ICITR - 2020

Permanent URI for this collectionhttp://192.248.9.226/handle/123/16316

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Now showing 1 - 20 of 27

item: Conference-Full-text
Building social resilience during disasters: an investigation into the role of online social media networks
(Faculty of Information Technology, University of Moratuwa., 2020-12) Firdhous, MFM; Karunananda, AS; Talagala, PD
Within the last ten years, the world witnessed four serious epidemics. COVID-19 has been the most serious of these ones in terms of the number of people affected and the lives lost. In order to contain the spread of the disease many countries including Sri Lanka enforced 24 hour curfews. The social isolation created by lockdowns creates many problems in people including anxiety and depression. Many studies have been carried out on effect of lockdowns on mental well being of people. But, so far nobody has studied whether online social me can help people overcome the negative effects of lockdowns. This research was carried out to fill this gap. An online survey was carried out to understand how people used social media during the continuous curfew enforced by the Sri Lankan government. The research found that the average time spent using social media has increased compared to normal days. Also, majority of the users agreed that the social media helped them overcome the boredom created by the lockdown. This fact was confirmed using statistical tests in this study.
item: Conference-Full-text
Arima and ann approach for forecasting daily stock price fluctuations of industries in Colombo stock exchange, Sri Lanka
(Faculty of Information Technology, University of Moratuwa., 2020-12) Wijesinghe, GWRI; Rathnayaka, RMKT; Karunananda, AS; Talagala, PD
Time series forecasting is regarded as the most successful criterion among several factors involved in the decision-making process to pick a correct prediction model. Improving predictability has become crucial for decision-makers and managers, especially time series forecasts, in various fields of science. Using K-mean clustering and Principle Component Analysis, the dataset is clustered based upon a central point selection and the Euclidian distance measurement. The results define the main contribution sector for CSE, and the business in the selected sector in the 2008-2017 period in accordance with the clustering results. In particular, ARIMA has demonstrated its performance in predicting the next lags in precision and accuracy. With regard to Colombo Stock Exchange (CSE), there are very few studies in the literature that have focused on new approaches to forecasts of high volatility stock price indexes. Different statistical methods and economic data techniques have been widely applied in the last decade in order to classify CSE's stock price, patterns and trade volumes. This article looks at the best sector and organization to invest in and discusses whether and how the deep-learning algorithms for time series data projection, such as the Back Propagation Neural Network, are better than traditional algorithms. The results show that Deep learning algorithms like BPNN outperform traditionally based algorithms like the model ARIMA. For ARIMA and ANN, MAPE values are 0.472206 and 0.1783333 respectively. MAE values are 29.6975 and 4.708423 respectively results for ARIMA and ANN. The MAE and MAPE values relative to ARIMA and BPNN, which suggests BPNN `s superiority to ARIMA.
item: Conference-Full-text
The public sentiment analysis within big data distributed system for stock market prediction– a case study on colombo stock exchange
(Faculty of Information Technology, University of Moratuwa., 2020-12) Malawana, MADHP; Rathnayaka, RMKT; Karunananda, AS; Talagala, PD
Stock price prediction plays an important role on the journey of investors on the stock market. The prices of the company stocks on the market are performed by different deliverables. Social media data sets, news sites, feedback and reviews are some kind of online tools that can affect the stock market. It is often worth using this context to predict the performance of market shares. We take the advantage of Sentiment analysis on Market related announcement and respective public opinions for stock market trend predictions for more accurate recommendations. Sentiment Analysis is a machine learning program for extracting opinions from a text section that is designed to support any product, company, individual or other entity (positive, negatively, neutral). In this research calculations and data processing were performed within machine learning approach with use of Spark model on Google cloud platform. Among most of the stock prediction researches, only few researchers have done their researches on sentiment analysis within big data distributed environment. Logistic Regression and Naïve Bayes perform well in sentiment classification. Main finding of this research is that public opinion significantly influences the fluctuations of market forces and economic factors such as monetarism, government reforms, unforeseen pandemics, interest rates, public trust, and faith in bond market trust. The detection of the feelings pattern will enhance the market prediction as it ensures the consistency of decision.
