1st International Conference on Advancements in Computing [ICAC] 2019

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/1599

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SmartOne: IoT-based Smart Platform to Manage Personal Water Usage
(IEEE, 2019-11-21) Vithanage, J; de Silva, R; Karunaratne, K; Silva, M. D; Bogoda, P; Kankanamge, R; Kehelella, P; Jayakody, K. D; Wijekoon, J
The origin of life is water, and consistent water consumption is essential for the proper functioning of human organs. Thus, regular hydration is vital for human beings because improper hydration leads to diseases such as cholera, diarrhea, bladder stones, and kidney issues. However, maintaining a sufficient and regulated water intake is challenging for many personal as the livelihood, i.e., busy life schedule, is getting complicated, and sometimes due to limited access to clean water. To this end, the SmartOne water bottle was introduced to enable effective management of daily water requirements and to ensure users drink good quality water. The initial prototype of the water bottle was implemented as a combination of hardware and mobile application, and it was evaluated in terms of measuring water goal, quality, drink and sift event detection.
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Plus Go: Intelligent Complementary Ride-Sharing System
(IEEE, 2019-11-21) Wickramasinghe, V; Edirisinghe, A; Gunawardena, S; Gunathilake, A; Kasthurirathna, D; Wijekoon, J
Currently the world population is gathering to the cities making huge traffic congestion throughout the day. This has drawn serious attention to the society incurred to implement smart solutions for traffic management. One of the prominent problems for traffic congestion is the number of vehicles entering the cities is high. It is a popular fact that the solitary travelers coming to a defined destination make the vehicles underutilized. Therefore, this study proposes a solution to implement a new ride-sharing platform: Plus Go, to reduce this underutilization. Plus Go matches the travelers by considering the designation, traveler preferences, shortest path details, and the ratings of the users. Moreover, Plus Go intelligently estimates the traveling cost based on the fuel consumption of the vehicle, distance traveled, and the time taken to reach the destination. The proposed solution matches the travelers with 98% accuracy ensuring that ride-sharing is an effective solution to reduce the number of vehicles entering the cities.
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Recognition and translation of Ancient Brahmi Letters using deep learning and NLP
(IEEE, 2019-12) Wijerathna, K. A. S. A. N; Sepalitha, R; Thuiyadura, I; Athauda, H; Suranjini, P. D; Silva, J. A. D. C; Jayakodi, A
Inscriptions are major resources for studying the ancient history and culture of civilization in any country. Analyzing, recognizing and translating the ancient letters (Brahmi letters) from the inscription is a very difficult work for present generation. There is no any automatic system for translating Brahmi letters to Sinhala language. However, they are using manual method for translating inscriptions. The method that used in epigraphy is being taken a long period to decipher, analyze and translate the inscribed text in inscriptions. This research mainly focuses on recognition of ancient Brahmi characters written the time period between 3 rd B.C and 1 st A. D. First, we remove the noise, segment the letters from the inscription image and convert it into the binary image using image processing techniques. Secondly, we recognize the correct Brahmi letters, broken letters and then identify the time period of the inscriptions using Convolution Neural Networks in deep learning. Finally, the Brahmi letters are translated into modern Sinhala letters and provide the meaning of the inscription using Natural Language Processing. This proposed system builds up solution to overcome the existing problems in epigraphy.
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VTutor: A Platform for Improving Searchability and Interactivity of Recorded Lectures
(IEEE, 2019-12-05) Karunaratna, D; Hettiarachchi, I; Fernando, S; Epa, S; Kodagoda, N; Suriyawansa, K
Recorded lectures have gained popularity as a method of delivering lecture content as they give learners a host of distinct advantages such as the ability to follow lectures without time or location constraints and to consume the lectures at their own pace. However, despite such benefits, they have a tendency to be lengthy and tedious to watch. They also prove cumbersome when precise information needs to be extracted from the content. Another drawback is that recorded lecture videos fail to show the connection between the lecture and its support material such as slides and questionnaires. Though many of the existing platforms allow editing lecture videos for more interactivity, the methods employed by these platforms have always been manual, and therefore time intensive. VTutor is a web platform that aims to address these drawbacks by introducing automation into the video enhancement process, eventually combining the lecture material to create an enhanced user experience. Specifically, VTutor allows users to navigate through a lecture video using subtopics, its corresponding slides and code samples. Furthermore, it is equipped with the ability to automatically generate questions by scraping the internet based on provided keywords thus improving the level of engagement that a learner has with the lecture.
