Absorption, fluorescence and sensing quality of Rose Bengal dye-encapsulated cinnamon nanoparticles

Salim, A.A.; Ghoshal, Sib Krishna; Shamsudin, M. S.; Rosli, Muhammad Izz; Aziz, M. S.; Harun, S. W.; Krishnan, Ganesan; Bakhtiar, Hazri

doi:10.1016/j.sna.2021.113055

Cited by 12 publications

(8 citation statements)

References 14 publications

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“…The COVID-19 class contains 822 images, while the NORMAL class contains 1577 images [24][25][26][27]. However, we used the proposed method to evaluate ACC, MCC, SEN, F1, PRE, and SP metrics (equations [25][26][27][28][29][30][31][32].…”

Section: Resultsmentioning

confidence: 99%

“…In this experiment, we calculate the evaluation metrics of the SVM and the KNN classifiers for the GLCM1, GLCM2, GLCM3, GLCM4, and GLCMA datasets. Furthermore, this subsection compares the proposed method with state-of-the-art algorithms [28][29][30][31][32][33]. Furthermore, the experimental results of the proposed method are collected on the COVID GLCM dataset using SVM and KNN classifiers.…”

Section: Resultsmentioning

confidence: 99%

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Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Najjar,

Kadhim,

Kareem

et al. 2023

Mal. J. Fund. Appl. Sci.

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As the world continues to battle the devastating effects of the COVID-19 pandemic, it has become increasingly crucial to screen patients for contamination accurately and effectively. One of the primary screening methods is chest radiography, utilizing radiological imaging to detect the presence of the virus in the lungs. This study presents a cutting-edge solution to classify COVID-19 infections in chest X-ray images by utilizing the Gray-Level Co-occurrence Matrix (GLCM) and machine learning algorithms. The proposed method analyzes each X-ray image using the GLCM to extract 22 statistical texture features and then trains two machine learning classifiers - K-Nearest Neighbor and Support Vector Machine - on these features. The method was tested on the COVID-19 Radiography Database and was compared to a state-of-the-art method, delivering highly efficient results with impressive sensitivity, accuracy, precision, F1-score, specificity, and Matthew's correlation coefficient. The proposed approach offers a promising new way to classify COVID-19 infections in chest X-ray images and has the potential to play a crucial role in the ongoing fight against the pandemic.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Najjar,

Kadhim,

Kareem

et al. 2023

Mal. J. Fund. Appl. Sci.

View full text Add to dashboard Cite

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“…Of course, it suffers during its performance from small key spaces and the possibility of detecting its orbits in addition to its limited ranges, and several other performance limitations. The authors proposed a high-dimensional chaotic system and the possibility of more secure encryption when used in image encryption and named it Walid-Ali Chaotic Map (WAM) [26][27][28][29][30]. Based on these characteristics, this new chaotic system (WAM) has been used in this research to generate chaotic sequences, which can be described by the following equations:…”

Section: The Proposed Methodsmentioning

confidence: 99%

A Robust Image Encryption Scheme Based on Block Compressive Sensing and Wavelet Transform

Abed,

Mahmoud Al-Jawher

2023

Int J Innov Comp

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In this paper, a modified robust image encryption scheme is developed by combining block compressive sensing (BCS) and Wavelet Transform. It was achieved with a balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete wavelet transform (DWT) domain, and the coefficient vectors are confused using the coefficient random permutation strategy and encrypted into a secret image by compressive sensing. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme. The compressive encryption model using BCS with Walsh transform as sensing matrix and WAM chaos system, the scrambling technique and diffusion succeeded in enhancement of secure performance.

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“…The problem is introduced in a way that only Bob can notice and fix because he has the private key. The bounded distance decoding problem is NP-Complete, making it inefficient for the hacker to fix the mistake [26][27][28].…”

Section: Code-based Cryptographymentioning

confidence: 99%

Post-quantum Techniques in Wireless Network Security: An Overview

Fakhruldeen

Al-Kaabi

Jabbar

et al. 2023

Mal. J. Fund. Appl. Sci.

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Post quantum is a general name to all the techniques which are safe against the quantum computer attack. The wireless network is one of the most important means of communication. Wireless network security is a top priority. Wireless networks use conventional cryptography, which has various flaws, whereas quantum cryptography claims to be completely secure. It wasn't long after quantum computers became operational that people began to think about new ways to secure electronic communications. After considering all of the weaknesses in conventional cryptosystems, individuals began to look for new ways to secure electronic communications. Traditional cryptography has many problems, but quantum cryptography addresses nearly all of them.

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Absorption, fluorescence and sensing quality of Rose Bengal dye-encapsulated cinnamon nanoparticles

Cited by 12 publications

References 14 publications

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

Classification of COVID-19 from X-ray Images using GLCM Features and Machine Learning

A Robust Image Encryption Scheme Based on Block Compressive Sensing and Wavelet Transform

Post-quantum Techniques in Wireless Network Security: An Overview

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