Experimental validation of optimized fast terminal sliding mode control for level system.

This research article presents a process control application of a single-input single-output (SISO) level control system using the combination of fast terminal sliding mode control (FTSMC) and optimization method. Non-dominated sorted genetic algorithm-ii (NSGA-ii); a modern optimization technique is used to optimized the parameters of FTSMC. Here, a comparative analysis of conventional sliding mode control (SMC), FTSMC, NSGA tuned FTSMC and NSGA-ii tuned FTSMC has being carried out through MATLAB/ Simulink.

Gaussian process-based kernel as a diagnostic model for prediction of type 2 diabetes mellitus risk using non-linear heart rate variability features.

The main objective of the study was to develop a low-cost, non-invasive diagnostic model for the early prediction of T2DM risk and validation of this model on patients. The model was designed based on the machine learning classification technique using non-linear Heart rate variability (HRV) features. The electrocardiogram of the healthy subjects (n = 35) and T2DM subjects (n = 100) were recorded in the supine position for 15 min, and HRV features were extracted.

Heart Rate Variability measured during rest and after orthostatic challenge to detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification and Regression Tree model.

Background: According to the World Health Organization, one in ten adults will have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction is one of the significant complications of T2DM. Autonomic dysfunction is usually assessed by standard Ewing's test and resting Heart Rate Variability (HRV) indices.

Content based medical image retrieval based on new efficient local neighborhood wavelet feature descriptor.

This paper presents a new class of local neighborhood based wavelet feature descriptor (LNWFD) for content based medical image retrieval (CBMIR). To retrieve images effectively from large medical databases is backbone of diagnosis. Existing wavelet transform based medical image retrieval methods suffer from high length feature vector with confined retrieval performance.