دانلود کتاب مدل‌های معنایی در برنامه‌های IoT و سلامت الکترونیک

The Healthcare domain needs many health devices linked to the internet due to the emerging growth of Internet of Things (IoT) technologies. For the initial diagnosis and prevention, the Internet of Things plays an essential role in analyzing health issues and generating alerts with the help of IoT devices. IoT technologies play a significant role in providing efficient and effective solutions in health information system areas. The Healthcare domain is crucial for people and organizations to get the services and the delivery of services simultaneously. In this area, a massive collection of domain knowledge is gathered by the medical devices that efficiently conducts the activities for remote patient monitoring in a dynamic way. Different devices can be used to collect the patient data, such as monitoring wellness, elderly care, chronic diseases, fitness activities, and assuring sustainability. These devices (diagnostic, medical imaging, and sensor devices) are significant and effectively used for diagnosing, medicating, and treatment. Different types of sensing devices such as blood pressure monitors, glucose meters, thermometers, and heart rate sensors can be the best examples of IoT devices in health care. The stored and analyzed health data gathered from devices are later used for querying and generate alerts by defining rules under emergency conditions. These applications provide IoT-based medical services for reducing the cost, monitoring the patients, reducing healthcare providers’ time, and increasing patient guidance. These sensing devices can exchange the collected data (user information, health sensing data, history, device information, or other domain data). During the interchange of data by IoT devices, it is necessary to ensure the confidentiality and privacy of the data and exchange the data without losing its meaning. Semantic web technologies such as ontologies and rules are the best effort to maintain the consistency of data. These technologies have proven to be a challenging task for the exploitation and integration of data from different sources. IoT-based applications are included in semantic web technologies for providing sensor-based services and remote monitoring by linking the end-users. There is a need to present the semantic modeling of data with IoT devices description. IoT devices generate massive data every day by observations and actuations; every device has its own information and it becomes challenging to access the information by different applications. This problem can be resolved by involving semantics to reduce the ambiguity and increase the reusability of data for a significant decision  Semantic modeling generalizes the entities with their description and depicts the relationship among entities to structure the data with its meaning. It is presented as a conceptual model to include semantic data representation with its meaning and the possible relationship between them. In the semantic web approach, data is modeled and organized to advance the knowledge base and maintain data consistency as information is updated remotely. A single entity, word, or data cannot interpret the context or meaning to humans, but linked entities can always provide the meaning with a context. For example, the context of data in a database is expressed mainly by its structure but not focused on its meaning, while in semantic modeling, the structure is described inherently with the meaning and description of data. The hierarchy of classes and subclasses can describe semantic models as data classification, and classes are related to the properties to generalize the structure. This abstraction of classification provides strong readability and interpretation to both humans and machines, that was the semantic web’s central aspect. Ontologies are presented as the backbone to achieve the goal of the semantic web to define the concepts explicitly and presented as a semantic model. Abstractions can be presented in a semantic data model as

• Classification – “instance_of” relations
• Aggregation – “has_a” relation
• Generalization – “is_a” relation