The investigation of signature validation is crucial to the field of personal authenticity. The biometrics-based system has been developed to support some information security features.Aperson’s signature, an essential biometric trait of a human being, can be used to verify their identification. In this study, a mechanism for automatically verifying signatures has been suggested. The offline properties of handwritten signatures are highlighted in this study which aims to verify the authenticity of handwritten signatures whether they are real or forged using computer-based machine learning techniques. The main goal of developing such systems is to verify people through the validity of their signatures. In this research, images of a group of signatures, numbering 70 images, were used. Image preprocessing steps were performed on them, and their features were extracted using the median filter. After that, the eigenvector and eigenvalue were calculated using the PCA algorithm. Then the backpropagation neural network algorithm was applied for training and testing where the performance reached 6.7995e−07 for 82 epochs and the accuracy was 99.98%.
The process of combining the significant information from a series of images into a single image called image sharpening or image fusing, where the resultant fused image will be having more spatial and spectral information than any of the input images. in this research two images of the same place in different spatial resolution have been used the first one was panchromatic and the second image was multispectral with spatial resolution 0.5m and 2 m respectively. These images were captured by world view-2 sensor. This research resent four pan sharpening methods like (HSV, Brovey (color normalizes) , Gram shmidt and PCA)these methods were used to combine the adopted images to get multispectral image
... Show MoreFace Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a
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Face Identification is an important research topic in the field of computer vision and pattern recognition and has become a very active research area in recent decades. Recently multiwavelet-based neural networks (multiwavenets) have been used for function approximation and recognition, but to our best knowledge it has not been used for face Identification. This paper presents a novel approach for the Identification of human faces using Back-Propagation Adaptive Multiwavenet. The proposed multiwavenet has a structure similar to a multilayer perceptron (MLP) neural network with three layers, but the activation function of hidden layer is replaced with multiscaling functions. In experiments performed on the ORL face database it achieved a
... Show MoreIn this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermal conductivity of nanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and
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The dubbing process for the Foreign Dramas by the Kurdish language became a serious phenomenon in the Kurdish satellite TV, especially in the past few years. It attracted a wide audience, especially young adolescents. And prepared by some breakthrough hostile to Kurdish culture and value coordinated by others remedy for the structural gaps and lapses have, as a result of climate which is brought into existence globalization and secretions variety dimensional, saturated value and cognitive effects bearing a strong identity of its elements. From here, the problem of this research is the ambiguity of their role in a series of developmental processes Kurdish awareness of young people and the extent of the impact that caused it.
This
... Show MoreThis paper deals with an analytical study of the flow of an incompressible generalized Burgers’ fluid (GBF) in an annular pipe. We discussed in this problem the flow induced by an impulsive pressure gradient and compare the results with flow due to a constant pressure gradient. Analytic solutions for velocity is earned by using discrete Laplace transform (DLT) of the sequential fractional derivatives (FD) and finite Hankel transform (FHT). The influences of different parameters are analyzed on a velocity distribution characteristics and a comparison between two cases is also presented, and discussed in details. Eventually, the figures are plotted to exhibit these effects.
Extracorporeal Shock Wave Lithotripsy (ESWL) is the most commonplace remedy for kidney stone. Shock waves from outside the body frame are centered at a kidney stone inflicting the stone to fragment. The success of the (ESWL) treatment is based on some variables such as age, sex, stone quantity stone period and so on. Thus, the prediction the success of remedy by this method is so important for professionals to make a decision to continue using (ESWL) or tousing another remedy technique. In this study, a prediction system for (ESWL) treatment by used three techniques of mixing classifiers, which is Product Rule (PR), Neural Network (NN) and the proposed classifier called Nested Combined Classi
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Forest fires continue to rise during the dry season and they are difficult to stop. In this case, high temperatures in the dry season can cause an increase in drought index that could potentially burn the forest every time. Thus, the government should conduct surveillance throughout the dry season. Continuous surveillance without the focus on a particular time becomes ineffective and inefficient because of preventive measures carried out without the knowledge of potential fire risk. Based on the Keetch-Byram Drought Index (KBDI), formulation of Drought Factor is used just for calculating the drought today based on current weather conditions, and yesterday's drought index. However, to find out the factors of drought a day after, the data
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Voice Activity Detection (VAD) is considered as an important pre-processing step in speech processing systems such as speech enhancement, speech recognition, gender and age identification. VAD helps in reducing the time required to process speech data and to improve final system accuracy by focusing the work on the voiced part of the speech. An automatic technique for VAD using Fuzzy-Neuro technique (FN-AVAD) is presented in this paper. The aim of this work is to alleviate the problem of choosing the best threshold value in traditional VAD methods and achieves automaticity by combining fuzzy clustering and machine learning techniques. Four features are extracted from each speech segment, which are short term energy, zero-crossing rate, auto
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