item: Conference-Full-text
An automated decision-making framework for precipitation-related workflows
(Faculty of Information Technology, University of Moratuwa., 2020-12) Adikari, AMH; Bandara, HMND; Herath, S; Chitraranjan, C; Karunananda, AS; Talagala, PD
Due to weather’s chaotic nature, static workflow managers are ineffective in integrating multiple Numerical Weather Models (NWMs) with cascading relationships. Unexpected events like flash floods and breakdown in canal water control systems or reservoirs make decision-making in workflow management further complicated. To enable dynamic decision-making, we need to update part or entire workflow, terminate unfitting NWM executions, and trigger parallel NWM workflows based on recent results from NWMs and observed conditions. Most of the existing weather-related decision support systems cannot trigger or create workflows dynamically. They are also designed for specific geography or functionality, making it challenging to customize for regions with different weather patterns. In this paper, we present an automated decision-making framework for precipitation-related workflows. The proposed framework can manage complex weather-related workflows dynamically in response to varying weather conditions, automatically control and monitor those workflows, and update workflow paths in response to unexpected weather events. Using significant flood-related datasets from the Colombo catchment area, we demonstrate that the proposed framework can achieve 100% accuracy in dynamic workflow generation and path updates compared to manual workflow controlling. Also, we demonstrate that unexpected event identification and pumping station controlling workflow triggers could be improved with advance rule sets.
item: Conference-Full-text
Energy and power consumption analysis of a wireless sensor node without a voltage regulator
(Faculty of Information Technology, University of Moratuwa., 2020-12) Pieris, TPD; Chathuranga, KVDS; Kulasekera, AL; Guha, P; Mukhija, P; Karunananda, AS; Talagala, PD
Wireless sensor nodes are used in a wide range of areas such as environment monitoring, health monitoring, military and engineering applications to transfer sensor data from one location to another location. These sensor nodes usually have sensors, a microprocessor, transceiver, and a limited power supply. Most sensor nodes are configured to be in sleep mode and wakes up periodically and transfers the sensor data. In the sleep mode they consume less energy and most of the power is consumed in the wake-up time. The power consumption directly affects the life span of the sensor node. Sensor nodes typically use linear voltage regulators rather than switching ones to prevent switching noise, switching voltage ripples and to limit the footprint of the switching circuitry. Low dropout regulators are also used as they consume less energy than standard regulators and saves the battery energy. Even the low dropout regulators consume considerable amount of power at the high battery voltages and considerably high current flows through them. If there is way to save this energy, the lifespan of the sensor node can be extended. One way is avoiding the voltage regulator completely. Modern electronic components such as microcontrollers, sensors and transceivers can work in a wide range of voltages and they have internal voltage references. Therefore, the design of a sensor of a sensor node without voltage regulator is possible. In this paper we implemented a sensor node design without a voltage regulator, and we have evaluated and concluded that this design has up-to 40% energy saving compared with same sensor node design with a voltage regulator.
item: Conference-Full-text
Design and evaluation of a capacitive sensor for real time monitoring of gravimetric moisture content in soil
(Faculty of Information Technology, University of Moratuwa., 2020-12) Pieris, TPD; Chathuranga, KVDS; Karunananda, AS; Talagala, PD
Soil moisture content measurements are required widely in the fields of lawn maintenance, irrigation systems of farming, soil processing in civil engineering etc. The paper presents a novel two terminal sensor for real-time volumetric soil moisture content measurement. The sensor was designed and tested with two terminals and an epoxy layer. This sensor was designed such that the range of the sensor in the range of the moisture measurement. An a-stable multi vibrator-based circuit was used to measure the capacitance across the two terminals when inserted into soil. A microcontroller-based system was used to measure, calculate the volumetric soil moisture content and save to a SD card. The results validate the effective applicability of the developed sensor in volumetric soil moisture content measurement.
