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Web-Enhanced Vision Transformers and Deep Learning for Accurate Event-Centric Management Categorization in Education Institutions

Authors :
Khalied M. Albarrak
Shaymaa E. Sorour
Source :
Systems, Vol 12, Iss 11, p 475 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

In the digital era, social media has become a cornerstone for educational institutions, driving public engagement and enhancing institutional communication. This study utilizes AI-driven image processing and Web-enhanced Deep Learning (DL) techniques to investigate the effectiveness of King Faisal University’s (KFU’s) social media strategy as a case study, particularly on Twitter. By categorizing images into five primary event management categories and subcategories, this research provides a robust framework for assessing the social media content generated by KFU’s administrative units. Seven advanced models were developed, including an innovative integration of Vision Transformers (ViTs) with Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, VGG16, and ResNet. The AI-driven ViT-CNN hybrid model achieved perfect classification accuracy (100%), while the “Development and Partnerships” category demonstrated notable accuracy (98.8%), underscoring the model’s unparalleled efficacy in strategic content classification. This study offers actionable insights for the optimization of AI-driven digital communication strategies and Web-enhanced data collection processes, aligning them with national development goals and Saudi Arabia’s Vision 2030, thereby showcasing the transformative power of DL in event-centric management and the broader higher education landscape.

Details

Language :
English
ISSN :
20798954
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Systems
Publication Type :
Academic Journal
Accession number :
edsdoj.0f4078ed448402d9a706f641b3b4140
Document Type :
article
Full Text :
https://doi.org/10.3390/systems12110475