Research Papers - Dept of Information Technology

Permanent URI for this collectionhttps://rda.sliit.lk/handle/123456789/593

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End date: 2019Subject: search.filters.subject.Machine Learning

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Now showing 1 - 7 of 7
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    PublicationOpen Access
    A Review of Data Mining Methods for Educational Decision Support
    (Faculty of Graduate Studies, University of Kelaniya, Sri Lanka, 2016) Kasthuriarachchi, K. T. S; Liyanage, S. R
    Data mining is a computer based information system which is devoted to scanning huge data repositories, generate information and discover knowledge. It attempts to uncover data patterns, organize information of hidden relationships, structure association rules and many more operations that cannot be performed using traditional computer based information systems. Therefore, data mining outcomes represent a valuable support for decisions making in various industries and education is one domain that can benefit from data mining. Application of data mining in education is living in its spring time and preparing for a hot summer season. Educational data mining emerges as a paradigm oriented to design models, tasks, methods, and algorithms for exploring data from educational settings. Educational Data Mining develops and adopts statistical methods, machine- learning and data mining methods to study educational data generated basically by students and educational instructors. The main goal of applying data mining in education is largely to improve learning by enabling data driven decision making for improve current educational practices and learning materials. Educational knowledge discovery, in data mining point of view can be seen as a similar process of applying the general knowledge discovery and data mining process and in experimental point of view, it can be seen as an iterative cycle of hypothesis formation, testing and refinement which not just turn data into knowledge but, also to filter the mined knowledge for decision making. There are many applications in education arena that have been resolved using data mining. There are more research studies have also been conducted under various educational problem categories and also there are a number of frequently used data mining methods use in Educational Data Mining. Various open source and commercial tools are available to apply data mining methods on the educational data. This study focuses on the identification of various educational problem domains where data mining methods can be applied and to study the suitability of the available data mining methods and the tools to perform Educational Data Mining in Sri Lankan Educational Institutes. The knowledge discovered by this review is expected to generate meaningful insight and provide guidance for important decisions made by educators
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    PublicationOpen Access
    Recommendations for Students in Higher Education: A Machine Learning Approach.
    (International Postgraduate Research Conference (IPRC) , Faculty of Graduate Studies, University of Kelaniya, 2017) Kasthuriarachchi, K. S. T; Liyanage, S. R
    Educational Data Mining is a rising discipline in Data Mining setting which concentrated on creating systems for investigating one of a kind data that starts from educational settings, and utilizing those procedures to better comprehend students and the settings which they learn in. There were numerous potential circumstances for applying data mining in education, such as; predicting the performance of students in education domain, advancement of student models, making methodologies for instructive help, settling on decisions to growing better learning systems, upgrading the execution of students and lessening the dropout rate of students and so on. There were sure examinations directed in dissecting students' data to foresee the execution in light of data mining approaches utilizing machine learning algorithms. However, a few of them were guiding the students using the recommendations of educators to success in their academic lives. The key objective of this research is to provide educators‘ recommendations to students in higher education through data analysis using machine learning algorithms. In this experiment, the data about more than 3000 students with eight attributes; age, gender, A/L Stream, A/L English Grade, does the student has repeat modules, GPA of Semester1, GPA of Semester 2 and Pass status of year 1 were included into the research sample who registered and were following their first academic year of an Information Technology degree in an institute. Three classification type machine learning algorithms were used to build the predictive model. They were Naïve Bayes algorithm, Decision Tree algorithm and Support Vector Machine algorithm. The accuracy of the models built by each algorithm have been tested against each other to identify the best model and extracted the most influencing/ important attributes in the model to predict the final grade (pass/ fail) in the end of first year of the students. Accordingly, the accuracy measures of Naïve Bayes, Decision tree andSupport Vector Machine were recorded as 74.67%, 74.01% and 74.01% respectively and it was clear that all three algorithms were holding almost same accuracy level. However, the model generated by Naïve Bayes algorithm has been selected since it was outperformed the rest. Then rank features by importance method was used as the feature selection method to identify the most influencing factors of the predictive model. As the result of it, past repeat modules, GPA of Semester1, GPA of Semester 2 were extracted as the most influencing attributes. Furthermore, these attributes were tested using correlation analysis to measure the significance of the relationship with the target attribute. According to this study, the educators will be able to recommend the students to score good marks for assessments of the subjects to obtain a better GPA to semester 1 and semester 2 without failing the modules to successfully complete the first year of the degree course which make more beneficial for educators as well as students to be success.
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    PublicationEmbargo
    Intelligent Trainer for Athletes using Machine Learning
