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EGNN-IDG: enhanced generative neural network model for image description generation.

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Title: EGNN-IDG: enhanced generative neural network model for image description generation.
Authors: Mujawar, Shaheen Raphiahmed1,2 (AUTHOR) shaheenmujawar156@gmail.com, Iyer, Sridhar3 (AUTHOR)
Source: Journal of Experimental & Theoretical Artificial Intelligence. Nov2025, Vol. 37 Issue 8, p1501-1520. 20p.
Subjects: Deep learning, Optimizers (Computer software), Image processing, Generative artificial intelligence, Photograph captions
Abstract: Image description creation is utilised in a variety of applications, such as automatic script development, medical image annotation, and news reporting. However, the detailed description of the image with the relevant region is complicated using deep learning approaches. In this paper, an intelligent Red-tailed Hawk Generative Deep Learning Model is presented for generating image descriptions. The suggested technique reduces the loss function of the network model while also updating the weight parameter using the meta-heuristic Red-tailed Hawk Optimizer (RTHO). Extensive simulation experiments are carried out to assess the suggested model. The obtained results demonstrate that utilising the proposed approach improves the BiLingual Evaluation Understudy (BLEU)-1, BLEU-2, and METEOR scores to 0.74, 0.44, and 0.53, respectively. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: EGNN-IDG: enhanced generative neural network model for image description generation.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+Experimental+%26+Theoretical+Artificial+Intelligence%22">Journal of Experimental & Theoretical Artificial Intelligence</searchLink>. Nov2025, Vol. 37 Issue 8, p1501-1520. 20p.
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  Data: <searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Optimizers+%28Computer+software%29%22">Optimizers (Computer software)</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Generative+artificial+intelligence%22">Generative artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Photograph+captions%22">Photograph captions</searchLink>
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  Label: Abstract
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  Data: Image description creation is utilised in a variety of applications, such as automatic script development, medical image annotation, and news reporting. However, the detailed description of the image with the relevant region is complicated using deep learning approaches. In this paper, an intelligent Red-tailed Hawk Generative Deep Learning Model is presented for generating image descriptions. The suggested technique reduces the loss function of the network model while also updating the weight parameter using the meta-heuristic Red-tailed Hawk Optimizer (RTHO). Extensive simulation experiments are carried out to assess the suggested model. The obtained results demonstrate that utilising the proposed approach improves the BiLingual Evaluation Understudy (BLEU)-1, BLEU-2, and METEOR scores to 0.74, 0.44, and 0.53, respectively. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Journal of Experimental & Theoretical Artificial Intelligence is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/0952813X.2025.2481044
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      – Code: eng
        Text: English
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        PageCount: 20
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Optimizers (Computer software)
        Type: general
      – SubjectFull: Image processing
        Type: general
      – SubjectFull: Generative artificial intelligence
        Type: general
      – SubjectFull: Photograph captions
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      – TitleFull: EGNN-IDG: enhanced generative neural network model for image description generation.
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              Text: Nov2025
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              Y: 2025
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