In the current digitalized world, cloud computing becomes a feasible solution for the virtualization of cloud computing resources. Though cloud computing has many advantages to outsourcing an organization’s information, but the strong security is the main aspect of cloud computing. Identity authentication theft becomes a vital part of the protection of cloud computing data. In this process, the intruders violate the security protocols and perform attacks on the organizations or user’s data. The situation of cloud data disclosure leads to the cloud user feeling insecure while using the cloud platform. The different traditional cryptographic techniques are not able to stop such kinds of attacks. BB84 protocol is the first quantum cryptography protocol developed by Bennett and Brassard in the year 1984. In the present work, three ways BB84GA security systems have been demonstrated using trusted cryptographic techniques like an attribute-based authentication system, BB84 protocol, and genetic algorithm. Firstly, attribute-based authentication is used for identity-based access control and thereafter BB84 protocol is used for quantum key distribution between both parties and later the concept of genetic algorithm is applied for encryption/decryption of sensitive information across the private/public clouds. The proposed concept of involvement of hybrid algorithms is highly secure and technologically feasible. It is a unique algorithm which may be used to minimize the security threats over the clouds. The computed results are presented in the form of tables and graphs.
Finding orthogonal matrices in different sizes is very complex and important because it can be used in different applications like image processing and communications (eg CDMA and OFDM). In this paper we introduce a new method to find orthogonal matrices by using tensor products between two or more orthogonal matrices of real and imaginary numbers with applying it in images and communication signals processing. The output matrices will be orthogonal matrices too and the processing by our new method is very easy compared to other classical methods those use basic proofs. The results are normal and acceptable in communication signals and images but it needs more research works.
The objective review is to inspect the involvement of Interleukin-6 (IL-6) in rheumatoid arthritis (RA) and to highlight the role of IL-6 and its variants in the pathogenesis of RA and response to anti-IL-6 agents. Several genetic and environmental risk factors and infectious agents contributed to the development of RA. Interleukin-6 is engaged in self-targeted immunity by modifying the equilibrium between T regulatory (T-reg) and T helper-17 (Th-17) cells. The evidences reported that IL-6 parti
Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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This research aims to the importance of oil in achieving economic
security in the Arab. Oil is not an ordinary subject and returns it significance to
the followings:
1. The importance of skipping a source of energy.
2. The importance of oil as raw material for petrochemical industry.
3. The importance of the oil sector as an area of foreign investment
4. The importance of oil in the marketing activities, transport, insurance
and various services
In addition to the importance of oil in general and the Arab oil has
additional strategic advantages such as geographic location, And the
magnitude of reserves and production of heavy investment costs are relatively
simple, And the ability to meet the
Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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