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Journal of Information and Optimization Sciences cover
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

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Open Access Research Article

Ensemble deep learning technique for optimized informative query assessment for visual images

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pp. 427–437Vol. 46Issue 2March 2025DOI: 10.47974/JIOS-1925XML
Received:
12 Nov 2024
Published Online:
17 Mar 2025
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1925
Pages:
427–437

Abstract

Traditional data analysis is not up to pace with the quick rate at which multimedia repositories are growing. For finding pertinent images with optimized accuracy, a sophisticated visual informative retrieval (VIR) model is necessary. Currently, the techniques of deep learning are playing a vital role for facing this challenging issue. In addition to ignoring the emphasis on particular channels or locations, deep learning models prioritize the most useful sections of the feature maps. This will result in optimal feature representations of visual images that are less effective since they have various levels of relevance across different channels or regions. This work aimed to address this issue by focusing on improving the feature maps through the use of channels. The experiments conducted with benchmarked datasets for validating the proposed work. The comparative analysis demonstrated that proposed ensemble deep learning technique outperformed the existing deep learning techniques with notable optimized informative query assessment results. 

Keywords

Subject Classifications

Primary 93A00Secondary 94A00

References

[1] S. R. Dubey, “A decade survey of content-based image retrieval using deep learning,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 32, no. 5, pp. 2687–2704 (2021).
[2] G. Sucharitha and R. K. Senapati, “Biomedical image retrieval by using local directional edge binary patterns and Zernike moments,” Multimedia Tools and Applications, vol. 79, no. 3, pp. 1847–1864 (2020).
[3] G. Sucharitha and R. K. Senapati, “Local extreme edge binary patterns for face recognition and image retrieval,” Journal of Advanced Research in Dynamical and Control Systems, vol. 10, pp. 644–654 (2018).
[4] C. Zhang and J. Liu, “Content based deep learning image retrieval: A survey,” Proceedings of the 2023 9th International Conference on Communication and Information Processing (2023).
[5] M. S. Basha, K. R. Prasad, S. K. Mouleeswaran, R. C. Poonia, and S. Sebastian, “Multi-disease detection system with X-ray images using deep learning techniques,” Journal of Information and Optimization Sciences, vol. 45, no. 5, pp. 1379–1388 (2024), doi: 10.47974/JIOS-1710.
[6] M. Sivalakshmi, K. R. Prasad, and C. S. Bindu, “Improved privacy protection technique for enhancing security of real-time video surveillance,” Journal of Information and Optimization Sciences, vol. 45, no. 5, pp. 1389–1399 (2024), doi: 10.47974/JIOS-1711.
[7] G. Sucharitha, R. K. Senapati, and A. B. Ranjan, “Secure and efficient content-based image retrieval using dominant local patterns and watermark encryption in cloud computing,” Cluster Computing, pp. 1–17 (2024).
[8] N. Perveen, D. Roy, and C. Krishna Mohan, “Facial Expression Recognition in Videos using Dynamic Kernels,” IEEE Transactions on Image Processing, vol. 29, pp. 8316–8325 (2020), doi: 10.1109/TIP.2020.3011846.
[9] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “ImageNet classification with deep convolutional neural networks,” Communications of the ACM, vol. 60, no. 6, pp. 84–90 (2017).
[10] L. Zhang, Y. Zhang, and X. Liu, “A transfer residual neural network based on ResNet-50 for detection of steel surface defects,” Applied Sciences, vol. 13, no. 9, p. 5260 (2023).
[11] D. Roy, T. Ishizaka, C. Krishna Mohan, and A. Fukuda, “Detection of collision-prone vehicle behavior at intersections using Siamese interaction LSTM,” IEEE Transactions on Intelligent Transportation Systems (2020), doi: 10.1109/TITS.2020.3031984.
[12] D. Singh and C. Krishna Mohan, “Graph formulation of video activities for abnormal activity recognition,” Pattern Recognition, vol. 65, pp. 265–273 (2017), doi: 10.1016/j.patcog.2017.01.001.
[13] K. Shaheed, M. A. Yaseen, and M. A. Aslam, “EfficientRMT-Net—An efficient ResNet-50 and Vision Transformers approach for classifying potato plant leaf diseases,” Sensors, vol. 23, no. 23, p. 9516 (2023).
[14] C.-L. Lin and K.-C. Wu, “Development of revised ResNet-50 for diabetic retinopathy detection,” BMC Bioinformatics, vol. 24, no. 1, p. 157 (2023).
[15] M. Muhathir, M. Sivalakshmi, and S. K. Rajendra Prasad, “Convolutional neural network (CNN) of ResNet-50 with InceptionV3 architecture in classification on X-ray image,” Computer Science On-line Conference, Cham: Springer International Publishing (2023).
[16] Z. Duan, X. Zhang, and Q. Li, “An adapted ResNet-50 architecture for predicting flow fields of an underwater vehicle,” IEEE Access (2024).
[17] L. Deng, Y. Li, Z. Zhan, and X. Xie, “Multi-level attention network: Mixed time–frequency channel attention and multi-scale self-attentive standard deviation pooling for speaker recognition,” Engineering Applications of Artificial Intelligence, vol. 128, p. 107439 (2024).
[18] J. Mo and L. Xu, “Weighted cluster-range loss and criticality-enhancement loss for speaker recognition,” Applied Sciences, vol. 10, no. 24, p. 9004 (2020).
[19] Corel, “Corel 10K Dataset,” Corel Corporation (2004). [Online]. Available: http://www.corel.com.
[20] A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” [Online]. Available: https://www.cs.toronto.edu/~kriz/cifar.html. Received June 2016.

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