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Stream/Area:
-
Subject:
-
Paper ID:
10.55041/IJSREM28939
Volume:
8 |
3 |
Mar 2024
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The existing literature survey offers a thorough exploration of automatic text summarization, speech-to-text conversion, and face recognition technologies, all of which are integral to the proposed model named as ConvoLogix. Historically, traditional methods for managing meetings and collaboration involved manual note-taking and attendance tracking. These processes were time-consuming, error-prone, and not conducive to optimization. In today’s fast-paced business environment, the absence of automated solutions hinders efficiency and collaboration. The survey underscores the significance of embracing advanced techniques in Machine Learning with Traditional and Deep Learning Models for Audio and video processing for process automation, emphasizing their pivotal role in streamlining meetings and attendance tracking. A key theme within the survey is the identification of limitations associated with conventional approaches to meeting minute generation and attendance recording. The ConvoLogix model’s core objective is to leverage these technologies to automate meeting minutes generation and attendance tracking, resulting in time savings, improved collaboration, and data-driven insights.
KEYWORDS
Publication Date
12/31/45352
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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