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All Journal Bulletin of Electrical Engineering and Informatics Journal of Applied Engineering and Technological Science (JAETS)
Karthik Chandran
Jyothi Engineering College
Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering

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A study on agricultural engineering equipment in South Tamilnadu using linear regression Chandrakumar Thangavel; Ramya Thangavel; Karthik Chandran; Gunnam Suryanarayana; Subrata Chowdhury; Nguyen Duc Uyen; Thi-Thu Nguyen; Duc-Tan Tran
Bulletin of Electrical Engineering and Informatics Vol 11, No 3: June 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i3.3325

Abstract

Economic growth in India purely depends on the Indian agricultural sector. In developing countries, the mechanization of agriculture plays a vital role in productivity. The research focuses on identifying which farmers in South Tamilnadu mostly use agricultural machinery. In this paper, we have taken farmer names and mobile numbers, choice of implement requirement into consideration by collecting the real data through DBT portal (https://agrimachinery.nic.in). This research work deals with five southern districts in Tamilnadu, namely Dindigul, Madurai, Theni, Ramnad, and Virudhunagar, in which we have predicted which machinery is suitable for that area. The linear regression model was used in this research by testing and training the dataset in all five data frames to get efficient results. Prediction of each data frame reveals the efficient working of the particular machinery for that specific area due to the different geographical features.
Students’ Activeness Measure in Moodle Learning Management System Using Machine Learning Chandrakumar Thangavel; Valliammai S E; Amritha P. P; Karthik Chandran; Subrata Chowdhury; Nguyen Thi Thu; Bo Quoc Bao; Duc-Tan Tran; Duc-Nghia Tran; Do Quang Trang
Journal of Applied Engineering and Technological Science (JAETS) Vol. 6 No. 1 (2024): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v6i1.6128

Abstract

Due to COVID-19, the need for online education has increased worldwide, prompting students to shift from traditional learning methods to online platforms as guided by higher education departments. Higher learning institutes are focused on developing constructive online learning platforms. This research aims to measure students’ academic performance on an online learning platform – Moodle Learning Management System (LMS) – using machine learning techniques. Moodle LMS, a popular free and open-source system, has seen significant growth since the COVID-19 lockdown. Many researchers have analyzed student performance in online learning, yet there remains a need to predict academic outcomes effectively. In this study, data were collected from a higher learning institute in Tamil Nadu, and linear regression was applied to predict students' final course outcomes. The analysis, based on students' activity in Moodle LMS across both theory and laboratory courses, helps faculty identify students at risk of failing and adjust instructional methods and assignments accordingly. This approach aims to reduce failure rates by providing timely warnings and encouraging students to improve their engagement with LMS resources.
Co-Authors Amritha P. P Bo Quoc Bao Chandrakumar Thangavel Chandrakumar Thangavel Do Quang Trang Duc-Nghia Tran Duc-Tan Tran Gunnam Suryanarayana Nguyen Duc Uyen Nguyen Thi Thu Ramya Thangavel Subrata Chowdhury Subrata Chowdhury Thi-Thu Nguyen Valliammai S E