Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on DWT-Gabor

Xiao, Xinping; Li, Jingbing; Dan, Yi; Fang, Yangxiu; Cui, Wenfeng; Bhatti, Uzair Aslam; Han, Baoru

doi:10.1007/978-981-16-3013-2_7

Cited by 13 publications

(3 citation statements)

References 18 publications

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“…The testing of the system suggests that the methodology used can differentiate the liver as having no damage, minimal damage, significant damage, severe damage, and cirrhosis. Additionally, the most widely used approach in this disease diagnosis is image processing [63][64][65][66]. But, this approach fails to detect it until or unless any physical symptoms have appeared in the liver.…”

Section: Defuzzifiermentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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The diagnosis of liver fibrosis (LF) is crucial as it is a deadly and life-threatening disease. Artificial intelligence techniques aid doctors by using the previous data on health and making a diagnostic system, which helps to take decisions about patients' health as experts can. The historical data of a patient's health can have vagueness, inaccurate, and can also have missing values. The fuzzy logic theory can deal with these issues in the dataset. In this paper, a multilayer fuzzy expert system is developed to diagnose LF. The model is created by using multiple layers of the fuzzy logic approach. This system aids in classifying the health of patients into different classes. The proposed method has two layers, i.e., layer 1 and layer 2. The input variables used in layer 1 for diagnosing liver fibrosis are Appetite, Jaundice, Ascites, Age, and Fatigue. Similarly, in layer 2, the input variables are Platelet count, White blood cell count, spleen, SGPT ALT (Serum Glutamic Pyruvic Transaminase Alanine Aminotransferase), SGOT ALT (Serum Glutamicoxalacetic Transaminase Alanine Aminotransferase), Serum bilirubin, and Serum albumin. The output variables for this developed system are no damage, minimal damage, significant damage, severe damage, and cirrhosis. This research work also presents the examination of results based on performance parameters. The proposed system achieves a classification accuracy of 95%. Moreover, other performance parameters such as sensitivity, specificity, and precision are calculated as 97.14%, 92%, and 94.44%, respectively.

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Section: Defuzzifiermentioning

confidence: 99%

Fuzzy Logic-Based System for Liver Fibrosis Disease

Alkhalifah¹,

Singla²,

Alurise³

2023

CSSE

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“…Single image rain removal algorithms are generally divided into conventional model-driven methods and data-driven deep learning methods [17,18]. Due to the strong automatic feature learning ability of deep networks, the single image rain removal algorithm based on deep learning has surpassed the conventional model-driven methods in recent years and caught the interest of field researchers [19,20].…”

Section: Related Workmentioning

confidence: 99%

Towards Robust Rain Removal with Unet++

Hu¹,

Sun²,

Yang³

et al. 2023

Computers, Materials &Amp; Continua

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Image deraining has become a hot topic in the field of computer vision. It is the process of removing rain streaks from an image to reconstruct a high-quality background. This study aims at improving the performance of image rain streak removal and reducing the disruptive effects caused by rain. To better fit the rain removal task, an innovative image deraining method is proposed, where a kernel prediction network with Unet++ is designed and used to filter rainy images, and rainy-day images are used to estimate the pixel-level kernel for rain removal. To minimize the gap between synthetic and real data and improve the performance in real rainy image handling, a loss function and an effective data optimization method are suggested. In contrast with other methods, the loss function consists of Structural Similarity Index loss, edge loss, and L1 loss, and it is adopted to improve performance. The proposed algorithm can improve the Peak Signal-to-Noise ratio by 1.3% when compared to conventional approaches. Experimental results indicate that the proposed method can achieve a better efficiency and preserve more image structure than several classical methods.

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“…Studies have shown that changes in the spatial and temporal patterns of climate may lead to changes in the geographical distribution of plants, putting their original habitats at risk [40][41]. To mitigate the effects of climate change on ecosystems, we used species distribution modelling to effectively identify conservation strategies to determine the areas where sensitive species are present or likely to be present [42] multiple periods and use machine learning to improve model accuracy further [23,43].…”

Section: Changes In the Potential Geographical Distribution Of Paeoni...mentioning

confidence: 99%

Climatic Changes in the Anthropocene Have Increased the Suitable Habitat Areas of <i>Paeonia delavayi</i> in China

Huang¹,

Zhao²,

Cai³

et al. 2023

Pol. J. Environ. Stud.

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Paeonia delavayi is a woody plant of the peony group, endemic to China, and has been a traditional medicinal herb since ancient times, as well as a valuable resource for breeding new peony species. Predicting the contemporary and future distribution patterns of Paeonia delavayi in suitable habitats under climate change conditions can provide scientific data to support the establishment of effective conservation. The potential distribution area of the endangered medicinal plant Paeonia delavayi was simulated by the Maxent model. The contribution rate of environmental factors and the knife-cut method were used to test and analyse the factors affecting the distribution of Paeonia delavayi. The response curves were used to determine the appropriate values of environmental factors. The AUC value for Paeonia lactiflora was 0.990, indicating excellent model accuracy. The current total suitable habitat area of Paeonia delavayi was 41.58×104 km 2 , located in the southwest region of China. The high suitable habitat area of Paeonia delavayi was 3.71×104 km 2 , accounting for 8.92% of the total suitable habitat area, mainly distributed in the south of Sichuan, north of Yunnan, and south of Chongqing in thin strips. The most important environmental factor affecting the geographic distribution of Paeonia delavayi is isothermal (Bio3). Under the future climate change scenario, the total suitable habitat area of Paeonia delavayi will increase significantly, and the area of highly suitable habitat will also increase. The centroid of the highly suitable area tends to migrate to high latitude and northwest direction, and the migration range is greater under the high-concentration emission scenario. The research results can provide a theoretical basis for the in-situ protection and sustainable utilization of Paeonia delavayi.

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Robust Zero Watermarking Algorithm for Encrypted Medical Images Based on DWT-Gabor

Cited by 13 publications

References 18 publications

Fuzzy Logic-Based System for Liver Fibrosis Disease

Fuzzy Logic-Based System for Liver Fibrosis Disease

Towards Robust Rain Removal with Unet++

Climatic Changes in the Anthropocene Have Increased the Suitable Habitat Areas of <i>Paeonia delavayi</i> in China

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