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

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Author: search.filters.author.Kasthurirathna, D

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Now showing 1 - 6 of 6
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    Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence
    (IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, L
    The Android operating system is one of the most used operating systems in the world and has become a target to malware authors. Traditional malware detection methods such as signatures find it impossible to deal with detecting complex and intelligent malware which are capable of obfuscating and repackaging to avoid being detected. There is therefore an increase in the need to have more efficient and intelligent forms of malware detection. Artificial intelligence has now been brought to the field of malware detection and classification. Due to its accuracy and intelligence it has become an ideal solution to bridge the gap between traditional classifiers and the intelligent malware. Currently, research is mainly being conducted using either machine learning or deep learning techniques to target all or a given malware family. This paper proposes a methodology which brings an ensemble solution between the Support Vector Machine algorithm and the Convolutional Neural Network to create a solution that provides a higher accuracy than available techniques.
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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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    Crowd-sourced Approach to Generate Real-time Passenger Train Time Table
    (IEEE, 2019-12-05) Weerathunga, D. C. B; Jayawickckrama, M. M. M; Jayasekara, U; Kasthurirathna, D; Wijetunga, P. S
    Availability of real-time public train transportation information can help to improve the commuters' transportation needs. Most of the time the guaranteed information is only supported in a closed system. Due to the administrative issue, there is no infrastructure to provide real-time data to any interested party. This paper proposed a framework that aims to provide a multiple-sourced crowdsourcing approach to generate Real-time Public Train information. The proposed system will enhance the accuracy as well as efficiency of the current system to provide accurate real-time train Status by crowdsourcing train location GPS (Global Positioning System) data from the passenger's smartphones. The tracing data are used to update the arrival/departure time using a predictive data source. The basic information is collected and distributed of each train route, stops and schedules. The `User Report Information' includes information related to trains and can be shared among the other interested parties through our System.
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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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    Predictive Analytics Platform for Organic Cultivation Management
    (IEEE, 2019-12-05) Rathnayake, R. M. S. M; Ekanayake, E. W. L. M. B; Kahandawala, K. A. I. P; de Silva, W. G. S. C; Nawinna, D. P; Kasthurirathna, D
    There is an increasing demand for organic farming as an environmentally friendly alternative to industrial agricultural system. It is a method of farming that does not involve pesticides, fertilizers, genetically modified organisms, and growth hormones. Organic farming yields vital benefits such as preservation of soil's organic composition, fertility, structure and biodiversity, reduce erosion and reduce the risks of human, animal, and environmental exposure to toxic materials. This paper presents design and development of a software platform for supporting sustainability of organic agriculture system, which has been implemented as a proof of concept in Sri Lanka. The predictive analytics based service platform that not only supports farming decisions of organic farmers but also offers an electronic market place for organic foods. The proposed system is capable of predicting organic harvests, prices and provide decision support on crop selection for upcoming cultivations. To implement this system, machine learning and optimization techniques have been used. In addition, it uses block chain technology to maintain authentication and identity management of organic farmers so that the consumers can trust they get genuine organic food.
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    Comparative analysis of the application of Deep Learning techniques for Forex Rate prediction
    (IEEE, 2019-12-05) Aryal, S; Nadarajah, D; Kasthurirathna, D; Rupasinghe, L; Jayawardena, C
    Forecasting the financial time series is an extensive field of study. Even though the econometric models, traditional machine learning models, artificial neural networks and deep learning models have been used to predict the financial time series, deep learning models have been recently employed to do predictions of financial time series. In this paper, three different deep learning models called Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN) and Temporal Convolution Network (TCN) have been used to predict the United States Dollar (USD) to Sri Lankan Rupees (LKR) exchange rate and compared the accuracy of the models. The results indicate the superiority of CNN model over other models. We conclude that CNN based models perform best in financial time series prediction.