The expanding importance of technology, particularly mobile banking, in the financial industry, is examined in this literature review, as well as the crucial role that cybersecurity knowledge plays in protecting online transactions. Users now have the flexibility to conduct payments whenever and wherever they wish thanks to the advent of mobile banking. Further consumer behavior study is necessary due to difficulties with its acceptability. Given the hazards involved in online and mobile banking, cybersecurity is revealed as a critical component. Users’ actions might lead to financial losses since they represent security concerns. The evaluation places a strong emphasis on the necessity of increasing user cybersecurity knowledge and comprehension. Wireless banking is still in its early phases and needs more study on consumer acceptability and behavior despite the greater accessibility of technology. Furthermore, the research study emphasizes the socio‐technical difficulties governments encounter in tackling cybersecurity and emphasizes how urgently better readiness is needed in the face of cyberwarfare threats. It investigated how user behavior in mobile banking in particular geographic areas, such as Thailand, relates to cyberspace knowledge and consciousness. The assessment emphasizes the value of technology in banking, the difficulties associated with cybersecurity, and the demand for increased customer knowledge and comprehension to ensure safe digital transactions. To conduct this research activity, standardized questionnaires are used. The technique employed to get this data was convenience sampling. The statistics collection size stood at 500 and was gathered from males as well as females of all ages, belonging to diverse revenue groups, and numerous professional backgrounds. The survey finds that even while these services are becoming widespread in the UAE, customers’ awareness and understanding of cyber security are still insufficient. Users frequently underrate the security dangers involved with online transactions, which might create openings. Additionally, the study underlines the necessity of more effective training programs and efforts to raise mobile banking consumers’ knowledge of cybersecurity issues. It also emphasizes how crucial it is to build cybersecurity precautions into the structure and functioning of services for mobile banking. The purpose of this study was to examine the demographic characteristics of consumers and companies (online transactions) that use mobile banking apps to get special advantages. It was shown that people’s satisfaction with mobile banking applications was influenced by a number of criteria, including age, employment, income, marital status, and educational attainment. Younger consumers, such as students and recent graduates, are seen to be happy than customers of various ages and vocations, and males are thought to be happier than women.

In the era of advancement in information technology and the smart healthcare industry 5.0, the diagnosis of human diseases is still a challenging task. The accurate prediction of human diseases, especially deadly cancer diseases in the smart healthcare industry 5.0, is of utmost importance for human wellbeing. In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a dizzying pace, from a small wristwatch to a big aircraft. With this advancement in the healthcare industry, there also rises the issue of data privacy. To ensure the privacy of patients’ data and fast data transmission, federated deep extreme learning entangled with the edge computing approach is considered in this proposed intelligent system for the diagnosis of lung disease. Federated deep extreme machine learning is applied for the prediction of lung disease in the proposed intelligent system. Furthermore, to strengthen the proposed model, a fused weighted deep extreme machine learning methodology is adopted for better prediction of lung disease. The MATLAB 2020a tool is used for simulation and results. The proposed fused weighted federated deep extreme machine learning model is used for the validation of the best prediction of cancer disease in the smart healthcare industry 5.0. The result of the proposed fused weighted federated deep extreme machine learning approach achieved 97.2%, which is better than the state‐of‐the‐art published methods.