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In this paper, we present an approach for the diagnosis of Autism Spectrum Disorder (ASD) from Magnetic Resonance Imaging (MRI) scans with Voxel-Based Morphometry (VBM) detected features using Projection Based Learning (PBL) algorithm for a Meta-cognitive Radial Basis Function Network (McRBFN) classifier. McRBFN emulates human-like meta-cognitive learning(More)
AIM This study aims at evaluating stresses generated in a fixed tooth to implant connected fixed partial denture (FPD) by finite element method by varying implant design and position, in unilateral distal extension cases, i.e. for replacing mandibular 1 st and 2 nd molars. MATERIALS AND METHODS Four models were created, Model 1-A finite element model(More)
This paper presents an automatic, non-invasive method for detecting Autism Spectrum Disorders (ASD) among males using structural Magnetic Resonance Imaging. Whole brain Voxel Based Morphometry (VBM) analysis is first used to identify the brain regions that are affected for ASD patients and gray matter probability in these regions are used as features for(More)
This paper presents an approach for automatic diagnosis of Autism Spectrum Disorder (ASD) among males using functional Magnetic Resonance Imaging (fMRI). fMRI has the capability to identify any abnormal neural interactions that may be responsible for behavioral symptoms observed in ASD patients. In this paper, the regional homogeneity of the voxels in the(More)
Autism Spectrum Disorders (ASD) represent a cluster of relatively common developmental conditions which require an early and accurate diagnosis for an effective remedial therapy. Resting state functional MRI (rs-fMRI) is considered an important tool to investigate the differences in functional connectivity due to ASD. In this paper, an Enhanced Effect-Size(More)
Reducing the power consumed by the device is the emerging trend. The aim of this project is to reduce the leakage current of the circuit by using the Sub Clocking technology. It is the process of switching the circuit by means of partially ON to reduce the power consumption. In this paper there are two modes of operation are implemented.1. Half mode(More)
Extreme Learning Machine (ELM) has recently emerged as a fast classifier giving good performance. Circular–Complex extreme learning machine (CC-ELM) is recently proposed complex variant of ELM which has fully complex activation function. It has been shown that CC-ELM outperforms real valued and other complex valued classifiers. In both CCELM & ELM(More)
In this paper, a new meta-cognitive RBF neural network classifier that uses a q-Gaussian activation function is presented. The q-Gaussian activation function has the capability to extend or contract the shape/response of the radial basis activation function, based on the value of the parameter q. This property is used to avoid a sharp fall in the response(More)
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