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Article type: Research Article
Authors: Haripriya, V.a; * | Vishal Gupta, Mohanb | Nadkarni, Nikitac | Malik, Surajd | Yadav, Adityae | Joshi, Apoorvaf
Affiliations: [a] Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, India | [b] College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India | [c] Department of ISME, ATLAS SkillTech University, Mumbai, Maharashtra, India | [d] Department of Computer Science & Engineering, IIMT University, Meerut, Uttar Pradesh, India | [e] Maharishi School of Business Management, Maharishi University of Information Technology, Uttar Pradesh, India | [f] Masters in Computer Applications, Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
Correspondence: [*] Corresponding author. V. Haripriya, Assistant Professor, Department of Computer Science and Information Technology, Jain (Deemed to be University), Bangalore, India. E-mail: haripriya64694@gmail.com.
Abstract: From online social networks to life-or-death security systems, multimedia files (photos, movies, and audio recordings) have grown common in today’s digital culture. Protecting people, businesses and infrastructure requires strict adherence to the encryption and decryption of multimedia data. We suggested an Ensemble Whale Optimized Recurrent Neural Network (EWO-RNN)used in this study to overcome these issues. With the help of this study, multimedia security will be evaluated in more accurate and comprehensive manner. Smarter decisions and proactive security measures may follow as a result of this. To increase the system quality and the overall performance, the collected data is pre-processed for normalized data by using Min-Max Normalization. Pre-processed data is extracted by using Kernel Principle Component Analysis (K-PCA). The EWO-RNN evaluates the effectiveness and efficiency of an approach by analyzing the performance of Accuracy (97.85%), Precision (92.2%), F1-score (96.1%), Mean Square Error (MSE) (0.086), Root Mean Square (RMSE) (0.12%) and Sensitivity (95%). The Enhanced Radial Base Deep Learning Algorithm for Predicting Multimedia Security Issues proposes a solution with improved resilience, accuracy, generalization, and decision-making capabilities. In a dynamic and evolving digital environment, this makes the algorithm a viable tool for multimedia security assessments.
Keywords: Multimedia, security issues, ensemble whale optimized recurrent neural network (EWO-RNN), min-max normalization, kernel principle component analysis (K-PCA)
DOI: 10.3233/JIFS-237041
Journal: Journal of Intelligent & Fuzzy Systems, vol. 46, no. 2, pp. 4829-4840, 2024
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