Department of Computer Science

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http://ctuap.ndl.gov.in/handle/123456789/33

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Now showing 1 - 6 of 6
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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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    Analyzing the impact of security risk for securing healthcare information systems
    (2025) Bonthu, Kotaiah
    In recent years, the world has witnessed a dramatic shift towards the digital realm, with the increasing popularity of web-based applications taking center stage [1-3]. These applications have not only transformed the way we interact with technology but have also beckoned the attention of a shadowy and sophisticated counterpart-the hacker community [4-7]. In this dynamic landscape, the security risks associated with web-based services, particularly within the critical domain of healthcare, are mounting at an alarming rate [8]. © 2025 selection and editorial matter, Suhel Ahmad Khan, Mohammad Faisal, Nawaf Alharbe, Rajeev Kumar and Raees Ahmad Khan. All rights reserved.
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    Deep learning based Identification of Solid Waste Management in Smart Cities through Garbage Separation and Monitoring
    (2023) Suryanarayana N.V.S.
    The solid waste management is the process of proper decomposition of waste materials within a period of time. This includes the collection of garbage's and then proceeded through certain measures for decomposition. There are various methods adopted in the garbage separation process. This includes the artificial intelligence techniques for the estimation and determination of the solid waste through automatic detection and separation of the garbage waste using control and sensing units. They are integrated with the internet of things to enable the two way communication system. This helps to visualize the functioning of the system adopting the digital platform. The proposed system is implemented through the smart dust bin held in every household that automatically senses the non-biodegradable and biodegradable waste materials. The classification of the waste materials are identified through the image processing techniques. © 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
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    IoT-based Smart Home Automation Systems for Energy Conservation
    (2023) Suryanarayana N.V.S.
    This research study explores the numerous components of smart home automation systems, including actuators, sensors, and controllers, and the integration of these components with Internet of Things (IoT) technology. To demonstrate the system's ability to reduce energy consumption, this study involves an experimental analysis of a prototype system. In order to scientifically evaluate the system's performance, the test data must be statistically analysed. The results of this research study have the potential to contribute towards the enhancement of energy efficiency and sustainability of the proposed system by offering insights for the construction of sustainable residential settings. This study investigates the effects of the residential environments. The use of complex and innovative systems has the potential to significantly improve the efforts to save energy and promote a sustainable future. © 2023 IEEE.