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Journal of Information and Optimization Sciences cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to: • Information Sciences • Optimization Sciences • Control Theory • Operational Research • Decision Sciences • Information Theory • Information Technology • Computer Networks and Communications • Mathematical Programming • Modelling and Simulation • Database Management • Applications to Engineering Sciences • Applications to Technology

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Open Access Research Article

Critical analysis of hybrid learning models to detect morphed images

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pp. 659–675Vol. 45Issue 3April 2024DOI: 10.47974/JIOS-1336XML
Received:
04 Oct 2022
Published Online:
27 Apr 2024
Article type:
Research Article
Language:
EN
Article no.:
JIOS-1336
Pages:
659–675

Abstract

Machine learning (ML) algorithms produce different results with the kind of input data set or parameters passed to the algorithm. As per input data set, the analysis generally depends on the data type and amount of data set being used to train and test.  Other parameters which affect the machine learning analysis are the CNN parameters or the hybrid approach being used. As there is no similar result for ML analysis on various data set, it is hard to predict which model will work most efficiently on the given data set. In this paper an analytical analysis of machine learning algorithms has been done to examine the working of various algorithms individually or in Hybrid mode. This paper studies CNN, CNN + RF, CNN +SVM approach and the theoretical parameters affecting them which helps in deciding the best suited algorithm with the given data set. 

Keywords

Subject Classifications

68T4568T0568M0168M25

References

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