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Stellar Classification is based on their spectral characteristics. In order to improve performance rates previously reported, like those based on statistical analysis or data transformations, classifiers based on computational intelligence provide a high level of accuracy no matter the presented high level of non-linearity or high dimensionality characteristics of data. In this paper, the star's classification...
How to accurately estimate facial age is a difficult problem due to insufficiency of training data. In this paper, an effective approach is proposed to estimate facial age by means of extreme learning machine (ELM). In the proposed method, a set of features is randomly selected from the original features to consist of a feature subspace. Given an initial weight matrix, the training samples within...
Extreme Learning machines (ELM) and Support Vector Machines have become two of the most widely used machine learning techniques for both classification and regression problems of recent. However the comparison of both ELM and SVM for classification and regression problems has often caught the attention of several researchers. In this work, an attempt has been made at investigating how SVM and ELM...
The purpose of this research was to optimize the backpropagation algorithm process by adding the Nguyen-Widrow method in input layer of feed-forward process and adapting the learning rate parameter in backward process in the backpropagation. In the preprocessing usually the data have not been normalized so the significant to the target output need to be reduce in the input layer process [1]. By embedded...
The determination of the fruit taste and grade depends on the internal quality of the fruit such as total soluble content, pH, and acidity. This paper investigates the feasibility of a non-destructive method to classify the internal quality of the pineapples using near infrared light and artificial neural network. Five near infrared light emitting diodes (LEDs) were used as the light source to emit...
This paper presents the modelling of agarwood oil (AO) significant compounds by different qualities using Scaled Conjugate Gradient (SCG) algorithm. This technique involved of data collection from Gas Chromatography-Mass Spectrometry (GC-MS) for compound extraction. The development of Multilayer perceptron (MLP) is used to discriminate the qualities of AO chemical compounds to the high and low quality...
To verification of concept, a spectroscopic method for measurement of pH in human blood through the syringe based on backpropagation artificial neural network (BP-ANN). In this paper the feasibility of design and fabricate measurement of pH was consist of 5LEDs as light source, 2 photodiodes as sensor to measure the light intensity and calculate the blood pH. The spectral data of 48 subjects were...
This study evaluates the relationship between near infrared light and glucose concentration by means of adaptive linear neuron. Firstly, the design and the development of the proposed glucose measurement device are presented. After that, the experiment design of acquiring sufficient near infrared data for training and testing is described. Next, adaptive linear neuron was trained and validated to...
In this paper, an approach for increasing the sustainability of inverter-based memristive neuromorphic circuits in the presence of process variation is presented. The approach works based on extracting the impact of process variations on the neurons characteristics during the test phase through a proposed algorithm. In this method, first, some combinations of inputs and weights (based on the neuromorphic...
This paper introduces to diagnosis of Dyslexia using computing system, considered people difficulties in reading, spelling, writing and speaking. Consequently, a computational analysis classifier will be achieved using dyslexia metrics techniques. Accordingly, Gibson test of brain skills will be used with effect of working memory, auditing (hearing and speech) and visual memory and cognition, visual...
It is a challenging task to recognize fine-grained subcategories due to the highly localized and subtle differences among them. Different from most previous methods that rely on object/part annotations, this paper proposes an automatic fine-grained recognition approach, which is free of any object/part annotation at both training and testing stages. The key idea includes two steps of picking neural...
Recent advances in the area of Deep Convolutional Neural Networks have led to steady progress, mainly observed in the field of object classification and localization. Extensive testing helped generate frameworks guaranteeing the initiation of successful network architectures. For this reason, the authors focus on bringing added value on specific nodes of a generic network configuration. We propose...
Inspired by Gustave Lebon's idea of crowds as single-minded entities, we present a novel approach to describe the behavior of a crowd as a single entity, based on the global movement of the entire aggregate of people conforming the crowd. The present work significantly differs from existing literature where the behavior of single individuals within the crowd are the building blocks to describe crowd...
With the integration of EVs into the power grid, smart metering using machine-to-machine (M2M) communication is likely to play an important role in real-time energy management and control. Smart devices embedded with advanced metering infrastructure (AMI) can forecast the energy demand as well as perform energy pricing in real time. In this paper, an artificial neural network (ANN) based intelligent...
Compared with deep neural network which is trained using back propagation, the extreme learning machine (ELM) learns thousands of times faster but still produces good generalization performance. To better understand the ELM, this paper studies the effect of noise on the input nodes or hidden neurons. It was found that there is no effect on the performance of ELM when small amount of noise is added...
One of the mostly used commodities in investment is gold. However, gold price tends to have fluctuation. This paper proposed an Evolving Multi-Layer Perceptron (eMLP) to forecast accurately the gold price by considering its daily fluctuate price and utilizing information from a big data of actual dataset. The proposed eMLP algorithm combines the concept of evolving connectionist system and multi-layer...
We consider the problem of inferring time-varying Granger causal interactions among multiple simultaneously recorded spike trains from a neuronal ensemble. We present a dynamic Granger causality measure with sparsity and adaptivity features for point process observations, and estimate it recursively. We develop a statistical inference framework based on asymptotic analysis of deviance, and perform...
In this paper, fast and reliable operation fault classification is carried out using energy of the detail coefficients of the phase signals. Daubechies 4 wavelet was chosen because of its success in detecting abrupt changes in power system signals. The MATLAB 7.10 version is used to generate the fault signals and verification the algorithm. A scheme is proposed for 220KV Karad to Miraj STATCOM connected...
When processing video, it is normally assumed that cameras are vertically oriented such that people appear upright, which helps simplify subsequent processing such as person detection. In real situations, due to the need to provide maximum coverage of the viewing space, cameras are usually placed with arbitrary orientations so the apparent vertical axis of the videos captured may not correspond to...
Effective generation of hash function is very important for an achievement of a security of today networks. A cryptographic hash function is a transformation that takes an input and returns a fixed-size value, which is called the hash value. An artificial neural network (ANN), as a possible approach, could be used for the hash function generation. The performance of the ANN was validated by software...
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