javad vahidi

This paper studies advanced mathematical methods like sin-cos and sinh-cosh approachesto find precise solutions by considering inter-modal dispersion and spatio-temporal dynamics with kerrlaw nonlinearity in the Resonant shrödinger equation. These methods are useful for solving nonlinearpartial differential equations. To obtain the ordinary differential equation for the traveling wave solution,we initially deal with the general partial differential equation (PDE). Then, a series of optical solitonsolutions, including cusp and dark solitons, are derived for the Resonant shrödinger equation using thiseffective approach.

Today, computer network fault diagnosis is one of the key challenges experts are facing in the field of computer networks.  Therefore, achieving an automatic diagnosis system which is based on artificial intelligence methods and is able to diagnose faults with maximum accuracy and speed is of high importance. One of the methods which is studied and utilized up to now is artificial neural networks with a back propagation algorithm while using neural networks with a back propagation algorithm has two main challenges in front. The first challenge is related to the backpropagation learning type as it is a supervised learning requiring inductive knowledge driven from previous conditions. The second challenge is the long time required for training such a neural network. In this work, combining neural networks with a backpropagation algorithm and fuzzy logic is applied as a method for confronting these challenges. The result of this study shows that fuzzy clustering is able to provide the inductive knowledge required for backpropagation learning by determining the membership degree of training samples to different clusters of network faults. Also, according to the simulations taken place, implementing a fuzzy controller in determining the learning rate in each backpropagation iteration has resulted in successful outcomes. Thus, the learning speed of this algorithm has been increased in comparison to the constant learning rate mode resulted in reducing the training time duration of this neural networks.

‎This ‎paper ‎adopts‎ the extended rational sinh-cosh as well as sine-cosine procedures to find precise solutions to the Hirota equation and Hirota-Maccari equation‎. ‎It is illustrated that seeking the precise solutions for these equations plays a foremost and effectual role in solving the numerous kinds of PDEs applied in optics, fluid mechanics, plasma physics and solid physics.‎‎ ‎Furthermore‎, ‎we are able to obtain some consequences of dark and cusp wave solutions‎. ‎Besides‎, ‎two-dimensional and three-dimensional surfaces have been drawn in order to acknowledge the concept of the acquired equations‎.

Generally, jobs are divided into smaller portions, in parallel and according to distributed processing, and each portion is called a task. Each task can execute dependently or independently. When introducing heterogeneous systems, it is desirable that tasks can run on these systems. Since it is advantageous that tasks running on heterogeneous systems are completed faster, the optimization of task scheduling is of great importance. Actually, task scheduling problems in heterogeneous systems are NP-hard and it is a crucial issue. In such problems, Directed Acyclic Graphs (DAGs) can be used as task graphs to be scheduled on heterogeneous systems. The proposed method presents a genetic algorithm with new operators and final scheduler to be scheduled on heterogeneous systems. The practicality and convergence of the algorithm are proved by Markov’s chain theory. The findings reveal that the currently proposed algorithm is more efficient in comparison to previously presented ones and also has a better make span. Moreover, it is concluded that the Enhanced Genetic Algorithm (EGA) achieves the solution faster in early generations.

In this paper, we introduce a new iterative method for finding the fixed point of a nonlinear function. In fact, we want to offer a new way to obtain the fixed point of various functions using the Grey Wolf Optimizer algorithm. This method is new and very efficient for solving a nonlinear equation. We explain this method with three benchmark functions and compare results with other methods, such as ALO, MVO, MFO and SCA.

The essential purpose of this paper is to obtain the fixed point of different functions by using a modern repetitive method. We incorporate concepts suggested in the Bisection method and the Moth-Flame Optimization algorithm. This algorithm is more impressive for finding fixed point functions. We also implement this method for four functions and finally compare the current method with other methods such as ALO, MVO, SSA, SCA algorithms. the proposed method shows a decent functionality than the other four methods.

