Abstract :

The Rapid Growth Of Online Educational Platforms Has Led To An Increasing Volume Of Visual Learning Content, Necessitating Effective Image Classification Methods To Improve Content Organization And Accessibility. Unlike Traditional Single-label Classification, Educational Images Often Belong To Multiple Categories Simultaneously, Such As Subject Areas, Concepts, And Instructional Contexts, Which Makes Multi-label Classification Essential. This Study Proposes A Robust Multi-label Image Classification Framework Tailored For Online Educational Platforms, Leveraging Convolutional Neural Networks (CNNs) Trained With Stochastic Gradient Descent (SGD) Optimization. Our Approach Employs A Deep CNN Backbone Model Adapted For Multilabel Tasks By Utilizing Sigmoid Activation Functions On The Output Layer To Predict The Presence Of Multiple Labels Independently. The Model Is Trained Using A Binary Cross-entropy Loss Function, Which Effectively Handles Multi-label Data By Optimizing Each Label Prediction Independently. SGD Is Chosen As The Optimizer Due To Its Proven Efficiency And Strong Generalization Capabilities In Large-scale Image Classification Tasks, With Momentum And Learning Rate Scheduling Employed To Enhance Convergence And Accuracy. We Validate The Proposed Framework On A Curated Dataset Consisting Of Diverse Educational Images Annotated With Multiple Labels, Representing Different Subjects And Pedagogical Themes. The Evaluation Uses Metrics Such As Mean Average Precision (mAP), F1-score, Precision, And Recall To Assess Classification Performance Comprehensively. Experimental Results Demonstrate That The Model Achieves High Accuracy And Reliable Multi-label Prediction, Outperforming Baseline Approaches And Confirming The Suitability Of SGD For Optimizing Multi-label Classifiers In This Domain. This Multi-label Classification System Significantly Improves The Automatic Tagging And Retrieval Of Educational Images, Facilitating Personalized Learning Experiences And Efficient Content Management On Online Platforms. By Accurately Categorizing Images Across Overlapping Categories, Educators And Learners Can Benefit From Enhanced Searchability And Better Alignment With Curriculum Objectives. In Conclusion, The Integration Of SGD-optimized Deep Learning Models For Multi-label Image Classification Presents A Scalable And Effective Solution For Managing Complex Educational Content. Future Work Will Explore The Incorporation Of Additional Modalities, Such As Text And Metadata, To Further Enhance Classification Accuracy And Contextual Relevance In Online Educational Ecosystems.

Published:

30-7-2026

Issue:

Vol. 26 No. 7 (2026)

Page Nos:

1267-1273

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite