A hybrid model for forecasting of particulate matter concentrations based on multiscale characterization and machine learning techniques.

Accurate prediction of particulate matter (PM) using time series data is a challenging task. The recent advancements in sensor technology, computing devices, nonlinear computational tools, and machine learning (ML) approaches provide new opportunities for robust prediction of PM concentrations. In this study, we develop a hybrid model for forecasting PM and PM based on the multiscale characterization and ML techniques.

Multiscale based nonlinear dynamics analysis of heart rate variability signals.

Acceleration change index (ACI) is a fast and easy to understand heart rate variability (HRV) analysis approach used for assessing cardiac autonomic control of the nervous systems. The cardiac autonomic control of the nervous system is an example of highly integrated systems operating at multiple time scales. Traditional single scale based ACI did not take into account multiple time scales and has limited capability to classify normal and pathological subjects.

An Analysis of Societal Determinant of Anemia among Adolescent Girls in Azad Jammu and Kashmir, Pakistan.

Societal determinants of health are of recognized importance for understanding the causal association of society and health of an individual. Iron deficiency anemia (IDA) is a challenging public health problem across the globe instigating from a broader sociocultural background. It is more prevalent among pregnant women, children under the age of five years, and adolescent girls.

Studying the dynamics of interbeat interval time series of healthy and congestive heart failure subjects using scale based symbolic entropy analysis.

Considerable interest has been devoted for developing a deeper understanding of the dynamics of healthy biological systems and how these dynamics are affected due to aging and disease. Entropy based complexity measures have widely been used for quantifying the dynamics of physical and biological systems. These techniques have provided valuable information leading to a fuller understanding of the dynamics of these systems and underlying stimuli that are responsible for anomalous behavior.

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states.

Objective: Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.

Statistical study of the risk factors of myocardial infarction in the patients of district Muzaffarabad capital of Azad Jammu and Kashmir.

Coronary artery disease (CAD) is a leading cause of mortality in the developing countries. The aim of the study was to check the association of Myocardial infarction (MI) with several factors such as smoking & smoking exposure, blood pressure, sugar & cholesterol level, stress, anxiety & lifestyle. A cross sectional community based survey was conducted involving 469 patients having one or more risk factors or having complains regarding MI & already diagnosed MI, was taken using Multistage sampling technique from Sheikh Zaid Hospital & Abbas Institute of Medical Sciences.

Classification of heart rate signals of healthy and pathological subjects using threshold based symbolic entropy.

The dynamical fluctuations of biological signals provide a unique window to construe the underlying mechanism of the biological systems in health and disease. Recent research evidences suggest that a wide class of diseases appear to degrade the biological complexity and adaptive capacity of the system. Heart rate signals are one of the most important biological signals that have widely been investigated during the last two and half decades.