## 4 EC, DL, IoMT, and Drug Discovery
Equipping IoMT devices with wearable medical sensors can create a powerful tool for monitoring the health information of millions of people, since, in this way, IoMT devices can permanently transmit the medical information of a vaccinated person to the nearest ED. Once the ED receives the data, it classifies and analyzes the information and sends the results where physicians and researchers at medical centers in the form of charts, graphs, tables, and notes. In an emergency, the system immediately alerts an ambulance and relevant doctors. To facilitate the analysis of large amounts of data, the platform must be connected to CC servers to enable easy updates. If there are any bugs in or updates to the DL methods or the natural language processing (NLP) programs installed on the ED, the connection to CC allows the system to be updated easily to keep up with the latest advancements in COVID-19 diagnosis and treatment, as well as AI methods. The proposed framework for developing drugs and vaccines in trial stages involves using NLP to analyze clinical-examination reports and other unstructured data. This analysis can help identify patterns and insights that can be useful in drug development and vaccine trials. Natural language processing can assist in detecting potential health risks and generating control comments, which frees up healthcare professionals to focus on patient care rather than manual data analysis. The ability of NLP to quickly and accurately process large amounts of data is valuable in extracting important information from unstructured data sources. This information can be used to identify areas that need improvement or  
Figure 2: Layer Six: Architecture for Vaccinated Persons’ Health-Data Monitoring and Control. The proposed architecture for Layer 6 in a framework for monitoring and controlling vaccinated persons’ health data is composed of three main sections. Firstly, a number of sensors and IoMT devices are used to collect health data from individuals. Secondly, an EC (edge computing) platform processes the collected data at maximum speed, and a network of physicians, researchers, and natural language processing (NLP) tools supervise the process. Thirdly, CC (cloud computing) servers support the EC-platform infrastructure but do not directly participate in processing the health data [29]. This framework provides real-time monitoring and analysis of health data, which can be used to identify potential health concerns or track the effectiveness of vaccination programs. The use of sensors, IoMT devices, and NLP tools ensures that the framework operates efficiently and accurately. potential health risks. In addition, NLP can improve the accuracy of diagnoses by detecting patterns and trends that humans might miss. This leads to the earlier identification of health problems and better treatment options. By using NLP in health-data monitoring and control, healthcare processes can be made more efficient and accurate, resulting in better patient outcomes and improved overall health in the context of drug- and vaccine-development trials. Furthermore, IoMT devices may require updates due to medical or technical reasons, and with millions of devices connecting to thousands of EDs, coordinating the software, hardware, and data can be challenging. However, the primary advantage of this approach is its support for CC servers. Although CC servers do not have real-time connections with the IoMT devices or managing platforms at medical centers, they can receive the latest instructions from operators via EDs. Consequently, they can update the DL methods based on necessary modifications, and IoMT devices receive essential updates from the EDs. As a result, the whole system can be updated quickly, depending on the volume of updated requirements. Therefore, this proposed method not only accelerates monitoring operations but also ensures that the system is continually updated.