Recently, there has been enormous development due to advancements in technology. Industries and enterprises are moving towards a digital system, and the oil and gas industries are no exception. There are several threats and risks in digital systems, which are controlled through cyber-security. For the first time in the theory of fuzzy sets, this research analyzes the relationships between cyber-security and cyber-crimes in the oil and gas sectors. The novel concepts of complex intuitionistic fuzzy relations (CIFRs) are introduced. Moreover, the types of CIFRs are defined and their properties are discussed. In addition, an application is presented that uses the Hasse diagram to make a decision regarding the most suitable cyber-security techniques to implement in an industry. Furthermore, the omnipotence of the proposed methods is explained by a comparative study.

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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.

Applications based on video and image in wireless sensor network are highly attractive due to their wealth of information. In this context, application for object recognition and tracking using image and video information is one the attractive approaches that can be applied for event detection and localization, security processes, following of the rare animal species and control of road traffic, and so forth. However, the implementation of such approach with WMSN requires a specific image processing scheme and efficient transmission protocol. In fact, because of the limited energy of the batteries embedded in motes, the power consumption is the major constraint facing network life time and reliability in WMSN. The efficiency and the validity of these multimedia applications over wireless sensor networks are then dependent on the capabilities of the designer to provide low-power scheme for data processing and energy-aware transmission protocols. This paper presents a contribution to the design of low complexity scheme based on object identification for efficient sensing of multimedia information in wireless multimedia sensor networks. It proposes a new solution and explores the associated performances of this scheme. The presented results in this paper attest the high efficiency to achieve low-power objects identification when implemented in wireless motes.