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A real-time neuro car detection system based on the Haar-like feature is presented in this paper. The proposed system relies on an artificial neural network (ANN) to recognize the car object. ANN was trained using the Haar-like features extracted from the negative and positive car image data. The car objects vary with their sizes and trademarks. However, they have common features which can be assumed...
Biometrics play a crucial role in establishing an individuals identity. A signature is one of the most widely recognized way to authorize transactions and authenticate the human identity as compared to other electronic identification methods such as fingerprint and retina scans. Due to a huge demand for authentication, fast algorithms need to be assimilated for signature recognition and verification...
Face recognition has been widely used as biometric systems currently. Because of its ability to recognize a person identity reliably and accurately based on face. This research aims to develop a method that can be used for face recognition system. The proposed approach works as follows:(i) preprocess input images using gray-scaling, contrast stretch and Amoeba Median Filter, (ii) extract its characteristic...
Diabetes is worldwide problem. It is rapidly increase disease in the world. Diabetes, referred as diabetes mellitus it is organic process in which the person has increase blood glucose (blood sugar), either because insulin origination is deficient, or body's cells do not behave properly to insulin which is produce. Early investigate of diabetes is an important objection. Existing system had so many...
Camera calibration is necessary in machine vision application field. Calibration model has nonlinear characteristics, and establishment of mathematical model is often a complicated process, but neural network can solve the complex nonlinear problem effectively, neural network has strong nonlinear approximation ability, adaptive network parameters and fast learning. This paper presents a neurocalibration...
Cascade-Forward Neural Network (CFNN) performance is explored in this paper for blood stain image analysis. The blood stain images of various size, shape and impact angles are captured through experimentation. Each blood stain in the image is first detected using sobel edge detector. After the image has been thresholded and the noise removed, geometric properties of the blood drop is measured with...
Label-deficient semi-supervised learning is a challenging setting in which there is an abundance of unlabeled data but a dearth of labeled data. A hybrid network that mixes an autoencoder, capable of extracting information from unlabeled data, and a neural network classifier, which incorporates information from labeled data, can be useful in a label-deficient setting. In this case study, we examine...
Creating a neural network based classification model is commonly accomplished using the trial and error technique. However, this technique has several difficulties in terms of time wasted and the availability of experts. In this article, an algorithm that simplifies structuring neural network classification models is proposed. The algorithm aims at creating a large enough structure to learn models...
This paper presents a simulated memristor crossbar implementation of a deep Convolutional Neural Network (CNN). In the past few years deep neural networks implemented on GPU clusters have become the state of the art in image classification. They provide excellent classification ability at the cost of a more complex data manipulation process. However once these systems are trained, we show that the...
The paper presents a method to predict disulfide bond structure based on sample selection and Classifiers Fusion Technology. Firstly, the codes of the selected protein sequence are used as the input data of RBF neural network. Then the different sizes of the information windows were selected to construct the prediction models of disulfide bond. At last, the final prediction will be obtain from fusing...
Neural networks (NNs) have been widely used in microwave device modeling. One of the greatest challenges is how to speed up the model training process and reduce the development cost. To address the issue, this paper exploits FPGAs to accelerate NN training. Experimental results demonstrate that the model training time can be reduced by up to 99.1%, compared to the traditional software implementation.
As a type of clean and renewable energy source, wind power is being widely used all around the world. However, owing to the uncertainty and instability of the wind power, it is important to build an accurate prediction model for wind power for the grid-connected security operation. The performance of hybrid method is always better than that of single ones in the wind power prediction. Actual wind...
The power flow in distribution grids is becoming more complicated as reverse power flows and undesired voltage rises might occur under particular circumstances due to integration of renewable energy sources, increasing the occurrence of critical bus voltages. To identify these critical feeders the observability of distribution systems has to be improved. To increase the situational awareness of the...
Short text is prevalent on the Web, but it brings challenges to content analysis methods for the lack of contextual information. Biterm topic model (BTM) is a variant of latent Dirichlet allocation, which effectively infers the latent topic distribution of short text by modeling the generation of biterms in the whole corpus. However, it needs fine-tuning from labels to reduce noise when applied to...
An adaptive learning algorithm for Radial Basis Functions Neural Networks, RBFNNs, is provided. In recent years, RBFs have been subject to extensive areas of interests. But the setting up of RBFs in a network architecture can be time consuming, computationally deficient and unstable. Thus we have developed an efficient adaptive algorithm in a feedforward neural architecture in which the hidden neurons...
Anti-Malware industry faces the challenge of evaluating huge amount of data for potential malicious contents. This is due to the fact that hackers introduce polymorphism to the existing malicious groups/classes. Effective feature extraction and classification of malware data is necessary to tackle such issues. In this paper, we visualize viruses in an image as they capture minor changes while retaining...
In this paper the approach to the temperature parameters forecasting to avoid overheating of the spacecraft equipment at the end of the data transmission session is considered. To determine temperature values at the indicated times of the spacecraft components algorithms of historical data processing are proposed. The software is provided. The conducted experiments proved the ability to reveal anomaly...
Cognitive radio could detect the white space of spectrum and utilize spectrum resource efficiently. In a cognitive radio system, the recognition of signal modulation is a key technology, which would help the cognitive radio system to configure and realize intelligent green communication. In general, the recognition of signal modulation is not a linear classification. Back propagation (BP) neural network...
In this paper, a CMAC (cerebellar model articulation controller) neural network application on fault diagnosis of automobile automatic transmission system is proposed. Firstly, we build a CMAC neural network based diagnosis system with different coding scheme depending on the fault types. Secondly, the fault patterns, obtained from the China scholar's technical data, would be used to train the CMAC...
An Artificial Neural Network has been proposed as predicting the performance of the Software Defined Network according to effective traffic parameters. Those used in this study are round-trip time, throughput and the flow table rules for each switch, POX controller and OpenFlow switches, which characterize the behaviour of the Software Defined Network, have been modelled and simulated via Mininet...
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