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Showing 96 results for reza
Arezoo Karimizadeh, Mansour Vali, Mohammadreza Modaresi,
Volume 17, Issue 2 (9-2020)
Volume 17, Issue 2 (9-2020)
Abstract
Cystic fibrosis (CF) is the most common autosomal recessive disorder in white skinned individuals. Chronic lung infection is the main cause of mortality in this disease. Approximately 60–75 % of adult CF patients frequently suffer from Pseudomonas aeruginosa (PA) infection that is strongly associated with inflammation, lung destruction, and increased mortality. Therefore, CF patients should be followed up by physicians to diagnose infection in the primary stage, start treatment, and reduce the risk of chronic infection. Although sputum culture is the gold standard for diagnosis of PA infections, a rapid and accurate diagnostic method can facilitate early initiation of appropriate therapy and easy monitoring of the condition. The aim of this study was to diagnose CF patients with infection using their lung sound.
In this study, the symmetry of frequency information in right and left lung was investigated in CF patients with positive sputum culture results, negative sputum culture results, and patients who underwent treatment with antibiotics. Respiretorysounds were acquired from 34 CF patients (16 female, 18 male) who were being followed-up at the Pediatric Respiratory and Sleep Medicine Research Center of Children's Medical Center. The patient selection was based on their sputum microbiology culture. The selection category was as follows: 12 patients with normal flora culture results and 11 patients with PA infection. Also, respiratory sounds of 11 patients were recorded one month after antibiotic treatment and they used to investigate the effectiveness of the proposed method.
In the preprocessing step, cardiac sound was removed, respiratory sound cycles were separated and the signals were divided into 64 milisecond frame and 15 features were extracted from each frame. Differences between these features were computed between right and left lungs for early, middle and late section of the respiratory cycle using the new proposed feature. Then, the best group of features was selected by applying Genetic Algorithm. The selected group of features was fed into Support Vector Machine, K Nearest Neighbor and Naïve Bayesian classifier. Also, an Ensemble classifier was examined. The best result was obtained by Ensemble classifier that diagnosed infection by the accuracy of 91.3% and differentiates a group of CF patients with infection from CF patients who underwent treatment with an accuracy of 90.9%. This study describes a novel method of infection detection in CF patients based only on respiratory sound analysis. The proposed method is a simple and available way for early diagnosis of infection and initiating therapeutic strategies.
In this study, the symmetry of frequency information in right and left lung was investigated in CF patients with positive sputum culture results, negative sputum culture results, and patients who underwent treatment with antibiotics. Respiretorysounds were acquired from 34 CF patients (16 female, 18 male) who were being followed-up at the Pediatric Respiratory and Sleep Medicine Research Center of Children's Medical Center. The patient selection was based on their sputum microbiology culture. The selection category was as follows: 12 patients with normal flora culture results and 11 patients with PA infection. Also, respiratory sounds of 11 patients were recorded one month after antibiotic treatment and they used to investigate the effectiveness of the proposed method.
In the preprocessing step, cardiac sound was removed, respiratory sound cycles were separated and the signals were divided into 64 milisecond frame and 15 features were extracted from each frame. Differences between these features were computed between right and left lungs for early, middle and late section of the respiratory cycle using the new proposed feature. Then, the best group of features was selected by applying Genetic Algorithm. The selected group of features was fed into Support Vector Machine, K Nearest Neighbor and Naïve Bayesian classifier. Also, an Ensemble classifier was examined. The best result was obtained by Ensemble classifier that diagnosed infection by the accuracy of 91.3% and differentiates a group of CF patients with infection from CF patients who underwent treatment with an accuracy of 90.9%. This study describes a novel method of infection detection in CF patients based only on respiratory sound analysis. The proposed method is a simple and available way for early diagnosis of infection and initiating therapeutic strategies.
