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The leading cause of death worldwide is cardiac disease, which kills an estimated 27.9 million people each year and is responsible for 31% of all fatalities. Heart failure is frequently brought on by cardiovascular problems. It can be identified by the heart's inability to deliver enough blood to the body. All of the body's fundamental functions are affected when there is insufficient blood flow. Heart failure is a condition or set of symptoms that weakens the heart. Three important aspects form the foundation of the research study's main results. Given that it essentially measures the efficiency of the heart, this is to be expected. The patient's age is the last factor that is most closely associated. The heart's performance progressively deteriorates with age. The data was modeled using machine learning and ANN with an accuracy of about 80%, showing how effective the framework is at detecting cardiac arrest. Deep learning models' accuracy might rise to 90–95%.

The objective of this paper is to design a robust PI controller for a second order system plus delay (SOSPD) which has an inherent nonlinearity by the virtue of its structural dynamics. An example of Dual Spherical Tank Liquid Level System (DSTLLS), which is non linear, has been chosen to analyse the performance of the robust PI controller designed. The DSTLLS has been linearized and modelled using black box modelling technique by performing the real-time experimentation and the performance of the applied controller has been analysed in simulation by MATLAB Simulink. This paper also discusses about the simulation efficiency using the performance indices like Integrated Squared Error (ISE) and Integrated Absolute Error (IAE).

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