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Gobinath, T.
- Enhanced AI Based Feature Extraction Technique in Multimedia Image Retrieval
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Authors
Affiliations
1 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
2 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, University College of Engineering, IN
4 Department of Master of Business Administration, Koneru Lakshmaiah Education Foundation, IN
1 Department of Electronics and Communication Engineering, Rajalakshmi Engineering College, IN
2 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
3 Department of Computer Science and Engineering, University College of Engineering, IN
4 Department of Master of Business Administration, Koneru Lakshmaiah Education Foundation, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 4 (2023), Pagination: 3021-3027Abstract
In the era of rapid technological advancements, the demand for efficient and accurate identification and retrieval of information from multimedia images has seen a substantial increase. To meet this growing demand, artificial intelligence (AI)-based technologies, particularly feature extraction techniques, have gained significant popularity. Feature extraction involves the extraction of salient features from multimedia images, such as edges, lines, curves, textures, and colors, with the aim of representing the data in a more suitable format for analysis. This paper presents an enhanced AI-based feature extraction technique for multimedia image retrieval. The proposed method introduces a novel approach that combines the power of deep learning and evolutionary algorithms in a neuro-symbolic computation framework. Specifically, the renowned VGG16 deep learning algorithm is employed as the initial feature extractor. VGG16 is a state-of-the-art deep convolutional neural network that has demonstrated exceptional performance in various computer vision tasks, including image classification and feature extraction. The primary idea behind this approach is to leverage the capabilities of AI to extract the most discriminative features from the source images using VGG16. These features are then further refined using evolutionary algorithms, which employ a search and optimization process inspired by natural evolution. By iteratively improving the extracted features through the evolutionary algorithms, the method aims to enhance the discriminative power and representational quality of the extracted features. To evaluate the performance of the proposed approach, extensive experiments were conducted. The results demonstrate that the method achieves superior performance in terms of precision, recall, and F-measure when compared to conventional feature extraction techniques. Furthermore, a comprehensive comparison with state-of-the-art AI-based feature extraction techniques further highlights the potential and effectiveness of the proposed approach in multimedia image retrieval applications.Keywords
Information Retrieval, Feature Extraction, Multimedia, Images.References
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- Enhancing Blockchain Transaction Validation in Wireless Sensor Networks Using Random Forests
Abstract Views :114 |
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Authors
Affiliations
1 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Kamla Nehru Institute of Physical and Social Sciences, IN
3 Department of Computer Engineering, Vasantdada Patil Pratishthan College of Engineering and Visual Arts, IN
4 Department of Electronics and Communication Engineering, KSK college of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Chettinad College of Engineering and Technology, IN
2 Department of Computer Science and Engineering, Kamla Nehru Institute of Physical and Social Sciences, IN
3 Department of Computer Engineering, Vasantdada Patil Pratishthan College of Engineering and Visual Arts, IN
4 Department of Electronics and Communication Engineering, KSK college of Engineering and Technology, IN
Source
ICTACT Journal on Communication Technology, Vol 14, No 2 (2023), Pagination: 2901-2906Abstract
As a distributed and decentralized ledger that ensures secure and transparent transactions, blockchain technology has attracted considerable interest. In the context of wireless sensor networks (WSNs), where nodes with limited resources conduct transactions, ensuring efficient and trustworthy validation becomes a challenge. Using random forests, this paper proposes a novel method for enhancing blockchain transaction validation in WSNs. The proposed method enhances the accuracy and efficiency of transaction validation in WSNs by leveraging the ensemble-learning capabilities of random forests. The random forests model is trained with transaction content, originating node information, and network metrics extracted from WSN transactions. Experimental results indicate that the proposed method improves transaction validation precision and decreases validation time in comparison to conventional methods. In addition, the random forests model is resistant to multiple types of attacks, assuring the security and integrity of WSN transactions. The results demonstrate that random forests are a promising technique for improving blockchain transaction validation in wireless sensor networks.Keywords
Blockchain, Wireless Sensor Networks, Transaction Validation, Random Forests, Ensemble Learning, Resource-Constrained Nodes, Security, Integrity, Efficiency, Decentralized Ledger, Ensemble Learning.References
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