One of the numerous methods recently employed to study the health of structures is the identification of anomaly in data obtained for the condition of the structure, e. g. the frequencies for the structural modes, stress, strain, displacement, speed, and acceleration) which are obtained and stored by various sensors. The methods of identification applied for anomalies attempt to discover and recognize patterns governing data which run in sharp contrast to the statistical population. In the case of data obtained from sensors, data appearing in contrast to others, i. e. outliers, may signal the occurrence of damage in the structure. The present research aims to employ COMPUTER ALGORITHMS to identify structural defects based on data gathered by sensors indicating structural conditions. The present research investigates the performance of various methods including Artificial Neural Networks (ANN), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Manhattan Distance, Curve Fitting, and Box Plot in the identification of samples from damages in a case study using frequency values related to a cable-support bridge. Subsequent to the implementation of the methods in the datasets, it was shown that the ANN provided the optimal performance.

In this research a COMPUTER model formulated on the operating principle of genetic ALGORITHMS to serve as an analytical aid for pavement maintenance engineers is introduced. Analyses are conducted to show the characteristics of important operation parameter of the program. These parameters include: (1) Parent pool size; (2) Mutation rate in offspring generation. The effects of road maintenance parameter such as choice of warning level are analyzed. Examples are presented to illustrate the application of program to the selection of maintenance warning levels. Finally the detailed maintenance and rehabilitation schedules with two objective functions; minimization the present worth of maintenance costs budget and maximization of network pavement condition index; are presented.

One of the new issues that have been raised in recent years is the hub network design problem. The hubs are collection and distribution centers that are used for the purpose of less connections and more of indirect than direct communications. They are interface facilities which are used as switch centers to collect and distribute flows in the network. They determine routes and organize traffic between source-destination in order to provide high performance and be more inexpensive. In the hub location problem, the aim is to find a suitable location for the hub and routes for sending information from a source to a destination, in order to reduce costs and gain desired purpose by multiple transfers between the hubs. In this paper, teaching and learning based optimization, particle swarm optimization and imperialist competitive algorithm were studied for locating optimally hubs and allocating nodes to the nearest located hub nodes. Experimental results show that optimal location for hubs by using cluster-based optimization algorithm (TLBO) successfully has been performed with extreme accuracy and precision.

Two new and efficient ALGORITHMS for evaluating the terminal reliability of parallel COMPUTER interconnection networks have been proposed. Both the ALGORITHMS are based on multiple decomposition approach. The former is meant for reliability evaluation of multi COMPUTERs while the latter is meant for multi stage interconnection networks. Using the first algorithm the reliability of some important multi COMPUTER networks based on cubic architecture have been evaluated. The second algorithm is a modified version of the first one, which takes into account the topological advantages of multi stage interconnection networks during the decomposition process. The terminal reliability of some important fault-tolerant multi-stage interconnection networks has been evaluated using the second algorithm for the purpose of comparison. The complexities of the proposed ALGORITHMS are found to be highly polynomial in nature. The proposed method of multiple decomposition is compared with some similar decomposition methods. The simulated results confirm that the proposed decomposition method is much better than its counter parts. The generality of the proposed method is assured by applying the same to evaluate the terminal reliability of Alternating group graph which is also an important interconnection network apart from cube like interconnection networks.

Internet applications spreading and its high usage popularity result in significant increasing of cyber-attacks. Consequently, network security has become a matter of importance and several methods have been developed for these attacks.For this purpose, Intrusion detection systems (IDS) are being used to monitor the attacks occurring on COMPUTER networks. Data mining Techniques, Machine Learning, Neural networks, Collective Intelligence, Evolutionary ALGORITHMS and Statistical methods are some of ALGORITHMS which have been used for classification, training and reviewing detection accuracy with analysis based on the standard datasets in Intrusion Detection Systems. In this Paper, the hybrid algorithm is introduced based on decision tree and support vector machine (SVM) using feature selection and decision rules to apply on IDS. The main idea is to use the strengths of both ALGORITHMS in order to improve detection, enhance the accuracy and reduce the rate of error detection of the results. In this algorithm, the best features are selected by SVM, afterwards decision tree is used to make decisions and define rules. The results of applying proposed algorithm are analyzed on the standard dataset KDD Cup99. The proposed method guarantees high detection rate which is proved by simulation results.