Reza Bayat, Dr Mehdi Sadeghi, Mohammad Reza Aref,
Volume 17, Issue 2 (9-2020)
Volume 17, Issue 2 (9-2020)
Abstract
Deep understanding of molecular biology has allowed emergence of new technologies like DNA decryption. On the other hand, advancements of molecular biology have made manipulation of genetic systems simpler than ever; this promises extraordinary progress in biological, medical and biotechnological applications. This is not an unrealistic goal since genes which are regulated by gene regulatory networks (GRNs) are the core governors of life processes at the molecular level. In fact, manipulation of GRNs would be the ultimate strategy for optimal purposeful control of cell’s life. GRNs are in charge of regulating the amounts of all the inter-cellular as well as intra-cellular molecular species produced all the time in all living organisms. Manipulation of a GRN requires comprehensive knowledge about nodes and interconnections. This paper deals with both aspects in networks having more than fifty nodes. In the first part of the paper, restrictions of probabilistic models in modeling node behavior are discussed, i.e.: 1) unfeasibility of reliably predicting the next state of GRN based on its current state, 2) impossibility of modelling logical relations among genes, and 3) scarcity of biological data needed for model identification. These findings which are supported by arguments from probability theory suggest that probabilistic models should not be used for analysis and prediction of node behavior in GRNs. Next part of the paper focuses on models of GRN structure. It is shown that the use of multi-tree models for structure for GRN poses severe limitations on network behavior, i.e. 1) increase in signal entropy while passing through the network, 2) decrease in signal bandwidth while passing through the network, and 3) lack of feedback as a key element for oscillatory and/or autonomous behavior (a requirement for any biological network). To demonstrate that, these restrictions are consequences of model selection, we use information theoretic arguments. At the last and the most important part of the paper we look into the gene perturbation experiments from a network-theoretic perspective to show that multi-perturbation experiments are not as informative as assumed so far. A generally accepted belief among researches states that multi-perturbation experiments are more informative than single-perturbation ones, i.e., multiple simultaneously applied perturbations provide more information than a single perturbation. It is shown that single-perturbation experiments are optimal for identification of network structure, provided the ultimate goal is to discover correct subnet structures.
Omid Mokhlessi, Seyedjavad Seyed Mahdavi Chabok, Aida Alirezaee,
Volume 17, Issue 3 (11-2020)
Volume 17, Issue 3 (11-2020)
Abstract
The heart is one of the most important organs in the body, which is responsible for pumping blood into the valvular systems. Beside, heart valve disorders are one of the leading causes of death in the world. These disorders are complications in the heart valves that cause the valves to deform or damage, and as a result, the sounds caused by their opening and closing compared to a healthy heart.
Obviously, due to the complexities of cardiac audio signals and their recording, designing an accurate diagnosis system free of noise and fast enough is difficult to achieve. One of the most important issues in designing an intelligent heart disease diagnosis system is the use of appropriate primary data. This means that these data must not only be recorded according to the patient's equipment and clinical condition, but also must be labeled according to the correct diagnosis of the physician.
However, in this study, an attempt has been made to provide an intelligent system for diagnosing valvular heart failure using phonocardiographic sound signals to have maximum diagnostic power. For this purpose, the signals are labeled and used under the supervision of a specialist doctor.
The main goal is to select the effective feature vectors using the genetic optimization method and also based on the evaluation function by Pearson correlation coefficients.
Before extraction feature step, preprocessing from data recording, normalization, segmentation, and filtering were used to increase system performance accuracy. For better result, Signal temporal, wavelet and signal energy components are extracted from the prepared signal as feature extraction step.
Whereas extracted problem space were not correlated enough, in next step principal component analysis, linear separator analysis, and uncorrelated linear separator analysis methods were used to make feature vectors in a final correlated space.
In selecting step, an efficient and simple method is used inorder to estimate the number of optimal features. In general, correlation is a criterion for determining the relationship between variables. The difference between the correlations of all feature subsets is calculated (for both in-class and out-of-class subsets) and then categorized in descending order according to the evaluation function.
As a result, in the feature selection step the evaluation function is based on the Pearson statistical method, which is evaluated by a genetic algorithm with the aim of identifying more effective and correlated features in the final vectors.
Eventually In this paper, two widely used neural networks with dynamic and static structure including perceptron and Elman neural networks have been used to evaluate the accuracy of the proposed vectors. The results of modeling the process of selecting effective features and diagnosing the disease show the efficiency of the proposed method.