The problem of course timetabling problem deals with the creation of a timetable, the purpose of which is to assign courses to different time periods in the week and to determine an arrangement of courses that is acceptable and feasible for teachers, students, and higher education institutions while complying with the educational regulations. In this work, we attempted to consider a scheduling problem in which the compactness of the curriculum, the distribution of the course schedule in the time frame, the preferences of the professors, the minimum number of working days, the maximum capacity, and the sustainability of the classes (with the goal of minimizing the daily commute of students between classes) should be considered. To model the problem, we considered a mathematical programming problem of nonlinear integer type with large dimensions. The solution of the mathematical model with using the software GAMS and the results of course planning for the second semester of the master's program in applied mathematics at Iran University of Science and Technology are reported at the end.

Delay differential equations have a wide range of applications in science and engineering. When these equations are nonlinear and complex the exact solution can usually not be calculated. So finding a numerical solution with high precision for these equations is essential. In this paper we present a numerical method based on the transferred Legendre polynomials to solve multiple pantograph delay differential equations. In this method we use the Legendre-Gauss-Lobato collocation points to discretize the problem and turn the problem into a nonlinear programming problem. From solving this nonlinear programming problem we get an approximate solution for the the main multiple pantograph delay differential equation. We analyse the feasibility of the nonlinear programming problem and the convergence of the obtained approximate solution to the exact solution. In addition by solving several numerical examples and comparing the method with other methodsWe show the efficiency and the capability of the proposed method.

Rapid, low-cost and accurate estimation of tree metrics in the forest ecosystem is one of the challenges facing forest managers and inventories. Therefore, given that the forest areas of northern of Iran, mainly deciduous and uneven-aged forests, are covered with various tree ‎species of different sizes, mensuration techniques could be developed and established to suit the particular ‎circumstances. Methods used in forest inventory should be user-friendly, repeatable, relatively low cost, and also have high accuracy and precession for forest inventory operation.

Thanks to technological advances in the photography industry, one of the attractive and proposed methods could be the use of digital cameras, in which the potential and performance of this method was evaluated to estimate diameter at different heights of trees. For this purpose, using a digital camera which is mounted on a pre-designed horizontal index and in different shooting distance, diameter at stump height, DBH, and diameter at top of trunk for 108 trees, in REFTMU and in the altitude range of 750 to 1100 meters above sea level and in autumn was estimated. The distance from each shooting station to each tree was captured using the audio and laser rangefinder of the Vertex device as well as the laser meter.

The overall results showed that at 95% significance level, there was a significant difference with real data only in estimating the diameter at stump height. It is also showing an underestimation in estimating the diameter at breast height and at the top of trunk, and in estimating the upper trunk diameter, it was overestimated. The lowest amount of %CV.RMSE was also observed in estimating breast diameter (4.99%).

The evaluated method showed that in the middle classes of metrics captured data, it was more accurate and less error than the upper and lower classes. The results also showed that with increasing the distance of the shooting point, the error rate was increased. Consequently, the results of time study showed that the average operational time for capturing data in digital camera and the traditional method were 3.88 min.tree-1, and 3.42 min.tree-1, respectively.

Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed Incrimental Intrusion Detetection Model using Correlator (IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.

In this manuscript, we investigate solutions of the partial differential equations (PDEs) arising in mathematical physics with local fractional derivative operators (LFDOs). To get approximate solutions to these equations, we utilize the reduce differential transform method (RDTM) which is based upon the LFDOs. Illustrative examples are given to show the accuracy and reliable results. The obtained solutions show that the present method is an efficient and simple tool for solving the linear and nonlinear PDEs within the LFDOs.

