An Optimized Genetic Algorithm-Based Non-Commutative Encryption Method for Securing Data in the Cloud

This research introduces a novel non-commutative encryption approach designed to enhance data protection in the context of cloud computing. Leveraging the power of Optimized Genetic Algorithms (OGA), the proposed method aims to fortify the security of sensitive information by introducing non-commutative cryptographic techniques. Cloud computing, while offering unparalleled convenience and scalability, poses inherent security challenges, making robust encryption crucial for safeguarding user data. Through the use of a non-commutative encryption technique, this work presents a novel approach to Quantum Key Distribution (QKD). The integration of genetic algorithms serves to optimize the encryption process, ensuring a balance between computational efficiency and heightened security. There have been several data recovery procedures proposed by researchers, but none of them have shown to be dependable or useful. The suggested method allows users to access data from any backup server if the main cloud server becomes unreliable and cannot provide users with data. In this paper, they perform the analysis based on several parameters such as encryption time, decryption time, success rate, failure rate, throughput, and Avalanche effect. After comparing the proposed work with existing methods, the proposed method has low encryption (312ms)/decryption time (314ms), and a high success rate (100ms)/ failure rate (96ms)

A CONCEPTUAL STUDY ON MODE OF ACTION OF NASYA

In Ayurveda, Panchkarma therapy is used for the maintenance of health and eradication of diseases from their root and Nasya Karma is one among them. In this therapy, the medicine is administered through nose either in the form of ghee, oil, powder, liquid or smoke. It is particularly useful in the treatment of diseases occurring in the organs situated above the clavicle but indirectly it works on the whole body by improving the functioning of the endocrine glands and nervous system. Nasa is said to be the main doorway to Shiras. Nasyaaushadhi reaches to brain via nasal route and acts on higher centers of brain controlling different neurological, endocrinal and circulatory functions and thus showing local as well as systemic effects. This administration of drugs through nasal route opens a new hope for the both local and systemic drug administration. Nasal route drug administration is a promising alternative route of drug administration for local, systemic and central nervous system action. So here a review is presented on mode of action of Nasya procedure according to Ayurveda and modern science.

Block chain-Enhanced Security for Financial Institution Electronic Records Management System

With an emphasis on banking systems, this article explores how blockchain technology can be used to manage electronic records in the financial sector. This research looks at how well blockchain-based solutions work for ERM in terms of improving privacy, security, and data integrity. The research emphasizes the significance of cryptographic techniques, consensus protocols, access controls, and data integrity measures in guaranteeing the secrecy and dependability of financial data through a thorough examination of these components. In comparison to other studies, this one shows a small drop in accuracy with a precision ratio of. Blockchain technology has the potential to greatly improve the safety of financial institutions' electronic records, as this ratio is still very high. While there is certainly space for development, the results show that blockchain-based solutions have potential to strengthen the reliability and honesty of monetary systems

Enhancing the functional content of protein interaction networks

Protein interaction networks are a promising type of data for studying complex biological systems. However, despite the rich information embedded in these networks, they face important data quality challenges of noise and incompleteness that adversely affect the results obtained from their analysis. Here, we explore the use of the concept of common neighborhood similarity (CNS), which is a form of local structure in networks, to address these issues. Although several CNS measures have been proposed in the literature, an understanding of their relative efficacies for the analysis of interaction networks has been lacking. We follow the framework of graph transformation to convert the given interaction network into a transformed network corresponding to a variety of CNS measures evaluated. The effectiveness of each measure is then estimated by comparing the quality of protein function predictions obtained from its corresponding transformed network with those from the original network. Using a large set of S. cerevisiae interactions, and a set of 136 GO terms, we find that several of the transformed networks produce more accurate predictions than those obtained from the original network. In particular, the $HC.cont$ measure proposed here performs particularly well for this task. Further investigation reveals that the two major factors contributing to this improvement are the abilities of CNS measures, especially $HC.cont$, to prune out noisy edges and introduce new links between functionally related proteins