Dr Mohammad Reza Rezaeian,
Volume 17, Issue 3 (11-2020)
Volume 17, Issue 3 (11-2020)
Abstract
Magnetic resonance based on molecular imaging allows tracing contrast agents thereby facilitating early diagnosis of diseases in a non-invasive fashion that enhances the soft tissue with high spatial resolution. Recently, the exchange of protons between the contrast agent and water, known as the chemical exchange saturation transfer (CEST) effect, has been measured by applying a suitable pulse sequence to the magnetic resonance imaging (MRI) scanner. CEST MRI is increasingly used to probe mobile proteins and microenvironment properties, and shows great promise for tumor and stroke diagnosis. This effect leads to a reduction in magnetic moments of water causing a corresponding decrease in the gray scale intensity of the image, providing a negative contrast in the CEST image. The CEST effect is complex, and it depends on the CEST agent concentration, exchange rates, the characteristic of the magnetization transfer (MT), and the relaxation properties of the tissue. The CEST contrast is different from the inherent MT of macromolecule bounded protons which evidently occurs as a dipole-dipole interaction between water and macromolecular components. Recently it was shown that CEST agents can be strongly affected by the MT and direct saturation effects, so corrections are needed to derive accurate estimates of CEST contrast. Specifically, the existence of an analytical relation between the chemical exchange rate and physiological parameters such as the core temperature, glucose level, and PH has generated more interest in quantification of the CEST contrast. The most important model was obtained by analyzing water saturation spectrum named magnetization transfer ratio spectrum that was quantified by solving Bloch equations. This paper provides an analytical closed-formula of CEST contrast under steady state and transient conditions based on the eigenspace solution of the Bloch-McConnell equations for both of the MT and CEST effects as well as their interactions. In this paper, the CEST contrast has been modeled in two- and three-pool systems using measured (experimental- real data) and fitted data similar to the muscle tissue by considering interfering factors. The resulting error was characterized by an average of relative sum-square between three experimental data and fitted CEST contrast based on the proposed formulation lower than 4 percent. For further validation, these formulations were compared to the empirical formulation of the CEST effect based on a diamagnetic contrast agent introduced in the two-pool system. Using the proposed analytical expression for the CEST contrast, we optimized critical parameters such as concentration contrast agent, chemical exchange rate and characteristics of the electromagnetic radio frequency pulse via amplitude and pulse width in the rectangular pulse.
Ladan Riazi, Alireza Pourebrahimi, Mahmood Alborzi, Reza Radfar,
Volume 17, Issue 4 (2-2021)
Volume 17, Issue 4 (2-2021)
Abstract
This paper presents a method for improving steganography and enhancing the security using combinatorial Meta-heuristic algorithms. The goal is to achieve an improved PSNR value in order to preserve the image quality in the steganography process.
Steganography algorithms, in order to insert message signal information inside the host data, create small changes based on the message signal in the host data, so that they are not visible to the human eye. Each cryptographic algorithm has two steps: insert a stego signal and extract it. You can use the area of the spatial or transformation area to insert the stego signal. Extraction can be done using the correlation with the original watermark or independently of it. Clearly, the choice of insertion method and how to extract are interdependent. In spatial techniques, information is stored directly in pixel color intensity but in the transform domain, the image is initially converted to another domain (such as frequency), and then the information is embedded in the conversion coefficients. Using optimization algorithms based on Metahuristic algorithms in this field is widely used and many researchers have been encouraged to use it. Using a suitable fitness function, these methods are useful in the design of steganography algorithms.
In this research, seven commonly used Metahuristic algorithms, including ant colony, bee, cuckoo search, genetics, Particle Swarm Optimization, Simulated Annealing and firefly were selected and the performance of these algorithms is evaluated individually on existing data after being applied individually.
Among the applied algorithms, cuckoo search, firefly and bee algorithms that have the best fitness function and therefore the highest quality were selected. All 6 different modes of combining these 3 algorithms were separately examined. The best combination is the firefly, bee and cuckoo search algorithms, which provides a mean signal-to-noise ratio of 54.89.
The proposed combination compared to the individual algorithms of optimization of ant colony, bee, cuckoo search, genetics, Particle Swarm Optimization, Simulated Annealing and firefly, provides 59.29, 29.61, 37.43, 52.56, 54.84, 57.82, and 3.82% improvement in the PSNR value.
Steganography algorithms, in order to insert message signal information inside the host data, create small changes based on the message signal in the host data, so that they are not visible to the human eye. Each cryptographic algorithm has two steps: insert a stego signal and extract it. You can use the area of the spatial or transformation area to insert the stego signal. Extraction can be done using the correlation with the original watermark or independently of it. Clearly, the choice of insertion method and how to extract are interdependent. In spatial techniques, information is stored directly in pixel color intensity but in the transform domain, the image is initially converted to another domain (such as frequency), and then the information is embedded in the conversion coefficients. Using optimization algorithms based on Metahuristic algorithms in this field is widely used and many researchers have been encouraged to use it. Using a suitable fitness function, these methods are useful in the design of steganography algorithms.
