Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
2 results
Search Results
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 A Machine Learning Approach for Daily Temperature Prediction Using Big Data(Springer Science and Business Media Deutschland GmbH, 2022) Divakarla, U.; Chandrasekaran, K.; Hemant Kumar Reddy, K.H.K.; Reddy, R.V.; Rao, M.Due to global warming, weather forecasting becomes complex problem which is affected by a lot of factors like temperature, wind speed, humidity, year, month, day, etc. weather prediction depends on historical data and computational power to analyze. Weather prediction helps us in many ways like in astronomy, agriculture, predicting tsunamis, drought, etc. this helps us to be prepared in advance for any kinds disasters. With rapid development in computational power of high end machines and availability of enormous data weather prediction becomes more and more popular. But handling such huge data becomes an issue for real time prediction. In this paper, we introduced the machine learning-based prediction approach in Hadoop clusters. The extensive use of map-reduce function helps us distribute the big data into different clusters as it is designed to scale up from single servers to thousands of machines, each offering local computation and storage. An ensemble distributed machine learning algorithms are employed to predict the daily temperature. The experimental results of proposed model outperform than the techniques available in literature. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.