Conference Papers

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    Comparative Performance Evaluation of Web-Based Book Recommender Systems
    (Institute of Electrical and Electronics Engineers Inc., 2022) Bhat, S.S.; Pranav, P.; Shashank, K.V.; Raghunandan, A.; Mohan, B.R.
    In today's world, recommendation algorithms are popularly utilised for personalization. To improve their business, e-commerce behemoths rely heavily on their recommendation algorithms. As a result, the quality of suggestions can have a big impact on how much money they make. As a result, effective evaluation of recommender systems is critical. Traditional evaluation measures are limited to error-based and accuracy-based metrics, and do not account for characteristics such as novelty, informedness, markedness, and so on. This research study aims to compare the effectiveness of two web-based book recommendation systems by using the measures like diversity, informedness, and markedness, which are less well-known but equally essential. © 2022 IEEE.
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    Enhancing Data Quality in Hybrid Cloud Architectures
    (Institute of Electrical and Electronics Engineers Inc., 2024) Fernandes, G.H.; Divakarla, U.; Chandrasekaran, K.
    The emergence of the hybrid cloud model has completely changed how businesses handle, store and use their data. A common option for businesses looking to combine the benefits of cloud services and on-premises infrastructure is the hybrid cloud strategy. However, because of the complexity of data management across contexts and the integration of disparate systems, this paradigm presents serious hurdles to preserving data quality. Since data is the primary source for many important business decisions, ensuring data quality - which includes correctness, consistency, completeness, security, and reliability - remains a top priority. This study presents a unique method that makes use of cloud computing and machine learning (ML) algorithms to improve data quality in hybrid cloud environments. The detection, prevention and remediation of data quality issues by integrating state-of-the-art machine learning techniques into hybrid cloud systems is thoroughly examined in this study. The suggested architecture seeks to deliver more dependable and trustworthy data for decision-making processes by offering real-time monitoring, analysis and quality enhancement of data throughout the hybrid infrastructure. The efficacy of methodology in tackling data quality issues in hybrid cloud settings is illustrated using experiments and case studies. © 2024 IEEE.