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Efficient Agricultural Sensor Network with Disease Detection
(IEEE, 2019-12-05) Gunathilaka, M. D. N; Lokuliyana, S; Udurawana, A. W. G. C; Dissanayaka, D. M. A. S; Jayakody, A
The smart Agriculture concept is a new trending topic in making traditional agriculture task automation to make them more effective and efficient to suit current human requirements. With machine learning and image processing technologies those tasks are made more robust and accurate while maintaining the low cost made this research inspired to adopt Sri Lankan farmers to develop a real-time disease detection monitoring system with wireless sensor node for crops, so that would be able to harvest and store energy for battery-free operation using supercapacitors and technologies such as Maximum Power Point Tracking. The main outcomes of this nodes are to monitor the growth environment and also the crop for diseases by using image processing and machine learning techniques in order to cultivate a better fruit overall. The wireless sensor node can be adapted to be used on multiple types of remote farms. Pineapple (Ananas comosus) was selected as the test crop for the research which is a fruit grown widely in tropical countries in large fields. The texture, shape of the fruit and the taste of pineapple changes due to various conditions. The final system makes monitoring the crop for diseases a lot effective while making monitoring the growth conditions more efficient compared with what's available on the market.
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Ayurvedic Knowledge Sharing Platform with Sinhala Virtual Assistant
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Jayalath, A.D.A.D.S.; Nadeeshan, P.V.D.; Amarawansh, T.G.A.G.D.; Jayasuriya, H.P.; Nawinna, D. P.
Apart from western medicine methods Ayurveda medicinal system is a very huge and better resulting medicinal technique. In these Ayurveda methods identification of indigenous plants to predict the medicines is very important and must do very carefully. Generally main components that we use to identify a plant are leaf, flower, trunk and root etc. Among these features, we use images of leaves and flowers. To do this we are using deep learning based CNN approaches and machine learning and technologies. Those are OpenCV, and Tensorflow classification algorithm. According to the evidences that we gathered from surveys and interviews that we conducted with the responsible parties we could find out that lots of people don’t have much knowledge about indigenous medicinal plants and their Ayurveda treatment methods. To overcome this problem we implemented Ayurveda information centralized chatbot which is able to answer user’s questions relevant to the Ayurveda and indigenous medicinal plants. Chatbot will analyze the question that user asks and will provide answers according to that. Another useful feature of this system is it provides relevant information of Ayurveda doctors. So users can find doctors according to their needs and they are able to rate and give recommendations for the doctors. That will be help others to find doctors more easily and efficiently without any doubt.
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Comprehensive Forensic Data Extraction and Representation System for Windows Registry
(IEEE, 2019-12-05) W. De Alwis, C; Rupasinghe, L
Computer forensics is the process of methodically examining computer media (hard disks, diskettes, tapes, etc.) for evidence. When considering computer forensics, registry forensics plays a vital role because it helps identifying system configurations, application details, user configurations and helps in finding registry malware. Therefore, it is significant to extract this registry information to simplify the investigations for forensic professionals. At present, tools are limited to few commonly used registry information and there is a much border area to cover. Investigators have to manually search for the registries for required artifacts. But the nature and complexity of the registry file structure limits most of the investigators using these registries. Limiting this registry analysis only to the physical registry files and not considering the ability of extraction of registry information from Volatile Memory is another significant issue in registry forensics. Because these tools are only rely on the physical registry files and cannot extract registry artifacts from Volatile Memory. In order to cater to this problem, this research provide a comprehensive solution to registry analysis. This system is capable of extracting registry information from both physical registry files and Volatile Memory.
