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Secure and resilient improved image steganography using hybrid fuzzy neural network with fuzzy logic

Journal of Safety Science and Resilience 2024年第1期5卷 91-101页

作者： Sachin Dhawan Hemanta kumar Bhuyan subhendu kumar pani Vinayakumar Ravi Rashmi Gupta Arun Rana Alanoud Al Mazroa Dorset College King George BlvdSurreyBritish ColumbiaCanada Department of Information Technology Vignan’s Foundation for ScienceTechnology and Research(Deemed to be University)Andhra PradeshIndia Krupajal Computer Academy Biju Patnaik University of Technology(BPUT)Rourkela 751002India Center for Artificial Intelligence Prince Mohammad Bin Fahd UniversityKhobarSaudi Arabia NSUT East Campus(Formerly AIACTRDelhi 110031India(Affiliated to Guru Gobind Singh Inderaprastha University))Dwarka 110078India Department of Computer Science and Engineering Galgotias College of Engineering and Technology Greater Noida 201310Uttar PradeshIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman University(PNU)P.O.Box 84428Riyadh 11671Saudi Arabia

The exponential growth in communication networks,data technology,advanced libraries,and mainly World Wide Web services has played a pivotal role in facilitating the retrieval of various types of information as ***,this progress has also led to security concerns related to the transmission of confidential ***,safeguarding these data during communication through insecure channels is crucial for obvious *** emergence of steganography offers a robust approach to concealing confidential information,such as images,audio tracks,text files,and video files,in suitable media carriers.A novel technique is envisioned based on back-propagation *** to the proposed method,a hybrid fuzzy neural network(HFNN)is applied to the output obtained from the least significant bit substitution of secret data using pixel value dif-ferences and exploiting the modification *** simulation and test results,it has been observed that the proposed methodology achieves secure steganography and superior visual *** the experiments,we observed that for the secret image of the cameraman,the PSNR&MSE values of the proposed technique are 61.963895 and 0.041361,respectively.

关键词： Image enhancement Image processing Steganography Deep learning Fuzzy neural combination

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A Hybrid Parallel Multi-Objective Genetic Algorithm for 0/1 Knapsack Problem

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Journal of Software Engineering and Applications 2011年第5期4卷 316-319页

作者： Sudhir B. Jagtap subhendu kumar pani Ganeshchandra Shinde 不详

In this paper a hybrid parallel multi-objective genetic algorithm is proposed for solving 0/1 knapsack problem. Multi-objective problems with non-convex and discrete Pareto front can take enormous computation time to converge to the true Pareto front. Hence, the classical multi-objective genetic algorithms (MOGAs) (i.e., non- Parallel MOGAs) may fail to solve such intractable problem in a reasonable amount of time. The proposed hybrid model will combine the best attribute of island and Jakobovic master slave models. We conduct an extensive experimental study in a multi-core system by varying the different size of processors and the result is compared with basic parallel model i.e., master-slave model which is used to parallelize NSGA-II. The experimental results confirm that the hybrid model is showing a clear edge over master-slave model in terms of processing time and approximation to the true Pareto front.

关键词： Multi-Objective Genetic Algorithm Parallel Processing Techniques NSGA-II 0/1 Knapsack Problem Trigger Model Cone Separation Model Island Model

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