item: Conference-Full-text
Data mining approach for analyzing factors influencing vegetable prices
(Faculty of Information Technology, University of Moratuwa., 2020-12) Illankoon, IMGL; Kumara, BTGS; Karunananda, AS; Talagala, PD
Vegetables have a special place in the Sri Lankan economy. The price of vegetables, unlike the prices of other products, changes daily. There are several reasons for this and the examples include environmental conditions, supply variability, demand, festivals and seasonality, social environment, political conditions, etc. The main purpose of this research is to analyze and predict the factors influencing daily vegetable price fluctuations using data mining techniques. In this research, the most influential factors for vegetable prices were classified using the classification algorithms J48, Random Tree, Random Forest, and Support Vector Machine, taking into account the data obtained from the secondary data sources. The highest classification accuracy of 97.7143% was given by the Random Forest algorithm and it also recorded the best values for Precision, Recall, F-measure, and MCC comparing with the other three. Furthermore, it is clear that the Random forest algorithm is the most suitable to predict influential factors and it can be recommended for the purpose.
item: Conference-Full-text
Hybrid approach and architecture to detect fake news on twitter in real-time using neural networks
(Faculty of Information Technology, University of Moratuwa., 2020-12) Thilakarathna, MP; Wijayasekara, VA; Gamage, Y; Peiris, KH; Abeysinghe, C; Rafaideen, I; Vekneswaran, P; Karunananda, AS; Talagala, PD
Fake news has been a key issue since the dawn of social media. Currently, we are at a stage where it is merely impossible to differentiate between real and fake news. This directly and indirectly affects people's decision patterns and makes us question the credibility of the news shared via social media platforms. Twitter is one of the leading social networks in the world by active users. There has been an exponential spread of fake news on Twitter in the recent past. In this paper, we will discuss the implementation of a browser extension which will identify fake news on Twitter using deep learning models with a focus on real-world applicability, architectural stability and scalability of such a solution. Experimental results show that the proposed browser extension has an accuracy of 86% accuracy in fake news detection. To the best of our knowledge, our work is the first of its kind to detect fake news on Twitter real-time using a hybrid approach and evaluate using real users.
item: Conference-Full-text
Iot-enhanced smart laser fence for reducing human elephant conflicts
(Faculty of Information Technology, University of Moratuwa., 2020-12) Firdhous, MFM; Karunananda, AS; Talagala, PD
Human animal conflict is a serious problem in several countries in the Asian and African continents. The human animal conflict commonly results in the damage to properties and loss of human/animal life and limbs. The conflict between elephants and humans is considered to be one of the most serious conflicts due to the nature of losses incurred. The most commonly used deterrent used to keep the animals away from human settlements is the electric fence. Though, electric fences could act as a barrier for elephants to enter into villages, with time elephants learn to break them and enter villages. This paper presents the design of a smart laser fence that can be used for detecting elephants as well as chasing them back to the jungle with the help of other associated systems. The tests carried out on the system show that the concept is working and can be deployed in the field after an extensive field test.
item: Conference-Full-text
Human activity recognition using cnn & lstm
(Faculty of Information Technology, University of Moratuwa., 2020-12) Shiranthika, C; Premakumara, N; Chiu, HL; Samani, H; Shyalika, C; Yang, CY; Karunananda, AS; Karunananda, AS; Talagala, PD
In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset.