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An Automated Tool for Memory Forensics
(IEEE, 2019-12-05) Murthaja, M; Sahayanathan, B; Munasinghe, A. N. T. S; Uthayakumar, D; Rupasinghe, L; Senarathne, A
In the present, memory forensics has captured the world's attention. Currently, the volatility framework is used to extract artifacts from the memory dump, and the extracted artifacts are then used to investigate and to identify the malicious processes in the memory dump. The investigation process must be conducted manually, since the volatility framework provides only the artifacts that exist in the memory dump. In this paper, we investigate the four predominant domains of registry, DLL, API calls and network connections in memory forensics to implement the system `Malfore,' which helps automate the entire process of memory forensics. We use the cuckoo sandbox to analyze malware samples and to obtain memory dumps and volatility frameworks to extract artifacts from the memory dump. The finalized dataset was evaluated using several machine learning algorithms, including RNN. The highest accuracy achieved was 98%, and it was reached using a recurrent neural network model, fitted to the data extracted from the DLL artifacts, and 92% accuracy was reached using a recurrent neural network model, fitted to data extracted from the network connection artifacts.
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A Mobile Application to Predict and Manage High Blood Pressure and Personalized Recommendations
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Rajapaksha, S.; Abhayarathne, W.J.A.; Kumari, S.G.K.; De Silva, M.V.L.U.; Wijesuriya, W.M.S.M.
The purpose of this investigation is to present a mobile application using AI expert and how to predict and manage high blood pressure and provide personalized recommendations to lower it. Basically, the system interprets the inadequate and inappropriate intake of food is known to cause various health issues and diseases. Due to the diversity of food components and a large number of dietary sources, it is challenging to perform a real-time selection of diet patterns that must fulfill one’s nutrition needs and with considering your health issues and diseases. In this research, to address this issue to present an android based system, called Smart Blood Pressure Recommendation app. The purpose of this system is to allow patients to have an easy way to monitor their health and to see how their blood pressure has changed over time. This offer advice or suggestions, without having to schedule an appointment. As the system continues to gather data from a patient, it begins to offer advice its own if it finds that the patient’s current conditions fit a certain condition or pattern. To generate a recommendation, it refers to an Ontology based data model. The data model gains information about its knowledge by doctors and nutritionists that can be used by AI expert. This research helps users to identify their previous record charts of blood pressure, reliable alarms for user blood pressure medication, popup notifications, build health
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Analyzing Payment Behaviors And Introducing An Optimal Credit Limit
(2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Bandara, H.M.M.T.; Samarasinghe, D.P.; Manchanayake, S.M.A.M.
Identifying an optimal credit limit plays a vital role in telecommunication industry as the credit limit given to customers is influence on the market, revenue stabilization and customer retention. Most of the time service providers offer a fixed credit limit for customers which may cause customer dissatisfaction and loss of potential revenue. Therefore, it is essential to determine an optimal credit limit that maintains customer satisfaction while stabilizing the company revenue. Clustering algorithms were used to group customers with similar payment and usage behaviors. Then the optimal credit limit derived for each cluster is applicable to all the customers within the cluster. In order to identify the most suitable clustering algorithm, cluster validation statistics namely, Silhouette and Dunn indexes were used in this research. Based on the scores generated from these statistics KMeans algorithm was chosen. Furthermore, the quality of the KMeans clustering was evaluated using Silhouette score and the Elbow method. The optimal number of clusters are identified by those validation statistics. The significance of this approach is that the optimal credit limits generated by these clustering models suit dynamic behaviors of the customer which in turn increases customer satisfaction while contributing to reducing customer churn and potential loss of revenue.