In this research, seven commonly used Metahuristic algorithms, including ant colony, bee, cuckoo search, genetics, Particle Swarm Optimization, Simulated Annealing and firefly were selected and the performance of these algorithms is evaluated individually on existing data after being applied individually.
Among the applied algorithms, cuckoo search, firefly and bee algorithms that have the best fitness function and therefore the highest quality were selected. All 6 different modes of combining these 3 algorithms were separately examined. The best combination is the firefly, bee and cuckoo search algorithms, which provides a mean signal-to-noise ratio of 54.89.
The proposed combination compared to the individual algorithms of optimization of ant colony, bee, cuckoo search, genetics, Particle Swarm Optimization, Simulated Annealing and firefly, provides 59.29, 29.61, 37.43, 52.56, 54.84, 57.82, and 3.82% improvement in the PSNR value.
Fatemeh Najafi, Hamid Parvin, Kamal Mirzaei, Samad Nejatiyan, Seyede Vahideh Rezaie,
Volume 17, Issue 4 (2-2021)
Volume 17, Issue 4 (2-2021)
Abstract
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The input of this function is all base clusters and its output is a clustering called clustering agreement. This function is called an agreement function. Ensemble clustering has been proposed to increase efficiency, strong, reliability and clustering stability. Because of the lack of cluster monitoring, and the inadequacy of general-purpose base clustering algorithms on the other, a new approach called an ensemble clustering has been proposed in which it has been attempted to find an agreed cluster with the highest Consensus and agreement. In fact, ensemble clustering techniques with this slogan, the combination of several poorer models, is better than a strong model. However, this claim is correct if certain conditions (such as the diversity between the members in the consensus and their quality) are met. This article presents an ensemble clustering method. This paper uses the weak clustering method of fuzzy cmeans as a base cluster. Also, by adopting some measures, the diversity of consensus has increased. The proposed hybrid clustering method has the benefits of the clustering algorithm of fuzzy cmeans that has its speed, as well as the major weaknesses of the inability to detect non-spherical and non-uniform clusters. In the experimental results, we have tested the proposed ensemble clustering algorithm with different, up-to-date and robust clustering algorithms on the different data sets. Experimental results indicate the superiority of the proposed ensemble clustering method compared to other clustering algorithms to up-to-date and strong.
Mohammad Reza Hasni Ahangar, Ali Amiri Jezeh,
Volume 18, Issue 1 (5-2021)
Volume 18, Issue 1 (5-2021)
Abstract
Keywords can present the main concepts of the text without human intervention according to the model. Keywords are important vocabulary words that describe the text and play a very important role in accurate and fast understanding of the content. The purpose of extracting keywords is to identify the subject of the text and the main content of the text in the shortest time. Keyword extraction plays an important role in the fields of text summarization, document labeling, information retrieval, and subject extraction from text. For example, summarizing the contents of large texts into smaller texts is difficult, but having keywords in the text can make you aware of the topics in the text. Identifying keywords from the text with common methods is time-consuming and costly. Keyword extraction methods can be classified into two types with observer and without observer. In general, the process of extracting keywords can be explained in such a way that first the text is converted into smaller units called the word, then the redundant words are removed and the remaining words are weighted, then the keywords are selected from these words. Our proposed method in this paper for identifying keywords is a method with observer. In this paper, we first calculate the word correlation matrix per document using a feed forward neural network and Word2Vec algorithm. Then, using the correlation matrix and a limited initial list of keywords, we extract the closest words in terms of similarity in the form of the list of nearest neighbors. Next we sort the last list in descending format, and select different percentages of words from the beginning of the list, and repeat the process of learning the neural network 10 times for each percentage and creating a correlation matrix and extracting the list of closest neighbors. Finally, we calculate the average accuracy, recall, and F-measure. We continue to do this until we get the best results in the evaluation, the results show that for the largest selection of 40% of the words from the beginning of the list of closest neighbors, the acceptable results are obtained. The algorithm has been tested on corpus with 800 news items that have been manually extracted by keywords, and laboratory results show that the accuracy of the suggested method will be 78%.