item: Conference-Full-text
Machine learning approach for hairstyle recommendation
(Faculty of Information Technology, University of Moratuwa., 2020-12) Weerasinghe, H; Vidanagama, D; Karunananda, AS; Talagala, PD
According to aesthetic evaluations, hair is the most unique feature which can enhance the facial features of a person. Beauty experts have identified that 70% of overall face appearance completely depends on the haircut or hairstyle. The physical attributes such as the haircut is a major determinant of women's psychology. This is the essence of why a haircut which is matching a woman's face is necessary articulation. But selecting the right haircut or hairstyle is one of the most difficult decisions to take in a woman's life. This paper presents a novel framework to select the most suitable hairstyle or haircut by classifying the face shape. The author considers the shape of the face, beauty experts knowledge related to hair cuts and hairstyles and the length of the hair to develop a model to recommend the most suitable hairstyle or haircut. The author focused to recommend the haircuts and hairstyles for women which is a subsection of this large research area. According to beauty experts identifying the shape of the face is the most important step before selecting the right hairstyle or haircut. The proposed model has the ability to classify the face shape when a user uploaded a portrait of herself. Machine Learning libraries were used to identify the landmarks of the face image and classify the face in the correct shape. Naïve Bayes classification algorithm has used to recommend the most suitable hairstyle or haircut according to the detected face shape., hair length and information collected from the hair experts. User has given an option to share the recommended hair style or haircut with the beautician via “The Beauty Quest” Salon network platform. Five thousand images were trained, and python language has used as the programming language. The accuracy of the face shape classification model is 91% and the accuracy of the hair recommendation is also 83%.
item: Conference-Full-text
Automatic diagnosis of diabetic retinopathy using machine learning: a review
(Faculty of Information Technology, University of Moratuwa., 2020-12) Gunawardhana, PL; Jayathilake, R; Withanage, Y; Ganegoda, GU; Karunananda, AS; Talagala, PD
Diabetic Retinopathy is a popular cause of diabetes, causing vision-impacting lesions of the retina. Blindness may be avoided by early detection. The ophthalmologist's manual approach of diagnosing diabetic retinopathy is expensive and time consuming. At the same time, unlike computer assisted diagnostic systems, it may cause misdiagnosis. Deep learning has recently become one of the most effective approaches that has obtained better efficiency in the analysis and classification of medical images. In medical image analysis, convolutional neural networks are more commonly used as a deep learning approach and they are extremely effective. This paper assessed and addressed the new state-of-the-art Diabetic Retinopathy color fundus image classification and detection methodologies using deep learning and machine learning techniques. Additionally, various challenging issues that need further study are also discussed.
item: Conference-Full-text
Sinhala handwritten character recognition using convolutional neural network
(Faculty of Information Technology, University of Moratuwa., 2020-12) Mariyathas, J; Shanmuganathan, V; Kuhaneswaran, B; Karunananda, AS; Talagala, PD
Handwritten character recognition is widely used for the English language. It is difficult to create a character recognition model for south Asian languages because of its shape and compound characters. Among other South Asian languages (e.g.: - Tamil, Hindi, Malayalam, etc.) Sinhala characters are unique, because of their shape, which are having mostly curves and dots. These unique characteristics make it difficult to create a model to recognize Sinhala's handwritten characters. Recognizing handwritten characters rather than typed characters is more complicated because the handwriting of each individual is varying from each other. Therefore the recognition of Sinhala handwritten character need to be improved. Convolutional Neural Network (CNN) is playing a vital role in character recognition by supporting the more efficient image classification. This research focuses on recognizing Sinhala handwritten characters using CNN. Google colaboratory platform is used for the experiment, and python programming language is used for the implementation part. In total, around 110,000 image data were used for the experiment. CNN's performance was evaluated by training and testing the dataset by increasing the number of character classes. When it reaches 100 character class it shows reasonable accuracy of 90.27%. The model was trained by 5 sets of different 100 character classes. Finally, the overall accuracy of 82.33% is achieved for 434 characters. This model outerformed than similar systems.
