims using this knowledge.Publication Open Access Utalk: Sri Lankan Sign Language Converter Mobile App using Image Processing and Machine Learning(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Dissanayake, I.S.M.; Wickramanayake, P.J.; Mudunkotuwa, M.A.S; Fernando, P.W.N.Deaf and mute people face various difficulties in daily activities due to the communication barrier caused by the lack of Sign Language knowledge in the society. Many researches have attempted to mitigate this barrier using Computer Vision based techniques to interpret signs and express them in natural language, empowering deaf and mute people to communicate with hearing people easily. However, most of such researches focus only on interpreting static signs and understanding dynamic signs is not well explored. Understanding dynamic visual content (videos) and translating them into natural language is a challenging problem. Further, because of the differences in sign languages, a system developed for one sign language cannot be directly used to understand another sign language, e.g., a system developed for American Sign Language cannot be used to interpret Sri Lankan Sign Language. In this study, we develop a system called Utalk to interpret static as well as dynamic signs expressed in Sri Lankan Sign Language. The proposed system utilizes Computer Vision and Machine Learning techniques to interpret sings performed by deaf and mute people. Utalk is a mobile application, hence it is non-intrusive and cost-effective. We demonstrate the effectiveness of the our system using a newly collected dataset.Publication Embargo Smart Intelligent Advisory Agent for Farming Community(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Illandara, T.S.; De Silva, H.L.H.; Madurawala, K.S.H.; Dayasena, B.R.D.; Srimath, U.; Samaratunge Arachchillage, S.; Buddhika, T.The currently available agricultural services have few limitations because of the traditional cultivation methods and the unavailability of experts. This research attempts to solve the major problems faced by farmers using an Intelligent Expert Advisory Agent (EAA) that would act as a human counterpart to provide reliable solutions in real-time to the farmers using Machine Learning (ML), Image Processing (IP), and Internet of Things (IoT) technologies. A web application is developed to provide meaningful information to the user by representing agriculture instructors. Using the web application, the farmer can obtain information about predicted weather up to two months. Once the crop is selected, suitable organic fertilizers are suggested to maximize the productivity of the cultivation. After planting, the farmer can continuously monitor the condition of the plants in real-time using the IoT system. Based on this information, the farmer can check if the conditions are optimum for the growth of the plant by interacting with the knowledge base system. If the plants get infected with diseases, the user can capture an image of the diseased plant using the implemented mobile application and send to the IP system to identify the diseases and suggests remedies to overcome the situation.Publication Embargo ChildPath: Diagnose depression in pre-schoolers based on daily activ(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kirthika, L.; Abeykoon, J.To determine depression in pre-schoolers and validation of identifying depression based on daily activities. A comprehensive literature search, interviews with accredited mental health practitioners and a survey was conducted to validate the background aspects and existing diagnosis theories to map out based on daily activities. The results of the evaluation suggest a gap around diagnosis of depression in pre-schoolers due to lack of awareness and its distinctive nature to adult depression. This establishes a need for depression status calculation mechanism based on analysis of daily activities using machine learning to examine behaviour and speech patterns. Further, rule-based machine learning, will be implemented to offer personalized treatment plans if diagnosed with a status of depression.Publication Embargo PatientCare: Patient Assistive Tool with Automatic Hand-written Prescription Reader(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kulathunga, D.; Muthukumarana, C.; Pasan, U.; Hemachandra, C.; Tissera, M.; De Silva, H.Most people in the world prefer to be conscious of the medications prescribed by physicians. Especially, the importance of handwritten prescriptions is prodigious in Sri Lanka because they are widely used in the healthcare sector. However, due to the illegible handwriting and the medical abbreviations of the physicians, patients are unable to find the prescribed medication information. This research is an attempt to assist the patients in identifying the prescribed medicine information and minimizes misreading errors of medical prescriptions. When a patient uploads the image of a prescription, the system converts it into unstructured text data by using OCR and segmentation, then NER is used to categorize medical information from given text. According to the other research, some solutions exist in other domains for the above mechanisms. But they gave less accuracy when tried to apply for this research due to the domain specialty. Therefore, as a solution to overcome the above discrepancy this approach allows users to scan handwritten medical prescriptions and blood reports and obtain analyzed reports in medical history. Results have shown that this approach will give 64%-70% accuracy level in doctor's handwriting recognition and 95%- 98% accuracy in medical information categorization of the prescription format.