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This paper proposes a hybrid deep learning algorithm, namely, the Deep Boltzmann Functional Link Network (DBFLN) for classification problems. A Deep Boltzmann Machine (DBM) with two layers of Restricted Boltzmann Machine is the generative model that is used to generate stochastic features and input weights for the discriminative model. A discriminative Functional Link Network (FLN) uses these features...
Electroencephalographic (EEG) activity of brain function could provide a new non-muscular channel for giving messages and commands to the external world is known as brain computer interface (BCI). In a BCI system, humans can control many devices by using his or her imaginations. Today with the provision of affordable electronic systems and powerful processing tools has eased the bio-signal processing...
Z-source inverter is a power electronic converter with potential applications in areas like renewable energy, electric vehicles, etc. because unlike traditional inverters, it can provide a boost in output voltage without any additional DC-DC boost converter stage in between the DC source and inverter. This feature is particularly useful if the input DC source is of low voltage like battery or of fluctuating...
The membrane proteins are an important group of molecules whose 3-D structure is difficult to obtain experimentally. Membrane proteins are implicated as drug targets and play an important role in disease pathways. The computational structure prediction from membrane protein sequences aids understanding of the structures. The prediction of structural preferences of individual residues within a protein...
The use of large number of channels in EEG based Motor-imagery Brain Computer Interfaces (BCI) may cause long preparation time and redundancy of data. In this paper, we propose a Cohen's d effect-size based channel selection algorithm which eliminates the redundant channels while improving the classification performance. This method (referred to as Effect-size based CSP (E-CSP)) eliminates the channels...
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 116 regions of the automated anatomical labeling...
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 in the tail region, particularly when the...
Attention Deficiency Hyperactivity Disorder (ADHD) as a disruptive behavior disorder is receiving lots of attention because of its complexity and need for early detection. This paper presents a study on identification of potential biomarkers in the diagnosis of ADHD based on the structural-MRI of the brain obtained through ADHD-200 competition data set. The region of the brain considered here is "hippocampus"...
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