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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 119
Article Submitted : 23355
Article Published : 9033
Total Authors : 23952
Total Reviewer : 831
Total Countries : 162
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| Paper Title: | DEEP LEARNING-BASED STUDENT MONITORING IN COMPUTER LABS USING LSTM, HOI, AND FACIAL FEATURE EXTRACTION WITH NYN TRACK DASHBOARD |
| Authors Name: | Ms. N. Pushpa M.E., (Ph.D), , Mr. A. Ajay , Mr. R. R. Kavin , Mr. N. Gunaseelan |
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IJRTI_211416
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| Published Paper Id: | IJRTI2604126 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | |
| Abstract: | Computer laboratory sessions are essential for practical learning among technology students. However, due to manual monitoring practices in laboratories, attendance records often fail to accurately reflect students' actual work and the effective time spent during lab sessions. To validate staff who regularly use and supervise computer laboratories. The survey results revealed that a significant percentage of students reported experiencing distraction during lab periods, while a considerable proportion of staff indicated difficulties in continuously monitoring all students. These challenges negatively impact student productivity and learning habits, leading to abnormal activities such as mobile phone usage, talking, idle sitting, and leaving seats without supervision. To address these issues, this work proposed an automated laboratory monitoring solution that integrates overhead CCTV cameras and front-facing webcams installed in the laboratory environment. Overhead cameras are used to track student actions and seating behavior, while front-facing webcams are utilized to extract facial features and analyze |
| Keywords: | Student Behavior Analysis, CCTV Surveillance, Educational Surveillance, Automated Lab Monitoring |
| Cite Article: | "DEEP LEARNING-BASED STUDENT MONITORING IN COMPUTER LABS USING LSTM, HOI, AND FACIAL FEATURE EXTRACTION WITH NYN TRACK DASHBOARD ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.a933-a954, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604126.pdf |
| Downloads: | 00053 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2604126
Registration ID:211416
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier):
Page No: a933-a954 Country: -, -, India Research Area: Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604126 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604126 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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