In recent years, the needs of the Internet are felt in lives of all people. Accordingly, many studies have been done on security in virtual environment. Old technics such as firewalls, authentication and encryption could not provide Internet security completely; So, Intrusion detection system is created as a new solution and a defense wall in cyber environment. Many studies were performed on different ALGORITHMS but the results show that using machine learning technics and swarm intelligence are very effective to reduce processing time and increase accuracy as well. In this paper, hybrid SVM and ABC ALGORITHMS has been suggested to select features to enhance network intrusion detection and increase the accuracy of results. In this research, data analysis was undertaken using KDDcup99. Such that best features are selected by Support vector machine, then selected features are replaced in the appropriate category based on artificial bee colony algorithm to reduce the search time, increase the amount of learning and improve the authenticity of intrusion detection. The results show that the proposed algorithm can detect intruders accurately on network up to 99.71%.

Plum is one of the nutritious and popular fruits in Iran. Depending on the quality of the fresh fruit before harvesting and the drying process, different quality grades of this fruit are produced as dried plums. In this research, a COMPUTER vision system and machine learning ALGORITHMS were used to classify dried plums into three different quality grades. Different color, shape, and texture features were extracted from the images of dried plum samples, and were used separately and in combination with each other for developing classification ALGORITHMS of Multilayer Perceptron (MLP) neural networks, Support Vector Machine (SVM), Linear Discrimination Analysis (LDA) and Decision Tree (DT). In order to reduce the number of features and to extract more important features, the Correlation-based Feature Selection (CFS) method was used. Results showed that the combination of different image-extracted features increases the classification accuracy, compared to individual color, shape, or texture features. In this regard, the Random Forest (RF) DT model using the combination of image features and CFS feature selection algorithm had the highest classification accuracy in the training and evaluation stages. The values of Root Mean Squared Error (RMSE) and accuracy of this model were obtained equal to 0.1958 and 93.75% in the training stage, and equal to 0.2110 and 91.67% in the evaluation phase, respectively. Considering these performance parameters and the nature of machine vision systems, the results of this research can be used to develop an accurate, fast, and inexpensive system for the quality grading of dried plums.

Introduction: Pattern recognition field is necessary for the recognition of different sensorimotor tasks in Brain COMPUTER Interface systems. Reducing the number of features is an important step in Brain COMPUTER Interface systems and it can improve the accuracy and efficiency of the classification and reduce the costs.Methods: In this paper, features selection was performed through using Improved Binary Gravitational search algorithm and Advanced Binary Ant Colony Optimization on data related to brain signals of nine normal subjects for imagination of left and right hand movements. Features were extracted from six different frequency bands. Two classifiers including support vector machine and k- nearest neighbor were applied to separate the classes. Data were processed by EEGLAB toolbox and through matlab software.Results: The classification rate of the proposed method is 84.21%. Using feature selection methods, effective frequency bands and features for left and right hand movement classification were extracted.Conclusion: The results show the improvement in the classification rate by using Improved Binary Gravitational search algorithm and nearest neighbor classification.

Tumor detection and isolation in magnetic resonance imaging (MRI) is a significant consideration, but when done manually by people, it is very time consuming and may not be accurate. Also, the appearance of the tumor tissue varies from patient to patient, and there are similarities between the tumor and the natural tissue of the brain. In this paper, we have tried to provide an automated method for diagnosing and displaying brain tumors in MRI images. Images of patients with glioblastoma were used after applying pre-processing and removing areas that have no useful information (such as eyes, scalp, etc.). We used a bounding box algorithm, to create a projection for to determining the initial range of the tumor in the next step, an artificial bee colony algorithm, to determine an initial point of the tumor area and then the Grow cut algorithm for, the exact boundary of the tumor area. Our method is automatic and extensively independent of the operator. comparison between results of 12 patients in our method with other similar methods indicate a high accuracy of the proposed method (about 98%) in comparison s.