IoT (Internet of Things) devices are increasingly being used in healthcare to collect and transmit patient data, which can improve patient outcomes and reduce costs. However, this also creates new challenges for data security and privacy. Thus, the major demand for secure and efficient data-sharing solutions has prompted significant attention due to the increasing volume of shared sensor data. Leveraging a data-fusion-based paradigm within the realm of IoT-protected healthcare systems enabled the collection and analysis of patient data from diverse sources, encompassing medical devices, electronic health records (EHRs), and wearables. This innovative approach holds the potential to yield immediate benefits in terms of enhancing patient care, including more precise diagnoses and treatment plans. It empowers healthcare professionals to devise personalized treatment regimens by amalgamating data from multiple origins. Moreover, it has the capacity to alleviate financial burdens, elevate healthcare outcomes, and augment patient satisfaction. Furthermore, this concept extends to fortifying patient records against unauthorized access and potential misuse. In this study, we propose a novel approach for secure transmission of healthcare data, amalgamating the improved context-aware data-fusion method with an emotional-intelligence-inspired enhanced dynamic Bayesian network (EDBN). The findings indicated that F1 score, accuracy, precision, recall, and ROC-AUC score using DCNN were 89.3%, 87.4%, 91.4%, 92.1%, and 0.56, respectively, which was second-highest to the proposed method. On the other hand, the F1 score, accuracy, precision, recall, and ROC-AUC scores of FRCNN and CNN were low in accuracy at 83.2% and 84.3%, respectively. Our experimental investigation demonstrated superior performance compared with existing methods, as evidenced by various performance metrics, including recall, precision, F measures, and accuracy.

The operating system (OS) of a computer controls both its hardware and software. It handles necessary functions including input and output processing, file and memory management, and peripheral device management, including disc drives and printers. Programs created for particular purposes are referred to as application software. These programs, which are frequently open source and freely accessible, contribute to the growing number of downloads. This paper discusses the basics of Android malware, its evolution, and malware analysis tools and techniques. Providing the research gaps and giving a review of the literature on Android malware detection using machine learning and deep learning are its main objectives. It offers the knowledge gathered from the literature as well as potential avenues for future research, which may aid in the development of reliable and precise methods for classifying Android malware. This paper conducts a systematic and comprehensive assessment of the methods and tools utilized for the analysis, classification, and detection of malicious Android apps. Several research gaps are indicated based on the thorough literature evaluation. Additionally, the report offers insights on future research paths that may aid academics in developing novel and reliable methods for identifying and categorizing Android malware.