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Traffic Density Estimation and Traffic Control using Convolutional Neural Network
(IEEE, 2019-12-05) Ikiriwatte, A. K; Perera, D. D. R; Samarakoon, S. M. M. C; Dissanayake, D. M. W. C. B; Rupasignhe, P. L
The existing traffic light control systems are inefficient due to the usage of predefined algorithms on offline data. This causes in numerous problems such as long delays and a wastage of energy. Estimation of traffic density indirectly affects in decreasing the high traffic congestion which will occur due to the less planning of transportation infrastructure and the policies. The goal of this research is to introduce an applicable method to improve the existing static traffic signal system into a dynamic system. As an approach we analyze the use of machine learning algorithms to measure the traffic density to tackle this research problem of high traffic congestion. The main target is to implement this system for the four-way junctions since it is a place where the possibility of having a traffic congestion seems to be high. With use of these traffic density estimation algorithm, crowd density estimation and signal handling we conduct experiments on minimizing the congestion at four-way junctions. We decided on using convolutional neural networks as an advanced machine learning method to increase the accuracy of the learning algorithm.
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Responsive drone autopilot system for uncertain natural language commands
(IEEE, 2019-12-05) Rajapaksha, S. K; Illankoon, V; Halloluwa, N. D; Satharana, M; Umayanganie, D
The purpose of this investigation is to realize how possible is it to control a drone with human's English natural language commands, even the command contains some quantitative uncertain words. Basically, the system interprets the commands with uncertain words, into a machine understandable format. According to the literature review, human-robot association does not go with uncertain words and it is a considerable gap. This proposed system has a client side mobile application to input voice. To build a machine understandable command, it refers to an ontology based knowledge store. The knowledge store gains information and expands its knowledge by crawling websites. Once the user commands, the drone to make a movement, the system will analyze the instruction to check if there is uncertainty and if it points to a surrounding object that is visible to the drone's camera. For extracting the uncertainty it uses NLTK along with a specific grammar rule and that methodology is succeeded according to the results. After collecting that information the system can navigate through the user's instruction. This research helps non-skilled drone pilots to have a smooth flying experience, and also other researchers to discover about natural language processing with robotics.
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Secure Web Navigation with Intrusion Detection And Quota Management for SOHO and Small Scale Businesses
(IEEE, 2019-12-05) Perera, M. A. D. S. R; Hemapala, C; Udugahapattuwa, M; Senarathne, A. N
It's a modern day necessity and a trend to offer free and open web access to their customers and employees in small scale and Small Office Home Office (SOHO) business culture (restaurants, malls, coffee shops). Unfortunately, internet security and quota management are mostly overlooked which makes it an intruders' paradise. The existing solutions that incorporate machine learning based dynamic aspects, cannot be afforded by our target audience nor do they possess the extensive IT knowledge to configure and maintain them. To cater to this gap, this research proposes the network management device `Dynamic Defender', targeted for small scale and SOHO type businesses which focuses on affordability and user-friendliness as key factors while incorporating cutting edge machine learning technologies. The Dynamic Defender's Intrusion Detection Engine is comprised of 99.13% accuracy with its base run on Artificial Neural Networks. URL Classification Engine produced high accuracy on all 3 machine learning algorithms which were used. Specifically, Random Forest with 92.94 % precision, Artificial Neural Networks with 90.33% precision and Logistic Regression with 91.41% precision. The Dynamic Bandwidth Management System consisted of an 89% accuracy level on the hybrid module of Linear Regression and Decision Trees while the Quota Management System (QMS) provided an accuracy level of 82% in K-Nearest Neighbors and 89% on Decision Tree algorithm.
