AI-POWERED ANALYSIS
Fusion of deep transfer learning models with Gannet optimisation algorithm for an advanced image captioning system for visual disabilities
This in-depth analysis of "Fusion of deep transfer learning models with Gannet optimisation algorithm for an advanced image captioning system for visual disabilities" reveals key insights for enterprise decision-makers. Leveraging advanced AI, we've extracted critical data, identified strategic opportunities, and quantified potential impacts to guide your next-generation initiatives.
Executive Impact Summary
Our AI has synthesized the core findings of "Fusion of deep transfer learning models with Gannet optimisation algorithm for an advanced image captioning system for visual disabilities" into actionable insights, highlighting the quantifiable benefits and strategic implications for your enterprise.
- A novel FDTLGO-AICSVD model significantly enhances image captioning for visually impaired individuals.
- The model integrates DenseNet121, VGG19, MobileNetV2 for image feature extraction, and TF-IDF for text.
- Gannet Optimization Algorithm (GOA) is used for hyperparameter tuning, leading to precise, context-aware captions.
- Achieved superior BLEU-4 scores (45.11% on Flickr8K, 58.91% on Flickr30K) and CIDEr scores (63.17 on Flickr8K, 69.81 on Flickr30K).
- Demonstrated significantly reduced computational time compared to existing models.
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BLEU-4 Score (Flickr8K)
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CIDEr Score (Flickr8K)
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Computational Efficiency
Deep Analysis & Enterprise Applications
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Enterprise Process Flow
Image Preprocessing (Noise Removal, Contrast Enhancement)
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Text Preprocessing (Standardize, Prepare Textual Data)
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Feature Extraction (DenseNet121, VGG19, MobileNetV2, TF-IDF)
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Parameter Tuning (Gannet Optimization Algorithm)
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Performance Evaluation (BLEU, METEOR, ROUGE-L, CIDEr)
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BLEU-4 Score on Flickr8K Dataset
| Technique | BLEU-4 (Flickr8K) | CIDEr (Flickr8K) | Computational Time (sec) |
|---|---|---|---|
| NIC | 19.94% | 32.83 | 11.46 |
| Soft-Attention | 21.99% | 35.51 | 13.79 |
| Hard-Attention | 24.73% | 38.18 | 9.18 |
| FDTLGO-AICSVD (Proposed) | 45.11% | 63.17 | 5.12 |
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Highest CIDEr Score on Flickr30K Dataset
Case Study: Role of Gannet Optimization Algorithm
The Gannet Optimization Algorithm (GOA) is critical for hyperparameter tuning in the FDTLGO-AICSVD model. It ensures the model generates precise and context-aware captions by optimizing parameters across diverse testing scenarios. This meta-heuristic algorithm balances exploration and exploitation, effectively avoiding local minima and enhancing overall model stability and performance, especially in complex objective functions. Its robust capability in handling high-dimensional search spaces contributes significantly to the model's superior accuracy and language generation capabilities.
Case Study: Fusion of Deep Transfer Learning Models
The FDTLGO-AICSVD model's strength lies in its fusion of DenseNet121, VGG19, and MobileNetV2 for image feature extraction, combined with TF-IDF for textual data. DenseNet121 excels in feature reuse and mitigating vanishing gradients. VGG19 captures complex patterns, and MobileNetV2 provides a lightweight, efficient architecture for resource-constrained environments. This multi-model approach enables the system to leverage diverse visual patterns and textual importance effectively, improving multimodal feature representation for enhanced analysis and classification, outperforming single-model approaches in terms of accuracy, efficiency, and robustness.
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