The prediction of heart failure survivors is a challenging task and helps medical professionals to make the right decisions about patients. Expertise and experience of medical professionals are required to care for heart failure patients. Machine Learning models can help with understanding symptoms of cardiac disease. However, manual feature engineering is challenging and requires expertise to select the appropriate technique. This study proposes a smart healthcare framework using the Internet-of-Things (IoT) and cloud technologies that improve heart failure patients’ survival prediction without considering manual feature engineering. The smart IoT-based framework monitors patients on the basis of real-time data and provides timely, effective, and quality healthcare services to heart failure patients. The proposed model also investigates deep learning models in classifying heart failure patients as alive or deceased. The framework employs IoT-based sensors to obtain signals and send them to the cloud web server for processing. These signals are further processed by deep learning models to determine the state of patients. Patients’ health records and processing results are shared with a medical professional who will provide emergency help if required. The dataset used in this study contains 13 features and was attained from the UCI repository known as Heart Failure Clinical Records. The experimental results revealed that the CNN model is superior to other deep learning and machine learning models with a 0.9289 accuracy value.

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An important key challenge in Embedded Real Time Systems (ERTS) analysis is to provide a seamless scheduling strategy. Formal methods for checking the temporal characteristics and timing constraints at a high abstraction level have proven to be useful for making the development process reliable. In this paper, we present a Petri Net modeling formalism and an analysis technique which supports not only systems scheduling analysis but also the compositional specification of real time systems. The proposed Priority Time Petri Net gives determinism aspect to the model and accelerates its execution. Indeed, a compositional specification of a PTPN for complex application and multiprocessor architecture that solves the problem of hierarchy is presented.

This paper proposes a model driven approach for the schedulability analysis at an early stage of the embedded system development life-cycle. The activity diagram of Unified Modeling Language (UML) annotated with the profile for the Modeling and Analysis of Real-Time and Embedded systems (MARTE) is mapped into Priority Time Petri Net (PTPN) to enhance formal schedulability test of given real time tasks. The generated PTPN model is interpreted and executed to check whether a schedule of a task execution meets the imposed timing constraints. Therefore, the present paper focuses on the definition of temporal properties and tasks dependency by means of activity diagram and MARTE profile. Besides, it describes the transformation rules from analysis model to formal model.