Sharif Digital Repository

In this chapter we provide mathematical tools to study the stochastic process from the physical point of view. © 2019, Springer Nature Switzerland AG  

In this chapter we provide mathematical tools to study the stochastic process from the physical point of view. © 2019, Springer Nature Switzerland AG  

We introduce a homology-based technique for the classification of multiqubit state vectors with genuine entanglement. In our approach, we associate state vectors to data sets by introducing a metric-like measure in terms of bipartite entanglement, and investigate the persistence of homologies at different scales. This leads to a novel classification of multiqubit entanglement. The relative occurrence frequency of various classes of entangled states is also shown. © Rinton Press  

In this chapter, we introduce Symbiotic Evolutionary Algorithm (SEA) as a template for search and optimization based on partially specified chromosomes and symbiotic combination operator. We show that in contrast to genetic algorithms with traditional recombination operators, this template will not be bound to linkage problems. We present three implementations of this template: first, as a pure algorithm for search and optimization, second, as an artificial immune system, and third, as an algorithm for classifier rule base evolution, and compare implementation results and feature lists with similar algorithms. © 2008 Springer-Verlag Berlin Heidelberg  

A rotated general regression neural network is presented as an enhancement to the general regression neural network. A variable kernel estimate for multivariate densities is considered. A coordinate transformation is adopted which circumvent the difficulty of predicting multimodal distribution with large variance differences between modes which is associated with the general regression neural network. The proposed technique trains the network in a way that the variance differences between modes is kept small and in the same order. Further, the technique reduces the number of indispensable training parameters to two parameters and lowers the load of the computation as well as the time for... 

The process of monitoring the events occurring in a computer system or network and analyzing them for sign of intrusions is known as intrusion detection system (IDS). The objective of this paper is to extract fuzzy classification rules for intrusion detection in computer networks. The proposed method is based on the iterative rule learning approach (IRL) to fuzzy rule base system design. The fuzzy rule base is generated in an incremental fashion, in that the evolutionary algorithm optimizes one fuzzy classifier rule at a time. The performance of final fuzzy classification system has been investigated using intrusion detection problem as a high-dimensional classification problem. Results show... 

Local and global based methods are two main trends for face recognition. Local approaches extract salient features by processing different parts of the image whereas global approaches find a general template for face of each person. Unfortunately, most global approaches work under controlled envi-ronments and they are sensitive to changes in the illumination. On the other hand, local approaches are more robust but finding their optimal parameters is a challenging task. This work proposes a new local-based approach that automatically tunes its parameters. The proposed method incorporates different techniques. In the first step, convolutional neural network (CNN) is employed as a trainable... 

In this paper the Karnaugh and Quine-McCluskey methods are used for symbolic classification problem, and then these methods are compared with other famous available methods. Because the data in classification problem is very large, some changes should be applied in the original Quine-McCluskey (QMC) algorithm. We proposed a new algorithm that applies the QMC algorithm greedily calling it GQMC. It is surprising that GQMC results are most of the time equal to QMC. GQMC is still very slow classifier and it can be used when the number of attributes of the data is small, and the ratio of training data to the all possible data is high. © 2007 IEEE  

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five... 

Iran is home to a treasury of languages representing diverse language families: Iranic, Turkic, Semitic, Indic, Dravidian, Armenian, and Kartvelian, as well as sign languages. Despite valuable research carried out by Iranian and western scholars, there is still no comprehensive publication depicting the geographic distribution and linguistic status of language varieties in Iran. In order to work toward this goal, the Atlas of the Languages of Iran (ALI) (www.iranatlas.net) was officially launched in 2015 as an international, online research programme. The present study opens with a historical overview of the research context and underlines the ongoing necessity of constructing such an atlas... 

This paper presents a novel algorithm for detection of singular points, the core and delta points, in fingerprint images. The number and location of singular points, are used to classify fingerprint images into five general groups; and therefore to narrow down the search space in large fingerprint databases. Using the proposed directional masks in the first step, we detect the neighborhood of the singular points. In the second stage by implementing the proposed algorithm, an adaptive singular point detection method, is designed to extract the exact location of core and delta points. Usage of the proposed directional masks speeds up the process and the proposed adaptive singular point... 

Although exemplar based approaches have shown good accuracy in classification problems, some limitations are observed in the accuracy of exemplar based automatic speech recognition (ASR) applications. The main limitation of these algorithms is their high computational complexity which makes them difficult to extend to ASR applications. In this paper, an N-best class selector is introduced based on sparse representation (SR) and a tree search strategy. In this approach, the classification is fulfilled in three steps. At first, the set of similar training samples for the specific test sample is selected by k-dimensional (KD) tree search algorithm. Then, an SR based N-best class selector is... 

Inferior temporal (IT) cortex is the most important part of the brain and plays an important role in response to visual stimuli. In this study, object decoding has been performed using neuron spikes in IT cortex region. Single Unit Activity (SUA) was recorded from 123 neurons in IT cortex. Pseudo-population firing rate vectors were created, then dimension reduction was done and an Artificial Neural Network (ANN) was used for object decoding. Object decoding accuracy was calculated for various window lengths from 50 ms to 200 ms and various window steps from 25 ms to 100 ms. The results show that 150 ms length and 50 ms window step size gives an optimum performance in average accuracy  

Internet traffic classification is important in many aspects of network management such as data exploitation detection, malicious user identification, and restricting application traffic. Previously, features such as port and protocol numbers have been used to classify traffic, but these features can now be changed easily, making their use in traffic classification inadequate. Consequently, traffic classification based on machine learning (ML) is now employed. The number of features used in an ML algorithm has a significant impact on performance, in particular accuracy. In this paper, a minimum best feature set is chosen using a supervised method to obtain uncorrelated features. Outlier...