Steganography is one of the branches of information security field, it aims to hide information in unremarkable cover media so as not to arouse an eavesdropper's suspicion. The secret message is hidden in such a way that no significant degradation can be detected in the quality of the original image. The aim of this paper is to introduce an efficient steganographic scheme to hide data over gray scale images. This scheme is based on the property of the human eye, which is more sensitive to the change in the smooth area than the edge area using pixel value difference, besides employing the LSB substitution technique as a fundamental stage. The experimental results show that the proposed method could successfully achieve the goals of the high embedding capacity and maintaining the visual quality, in addition, provides more secure data hiding using selective pixel positions determined by a secret image (i.e. key). Moreover, based on that, the secret message is replaced with dynamic LSBs, our scheme can effectively resist several image steganalysis techniques.

Many industries are developing robust models, capable of analyzing huge and complex data by using machine learning (ML) while delivering faster and more accurate results on vast scales. ML is a subfield of artificial intelligence, which is broadly defined as the capability of a machine to imitate intelligent human behavior. ML tools enable organizations to swiftly identify profitable opportunities and potential risks. Besides these uses, ML also has a wide range of applications in our daily lives. So, the development in ML is most important in this age of digital system to solve more complex problems. In order to further develop ML and diminish the uncertainties to improve accuracy, an innovative concept of complex bipolar intuitionistic fuzzy sets (CBIFSs) is introduced in this article. Further, the Cartesian product of two CBIFSs is defined. Moreover, the complex bipolar intuitionistic fuzzy relations (CBIFRs) and their types with suitable examples are defined. In addition, some important results and properties are also presented. The proposed modeling techniques are used to study different ML factors and their interrelationship, so that the functionality of ML might be enhanced. Furthermore, the advantages and benefits of proposed methods are described by their side to side comparison with preexisting frameworks in the literature.

Artificial intelligence (AI) has made the life more efficient and powered many programs and services. AI is progressing rapidly, and the future is arriving faster than the predictions. Soon, AI will be more proficient as compared to humans in all aspects. Many industries are using AI for the analysis of data to find the best methods for investments. In this article, we developed the impacts of AI on different industries through the new concepts of complex bipolar picture fuzzy set (CBPFS) proposed in the current study. The CBPFS has an extensive structure that includes membership, abstinence, and nonmembership degrees with the ability to deal with multivariable problems. These degrees are fuzzy numbers between 0 and 1 inclusive; 0 being the lowest and 1 being the highest value for each degree, which reflect different meaning for membership, nonmembership, and abstinence. Furthermore, the paper explains the Cartesian product between CBPFSs and complex bipolar picture fuzzy relation (CBPFR) and its types with suitable example. Furthermore, through a comparison test with preexisting fuzzy set frameworks, some benefits of CBPFS are presented in this article.

No abstract

Wireless body sensor networks (WBSNs) pose significant security and privacy risks. The Medical Server (MS) will only allow legitimate stakeholders access to confidential patient medical records when successful mutual authentication between all registered users and the MS has been confirmed using preset secret attributes. This paper proposes a novel approach to overcome the security and privacy problems in WBSNs by using CP-ABE and a consortium blockchain for key management and authentication. In this paper, a fixed-size session key is computed by utilizing several attribute base rules and AND/OR logic gate combinations. IEEE 802.15.6 is also used to transmit the encoded patient data from the register and legitimately deployed biosensor nodes on a patient’s body to the Base Station nearby (BS). This was done in part by leveraging consortium blockchains to construct partial blocks and then, transmit the encrypted partial blocks to MS via peer-to-peer networks, as well as aggregating critical physiological information. MS is now validating partial blocks with a hash function to ensure their integrity before converting them all into full blocks, which are subsequently mined and put on the blockchain effectively and ideally using a consensus mechanism. When sessions between MS and stakeholders are established, all legitimate consumers can view the secure medical records of a registered patient in a hospital using their predefined access structure.. The resource-constrained environment of WBSNs can benefit from enhanced data security and privacy by using CP-ABE in conjunction with the organization’s consensus to encrypt the patient’s critical features or attributes. Automated Validation of Internet Security Protocol and Applications (AVISPA) tool is used to verify the validity and correctness of the proposed authentication mechanism. The proposed scheme reduces transmission, processing and storage costs and energy usage by a significant margin when compared to current state-of-the-art alternatives. It is also worth noting that a multicriteria decision making (MCDM) approach known as Evaluation Based on Distance from Average Solution (EDAS) is employed to provide the ranking and determine which strategy is optimal across all of the domains involved.