Fast and Accurate Diagnosis of Autism (FADA): a novel hierarchical fuzzy system based autism detection tool.

The main aim of this research work was to develop and validate a novel graphical user interface based hierarchical fuzzy autism detection tool named as "Fast and Accurate Diagnosis of Autism" for the diagnosis of autism disorder quickly and accurately and in addition, this tool also highlights the highly impaired area in each participant. Two groups of children had been participated in this study which includes autism group (N = 40) and normal group (N = 40). The hierarchical fuzzy expert system had been developed using IF-Then rules based on the experiences of the specialists and both the groups were tested on the designed system.

Influence of stimuli colour in SSVEP-based BCI wheelchair control using support vector machines.

This study aims to develop a Steady State Visual Evoked Potential (SSVEP)-based Brain Computer Interface (BCI) system to control a wheelchair, with improving accuracy as the major goal. The developed wheelchair can move in forward, backward, left, right and stop positions. Four different flickering frequencies in the low frequency region were used to elicit the SSVEPs and were displayed on a Liquid Crystal Display (LCD) monitor using LabVIEW.

K-nearest neighbour-based algorithm for P- and T-waves detection and delineation.

The aim of an automated Electrocardiogram (ECG) delineation system is the reliable detection of the characteristic waveforms and determination of peaks and limits of individual QRS-complex, P- and T-waves. In this paper, a classical statistical pattern recognition algorithm characterized with high accuracy and stability, i.e.

QRS detection using K-Nearest Neighbor algorithm (KNN) and evaluation on standard ECG databases.

The performance of computer aided ECG analysis depends on the precise and accurate delineation of QRS-complexes. This paper presents an application of K-Nearest Neighbor (KNN) algorithm as a classifier for detection of QRS-complex in ECG. The proposed algorithm is evaluated on two manually annotated standard databases such as CSE and MIT-BIH Arrhythmia database.