Faculty Publications
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Item Impact analysis of online education development and implementation using machine learning model(Bentham Science Publishers, 2024) Divakarla, U.; Chandrasekaran, K.Online education is becoming increasingly necessary and in high demand as a result of the current circumstances and the enormous expansion in internet users. Various studies have been done in this area to enhance the positive benefits of offering educational courses online. One of the most crucial concerns for learning contexts like schools and universities, especially during current epidemic period, is the prediction and analysis of students' performance since it aids in the development of practical mechanisms that enhance academic achievement and prevent dropout. Most educational institutions now place a high priority on forecasting and analysing student performance. That is necessary to assist at-risk students, ensure their retention, provide top-notch learning tools and opportunities, and enhance the university's ranking and reputation. This project aims to collect information related to online education and use Machine Learning to predict students' performance. © 2024 Bentham Science Publishers. All rights reserved.Item Deep Learning-Based Prediction, Classification, Clustering Models for Time Series Analysis: A Systematic Review(Springer Science and Business Media Deutschland GmbH, 2022) Naik, N.N.; Chandrasekaran, K.; Venkatesan, M.; Prabhavathy, P.Analysis of time series is a prominent issue in the field of data analysis. With large amount of existing data in time series, multiple algorithms for analyzing time series data are being proposed. A variety of deep learning models are being designed to enhance the diversity of datasets related to time series across different fields. In comparison with the existing methods, only few have incorporated deep neural networks to perform this task. In most of the cases, deep neural networks are being applied for image data but it can also be used for sequential data such as text and audio. Here, we throw light on the recent advancements in hybrid deep learning models which consist of combination of various frameworks of deep neural networks with statistical models that have led to an improvement in time series analysis. Deep learning models are categorized into discriminative, and generative models provide an insight into the data based on the perception of conditional or joint probability. In this paper, we have surveyed newly devised algorithms and limitations of prediction, classification and clustering for time series analysis which describes how the temporal information can be merged into the analysis of the time series data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.