Time series derived from simpler systems are single scale based and thus can be quantified by using traditional measures of entropy. However, times physical biological complex show structures on multiple spatio-temporal scales. Traditional approaches entropy complexity fail to account for scales inherent in these time series. Recently multi-scale (MSE) method was introduced, which provide a way measure over range MSE uses sample entropy, refinement approximate quantify the Nonstationarity,...

The dynamical fluctuations in the rhythms of biological systems provide valuable information about underlying functioning these systems. During past few decades analysis cardiac function based on heart rate variability (HRV; variation R wave to intervals) has attracted great attention, resulting more than 17000-publications (PubMed list). However, it is still controversial underling mechanisms HRV. In this study, we performed both linear (time domain and frequency domain) nonlinear HRV data...

The adaptability of heart to external and internal stimuli is reflected by the rate variability (HRV). Reduced HRV can be a predictor negative cardiovascular outcomes. Based on nonlinear, nonstationary, highly complex dynamics controlling mechanism system, linear measures have limited capability accurately analyze underlying dynamics. In this study, we propose an automated system signals extracting multimodal features capture temporal, spectral, Robust machine learning techniques, such as...

Human beings are continuously exposed to the radiations coming from outside and inside their bodies. Outside ground, building materials, food, air, universe even elements within human own According UNSCEAR 2000 report, background deliver an average effective dose of 2.4 mSv per person worldwide. Sustained exposure high radiation levels may pose substantial health threats general public. In current study we presenting results ambient outdoor gamma rates measured for Jhelum valley state Azad...

Electricity, a fundamental commodity, must be generated as per required utilization which cannot stored at large scales. The production cost heavily depends upon the source such hydroelectric power plants, petroleum products, nuclear and wind energy. Besides overproduction underproduction, electricity demand is driven by metrological parameters, economic industrial activities. Therefore, region specific accurate electric load forecasting can help to effectively manage, plan, schedule...

This Breast Cancer in women is the most frequency diagnosed and second leading cause of cancer deaths. Due to complex nature microcalcification masses, radiologist fail properly diagnose breast cancer. In past researchers developed Computer aided diagnosis (CAD) systems that help detect abnormalities an efficient manner. this research, we have employed robust Machine learning classification techniques such as Support vector machine (SVM) kernels Decision Tree distinguish mammograms from...

Twitter sentiment analysis is a challenging problem in natural language processing. For this purpose, supervised learning techniques have mostly been employed, which require labeled data for training. However, it very time consuming to label datasets of large size. To address issue, unsupervised such as clustering can be used. In study, we explore the possibility using hierarchical twitter analysis. Three hierarchical-clustering techniques, namely single linkage (SL), complete (CL) and...

Abstract Accurate classification of brain tumor subtypes is important for prognosis and treatment. In this study, we optimized applied non‐deep learning methods based on hand‐crafted features deep transfer using softmax as KNN SVM extracted from ResNet101. For techniques, multimodal input to machine classifiers. convolutional neural networks, GoogleNet ResNet101with approach. The performance was evaluated in terms sensitivity, specificity, positive predictive value (PPV), negative (NPV),...

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

Cardiotocography (CTG) is a worldwide method used for recording fetal heart rate and uterine contractions during pregnancy delivery. The consistent visual assessment of the CTG not only time consuming but also requires expertise clinical knowledge obstetricians. inconsistency in evaluation can be eliminated by developing decision support systems. During last few decades various data mining machine learning techniques have been proposed such In present study, bagging approach combination with...

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, use high ranking for detection CHF normal employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, RBF Polynomial. The performance was measured in terms...

Considerable interest has been devoted for developing a deeper understanding of the dynamics healthy biological systems and how these are affected due to aging disease. Entropy based complexity measures have widely used quantifying physical systems. These techniques provided valuable information leading fuller underlying stimuli that responsible anomalous behavior. The single scale traditional entropy yielded contradictory results about real world time series data pathological subjects....

Lung cancer is the major cause of cancer-related deaths worldwide with poor survival due to diagnostic system at advanced stage. In past, researchers developed computer-aided diagnosis (CAD) systems, which were greatly used by radiologist for identifying abnormalities and applied few features extracting methods. The physiology behavior various physiological systems can be best investigated using nonlinear dynamical measures capturing intrinsic dynamics, influenced multiple pathologies...

Societal determinants of health are recognized importance for understanding the causal association society and an individual. Iron deficiency anemia (IDA) is a challenging public problem across globe instigating from broader sociocultural background. It more prevalent among pregnant women, children under age five years, adolescent girls. Adolescent girls vulnerable to develop IDA because additional nutritional demand body needed growth spurt, blood loss due onset menarche, malnourishment,...

A new tone-mapping algorithm is presented for visualization of high dynamic range (HDR) images on low (LDR) displays. In the first step, real-world pixel intensities HDR image are transformed to a perceptual domain using perceptual-quantizer (PQ). This followed by construction histogram luminance channel. Tone-mapping curve generated from cumulative histogram. It known that histogram-based approaches can lead excessive stretching contrast in highly populated bins, whereas pixels sparse bins...

In this paper, we have employed K-d tree algorithmic based multiscale entropy analysis (MSE) to distinguish alcoholic subjects from non-alcoholic ones. Traditional MSE techniques been used in many applications quantify the dynamics of physiological time series at multiple temporal scales. However, algorithm requires O(N2), i.e. exponential and space complexity which is inefficient for long-term correlations online application purposes. current study, a recently developed approach compute The...

Radiation effects on magnetohydrodynamic (MHD) boundary-layer flow and heat transfer characteristic through a porous medium due to an exponentially stretching sheet have been studied. Formulation of the problem is based upon variable thermal conductivity. The analysis carried out for both prescribed surface temperature (PST) flux (PHF) cases. developed system nonlinear coupled partial differential equations transformed ordinary by using similarity transformations. series solutions were...

Cancer is the second leading cause of mortality across globe. Approximately 9.6 million people are estimated to have died due cancer disease in 2019. Accurate and early prediction can assist healthcare professionals devise timely therapeutic innervations control sufferings risk mortality. Generally, a machine learning (ML) based predictive system uses data (genetic profile or clinical parameters) algorithms predict target values for detection. However, optimization accuracy an important...

Coronavirus disease 2019 (COVID-19) is a respiratory illness that leads to severe acute syndrome and various cardiorespiratory complications, contributing morbidity mortality. Entropy analysis has demonstrated its ability monitor physiological states system dynamics during health disease. The main objective of the study extract information about control by conducting complexity OSV signals using scale-based entropy measures following two-month timeframe after recovery.

Abstract Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed ResNet101 deep learning method with transfer to analyze 2021 Radiological Society North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), post‐contrast (T1Gd), T2‐weighted (T2). assessed...