]>
2014
8
4
96
A Class of Multivalent Analytic Functions Defined by a New Linear Operator
A Class of Multivalent Analytic Functions Defined by a New Linear Operator
en
en
The main object of the present paper is to derive some results for multivalent analytic functions defined by a linear operator. Making use of a certain operator, which is defined here by means of Hadamard product, we introduce a subclasses \(S_{A,B}^{p,\gamma}(\alpha,\lambda,\mu,\nu,a,c)\) of the class \(A(p)\) of normalized p-valent analytic functions on the open unit disk. Also we have extended some of the previous results and have given necessary and sufficient condition for this class.
326
334
Rahim
Kargar
Abdoljalil
Bilavi
Salahaddin
Abdolahi
Salah
Maroufi
Analytic functions
Multivalent functions
Hadamard product
Subordination
Linear operators.
Article.1.pdf
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A Numerical Method for Optimal Control-state Problem with Bivariate B-spline Basis
A Numerical Method for Optimal Control-state Problem with Bivariate B-spline Basis
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en
We apply the bivariate B-spline basis to find an approximate solution for control-state
function in a constrained optimal control problem, whose constraint is an elliptic partial
differential equation (PDE) with Dirichlet boundary conditions. In this method, the PDE is
first discretized and then by using bivariate B-spline basis, a state function is obtained with
respect to some unknown coefficients. By applying generalized Newton method, the
optimal value for the control function is also determined. Finally, a numerical example is
given and the optimal solution is derived by using the bivariate B-spline basis.
335
342
Ali
Zakeri
Mohammad
Masjed-jamei
Amir Hossein Salehi
Shayegan
Bivariate B-spline basis
Optimal control problem
Elliptic equation
WEBspline finite element method.
Article.2.pdf
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K. Hollig, U. Reif, J. Wipper, B-spline approximation of Neumann problems, Preprint 2001-2, Universitat Stuttgart ()
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M. Weiser, T. Ganzler, A. Schtela, A control reduced primal interior point method for PDE constrained optimization , ZIB Report 04-38, Zuse Institute Berlin (2004)
]
Prediction of Saturated Vapor Pressures Using Non-linear Equations and Artificial Neural Network Approach
Prediction of Saturated Vapor Pressures Using Non-linear Equations and Artificial Neural Network Approach
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en
A new method to estimate vapor pressures for pure compounds using an artificial neural network (ANN) is presented. A reliable database including more than 12000 data point of vapor pressure for testing, training and validation of ANN is used. The designed neural network can predict the vapor pressure using temperature, critical temperature, and acentric factor as input, and reduced pressure as output with 0.211% average absolute relative deviation. 8450 data points for training, 1810 data points for validation, and 1810 data points for testing have been used to the network design and then results compared to data source from NIST Chemistry Web Book. The study shows that the proposed method represents an excellent alternative for the estimation of pure substance vapor pressures and can be used with confidence for any substances.
343
358
Mehrdad
Honarmand
Ehsan
Sanjari
Hamidreza
Badihi
Vapor pressure
ANN
neural network
correlation
non-linear
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Efficient Solution of Fractional Initial Value Problems Using Expanding Perturbation Approach
Efficient Solution of Fractional Initial Value Problems Using Expanding Perturbation Approach
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en
In this article, expanding perturbation approach is applied for solving the initial value problems with fractional coordinate derivatives. The fractional derivative is described in the Caputo sense. The response expressions are written in terms of the Mittag-Leffler functions. Convergence of the approach is proved. Comparisons are made to confirm the reliability and effectiveness of the present ideas.
359
366
Khosro
Sayevand
Expanding perturbation approach
fractional initial value problems
Caputo derivative.
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]
Image Segmentation with Improved Distance Measure in Som and K Means Algorithms
Image Segmentation with Improved Distance Measure in Som and K Means Algorithms
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en
This paper explains the task of segmenting image by improved distance measure in SOM and K means algorithms. Image segmentation, divides the image into its constituent regions. It can be said the most prominent features in segmenting is the image brightness for monochrome images and the color components of color images. Over all pixels of image analysis is difficult, Pixels with similar brightness, with the use of image segmentation are grouped together. To achieve higher accuracy of segmentation, we are used fit the soft computing techniques namely Fuzzy algorithms. Image segmentation in many cases (For example, the tumor area to help doctors detect tumor) only be used to assist human visual system. This paper compares segmentation-based methods, visual system and scoring is on him.
