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.

Due to a very large number of connected virtual objects in the surrounding environment, intelligent service features in the Internet of Things requires the reuse of existing virtual objects and composite virtual objects. If a new virtual object is created for each new service request, then the number of virtual object would increase exponentially. The Web of Objects applies the principle of service modularity in terms of virtual objects and composite virtual objects. Service modularity is a key concept in the Web Objects-Enabled Internet of Things (IoT) environment which allows for the reuse of existing virtual objects and composite virtual objects in heterogeneous ontologies. In the case of similar service requests occurring at the same, or different locations, the already-instantiated virtual objects and their composites that exist in the same, or different ontologies can be reused. In this case, similar types of virtual objects and composite virtual objects are searched and matched. Their reuse avoids duplication under similar circumstances, and reduces the time it takes to search and instantiate them from their repositories, where similar functionalities are provided by similar types of virtual objects and their composites. Controlling and maintaining a virtual object means controlling and maintaining a real-world object in the real world. Even though the functional costs of virtual objects are just a fraction of those for deploying and maintaining real-world objects, this article focuses on reusing virtual objects and composite virtual objects, as well as discusses similarity matching of virtual objects and composite virtual objects. This article proposes a logistic model that supports service modularity for the promotion of reusability in the Web Objects-enabled IoT environment. Necessary functional components and a flowchart of an algorithm for reusing composite virtual objects are discussed. Also, to realize the service modularity, a use case scenario is studied and implemented.