Face recognition is one of the most important tools of identification in biometrics. Face recognition has attracted great attention in the last decades and numerous algorithms have been proposed. Different researches have shown that face recognition with Sparse Representation based Classification (SRC) has great classification performance. In some applications such as face recognition, it is appropriate to limit the search space of sparse solver because of local minima problem. In this paper, we apply this limitation via two methods. In the first, we apply the nonnegative constraint of sparse coefficients. As finding the sparse representation is a problem with very local minima, at first we use a simple classifier such as nearest subspace and then add the obtained information of this classifier to the sparse representation problem with some weights. Based on this view, we propose Weighted Non-negative Sparse Representation WNNSR for the face recognition problem. A quick and effective way to identify faces based on the sparse representation (SR) is smoothed $L_0$-norm $(SL_0)$ approach. In this paper, we solve the WNNSR problem based on the $SL_0$ idea. This approach is called Weighted Non-Negative Smoothed $L_0$ norm $(WNNSL_0)$. The simulation results on the Extended Yale B database demonstrate that the proposed method has high accuracy in face recognition better than the ultramodern sparse solvers approach.

In this paper, a real-time denoising filter based on modelling of stable hybrid models is presented. The hybrid models are composed of the shearlet filter and the adaptive Wiener filter in different forms. The optimization of various models is accomplished by the genetic algorithm. Next, regarding the significant relationship between Optimal models and input images, changing the structure of Optimal models for image denoising is modelled by the ANFIS. The eight hundred digital images are used as train images. For eight hundred training images, Sixty seven models are found. For integrated evaluation, the amounts of image attributes such as Peak Signal to Noise Ratio, Signal to Noise Ratio, Structural Similarity Index, Mean Absolute Error and Image Quality Assessment are evaluated by the Fuzzy deduction system. Finally, for the features of a sample noisy image as test data, the proposed denoising model of ANFIS is compared with wavelet filter in 2 and 4 level , Fast bilateral filter, TV-L1, Median, shearlet filter and the adaptive Wiener filter. In addition, run time of proposed method are evaluated. Experiments show that the proposed method has better performance than others.

In this manuscript, we investigate solutions of the partial differential equations (PDEs) arising in mathematical physics with local fractional derivative operators (LFDOs). To get approximate solutions of these equations, we utilize the reduce differential transform method (RDTM) which is based upon the LFDOs. Illustrative examples are given to show the accuracy and reliable results. The obtained solutions show that the present method is an efficient and simple tool for solving the linear and nonlinear PDEs within the LFDOs.

Cloud computing is a massively distributed system in which existing resources interact with user-requested tasks to meet their requests. In such a system, the problem of optimizing Resource Allocation and Scheduling (RAS) is vital, because recourse allocation and scheduling deals with the mapping between recourses and user requests and also is responsible for optimal allocating of tasks to available resources. In the cloud environment, a user may face hundreds of computational resources to do his work. Therefore, manually recourse allocation and scheduling are impossible, and having a schedule between user requests and available recourses seems logical. In this paper, we used Whale Optimization Algorithm (WOA) to solve resource allocation and task scheduling problem in cloud computing to have optimal resource allocation and reduce the total runtime of requested services by users. The proposed algorithm is compared with the other existed algorithms. Results indicate the proper performance of the proposed algorithm than other ones.

Today, intrusion detection systems are extremely important in securing computers and computer networks. Correlated systems are next to intrusion detection systems by analyzing and combining the alarms received from them, appropriate reports for review and producing security measures. One of the problems face by intrusion detection systems is generating a large volume of false alarms, so one of the most important issues in correlated systems is to check the alerts received by the intrusion detection system to distinguish true-positive alarms from false-positive alarms. The main focus of this research is on the applied optimization of classification methods to reduce the cost of organizations and security expert time in alert checking. The proposed intrusion detection model using correlation(IIDMC) is tested on a valid test dataset and the results show the efficiency of the proposed model and consequently its high accuracy.

One of the ways to enhance the security and concealment of data used today is image watermarking. In image watermarking operation, we try to hide image inside another image without letting others know about the hidden image. In this paper, Contourlet Transform and SVD Transform are used to embedded watermark in the host image. The PSO Optimization Algorithm is also used in the watermark extraction step to find the best scale factor. The results of the proposed algorithm in this article show an improvement over the comparative methods.

