Research Papers - Dept of Information Technology

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

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Subject: search.filters.subject.Classification

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    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, A
    In 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.
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    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, I
    According 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.
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    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. R
    Educational 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.