Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Embargo Classification of Documents and Images Using an Enhanced Genetic Algorithm(IEEE, 2022-12-09) Athuraliya, N; De Silva, H; Dasanayake, D; Fernando, K; Haddela, P.S; Gunarathne, AIn 1975, John Holland proposed the Genetic Algorithm (GA). The algorithm is widely used to provide superior solutions for optimization and search problems by relying on biologically inspired operators including mutation, crossover, and selection. The fittest individuals are chosen for reproduction in this algorithm to generate the next generation’s offspring. Classification is a technique used in data mining to analyze the collected data and to divide them into different classes. The relationship between a known class assignment and the properties of the entity to be classed may serve as the foundation for the classification procedure. Through this research, it has mainly consider classification for documents and images using GA. In order to enhance the accuracy and to reduce the error rate of traditional models, a new approach is proposed which is based on GA. The primary benefit of using GA in conjunction with classification is the efficiency in which it can address optimization issues. The experiment results are used to verify the suggested algorithm using benchmark data sets gathered from the UCI machine learning repository.Publication Embargo Performance Comparison of Sea Fish Species Classification using Hybrid and Supervised Machine Learning Algorithms(IEEE, 2022-10-04) Nalmi, R; Rathnayake, N; Mampitiya, L.IIn the domain of autonomous underwater vehicles, the classification of objects underwater is critical. The hazy effect of the medium causes this obstacle, and these effects are directed by the dissolved particles that lead to the reflecting and scattering of light during the formation process of the image. This paper mainly focuses on exploring the best possible image classifier for the underwater images of the different fish species. The classifications were carried out by different hybrid and supervised machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), Neural Networks (NN), Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). This study compares the algorithms’ accuracy and time and analyzes crucial features to decide the most optimal algorithm. Furthermore, the results of this paper depict that using dimension reduction methods such as PCA and LDA increases the accuracy of some algorithms. Random Forest was able to outperforms with a higher accuracy of 99.89% with the proposed feature extraction methods.Publication Embargo Classification of Human Emotions using Ensemble Classifier by Analysing EEG Signals(IEEE, 2021-04-13) Mampitiya, L. I; Nalmi, R; Rathnayake, NThis study is based on EEG brain wave classification of a well-known dataset called the EEG Brainwave Dataset. The dataset combines three classes such as positive, negative, and neutral. The classification is performed using an ensemble classifier that combines RF, KNN, DT, SVM, NB, and LR. The meta classifier is LR, while the other five algorithms work as the base classifiers. Furthermore, PCA is used as the dimension reduction method to increase the accuracy of the final output. The results are evaluated under 11 different parameters. Moreover, the accuracy of this study is compared with the seven other EEG emotion classification methods. The proposing method attained 99.25% of accuracy, outperforming the other state-of-the-art algorithms.Publication Embargo Yuwathi: Early Detection of Breast Cancer and Classification of Mammography Images Using Machine Learning(IEEE, 2022-07-18) Diddugoda, D; Fernando, D. B; Munasinghe, S. M; Weerasinghe, L; Weerathunga, IAccording to the World Health Organization's (WHO) data and records, breast cancer is one of the most common diseases among women. As a result of the mutations of the genes within a cell, the cell starts growing uncontrollably and rapidly. Such a condition is known as cancer. Cancer tumors can be categorized into two major categories, benign and malignant. However, there is no existing solution in practice to automate early breast cancer identification and risk prediction using medical images (Mammograms). This paper discusses automating breast cancer detection, breast density identification, risk prediction, and solution suggestion using machine learning, image processing, and computer vision techniques. All the mentioned features can be accessed using the application "YUWATHI", and a user can take advantage of this application by using a smartphone also a web application. The objectives of the present study are mammographic mass detection without user intervention, identifying pectoral muscles and removing them, training a machine learning model to identify the future risk of breast cancers by obtaining clinical reports from the OCR application and suggesting solutions for the above problems using a computer-aided diagnosis (CADx) system that helps doctors to make decisions swiftly. The algorithms used for breast cancer detection, breast density classification, and future breast cancer risk prediction are Convolutional Neural Network (CNN), CNN and Logistic Regression with the accuracies 97.32%, 71.97% 74.76%, respectively.Publication Embargo A Data Mining Approach to Identify the Factors Affecting the Academic Success of Tertiary Students in Sri Lanka(Springer, Cham, 2018-02-11) Kasthuriarachchi, S; Bhatt, C. M; Liyanage, S. REducational Data Mining has become a very popular and highly important area in the domain of Data mining . Application of data mining to education arena arises as a paradigm oriented to design models, methods, tasks and algorithms for discovering data from educational domain. It attempts to uncover data patterns, structure association rules, establish information of unseen relationships with educational data and many more operations that cannot be performed using traditional computer based information systems. It grows and adopts statistical methods, data mining methods and machine-learning to study educational data produced mostly by students, educators, educational management policy makers and instructors. The main objective of applying data mining in education is primarily to advance learning by enabling data oriented decision making to improve existing educational practices and learning materials. This study focuses on finding the key factors affecting the performance of the students enrolled for technology related degree programs in Sri Lanka. The findings of this study will positively affect the future decisions about the progress of the students’ performance, quality of the education process and the future of the education provider.Publication Embargo Cricket Shot Image Classification Using Random Forest(IEEE, 2021) Devanandan, M; Rasaratnam, V; Anbalagan, M. K; Asokan, N; Panchendrarajan, R; Tharmaseelan, JCricket is one of the top 10 most played sport across the world regardless of age and gender. However, learning cricket has been quite challenging as the majority of the cricket-playing individuals are unable to afford quality infrastructure. While this has opened up many research opportunities to provide solutions to automatically learn cricket, very little work has been done in this era. In this paper, we focus on the batting skills of cricket players. We develop a Random Forest model to classify the cricket shot images using human body keypoints extracted with MediaPipe. Experiment results show the proposed model achieves an F1-score of 87% and outperforms the existing solution in a 5% margin. Further, we propose a similarity estimation approach to compare the user’s cricket image with popular international cricket players’ cricket shot images of the same type and retrieve the most similar one. The mobile application we developed based on our solution will enable cricket-playing individuals to analyze, improve and track their batting performances without the need of having a coach.Publication Embargo Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence(IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, LThe 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.Publication Embargo Mobile-based Malware Detection and Classification using Ensemble Artificial Intelligence(IEEE, 2019-12-05) Somasundaram, S; Kasthurirathna, D; Rupasinghe, LThe 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.