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On the effectiveness of using machine learning and Gaussian plume model for plant disease dispersion prediction and simulation
(IEEE, 2019-12-05) Miriyagalla, R; Samarawickrama, Y; Rathnaweera, D; Liyanage, L; Kasthurirathna, D; Nawinna, D; Wijekoon, J. L
Agriculture plays a vital role in the economic development of the entire world. Similarly, in Sri Lanka, 6.9% of the national GDP is contributed by the agricultural sector and more than 25% of Sri Lankans are employed in the field of agriculture. But the frequent fluctuations of climate conditions have caused the spread of diseases such as late blight which eventually has led to the devastation of entire plantations of Sri Lankans. To this end, this paper proposes to forecast the possible dispersion pattern and assist the farmers in identifying the possibility of the disease getting dispersed to nearby crops to provide early warning. Eventually, it leads the farmers to take precautions to save the plants before reaching a critical stage. The yielded results show that the proposed method successfully performed disease diagnosis and disease progression level identification with 90-94 % accuracy and dispersion pattern analysis.
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Dia-Shoe: A Smart Diabetic Shoe to Monitor and Prevent Diabetic Foot Ulcers
(IEEE, 2019-12-05) Kularathne, N; Wijayathilaka, U; Kottawaththa, N; Hewakoralage, S; Thelijjagoda, S
Diabetes is a continuous, lifetime disease that affects the body ability to use the energy found in food. Usually, the body splits down the sugars and carbohydrates you eat into a sugar called glucose. Glucose fuels the cells in the body. But the cells require insulin, a hormone, in the bloodstream to get in the glucose and utilize it for energy. Body doesn't make enough insulin, or it can't use the insulin it produces are the two main reasons to occur diabetes. Since the cells can't take in the glucose, it forms up in the blood. High levels of blood glucose can ultimately create nerve injury to the tissues in the extremities. The damage to nerves is a significant reason to occur foot ulcers. If a diabetic sufferer has foot ulcers, he/she need to manage it. Unless with the time they can drop their sensation in the foot which is known as neuropathy situation, which eventually appears in foot ulcers. Because of this, doctors advise using a diabetic shoe to prevent foot ulcers. These shoes decrease the peak plantar force exerted on the foot, but temperature, humidity and abnormal pressure are not managed, and this can be effective on the infection of wounds. As Diabetes is the main reason for the occurrence of these foot ulcers, maintaining blood glucose level is important. Diabetic patients can manage their blood glucose level by controlling daily calorie consumption and activities. For this self-maintenance of diabetes, patients should reduce their blood glucose level periodically and get notified of its divergence. The concept “Día shoe” proposed is an innovative mobile base plug and play device which can be attached to any sort of diabetic shoe to manage the foot ulcers and prevent them from spoiling, while monitoring temperature, humidity, weight and step count of the patient through a mobile application.
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Air Visio: Air Quality Monitoring and Analysis Based Predictive System
(IEEE, 2019-12-05) Dissanayaka, A. D; Taniya, W. A. D; De Silva, B. P. A. N; Senarathne, A. N; Wijesiri, M. P. M; Kahandawaarachchi, K. A. D. C. P
Sri Lanka is facing a serious air pollution problem that severely impacts the daily life of every Sri Lankan. The main source of ambient air pollution in Sri Lanka is vehicular emissions. A methodology to monitor the air quality in real-time with an overall coverage of Sri Lanka, and automatically process these huge data to identify air quality levels in a specific area is now becoming a timely research topic. An air quality monitoring and analysis based predictive system is proposed to monitor the ambient air quality, provides the best route with minimum polluted air, maps the heatmaps to identify the current air quality of an area easily and predict the future air quality of each area. The prototype was implemented by hierarchically deploying two different gas sensors, an Arduino Uno board and a wifi module, to implement in open spaces between smart buildings, and transfers the sensor data back to the information processing center by using IoT technology for real-time display. The information processing center stores real-time information which is collected from the sensors to the database. By reading sensor data stored in the database, the front-end system draws real-time, accurate air quality levels included maps and predicts the less polluted routes and the air quality level over an area. Further, an energy harvesting system is also presented for the power consumption of the device. A route is suggested in an accuracy of 70% from this system. The final product provides a low cost, highly portable and easily maintainable system for the users.
