Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient’s mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson’s product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.
Recent advancements in the Internet of Things (IoT) and the Web of Things (WoT) accompany a smart life where real world objects, including sensing devices, are interconnected with each other. The Web representation of smart objects empowers innovative applications and services for various domains. To accelerate this approach, Web of Objects (WoO) focuses on the implementation aspects of bringing the assorted real world objects to the Web applications. In this paper; we propose an emergency fire management system in the WoO infrastructure. Consequently, we integrate the formation and management of Virtual Objects (ViO) which are derived from real world physical objects and are virtually connected with each other into the semantic ontology model. The charm of using the semantic ontology is that it allows information reusability, extensibility and interoperability, which enable ViOs to uphold orchestration, federation, collaboration and harmonization. Our system is context aware, as it receives contextual environmental information from distributed sensors and detects emergency situations. To handle a fire emergency, we present a decision support tool for the emergency fire management team. The previous fire incident log is the basis of the decision support system. A log repository collects all the emergency fire incident logs from ViOs and stores them in a repository.
In the era of digital transformation, the Internet of Things (IoT) is emerging with improved data collection methods, advanced data processing mechanisms, enhanced analytic techniques, and modern service platforms. However, one of the major challenges is to provide an integrated design that can provide analytic capability for heterogeneous types of data and support the IoT applications with modular and robust services in an environment where the requirements keep changing. An enhanced analytic functionality not only provides insights from IoT data, but also fosters productivity of processes. Developing an efficient and easily maintainable IoT analytic system is a challenging endeavor due to many reasons such as heterogeneous data sources, growing data volumes, and monolithic service development approaches. In this view, the article proposes a design methodology that presents analytic capabilities embedded in modular microservices to realize efficient and scalable services in order to support adaptive IoT applications. Algorithms for analytic procedures are developed to underpin the model. We implement the Web Objects to virtualize IoT resources. The semantic data modeling is used to promote interoperability across the heterogeneous systems. We demonstrate the use case scenario and validate the proposed design with a prototype implementation.
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