Hypertension is a global challenge to the public health which can easily lead life-threatening vascular diseases unless control measures are adopted. Considering prevalence of and their fatality, early detection high-risk patients an important problem in present world. Heart rate variability (HRV) analysis be effective prognostic tool identify characteristics vulnerable patients, considering it...

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater in...

Purpose: The purpose of this paper is discriminating between tumor progression and response to treatment based on follow-up multi-parametric magnetic resonance imaging (MRI) data retrieved from glioblastoma multiforme (GBM) patients. Materials and Methods: Multi-parametric MRI data consisting of conventional MRI (cMRI) and advanced MRI [i.e., perfusion weighted MRI (PWI) and diffusion kurtosis ...

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for prediction of recovery moderate traumatic brain injury (TBI). However, brain’s recorded electrical activity has always been contaminated with artifacts, which turn further impede subsequent processing steps. As a res...

The problem of class imbalance along with classoverlapping has become a major issue in the domain of supervised learning. Most supervised learning algorithms assume equal cardinality of the classes under consideration while optimizing the cost function and this assumption does not hold true for imbalanced datasets which results in sub-optimal classification. Therefore, various approaches, such ...

247 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Pvt. Ltd. Abstract—Software quality prediction is a process of utilizing software metrics such as code-level measurements and defect data to build classification models that are able to estimate the quality of program modules. These kinds of estimations can help software managers to effectively allocate potentially limi...

A novel data-driven model for the prediction of bacteriological presence, in form total cell counts, treated water exiting drinking treatment plants is presented. The was developed and validated using a year hourly online flow cytometer data from an operational plant. Various machine learning methods are compared (random forest, support vector machines, k-Nearest Neighbors, Feed-forward Artific...