item: Conference-Full-text
Evaluation of re-identification risks in data anonymization techniques based on population uniqueness
(Faculty of Information Technology, University of Moratuwa., 2020-12) Bandara, PLMK; Bandara, HMND; Fernando, S; Karunananda, AS; Talagala, PD
With the increasing appetite for publicly available personal data for various analytics and decision making, due care must be taken to preserve the privacy of data subjects before any disclosure of data. Though many data anonymization techniques are available, there is no holistic understanding of their risk of re-identification and the conditions under which they could be applied. Therefore, it is imperative to study the risk of re-identification of anonymization techniques across different types of datasets. In this paper, we assess the re-identification risk of four popular anonymization techniques against four different datasets. We use population uniqueness to evaluate the risk of re-identification. As per the analysis, k-anonymity shows the lowest re-identification risk for unbiased samples of the population datasets. Moreover, our findings also emphasize that the risk assessment methodology should depend on the chosen dataset. Furthermore, for the datasets with higher linkability, the risk of re-identification measured using the uniqueness is much lower than the real risk of re-identification.
item: Conference-Full-text
Prediction of diabetes using cost sensitive learning and oversampling techniques on Bangladeshi and Indian female patients
(Faculty of Information Technology, University of Moratuwa., 2020-12) Pranto, B; Mehnaz, SK; Momen, S; Huq, SM; Karunananda, AS; Talagala, PD
Diabetes is a major non-communicable disease that is responsible for many associated health risks and is rapidly increasing in low and middle income countries like Bangladesh. Class imbalance existing in datasets is a dire issue that can result the predictions of diabetes to be biased towards the majority class - thus reducing the reliability of machine learning models. Considering the associated risks of diabetes, a decrease in recall can result in life threatening consequences. In order to tackle this problem, a cost-sensitive learning and synthetic minority oversampling technique (SMOTE) have been applied on the PIMA Indian dataset. After that, the models have been tested on PIMA test set as well as on dataset collected from Kurmitola General Hospital (KGH), Dhaka, Bangladesh. Our results demonstrate that this proposed approach has successfully improved the reliability of the previous ML models to predict diabetes among Bangladeshi female population.
item: Conference-Full-text
Prediction of absenteeism at work using data mining techniques
(Faculty of Information Technology, University of Moratuwa., 2020-12) Skorikov, M; Hussain, MA; Khan, MR; Akbar, MK; Momen, S; Mohammed, N; Nashin, T; Karunananda, AS; Talagala, PD
High absenteeism among employees can be detrimental to an organization as it can result in productivity and economic loss. This paper looks into a case of absenteeism in a courier company in Brazil. Machine learning techniques have been employed to understand and predict absenteeism. Understanding this would provide human resource managers an excellent decision aid to create policies that can aim to reduce absenteeism. Data has been preprocessed, and several machine learning classification algorithms (such as zeroR, tree-based J48, naive Bayes, and KNN) have been applied. The paper reports models that can predict absenteeism with an accuracy of over 92%. Furthermore, from an initial of 20 attributes, disciplinary failure turns out to be a very prominent feature in predicting absenteeism.
item: Conference-Full-text
Artificial neural network to estimate the paddy yield prediction using remote sensing, weather and non weather variable in Ampara district, Sri Lanka
(Faculty of Information Technology, University of Moratuwa., 2020-12) Wanninayaka, WMRK; Rathnayaka, RMKT; Udayakumara, EPN; Karunananda, AS; Talagala, PD
In Sri Lanka, seasonal paddy area mapping and rice prediction is based on the traditional methods with poor technologies. Ampara district has been chosen as the study area because its contribution is considered as the second highest paddy yield to the Sri Lankan rice harvest. This study focuses on developing models for precise mapping paddy and predicting the harvest of rice in the Ampara district. It helps the government and persons of authority to take decisions about how to manage the economy based on the rice quantity. Research includes the imageries of satellites sentinel-1 and sentinel-2 the period from April to September 2019. The two classification methods, Divisional Secretory Division (DSD) and maximum likelihood classification were used to identify the real paddy area. The accuracy rates of these classifications were 0.92 and 0.86 respectively. Artificial Neural Network (ANN) model was used to predict paddy rice harvest using sentinel 2 features extracts and round truth data. Mean square error of the model is 0.106 and mean absolute error is 0.245. Increasing the remote sensing imagery directly affects to enhance accuracy. Increasing the number of sample classes and number of classes in various types will raise-up higher accuracy than in here.
