Department of BCA

Permanent URI for this collection

http://rcca.ndl.gov.in/handle/123456789/367

Browse

Browsing Department of BCA by Author "Almeida, Tracy"

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    A COMPARATIVE STUDY OF 10+2 LEVEL COURSES AND ITS IMPACT ON THE ACADEMIC PERFORMANCE OF THE STUDENTS STUDYING IN THIRD YEAR, BACHELOR IN COMPUTER APPLICATIONS
    (SHODH SARITA Vol. 7, Issue 26, April-June, 2020, 2020) Almeida, Tracy
    The evolving needs of technical education compel students at the 10+ 2 level to adhere to meet the needs of the entry level eligibility criteria. Therefore students who are unable to pursue Higher Education (HE) in engineering and other technical disciplines now stand a chance to enhance their technical knowledge through educational programmes like Bachelors in Computer Science, Bachelors in Computer Applications (BCA) and Bachelors in Information Technology. BCA is one such programme offered at the Goa University in the ten colleges across the state of Goa. College courses are fundamentally different from high school courses and there needs to be a proper choice of subject courses at 10+2 level that will help students to bridge the gap and to meet the standards of HE (Conley et al, 2007). This paper seeks to ascertain the influence of a few educational attributes at the 10+2 level on the performance at TY in the BCAprogramme. It also intends to provide suggestions to students who intend doing HE in the BCAprogramme. Keywords : Bachelor in Computer Applications (BCA), Mathematics (Math), Higher Education (HE), Computer Techniques (CT), 10+2 level, First Year (FY), Third Year(TY).
  • Thumbnail Image
    Item
    Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization
    (Data Science and Security, 2024) Almeida, Tracy; Khan, Salman
    The paper focuses on High-Utility Itemset Mining (HUIM). The algorithms to mine high utility itemsets face exponential search time due to the growing number of transactions. An algorithm that employs genetic approach has been proposed in this paper to address this challenge. It utilizes techniques of genetic approach to avoid unfit individuals. Transaction clustering is also applied to the database, reducing the time required for database scanning during itemset utility calculations.
  • Thumbnail Image
    Item
    Enhancing High-Utility Itemset Mining Efficiency Using Crow Search Algorithm with High-Utility Item Prioritization
    (Data Science and Security, 2025) Almeida, Tracy
    This study presents an enhanced Crow Search Algorithm (CSA) for High-Utility Itemset Mining (HUIM), aimed at improving efficiency in large-scale datasets. HUIM focuses on discovering itemsets based on utility, such as profit or importance, rather than mere frequency. Traditional algorithms struggle with large datasets due to the extensive search space, while metaheuristic approaches like CSA show promise but remain inefficient in handling low-utility itemsets. To address this, we introduce a high-utility item prioritization list to guide the CSA in selecting valuable itemsets, reducing computational complexity. Experimental results, using synthetic datasets, demonstrate that the enhanced CSA significantly reduces execution time as the minimum utility threshold increases. Despite the study’s limitations—such as the use of synthetic data and lack of comparisons with other algorithms—the proposed method showcases potential for practical applications like retail analysis.
  • Thumbnail Image
    Item
    High Utility Itemset Mining Using Genetic Approach
    (Advanced Informatics for Computing Research pp 143–150, 2022) Almeida, Tracy; Khan, Salman
    Frequent Itemset Mining(FIM) aims to generate itemsets having their frequency of occurrence not lesser than minimum support specified by the user. FIM does not consider the itemset utility which is the it’s profit value. High-utility itemset mining(HUIM) mines high-utility itemsets(HUI) from data. HUIM is a combinatorial optimization. With HUIM algorithms, the time required to search increases exponentially with an increasing number of transactions and database items. To address this issue an efficient algorithm to mine HUIs is proposed. The proposed algorithm uses a compact form of chromosome encoding by eliminating the itemsets with low transactional utilities. The algorithm employs methodology of self mutation to reduce generation of unwanted chromosomes. Experimental results have shown that the proposed algorithm finds HUIs for a given threshold value. The proposed algorithm consumes less time as compared to another HUIM algorithm HUIM-IGA.