Smart Computing and Systems Engineering - 2018 (SCSE 2018)

Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/18937

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Subject: search.filters.subject.Machine learning

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    A study on classifying the store positioning from the transactional data
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Takahashi, M.; Tanaka, Y.
    This paper describes a customer analysis for store positioning, using data gathered from supermarkets in Japan. Among the retail industry in Japan, there are many types of reward cards used for customer retention purposes. The rewards cards or “Point Card”, is originally aimed for customer analysis purposes, but at present the full benefits have not been extracted due to issues in data analytics. This reward card has only become a method of simply distributing “virtual money” to the customer. For the efficient use of gathering data, we propose a classification method of the customer based on the objectives of visiting stores. In this study, the customers were classified into their objectives.
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    An assessment of machine learning-based training tools to assist Dyslexic patients
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Sathsara, G.W.C.; Rupasinghe, T.D.; Sumanasena, S.P.
    Dyslexia is a language based disability, where the patients often have difficulties with reading, spelling, writing and pronouncing words. The reading speed of Dyslexics tend to be lower than their equivalents, because of slow letter and word processing. Inspite of this disorder, a dyslexic person can be trained to read in normal speed. There are manual methods and some technical improvements can be reported such as the live-scribe smart pen, Dragon Naturally Speaking, Word processors, and Video Games. This study provides an assessment about the Machine Learning (ML) based techniques used for Dyslexic patients via a systematic review of literature, and a proposed ML based algorithm that will lay foundation for future research in the areas of machine learning, augmented and healthcare training devices.
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    Analysis and detection of potentially harmful Android applications using machine learning
    (International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Kavneth, G.A.S.; Jayalal, S.
    With the rapid advancement of technology today, smartphones have become more and more powerful and attract a huge number of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86% of the total market in 2017 (Gartner, 2017) and became the most popular mobile operating system. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. This paper proposes a static-dynamic hybrid malware detecting scheme for Android applications. While the static analysis could be fast, and less resource consuming technique and dynamic analysis can be used for high complexity and deep analysis. The suggested methods can automatically deliver an unknown application for both static and dynamic analysis and determine whether Android application is a malware or not. The experimental results show that the suggested scheme is effective as its detection accuracy can achieve to 93% ∼ 100%. The findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors. Furthermore, this research compares the machine learning algorithms for static and dynamic analysis of the Android malwares and compare the accuracy by the data used to train the machine learning models. It reveals Deep Neural Networks and SVM can be used for and higher accuracy. In addition, era of the training and testing dataset highly effect the accuracy of the results regarding Android applications.