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Therapy Tool for Adolescents with ADHD
(IEEE, 2019-12-05) Nugawela, R. C. W. M. R. H. E; Fonseka, W. G. S; Perera, G. U. C; Mihirangi, S. K. S; Kahandawaarachchi, K. A. D. C. P
Attention deficit hyperactivity disorder (ADHD) is one of the most common disorders widespread among children and adolescents. Adolescence is the transitional phase between childhood and adulthood. The main three symptoms of ADHD are hyperactivity, impulsivity and inattention. Prevalence of ADHD among adolescents is approximately 5% in Sri Lanka. Treatment is a vital factor in any ADHD patient's lifestyle which involves a combination of pharmaceutical medications as well as therapeutic treatments with the help of the patient's family and friends. With no proper treatment, children and adolescents with ADHD are faced with the risk of carrying on the symptoms of the disorder into adulthood. Hence, the importance of treatments for patients diagnosed with ADHD is clearly evident. The solution derived at is an Android Application to provide a therapeutic treatment acceptable to the three groups of symptoms in ADHD and to track and maintain a log of records of the patient's performance and progression. For this research, one participant each was dedicated for each testing module in order to eliminate subjectivity and ensure accuracy. The Android Application developed consists of activities targeted at treating all main groups of ADHD symptoms and offers a feasible solution for patients, parents and specially for doctors to monitor the patient's progress.
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Security Platform for Mobile OS
(IEEE, 2019-12-05) Benett, A. S. B; Vinushanth, K; Ranjitha, L; Abisherk, R. S; Senarathne, A. N
Evolution of human is evident in everything that we see, feel and use today. History of phone is one such example we can see. Modern devices have all the features that helped it to become the ultimate source of data for an individual. It was easy for an individual to keep all his data intact with him in his hands. There are vulnerable points which can be exploited to acquire the personal and sensitive data from the device in order to gain unethical advantage over an individual. Bluetooth, Wi-Fi and human errors are some of those vulnerable points. In this paper, multilevel malware detection with the help of machine learning, Rogue access point detection and accidental data leakage prevention are proposed with an emphasis on Android mobile operating system. As a result, accidental leakage of sensitive data by the user can be prevented. Further, rogue access point detection will help the user to prevent data loss through wireless network and the malware detection can prevent all the known and 85% of the unknown malwares.
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Stock Market Prediction Using Machine Learning Techniques
(IEEE, 2019-12-05) Sirimevan, N; Mamalgaha, I. G. U. H; Jayasekara, C; Mayuran, Y. S; Jayawardena, C
Predicting stock market prices is crucial subject at the present economy. Hence, the tendency of researchers towards new opportunities to predict the stock market has been increased. Researchers have found that, historical stock data and Search Engine Queries, social mood from user generated content in sources like Twitter, Web News has a predictive relationship to the future stock prices. Lack of information such as social mood was there in past studies and in this research, we discuss an effective method to analyze multiple information sources to fill the information gap and predict an accurate future value. For this, LSTM - RNN models were employed to analyze sperate sources and Ensembled method with Weighted Average and Differential Evolution technique were used for more accurate prediction of the stock prices. And highly accurate predictions were made to one-day, seven-days, 15-days and 30 days for the future. So that investors could gain an insight into what they are inventing for and the companies to track how well they will perform in the stock market.
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Automated Smart Checkup Portal Network System to Check the Vision and Hearing of the Patients
(IEEE, 2019-12-05) Dias, A. A. T. K; Vithusha, J; Liyadipita, L. A. M. T. J; Abeygunawardhana, P. K. W
The human eye and ear are impressive systems in the body. Vision and Hearing are the main functions of those organs. We should regularly check our vision and hearing, It's the most reliable ways to maintain good vision and hearing. Not only that, every patient must keep a medical history and previous checkup records, those related to vision and hearing and those results should be real-time processed. Therefore, we have built an Automated Centralized Smart EE (eye and ear) Checkup Portal Network System. We have designed and developed an automated centralized vision and hearing checkup rooms network, Automated centralized live traffic indicating cloud-based web application to establish in every hospital.