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INTERNATIONAL JOURNAL OF SCIENTIFIC DEVELOPMENT AND RESEARCH International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2455-2631 | Impact factor: 8.15 | ESTD Year: 2016 open access , Peer-reviewed, and Refereed Journals, Impact factor 8.15 |
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| Paper Title: | Machine Learning Based Mobile Application For Academic Tracking |
| Authors Name: | Amrutha K.R , Bismi Rahim T.A , Eldhose Siby , Midhun Mohan , Dr. Sujith Kumar P.S |
| Unique Id: | IJSDR2306166 |
| Published In: | Volume 8 Issue 6, June-2023 |
| Abstract: | Portable application and other integration of data and communication innovation have ended up well known in instruction to screen educating and learning activities. And with the progression in technology and life fashion there’s require of speedier and simple arrangement. The system is to supply an understudy information framework and client interface is to alter the current paper records and makes a difference to upgrade the teaching and instruction of understudies. And these days, request of mechanized framework is higher, so that educational infrastructures like colleges required their manual framework to operate on versatile frameworks. For this reason, we plan and actualize a Portable Application for Scholarly Following. In this application, faculty can oversee all their paper work like participation record, marks record, transfer task, inform parents almost gatherings etc. In our proposed framework, the faculty can log into their college account through the app itself and overhaul the scholarly result, take participation utilizing savvy phone and store record of students for their persistent advancement. The information will be kept within the cloud server/college server. Students are also able to see their scholarly comes about. Students will also able to see scholastic results,attendance, inside marks as well as task, notes upgrade from the faculty utilizing Android phones. The student will too get an alert message when his/her participation become less than 75 percent. Too the students can effortlessly track their participation rate and class schedule daily. Based on this records of the students the proposed model will also able to predict the student’s performance . And by utilizing this the staff can recognize the students who require additional care and taking the fitting activities to improve their academic performance.The proposed method will be give applications such as online think about fabric, takes note, academic calendar and online updates of examination, online participation record, execution record, and parent insinuation framework. After particular time guardians will be given advance reports of the corresponding understudy consequently. Application framework will keep full record of their daily and month to month attendance. Educator will be given offices to download or print the understudy participation and inside evaluation report effectively. |
| Keywords: | Monitoring, Machine learning, Academic monitoring system (AMS). |
| Cite Article: | "Machine Learning Based Mobile Application For Academic Tracking", International Journal of Science & Engineering Development Research (www.ijsdr.org), ISSN:2455-2631, Vol.8, Issue 6, page no.1186 - 1193, June-2023, Available :http://www.ijsdr.org/papers/IJSDR2306166.pdf |
| Downloads: | 000337352 |
| Publication Details: | Published Paper ID: IJSDR2306166 Registration ID:206752 Published In: Volume 8 Issue 6, June-2023 DOI (Digital Object Identifier): Page No: 1186 - 1193 Publisher: IJSDR | www.ijsdr.org ISSN Number: 2455-2631 |
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