    (IEEE, 2019-09-27) Attigala, D. A; Weeraman, R; Fernando, W. S. S. W; Mahagedara, M. M. S. U; Gamage, M. P. A. W; Jayakodi, T
    International professional athletes are looked after and trained by a team of professionals consisting of trainers and medical professionals among other. They make sure that the athlete is physically and mentally prepared to compete in a competition, and often train for years for the perfect results. Sri Lankan athletes however do not have the same luxury of being taken cared by a team of such professionals since they are young due to the lack of adequate resources in the country. `Optio' mobile application aims to provide a solution for this problem by creating a mobile application that the athlete constantly has access to, which will provide him/her with dietary, exercise and health related advice catered and customized to each individual athlete's needs. Consequently, this will provide a method which will let the athlete's trainer monitor their athletes easily as well as let them pick the most suitable athlete for a competition.
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    PublicationOpen Access
    Agro-Genius: Crop Prediction Using Machine Learning
    (https://ijisrt.com/agrogenius-crop-prediction-using-machine-learning, 2019-10) Gamage, M. P. A. W; Kasthurirathna, D; Paresith, M. M; Thayakaran, S; Suganya, S; Puvipavan, P
    This paper present a way to aid farmers focusing on profitable vegetable cultivation in Sri Lanka. As agriculture creates an economic future for developing countries, the demand of modern technologies in this sector is higher. Key technologies used for this problem are Deep Learning, Machine Learning and Visualization. As the product, an android mobile application is developed. In this application the users should input their location to start the prediction process. Data preprocessing is started when the location is received to the system. The collected dataset divided into 3 parts. 80 percent for training, 10 percent for testing and 10 percent for validation. After that the model is created using LSTM RNN for vegetable prediction and ARIMA for price prediction. Finally, for given location profitable crop and predicted future price of vegetables are shown in the application. Other than the prediction, optimizing for multiple crop sowing according to the user requirements and visualizing cultivation and production data on map and graphs are also given in the application. This paper elaborates the procedure of model development, model training and model testing.
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    A Mobile App for Location Based Customer Notifications about Sales Offers
    (2019 1st International Conference on Advancements in Computing (ICAC), SLIIT, 2019-12-05) Saluwadana, R.B.; Hemachandra, K.A.N.W.; Jayasinghe, L.M.R.; Ahnaf Hassanar; Gamage, M.P.
    Nowadays merchants’ focus on sending specifics about their sales offers to prospective customers through electronic means. But customers are neutral about those messages if they are away from those shops. Therefore, the authors decided to implement a mobile application to send location-based sales offer notifications to customers in order to overcome this problem, with some additional features. The main features in the proposed system are to filter out sales offer details from social media, send location-based notifications containing details of offers to customers, provide personalized search predictions during search, and provide recommendations to merchants to improve their business. Modern technologies like Machine Learning (ML), Deep Learning (DL) and Natural Language Processing (NLP) are used to build the solution for this problem. The main advantage of the proposed system is that customers are attracted more towards the sales offers since they receive them when they are close by to the relevant shop. Also, merchants can reach targeted customers resulting in a more effective marketing campaign. The survey conducted proved that both customers and merchants are highly satisfied with the effectiveness of the product.
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    PublicationEmbargo
    Machine Learning Based Automated Speech Dialog Analysis Of Autistic Children
    (IEEE, 2019-10-24) Wijesinghe, A; Samarasinghe, P; Seneviratne, S; Yogarajah, P; Pulasinghe, K
    Children with autism spectrum disorder (ASD) have altered behaviors in communication, social interaction, and activity, out of which communication has been the most prominent disorder among many. Despite the recent technological advances, limited attention has been given to screening and diagnosing ASD by identifying the speech deficiencies (SD) of autistic children at early stages. This research focuses on bridging the gap in ASD screening by developing an automated system to distinguish autistic traits through speech analysis. Data was collected from 40 participants for the initial analysis and recordings were obtained from 17 participants. We considered a three-stage processing system; first stage utilizes thresholding for silence detection and Vocal Activity Detection for vocal isolation, second stage adopts machine learning technique neural network with frequency domain representations in developing a reliant utterance classifier for the isolated vocals and stage three also adopts machine learning technique neural network in recognizing autistic traits in speech patterns of the classified utterances. The results are promising in identifying SD of autistic children with the utterance classifier having 78% accuracy and pattern recognition 72% accuracy.
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    PublicationEmbargo
    Smart Mirror with Virtual Twin
    (IEEE, 2019-12-05) Abeydeera, S. S; Bandaranayake, M; Karunarathna, H. U; Pallewatta, S; Dharmasiri, P; Gunathilake, B; Saparamadu, S; Senanayake, B; Jayawardena, C
    Smart Mirror with a virtual twin who helps the user as a close companion. The virtual twin monitors the user's physical appearance and tracks the data gathered from given inputs. Since this is an intelligent virtual twin it uses machine learning techniques. It helps to improve the user's mental and physical health by detecting medical conditions and providing suitable suggestions in a more personalized way. This virtual twin not only focuses on physical or mental health conditions but also gives friendly suggestions about suitable styles which helps to improve the person's life quality.