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 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.Publication Embargo Smart Backpack for Travelers(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Gunarathne, P.D.R.P.; Amarasuriya, R.M.C.I.; Wickramasinghe, W.A.D.D.; Witharana, A.H.T.N.; Abeygunawardhana, P.K.W.Smart backpack is an application-specific design which guarantees a safe journey for travelers. The smart backpack has a different combination of services connected to a single system. It has a unique design that helps to fulfill its services. The system provides the health status of travelers and environmental status by measuring the quality level of the nearby atmosphere. As a security feature, it contains a human detective sensor-based security system. As well as the research consists of an undying power resource which charges by solar cells, the power source can be used to power up the system and to recharge traveler's devices through a USB power outlet. The Backpack has a user-friendly mobile application. This system also provides a health monitoring feature, which monitors the heart rate and body temperature of the traveler. The traveler can share his/her health status with the system and compute the real-time health condition from the outputs of the health sensors integrated into the backpack. The bag model design and building play a major role and has removable unique mini compartments for all hardware components. It should carry maximum weight with minimum pressure for the back of the traveler with minimum cost.Publication Embargo Hand Rehabilitation Using Robot-Assisted Physiotherapy(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Madhushan, I.H.D.; Charnara, E.B.K.; De Zoysa, A.T.J.; Upeka, G.S.; Abhayasinghe, N.; Abeygunawardhana, P.Robotics technology in the modern world is currently being implemented in medical fields to improve the quality of care and patient outcomes. In the proposed system, the robotics technology is used for physiotherapy. In the existing physiotherapy robot devices, there is no feature that provides exercise for every joint of the fingers and the wrist. Therefore, in this system, we used forward kinematics technologies to address each joint of the fingers and wrist thatcan access by the physiotherapist. We have designed the robot hand using the solid work and implemented 3D model then assembled system was tested again using different scenarios. Most existing robotic systems provide finger and wrist exercises separately, but our system can provide all exercises simultaneously. In here, we can predict the next exercises that are given for the patient and the progress of the rehabilitation of the patient. For the prediction, we developed the models using the FB prophet algorithm. When using this device, the patient's hand exercises are monitored in real-time and the physiotherapist can see the angles of the hand movement while controlling the robot device. To control this robot device, we used a mobile application.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 Providing Electronic Health Care Services Through A Private Permissioned Blockchain(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Navaratna, L.; Wijesinghe, N.; Pilapitiya, U.Electronic Health Recording System is a patient focused system to allow users to manage medical data, including reports and prescriptions digitally. Current systems, while they solve the reports and data scattering, low durability of the prescription mediums, inefficiency in attaining the data and the high paper usage that is toxic to the environment, an emergence of an data integrity and security issue can be identified. The implemented system addresses the afore mentioned functionality issues and caters to the data integrity and security issue through a highly exclusive private blockchain. This system is web based to provide more flexibility when presenting data to its users. The technical aspects of the proposed system were successful which was executed with the minimum requirements of the blockchain network. This includes the number of organizations and its number of peer nodes that was required to cover the scope of the proposed system. Overall, the proposed system was a successPublication 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 Secured, Intelligent Blood and Organ Donation Management System - “LifeShare”(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Wijayathilaka, P.L.; Pahala Gamage, P.H.; De Silva, K.H.B.; Athukorala, A.P.P.S.; Kahandawaarachchi, K.A.D.C.P.; Pulasinghe, K.N.The scarcity and exigency for blood and organs has created many discrepancies in current approaches. These have created the criteria for malpractices such as organ trafficking and black market selling. This research presents a solution with a secured-smart blood and organ donation web developed system, allowing both patients and healthcare providers to access information about the blood and organ processing records. The database would be managed using the Blockchain technology which could be only accessed by authorized users. Finally, tracking all registered donors, the proposed system generates a smart identity developed by Ethereum Smart Contract (ESC). System predicts blood demand for the future ten years using Linear Regression Model with 0.998 of high R-squared accuracy value. This reduces shortages and wastage of blood. Also, using global positioning system and K-Nearest Neighbors Machine Learning algorithm, the system finds the best matches among donors and seekers according to the nearest location. Further, the system will automatically send questionnaires for registered users to identify and evaluate their awareness and issues about organ donation. Overall, this study aims for a secured and transparent web application. Thus, it facilitates an innovative and a productive blood donation and organ transplantation process in Sri Lankan healthcare sector.