item: Conference-Full-text
Real-time uber data analysis of popular uber locations in kubernetes environment
(Faculty of Information Technology, University of Moratuwa., 2020-12) Gunawardena, TM; Jayasena, KPN; Karunananda, AS; Talagala, PD
Data is crucial in today's business and technology environment. There is a growing demand for Big Data applications to extract and evaluate information, which will provide the necessary knowledge that will help us make important rational decisions. These ideas emerged at the beginning of the 21st century, and every technological giant is now exploiting Big Data technologies. Big Data refers to huge and broad data collections that can be organized or unstructured. Big Data analytics is the method of analyzing massive data sets to highlight trends and patterns. Uber is using real-time Big Data to perfect its processes, from calculating Uber's pricing to finding the optimal positioning of taxis to maximize profits. Real-time data analysis is very challenging for the implementation because we need to process data in real-time, if we use Big Data, it is more complex than before. Implementation of real-time data analysis by Uber to identify their popular pickups would be advantageous in various ways. It will require high-performance platform to run their application. So far no research has been done on real-time analysis for identifying popular Uber locations within Big Data in a distributed environment, particularly on the Kubernetes environment. To address these issues, we have created a machine learning model with a Spark framework to identify the popular Uber locations and use this model to analyze real-time streaming Uber data and deploy this system on Google Dataproc with the different number of worker nodes with enabling Kubernetes and without Kubernetes environment. With the proposed Kubernetes environment and by increasing the worker nodes of Dataproc clusters, the performance can be significantly improved. The future development will consist of visualizing the real-time popular Uber locations on Google map.
item: Conference-Full-text
Vision-based adaptive traffic light controller for single intersection
(Faculty of Information Technology, University of Moratuwa., 2020-12) Sutharsan, M; Rajakaruna, S; Jayaweera, SY; Jayaweera, JACM; Thayaparan, S; Karunananda, AS; Talagala, PD
In this paper, a vision-based adaptive traffic light controller is proposed. The proposed controller was successfully deployed and tested as a complete system in a complex roundabout in Colombo city at a highly congested time. There were two main parts to this implementation. The first part was a vision-based traffic monitoring system. In this part, a system was developed so that it monitored lanes in a junction with cameras and extracted a traffic index based on traffic density, vehicle type, and pixel-wise velocity of vehicles by processing the video streams coming from cameras. The traffic signal light controlling part was the second part of the project. This part dealt with estimating a better timing adjustment for the existing system using a mathematical modeling approach while taking the extracted traffic index as input. This system was operated with the existing system with minimum alterations for easy real-world implementation. The developed prototype was plugged into the existing system to change traffic light phase timing according to the existing traffic level.
item: Conference-Full-text
An elephant detection system to prevent human-elephant conflict and tracking of elephant using deep learning
(Faculty of Information Technology, University of Moratuwa., 2020-12) Premarathna, KSP; Rathnayaka, RMKT; Charles, J; Karunananda, AS; Talagala, PD
Human settlement is spreading to forest boundary areas because of the population growth, it triggers disputes between elephants and humans, leading to the loss of property and life. Continuous monitoring and tracking of elephants are difficult due to their large size and movement. Therefore, large-scale for real-time detection and alert of elephant intrusion into human settlements, monitoring is needed. Many methods had been implemented for the elephant’s intrusion detection and warning systems. Wildlife conservation and the management of human-elephant conflict require a cost-effective method of monitoring elephant behavior. In this paper, a method for the identification of the elephant as an object using image processing is proposed. The major aim of the study is to minimize the human-elephant conflict in the forest border areas and the conservation of elephants from human activities as well as protect human lives from elephant attacks. We used a data set containing elephants and we developed an approach to distinguish elephants and other animals. We used the Convolutional Neural Network and achieved a maximum accuracy of 94 percent. The proposed method outperformed existing approaches and robustly and accurately detected elephants. It thus can form the basis for a future automated early warning system for elephants.

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