首页|Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

Optimizing Image Retrieval in Cloud Servers with TN-AGW: A Secure and Efficient Approach

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The increasing use of cloud-based image storage and retrieval systems has made ensuring security and efficiency crucial. The security enhancement of image retrieval and image archival in cloud computing has received considerable attention in transmitting data and ensuring data confidentiality among cloud servers and users. Various traditional image retrieval techniques regarding security have developed in recent years but they do not apply to large-scale environments. This paper introduces a new approach called Triple network-based adaptive grey wolf (TN-AGW) to address these challenges. The TN-AGW framework combines the adaptability of the Grey Wolf Optimization (GW0) algorithm with the resilience of Triple Network (TN) to enhance image retrieval in cloud servers while maintaining robust security measures. By using adaptive mechanisms, TN-AGW dynamically adjusts its parameters to improve the efficiency of image retrieval processes, reducing latency and utilization of resources. However, the image retrieval process is efficiently performed by a triple network and the parameters employed in the network are optimized by Adaptive Grey Wolf (AGW) optimization. Imputation of missing values, Min-Max normalization, and Z-score standardization processes are used to preprocess the images. The image extraction process is undertaken by a modified convolutional neural network (MCNN) approach. Moreover, input images are taken from datasets such as the Landsat 8 dataset and the Moderate Resolution Imaging Spectroradiometer (MODIS) dataset is employed for image retrieval. Further, the performance such as accuracy, precision, recall, specificity, F1-score, and false alarm rate (FAR) is evaluated, the value of accuracy reaches 98.1%, the precision of 97.2%, recall of 96.1%, and specificity of 917.2% respectively. Also, the convergence speed is enhanced in this TN-AGW approach. Therefore, the proposed TN-AGW approach achieves greater efficiency in image retrieving than other existing approaches. This research contributes to the enhancement of secure and efficient cloud-based image retrieval systems, addressing modern challenges in data management and security.

Image retrievalAdaptive grey wolfFeature extractionModified convolutional neural networkTriple network

N. P. Ponnuviji、G. Nirmala、M. L. Sworna Kokila、S. Indra Priyadharshini

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Department of Computer Science and Engineering, RMK College of Engineering and Technology, Puduvoyal, Thiruvallur, India

Department of Computer Science and Engineering, RMD Engineering College, Chennai, India

Department of Computing Technologies, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India

School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

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2025

10.1007/s40031-024-01098-9

Journal of The Institution of Engineers (India), Series B. Electrical eingineering, electronics and telecommunication engineering, computer engineering

Journal of The Institution of Engineers (India), Series B. Electrical eingineering, electronics and telecommunication engineering, computer engineering

ISSN：2250-2106
年,卷(期)：2025.106(2)