Research Papers - Dept of Computer Systems Engineering

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    ARCSECURE: Centralized Hub for Securing a Network of IoT Devices
    (Springer, Cham, 2021-07-06) Yapa Abeywardena, K; Abeykoon, A. M. I. S; Atapattu, A. M. S. P. B; Jayawardhane, H. N; Samarasekara, C. N
    As far as it is considered, IoT has been a game changer in the advancement of technology. In the current context, the major issue that users face is the threat to their information stored in these devices. Modern day attackers are aware of vulnerabilities in existence in the current IoT environment. Therefore, securing information from being gone into the hands of unauthorized parties is of top priority. With the need of securing the information came the need of protecting the devices which the data is being stored. Small Office/Home Office (SOHO) environments working with IoT devices are particularly in need of such mechanism to protect the data and information that they hold in order to sustain their operations. Hence, in order come up with a well-rounded security mechanism from every possible aspect, this research proposes a plug and play device “ARCSECURE”.
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    CURETO: Skin Diseases Detection Using Image Processing And CNN
    (IEEE, 2020-11-17) Karunanayake, R. K. M. S. K; Dananjaya, W. G. M; Peiris, M. S. Y; Gunatileka, B. R. I. S; Lokuliyana, S; Kuruppu, A
    Busy lifestyles these days have led people to forget to drink water regularly which results in inadequate hydration and oily skin, oily skin has become one of the main factors for Acne vulgaris. Acne vulgaris, particularly on the face, greatly affects a person's social, mental wellbeing and personal satisfaction for teens. Besides the fact that acne is well known as an inflammatory disorder, it was reported to have caused serious long-term consequences such as depression, scarring, mental illness, including pain and suicide. In this research work, a smartphone-based expert system namely “Cureto” is implemented using a hybrid approach i.e. using deep convolutional neural network (CNN) and natural language processing (NLP). The proposed work is designed, implemented and tested to classify Acne density, skin sensitivity and to identify the specific acne subtypes namely whiteheads, blackheads, papules, pustules, nodules and cysts. The proposed work not only classifies Acne Vulgaris but also recommends appropriate treatments based on their classification, severity and other demographic factors such as age, gender, etc. The results obtained show that for Acne type classification the accuracy ranges from 90%-95% and for Skin Sensitivity and Acne density the accuracy ranges from 93%-96%.
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    PublicationOpen Access
    Implicit aspect detection in restaurant reviews using cooccurence of words
    (Proceedings of the 7th Workshop on computational approaches to subjectivity, sentiment and social media analysis, 2016-06) Panchendrarajan, R; Ahamed, N; Murugaiah, B; Sivakumar, P; Ranathunga, S; Pemasiri, A
    For aspect-level sentiment analysis, the important first step is to identify the aspects and their associated entities present in customer reviews. Aspects can be either explicit or implicit, where the identification of the latter is more difficult. For restaurant reviews, this difficulty is escalated due to the vast number of entities and aspects present in reviews. The problem of implicit aspect identification has been studied for customer reviews in different domains, including restaurant reviews. However, the existing work for implicit aspect identification in customer reviews has the limitation of choosing at most one implicit aspect for each sentence. Furthermore, they deal only with a limited set of aspects related to a particular domain, thus have not faced the problem of ambiguity that arises when an opinion word is used to describe different aspects. This paper presents a novel approach for implicit aspect detection, which overcomes these two limitations. Our approach yields an F1- measure of 0.842 when applied for a set of restaurant reviews collected from Yelp.
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    Infinity yoga tutor: Yoga posture detection and correction system
    (IEEE, 2020-12-02) Rishan, F; De Silva, B; Alawathugoda, S; Nijabdeen, S; Rupasinghe, L; Liyanapathirana, C
    Popularity of yoga is increasing daily. The reason for this is the physical, mental and spiritual benefits that could be obtained by practicing yoga. Many are following this trend and practicing yoga without the training of an expert practitioner. However, following yoga in an improper way or without a proper guidance will lead to bad health issues such as strokes, nerve damage etc. So, following proper yoga postures is an important factor to be considered. In this proposed system, the system is able to identify poses performed by the user and also guide the user visually. This process is required to be completed in real-time in order to be more interactive with the user. In this paper, the yoga posture detection was done in a vision-based approach. The Infinity Yoga Tutor application is able to capture user movements using the mobile camera, which is then streamed at a resolution of 1280 × 720 at 30 frames per second to the detection system. The system consists of two main modules, a pose estimation module which uses OpenPose to identify 25 keypoints in the human body, using the BODY_25 dataset, and a pose detection module which consists of a Deep Learning model, that uses time-distributed Convolutional Neural Networks, Long Short Term Memory and SoftMax regression in order to analyze and predict user pose or asana using a sequence of frames. This module was trained to classify 6 different asanas and the selected model which uses OpenPose for pose estimation has an accuracy of 99.91%. Finally, the system notifies the users on their performance visually in the user interface of the Mobile application.