Distinct optical response of colloidal gold-cinnamon nanocomposites: Role of pH sensitization

Salim, A.A.; Ghoshal, Subhasis; Danmallam, Ibrahim Mohammed; Sazali, E.S.; Krishnan, Gowri Shankar; Aziz, M. S.; Bakhtiar, Hazri

doi:10.1088/1742-6596/1892/1/012039

Cited by 9 publications

(8 citation statements)

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“…Then, we used COVID-19 Radiography Database (COVID-Dataset) with three classes (COVID, Pneumonia, and Normal). Next, we applied our enhancement technique to the COVID-Dataset [17][18][19]. Finally, the enhanced dataset images are trained with our CNN to generate a model to classify the images into COVID, Pneumonia, or Normal.…”

Section: Methodsmentioning

confidence: 99%

Coronavirus Classification based on Enhanced X-ray Images and Deep Learning

Najjar,

Waheed,

Mahdi

2023

Mal. J. Fund. Appl. Sci.

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In light of the fact that the global pandemic of Coronavirus Disease 2019 (COVID-19) is still having a significant impact on the health of people all over the world, there is a growing need for testing diagnosis and treatment that can be completed quickly. The primary imaging modalities used in the respiratory disease diagnostic process are the Chest X-ray (CXR) and the computed tomography scan. In this context, this paper aims to design a new Convolutional Neural Network (CNN) to diagnose COVID-19 in patients based on CXR images and determine whether they are COVID or healthy. We have tested the performance of our CNN on the COVID-19 Radiography Database with three classes (COVID, Pneumonia, and Normal). Also, we proposed a new enhancement technique to enhance the CXR image using the Laplacian kernel with Delta Function and Contrast-Limited Adaptive Histogram Equalization. The proposed CNN has been trained and tested on 15153 enhanced and original images, COVID (3616), Pneumonia (1345), and Normal (10192). Our enhancement technique increased the performance metrics scores of the proposed CNN. Hence, the proposed method obtained better results than the state-of-the-art methods in accuracy, sensitivity, precision, specificity, and F measure.

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Section: Methodsmentioning

confidence: 99%

Coronavirus Classification based on Enhanced X-ray Images and Deep Learning

Najjar,

Waheed,

Mahdi

2023

Mal. J. Fund. Appl. Sci.

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“…They were able to attain an 81% rate of correct categorization. In their research, Ayan and Unver [24][25][26] shown how data augmentation may boost the accuracy of deep learning-based melanoma categorization. They took 1000 original photos of varying sizes from the famous ISIC dataset, splitting them in half to create sets of 500 images representing malignant and benign tumors.…”

Section: Related Workmentioning

confidence: 99%

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

Waheed

Saadi

Rahim

et al. 2023

Mal. J. Fund. Appl. Sci.

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Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.

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“…In contrast, the value of the correlation coefficient is close to 0, in encrypted images. Therefore, one of the most important objectives of effective encryption algorithms is to obtain an encrypted image with the lowest possible correlation [43][44][45][46]. Fig.…”

Section: B Correlation Coefficientmentioning

confidence: 99%

WAM 3D Discrete Chaotic Map for Secure Communication Applications

Abdul-Kareem,

Mahmoud Al-Jawher

2023

Int J Innov Comp

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Chaotic systems have become widely adopted as an effective way for secure data communications, because of its simple mathematical complexity and good security. The relationship between encryption algorithms and chaos systems has gained a lot of attention in the past few years, since it avoids the data spreading as well as lower the transmission delay and costs. In this paper a novel 3D discrete chaotic map is proposed for data encryption and secure communication and named as WAM. For secure communication, the Pecora and Carroll (P-C) method was utilized to achieve synchronization between the master system and the slave system. The simulation results of WAM 3D discrete chaotic map showed that the system has a chaotic behavior and a characteristic randomness and can pass 0-1, Lyapunov exponent (LE) and NIST tests which are usually used to check chaotic behavior. The statistical outcomes of the LE test were 0.0193, the frequency test (FT) was 0.4237, and the run test (RT) yielded a value of 0.0607. As a result, it enrich the theoretical basis of the equations and implementation of chaos, and it is superior for encryption algorithms and communication security applications.

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Distinct optical response of colloidal gold-cinnamon nanocomposites: Role of pH sensitization

Cited by 9 publications

References 0 publications

Coronavirus Classification based on Enhanced X-ray Images and Deep Learning

Coronavirus Classification based on Enhanced X-ray Images and Deep Learning

Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms

WAM 3D Discrete Chaotic Map for Secure Communication Applications

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