Faculty publications @ CTUAP

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Browsing Faculty publications @ CTUAP by Author "Bonthu Kotaiah N"

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    A Comprehensive Review of Course Recommendation Systems for MOOCs
    (2024) Bonthu Kotaiah N
    In recent years, many students have accepted Massive Open Online Courses (MOOCs) as a means of education. Due to the enormous number of courses available through MOOC, students need help in identifying and selecting an appropriate course based on their profile and interests. To address this issue, MOOCs incorporate a course recommendation system that generates a list of courses based on the student’s prerequisites. This literature review attempts to detect and assess trends, processes employed, and developments in MOOC course RS through an exhaustive analysis of academic literature published between January 1, 2016, and November 31, 2023. The study includes the various methodologies employed, the datasets used for evaluations, the performance measures used, and the many issues encountered by Recommendation Systems. Literature published in ScienceDirect, Wiley, Springer, ACM, and IEEE, were chosen for review. After applying inclusion and exclusion criteria, 76 articles from the aforementioned databases, including journals, conferences, and book chapters, were selected. The investigation found that methods from Machine Learning and Deep Learning were widely deployed. Traditional approaches like ”content-based filtering, collaborative filtering, and hybrid filtering” were frequently employed in conjunction with other algorithms for more accurate and precise suggestions. It also underlines the need to take data sparsity, the cold start problem, data overload, and user preferences into account when designing a course recommendation system. This paper contributes to examining the cutting-edge course Recommendation System in depth, examining recent developments, difficulties, and future work in this field.
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    A Survey on Cryptocurrency Price Prediction using Hybrid Approaches of Deep Learning Models
    (2023) Bonthu Kotaiah N
    Deep-learning and machine-learning algorithms have recently become a prominent research topic for forecasting the price of cryptocurrencies. Some research indicates that deep learning models are incapable of accurately and promptly predicting daily cryptocurrency prices, whereas other research compares the efficacy of various models. Such techniques include machine learning, deep learning, and statistical models, among others. Several studies have devised hybrid approaches that combine novel methodologies in an effort to enhance the accuracy of bitcoin price forecasts. Complex models of deep learning and interdependent relationships are examples of these modern methods. To further improve the quality of survey data, there are additional datasets that making frequent errors. The search results indicate that efforts are being made to better bitcoin price estimations using deep learning and hybrid methods. © 2023 IEEE.
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    Facial emotion recognition using geometrical features based deeplearning techniques
    (2023) Bonthu Kotaiah N
    In recent years, intelligent emotion recognition is active research in computer vision to understand the dynamic communication between machines and humans. As a result, automatic emotion recognition allows the machine to assess and acquire the human emotional state to predict the intents based on the facial expression. Researchers mainly focus on speech features and body motions; identifying affect from facial expressions remains a less explored topic. Hence, this paper proposes novel approach for intelligent facial emotion recognition using optimal geometrical features from facial landmarks using VGG-19s (FCNN). Here, we utilize Haarcascade to detect the subject face and determine the distance and angle measurements. The entire process is to classify the facial ex-pressions based on extracting relevant features with the normalized angle and distance measures. The experimental analysis shows high accuracy on the MUG dataset of 94.22% and 86.45% on GEMEP datasets, respectively