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Enhancing image processing performance with attention long short-term domain adversarial crossover orchard algorithm

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Abstract

The usage of digital devices has increased across the world due to global digitalization. The global digitalization concept arises due to the easy accessibility of the internet around the world. One of the important data that contributes a large amount to the internet is the image data. Images are used in various fields for advertising the products, expressing the individual’s point of view, describing the diseases in the medical field, etc. Hence a large number of images need to be transformed into digital images to exchange them in the digital images. The process of converting a normal image into a digital image and then extracting information from it is known as image processing. The existing techniques for image processing exhibit issues like high error rates and poor image processing performance. To overcome these issues, this paper proposes an Attention Long Short-term Domain Adversarial Crossover Orchard (ALSDA-CO) algorithm. The proposed method is highly effective in image processing approach and extracted varied class labels clearly that are not visible effectively. The weight functions of each image are trained effectively and optimize the hyperparameters with best solution. To evaluate the performance of this algorithm, the Fruits 100 dataset is used. The proposed algorithm showed better performance than the existing methods such as pre-trained CNN, LS-SVM, MobileNetV2-LSTM, and DCNN in terms of all the performance measures used. The proposed algorithm attained 98.5% accuracy, 98.9% recall, 98.7% precision, and 98.5% F1 score in image processing. Compared to existing approaches the attained range of accuracy outperformed 1.1% of proposed method than existing approaches. The proposed algorithm also exhibited a reduced error rate and enhanced image processing.

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No datasets were generated or analysed during the current study.

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Authors and Affiliations

  1. Department of Computer Science Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Saveetha Nagar, Thandalam, Chennai, Tamil Nadu, India

    K. Venkatraman

  2. Computer Science and Engineerin, St. Joseph’s College of Engineering, Chennai, Tamil Nadu, India

    A. Chandrasekar

  3. Department of EEE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    S. Radhika

Authors
  1. K. Venkatraman
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  2. A. Chandrasekar
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  3. S. Radhika
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Venkatraman K], [Chandrasekar A], [Radhika S]. The first draft of the manuscript was written by [Venkatraman K] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to K. Venkatraman.

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Communicated by: Hassan Babaie

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Venkatraman, K., Chandrasekar, A. & Radhika, S. Enhancing image processing performance with attention long short-term domain adversarial crossover orchard algorithm. Earth Sci Inform 17, 3687–3703 (2024). https://doi.org/10.1007/s12145-024-01331-5

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  • DOI: https://doi.org/10.1007/s12145-024-01331-5

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