367
376
Khosro
Jalali
Mostafa
Heydari
Asma
Tanavar
Segmentation
Distance measure
K means algorithm
P-norm weigthed SOM
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A Novel Method for Extracting Classification Rules Based on Ant-miner
A Novel Method for Extracting Classification Rules Based on Ant-miner
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en
In this paper, we propose a new method for data mining based on Ant Colony Optimization (ACO). The ACO is a metheuristic inspired by the behavior of real ants, where they search for optimal solutions by considering both local heuristic and previous knowledge, observed by pheromone changes. Good results of the ant colony algorithms for solving combinatorial optimization problems suggest an appropriate effectiveness of the approach also in the task of discovering classification rules in data mining. Mining classification rules is an important research area in data mining. Ant-Miner is an Ant Colony Optimization algorithm for classification task. This paper proposes an improved version of Ant-Miner named Ant-Miner4, which is based on Ant-Miner3 By changing the heuristic function used in the Ant-Miner3, and implementing it based on correction function of Laplace, we tried to redesign Ant-Miner to gain rules with high predictive accuracy. We compared Ant-Miner4 with the previous version (Ant-Miner3) using four data sets. The results indicated that the accuracy of the rules discovered by the new version was higher than the ones gained by the previous version.
377
386
Babak
Fakhar
Ant Colony Optimization Algorithm
Classification Rules
Data Mining
Laplace Correction
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]
The Method of Gbr Optimization by Special Parameters to Decrease Energy Consumption in Wsns
The Method of Gbr Optimization by Special Parameters to Decrease Energy Consumption in Wsns
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en
One of the fundamental for the design of routing protocols in wireless sensor networks, is energy efficiency. And because of the limitation in energy supplement, one of the most important parameters in a wireless sensor network nodes. To increase energy efficiency, The use a well-know protocol as GBR. However, there are some shortcoming in the GBR project. Firstly, to increasing elected nodes to forward the data from the source node to the sink waste to energy. Secondly, Due to the competitive nature of the algorithm, To select the most appropriate nodes to forward the data from the source node to the sink to be limited. As result, for the above problems optimization procedure discussed GBR, The main idea of this algorithm is to reduce retransmissions and attempt to save energy by taking smaller positions for to forward data in this project. This proposed scheme results in better energy efficiency than traditional GBR offers.
387
397
Sina
Hedayati
Arash Ghorbannia
Delavar
Wireless sensor network
Gradient–Base Routing
Efficiency energy.
Article.7.pdf
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Multi-level Fusion to Improve Threat Pattern Recognition in Cyber Defense
Multi-level Fusion to Improve Threat Pattern Recognition in Cyber Defense
en
en
Considering fast growth of internet and related network infrastructures, it is important to detect the intrusion and respond to it in a timely manner. Network intrusion can make vital information systems and communication networks inaccessible and imposes high cost of communication infrastructures. In order to gain high degrees of success in providing services, current and future generation of networking and internet technologies, require a set of tools to analyze the network and to detect the threats and intrusion in network. Due to main weakness in terms of high rate of false alarms and low accuracy of detection, by which cyber space detection and identification systems are opposed, fusion theory in decision level provides a new method for data analysis from multiple nodes in order to increase the possibility of intrusion detection through improving pattern recognition. This paper aims to present a novel method of fusion in decision level based on complex event processing and show how this method would be successful in exposing cyber threats for timely response.
398
410
Alijabar
Rashidi
Kourosh Dadashtabar
Ahmadi
Ali
Jafari
Information fusion
complex event processing
cyber defense
pattern recognition
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]
Applications of Opls Statistical Method in Medicine
Applications of Opls Statistical Method in Medicine
en
en
Studies related to prognosis in medicine result in a large volume of variables if clinical and laboratory variables are simultaneously accompanied with new imaging techniques; this issue causes problems for classical statistical methods such as logistic and linear regression. Among these cases, emergence of multicollinearity or close linear correlation between regression variables when the number of regression variables is high can be pointed out. Emergence of multicollinearity is inappropriate for ordinary least squares of regression model. PLS is a well-known method for connecting two X and Y data matrices using a multicollinearity model. OPLS is the product of a change which has occurred on PLS method in recent years. Considering application problems of linear regression method, applying an alternative method is a requirement. Using OPLS method can reduce model complexity and develop its power.
411
422
Kianoush Fathi
Vajargah
Robabe
Mehdizadeh
Homayoun
Sadeghi-bazargani
Medical studies
linear regression
PLS
OPLS
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