Matematik Bölümü Yayın Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/413
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Article Citation - WoS: 11Citation - Scopus: 13Image Encryption Algorithm Based on New Fractional Beta Chaotic Maps(Tech Science Press, 2022) Natiq, Hayder; Alkhayyat, Ahmed; Farhan, Alaa Kadhim; Al-Saidi, Nadia M. G.; Baleanu, Dumitru; Ibrahim, Rabha W.In this study, a new algorithm of fractional beta chaotic maps is proposed to generate chaotic sequences for image encryption. The proposed technique generates multi random sequences by shuffling the image pixel position. This technique is used to blur the pixels connecting the input and encrypted images and to increase the attack resistance. The proposed algorithm makes the encryption process sophisticated by using fractional chaotic maps, which hold the properties of pseudo-randomness. The fractional beta sequences are utilized to alter the image pixels to decryption attacks. The experimental results proved that the proposed image encryption algorithm successfully encrypted and decrypted the images with the same keys. The output findings indicate that our proposed algorithm has good entropy and low correlation coefficients. This translates to enhanced security against different attacks. A MATLAB programming tool was used to implement and assess the image quality measures. A comparison with other image encryption techniques regarding the visual inspection and signal-to-noise ratio is provided.Article Citation - WoS: 1Citation - Scopus: 4Image Splicing Detection Using Generalized Whittaker Function Descriptor(Tech Science Press, 2023) Al-Shamayleh, Ahmad Sami; Ibrahim, Rabha W.; Baleanu, DumitruImage forgery is a crucial part of the transmission of misinfor-mation, which may be illegal in some jurisdictions. The powerful image editing software has made it nearly impossible to detect altered images with the naked eye. Images must be protected against attempts to manipulate them. Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications. Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images. Because image tampering detection targets processing techniques such as object removal or addition, identifying altered images remains a major challenge in research. In this study, a novel image texture feature extraction model based on the generalized k-symbol Whittaker function (GKSWF) is proposed for better image forgery detection. The proposed method is divided into two stages. The first stage involves feature extraction using the proposed GKSWF model, followed by classification using the "support vector machine" (SVM) to distinguish between authentic and manipulated images. Each extracted feature from an input image is saved in the features database for use in image splicing detection. The proposed GKSWF as a feature extraction model is intended to extract clues of tam-pering texture details based on the probability of image pixel. When tested on publicly available image dataset "CASIA" v2.0 (Chinese Academy of Sciences, Institute of Automation), the proposed model had a 98.60% accuracy rate on the YCbCr (luminance (Y), chroma blue (Cb) and chroma red (Cr)) color spaces in image block size of 8 x 8 pixels. The proposed image authentication model shows great accuracy with a relatively modest dimension feature size, supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.Article Citation - WoS: 25Citation - Scopus: 27Solution of Modified Bergman Minimal Blood Glucose-Insulin Model Using Caputo-Fabrizio Fractional Derivative(Tech Science Press, 2021) Baleanu, Dumitru; Mishra, Manvendra Narayan; Goswami, Pranay; Dubey, Ravi ShankerDiabetes is a burning issue in the whole world. It is the imbalance between body glucose and insulin. The study of this imbalance is very much needed from a research point of view. For this reason, Bergman gave an important model named-Bergman minimal model. In the present work, using Caputo-Fabrizio (CF) fractional derivative, we generalize Bergman's minimal blood glucose-insulin model. Further, we modify the old model by including one more component known as diet D(t), which is also essential for the blood glucose model. We solve the modified model with the help of Sumudu transform and fixed-point iteration procedures. Also, using the fixed point theorem, we examine the existence and uniqueness of the results along with their numerical and graphical representation. Furthermore, the comparison between the values of parameters obtained by calculating different values of t with experimental data is also studied. Finally, we draw the graphs of G(t), X(t), I(t), and D(t) for different values of tau. It is also clear from the obtained results and their graphical representation that the obtained results of modified Bergman's minimal model are better than Bergman's model.Article Citation - WoS: 11Citation - Scopus: 11Fractional Renyi Entropy Image Enhancement for Deep Segmentation of Kidney Mri(Tech Science Press, 2021) Al-Shamasneh, Ala'a R.; Shaiba, Hadil; Ibrahim, Rabha W.; Baleanu, Dumitru; Jalab, Hamid A.Recently, many rapid developments in digital medical imaging have made further contributions to health care systems. The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses. Many factors like image contrast and quality affect the result of image segmentation. Due to that, image contrast remains a challenging problem for image segmentation. This study presents a new image enhancement model based on fractional Renyi entropy for the segmentation of kidney MRI scans. The proposed work consists of two stages: enhancement by fractional Renyi entropy, and MRI Kidney deep segmentation. The proposed enhancement model exploits the pixel's probability representations for image enhancement. Since fractional Renyi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels, yielding an overall better details of the kidney MRI scans. In the second stage, the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans. The experimental results showed an average of 95.60% dice similarity index coefficient, which indicates best overlap between the segmented bodies with the ground truth. It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.Article Citation - WoS: 13Citation - Scopus: 20A New Medical Image Enhancement Algorithm Based on Fractional Calculus(Tech Science Press, 2021) Ibrahim, Rabha W.; Hasan, Ali M.; Karim, Faten Khalid; Al-Shamasneh, Ala'a R.; Baleanu, Dumitru; Jalab, Hamid A.The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images. The captured images may present with low contrast and low visibility, which might influence the accuracy of the diagnosis process. To overcome this problem, this paper presents a new fractional integral entropy (FITE) that estimates the unforeseeable probabilities of image pixels, posing as the main contribution of the paper. The proposed model dynamically enhances the image based on the image contents. The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels? probability. Initially, the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image. Next, the contrast of the image is then adjusted to enhance the regions with low visibility. Finally, the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image. Tests were conducted on brain MRI, lungs CT, and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality. The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores. Overall, this model improves the details of brain MRI, lungs CT, and kidney MRI scans, and could therefore potentially help the medical staff during the diagnosis process.