Project time and cost are one of the issues and challenges that employers and contractors are facing whit them especially in the forest area. One of the most effective tools that used in prediction and risk analysis is Monte Carlo analysis that Based on the production of random numbers, the number of single components of a set is computed and find out their effect on the whole set. In the traditional approach to project control that still dominates in most national projects, estimating the probable time and budget of the project is still unpredictable to managers and experts. In this work, we tried to present a model for considering the probability and risk in estimating the probability of completion of the project by given time and cost of project termination in a forest area using a case study and software capabilities (Primavera Pertmaster). The results of the probability distribution revealed that the probability of reaching the predicted budget was approximately 13% according to the Monte Carlo simulation. However, the difference between the maximum and minimum values varies from 39 to 650 million IRR, which results in a high risk of the operation of the land operations, which indicates the high risk of the earthwork operation. The real cost of earthwork (570 million IRR) in the study area at the cost allocation chart is estimated at 90%, which also confirms the riskiness of the road earthwork project. Finally, the Monte Carlo simulation method, with the successful prediction of the risk of earthwork operation project, showed that there is an acceptable way for contractors and forest road designers to deal with the negative aspects of the project. The results of this research can be used as a guide to better management of forest for future operations.

For designing forest roads that in addition to minimize the cost of constructing, satisfy environmentalists, it is necessary to provide designers and contractors accurate data from surface and subsurface conditions of the road area due to the large-scale of data using of optimization methods (metaheuristic algorithms) with the help of computers is necessary. For this purpose, this research was carried out on a proposed forest road whit length of 1 km in the forests of the district 4 of Babakoh in the city of Siahkal in Guilan province. The cycle of all earthwork work was studied by continuous time study method and the information of the subsurface layers terms of digging was carefully recorded. The results of comparing the method of the ant colony with the hand-made design scenario showed that the multi-objective algorithm presented in this study has great potential for reducing the cost of earthwork operations and its volume. Based on the results, the algorithm is able to reduce 58.55 and 64 percent the cost of earthwork operations and the volume of operations, respectively. Finally, the results of this research can be used as a guide to better management of forest areas, especially forest roads with the development of efficient methods in all forestry forests.

Watermarking is an operation to hide important information. In this paper, a new watermarking algorithm using Shearlet transform and GWO optimization algorithm as well as SVD transform is presented. The results of this paper show the improvement of robustness and transparency of the new algorithm.

In this paper, we study a positive-additive functional equation in intuitionistic fuzzy C ∗ -algebras. Using fixed point methods, we approximate the positive-additive functional equation in intuitionistic fuzzy C ∗ -algebras.

The optimal control of problem is about finding a control law for a given system such that a certain optimality criterion is achieved. Methods of solving the optimal control problems are divided into direct methods and mediated methods (through other equations). In this paper, the PSO- SVM indirect method is used to solve a class of optimal control problems. In this paper, we try to determine the appropriate algorithm to improve our answers to problems.

Background and Intelligent methods such as artificial neural networks (ANN) have been recently used as an efficient model for prediction and classification of tumors. Diagnosis of benign and malignant breast tumors based on morphological, clinical and demographic features without using invasive paraclinical methods is very important. The aim of this study was to provide a neural network model to predict the status of breast tumors and compare its efficacy with the common regression model. In this study, Wisconsin breast cancer database was used. It was obtained from cytology results of the breast tumors of 683 patients. In the proposed model different features such as clump thickness, uniformity of cell size, uniformity of cell shape, etc. were used as input variables. We applied the genetic algorithm (GA) for determination of the best structure and training of multi-layer NN model was implemented in MATLAB. The performance of proposed NN model was compared appling logistic regression (LR) in SPSS. 5-fold cross validation was used for accurate calculation of the performance of the models. The results found GA capable of determining the best structure for a multi-layer NN and train it properly. In different performances the best NN structure was NN(9-8-6-1) with an average accuracy, sensitivity, specificity, and AUC (area under ROC curve) of 0.971, 0.988, 0.962, and 0.9955, respectively, while the values of the corresponding parameters for LR were 0.968, 0.975, 0.964 and 0.9954, respectively. The achieved ANN model could be used as a method with high sensitivity and specificity alongside common non-invasive diagnostic methods as a diagnosis support system to identify benign and malignant breast tumors.