Improving Prediction Efficacy Through Abnormality Detection and Data Preprocessing

Improving the Prediction Accuracy of Text Data and Attribute Data Mining with Data Preprocessing

Improving the Performance of ICA Algorithm for fMRI Simulated Data Analysis Using Temporal and Spatial Filters in the Preprocessing Phase

Introduction: The accuracy of analyzing Functional MRI (fMRI) data is usually decreases in the presence of noise and artifact sources. A common solution in for analyzing fMRI data having high noise is to use suitable preprocessing methods with the aim of data denoising. Some effects of preprocessing methods on the parametric methods such as general linear model (GLM) have previously been evalua...

Deep Abnormality Detection in Video Data

Automated detection of anomalous events plays an important role in video surveillance systems in practice. This task, however, requires to deal with three challenging problems of the lack of annotated training data, the inexact description of what to be “abnormal” and the expensive feature engineering procedure. Most anomaly detection systems are only able to satisfy some of these challenges. I...

Feature Detection Techniques for Preprocessing Proteomic Data

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) a...

Improving the Quality of Association Rules by Preprocessing Numerical Data

Numerical attribute management is a usual preprocessing task in data mining. Most of the algorithms require the discretization of numerical attributes by splitting the continuous range of values into intervals. This task is critical in association rule induction since it has a significant influence in the quality of the induced rules. We have proposed a multivariate algorithm whose efficiency a...