IMAGE PATTERN RECOGNITION WITH AN IMPROVISED DEEP LEARNING REGRESSION TECHNIQUE Article Swipe
Michael Mahesh K
,
J. P.
,
N Ragunath
,
P Kanagaraju
,
Aditya Bommaraju
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21917/ijivp.2024.0485
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.21917/ijivp.2024.0485
Advancements in image pattern recognition have revolutionized diverse domains such as healthcare, autonomous systems, and security. Despite these advancements, existing deep learning techniques often encounter challenges in achieving high accuracy, particularly when handling complex image datasets with significant noise or variations. The need for an enhanced approach that balances computational efficiency with superior predictive performance has become critical. This study introduces an Improvised Deep Learning Regression Technique based on InceptionNet for robust image pattern recognition. The proposed method incorporates optimized inception modules with tailored hyperparameter tuning to address limitations in feature extraction and pattern generalization. By employing an adaptive learning rate and advanced regularization mechanisms, the model achieves better performance on large-scale, heterogeneous datasets. The experimental evaluation was conducted using publicly available image datasets, including CIFAR-10 and ImageNet, to ensure comprehensive benchmarking. The results show significant improvements over existing methods. The proposed InceptionNet model achieved an accuracy of 96.5% on the CIFAR-10 dataset and a mean absolute error (MAE) reduction of 15.2% compared to traditional regression techniques. On the ImageNet dataset, the model recorded an accuracy improvement of 7.8% and reduced training time by 12%, validating its computational efficiency. The incorporation of deep inception modules contributed to precise recognition of intricate patterns and subtle variations, making the technique suitable for real-time applications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21917/ijivp.2024.0485
- OA Status
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- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4409272321Canonical identifier for this work in OpenAlex
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https://doi.org/10.21917/ijivp.2024.0485Digital Object Identifier
- Title
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IMAGE PATTERN RECOGNITION WITH AN IMPROVISED DEEP LEARNING REGRESSION TECHNIQUEWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-01Full publication date if available
- Authors
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Michael Mahesh K, J. P., N Ragunath, P Kanagaraju, Aditya BommarajuList of authors in order
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https://doi.org/10.21917/ijivp.2024.0485Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://doi.org/10.21917/ijivp.2024.0485Direct OA link when available
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Artificial intelligence, Computer science, Pattern recognition (psychology), Regression, Image (mathematics), Regression analysis, Deep learning, Computer vision, Machine learning, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.particularly | 30 |
| abstract_inverted_index.recognition. | 74 |
| abstract_inverted_index.advancements, | 18 |
| abstract_inverted_index.applications. | 211 |
| abstract_inverted_index.benchmarking. | 131 |
| abstract_inverted_index.comprehensive | 130 |
| abstract_inverted_index.computational | 49, 187 |
| abstract_inverted_index.heterogeneous | 112 |
| abstract_inverted_index.incorporation | 190 |
| abstract_inverted_index.hyperparameter | 84 |
| abstract_inverted_index.regularization | 103 |
| abstract_inverted_index.revolutionized | 6 |
| abstract_inverted_index.generalization. | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.3670671 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |