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In recent years, inexact computing has been increasingly regarded as one of the most promising approaches for reducing energy consumption in many applications that can tolerate a degree of inaccuracy. Driven by the principle of trading tolerable amounts of application accuracy in return for significant resource savings — the energy consumed, the (critical path) delay and the (silicon) area being the...
Neural spikes define the human brain function. An accurate extraction of spike features leads to better understanding of brain functionality. The main challenge of feature extraction is to mitigate the effect of strong background noises. To address this problem, we introduce a new feature representation for neural spikes based on Cepstrum of multichannel recordings. Simulation results indicated that...
In neuroscience, the extracellular actions potentials of neurons are the most important signals, which are called spikes. However, a single extracellular electrode can capture spikes from more than one neuron. Spike sorting is an important task to diagnose various neural activities. The more we can understand neurons the more we can cure more neural diseases. The process of sorting these spikes is...
3D stacking is a promising technology (low latency/power/area, high bandwidth); its main shortcoming is increased power density. Simultaneously, motivated by energy constraints, architectures are evolving towards greater customization, with tasks delegated to ac-celerators. Due to the widespread use of machine-learning algorithms and the re-emergence of neural networks (NNs) as the preferred such...
In Machine Learning applications, the selection of the classification algorithm depends on the problem at hand. This paper provides a comparison of the performance of the Support Vector Machine (SVM) and the Artificial Neural Network (ANN) for food intake detection. A combination of time domain (TD) and frequency domain (FD) features, extracted from signals captured using a jaw motion sensor, were...
As neurobiological evidence points to the neocortex as the brain region mainly involved in high-level cognitive functions, an innovative model of neocortical information processing has been recently proposed. Based on a simplified model of a neocortical neuron, and inspired by experimental evidence of neocortical organisation, the Hierarchical Temporal Memory (HTM) model attempts at understanding...
This paper presents an approach for finding the effect of varying hidden neurons and data size on various parameters in neural ensemble classifier. The approach is based on incrementing hidden neurons in base classifiers and training them by decrementing the training data and testing using exactly same size data. The experimental analysis of hidden neurons and data size on clusters, layers, diversity...
Extreme Learning Machine (ELM) for Single-hidden Layer Feedforward Neural Network (SLFN) has been attracting attentions because of its faster learning speed and better generalization performance than those of the traditional gradient-based learning algorithms. However, it has been proven that generalization performance of ELM classifier depends critically on the number of hidden neurons and the random...
Handwriting stroke reflects how the author faced his world and the emotional honesty. By examining all elements of handwriting and interpreting them separately or integrated, we could generate a sketch of the writer's character traits, emotional disposition and social style using standard of graphology. As image, the analysis of graphology is divided into two approaches that graphics features and...
Credit scoring is always a hot topic for the researchers because of its profitability. In this paper, we proposed a novel data-distribution based imbalanced data classification method to construct the credit scoring model using BP neural networks. The method distinguished itself by focusing on the distribution of the data and artificially changes the probabilities of the sampling for the purpose of...
Diagnosis of the thyroid function abnormalities may take much precious time of the patient. So, a computer aided diagnosis system can guide physicians in diagnosis and can save time of the patient. In this study, seven different types of neural networks were implemented in order to realize more robust and reliable networks on thyroid diagnosis. The particle swarm optimization and artificial bee colony...
In this paper we compare the performance of back propagation and resilient propagation algorithms in training neural networks for spam classification. Back propagation algorithm is known to have issues such as slow convergence, and stagnation of neural network weights around local optima. Researchers have proposed resilient propagation as an alternative. Resilient propagation and back propagation...
For a multi script/lingual country like India Script identification is a complex real life problem for automation of document processing. Handwritten script identification is again much more complex compared to print one. Here scripts from multi script handwritten documents are identified and then performance is compared using different well known classifiers. We followed a two stage approach for...
This work investigates the effect of different data structures on the performance and accuracy of VG-RAM-based classifiers. This weightless neural model is based on RAM nodes having very large address input, what suggests the use of special data structures in order to deal with space and time computational costs. Four different data structures are explored, including the classical one used in recent...
In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network...
Semi-supervised Learning with Graphs can achieve good results in classification tasks even in difficult conditions. Unfortunately, it can be slow and use a lot of memory. The first important step of the graph-based semi-supervised learning approaches is the construction of the graph from the data, where each data-point usually becomes a vertex in the graph - a potential problem with large amounts...
Early detection of Alzheimer's Disease (AD) is important so that preventative measures can be taken. Current techniques for detecting AD rely on cognitive impairment testing which unfortunately does not yield accurate diagnoses until the patient has progressed beyond a moderate AD. In this project, we develop a new approach based on mathematical and image processing techniques for better classification...
In this paper, a Dynamic Structure Preserving Map (DSPM) is proposed to effectively recognize human actions in video sequences. Inspired by the latest feature learning methods, we modified and improved the adaptive learning procedure in self-organizing map (SOM) to capture dynamics of best matching neurons through Markov random walk. The DSPM can learn implicit spatial-temporal correlations from sequential...
In this paper, we give a comparison of four methods for solving clustering problems, including similarity-based fuzzy clustering (SFC), elliptic basis function (EBF), versatile elliptic basis function (VEBF), and similarity-based fuzzy clustering with principal component analysis (PCSFC). PCSFC is a modified version of SFC with rotation, while VEBF is a refined version of EBF. SFC and PCSFC are based...
In this paper, a Fourier Neural Network (FNN), which specializes in regression and classification tasks is introduced, together with its weights initializing and training algorithm. Experiments conducted on various datasets show that the proposed model converges much faster than Multilayer Perceptron (MLP) and has equally good predicting accuracy and generalization ability.
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