Yaser Rezaei, Alirezae Rezaee, Fateme Darakeh, Zeynab Azarakhsh,
Volume 18, Issue 1 (5-2021)
Volume 18, Issue 1 (5-2021)
Abstract
Classification of land cover is one of the most important applications of radar polarimetry images. The purpose of image classification is to classify image pixels into different classes based on vector properties of the extractor. Radar imaging systems provide useful information about ground cover by using a wide range of electromagnetic waves to image the Earth's surface. The purpose of this study is to present an optimal method for classifying polarimetric radar images. The proposed method is a combination of support vector machine and binary gravitational search optimization algorithm. In this regard, first a set of polarimetric features including original data values, target parsing features, and SAR separators are extracted from the images. Then, in order to select the appropriate features and determine the optimal parameters for the support vector machine classifier, the binary gravitational search algorithm is used. In order to achieve a classification system with high classification accuracy, the optimal values of the model parameters and a subset of the optimal properties are selected simultaneously. The results of the implementation of the proposed algorithm are compared with two states, taking into account all the selected features, and the genetic algorithm, the results of zoning for the three regions are examined. The separation of areas for the San Francisco and Manila regions, and the detection of oil slicks in the ocean surface of the Philippines, have been evaluated. The comparison with the genetic algorithm was approximately between 6% to 12% and the comparison with the presence of all features was between 13% and 20%. For the San Francisco area, the number of extraction properties was 101, which was selected using the proposed 47 optimal properties algorithm. For the city of Manila, after applying the algorithm, 31 optimal features have been selected from 65 features. For the oil slick of the city of the Philippines, we have reached the stated accuracy by selecting 33 features from 69 features, for the first two regions the number of initial population is 50 and the repetition period is 30, and for the third region with 30 initial population and the repetition period is 10.
Hamidreza Tahmasbi, Mehrdad Jalali, Hassan Shakeri,
Volume 18, Issue 1 (5-2021)
Volume 18, Issue 1 (5-2021)
Abstract
With the expansion of social networks, the use of recommender systems in these networks has attracted considerable attention. Recommender systems have become an important tool for alleviating the information that overload problem of users by providing personalized recommendations to a user who might like based on past preferences or observed behavior about one or various items. In these systems, the users’ behavior is dynamic and their preferences change over time for different reasons. The adaptability of recommender systems to capture the evolving user preferences, which are changing constantly, is essential.
Recent studies point out that the modeling and capturing the dynamics of user preferences lead to significant improvements in recommendation accuracy. In spite of the importance of this issue, only a few approaches recently proposed that take into account the dynamic behavior of the users in making recommendations. Most of these approaches are based on the matrix factorization scheme. However, most of them assume that the preference dynamics are homogeneous for all users, whereas the changes in user preferences may be individual and the time change pattern for each user differs. In addition, because the amount of numerical ratings dramatically reduced in a specific time period, the sparsity problem in these approaches is more intense. Exploiting social information such as the trust relations between users besides the users’ rating data can help to alleviate the sparsity problem. Although social information is also very sparse, especially in a time period, it is complementary to rating information. Some works use tensor factorization to capture user preference dynamics. Despite the success of these works, the processing and solving the tensor decomposition is hard and usually leads to very high computing costs in practice, especially when the tensor is large and sparse.
In this paper, considering that user preferences change individually over time, and based on the intuition that social influence can affect the users’ preferences in a recommender system, a social recommender system is proposed. In this system, the users’ rating information and social trust information are jointly factorized based on a matrix factorization scheme. Based on this scheme, each users and items is characterized by a sets of features indicating latent factors of the users and items in the system. In addition, it is assumed that user preferences change smoothly, and the user preferences in the current time period depend on his/her preferences in the previous time period. Therefore, the user dynamics are modeled into this framework by learning a transition matrix of user preferences between two consecutive time periods for each individual user. The complexity analysis implies that this system can be scaled to large datasets with millions of users and items. Moreover, the experimental results on a dataset from a popular product review website, Epinions, show that the proposed system performs better than competitive methods in terms of MAE and RMSE.
Recent studies point out that the modeling and capturing the dynamics of user preferences lead to significant improvements in recommendation accuracy. In spite of the importance of this issue, only a few approaches recently proposed that take into account the dynamic behavior of the users in making recommendations. Most of these approaches are based on the matrix factorization scheme. However, most of them assume that the preference dynamics are homogeneous for all users, whereas the changes in user preferences may be individual and the time change pattern for each user differs. In addition, because the amount of numerical ratings dramatically reduced in a specific time period, the sparsity problem in these approaches is more intense. Exploiting social information such as the trust relations between users besides the users’ rating data can help to alleviate the sparsity problem. Although social information is also very sparse, especially in a time period, it is complementary to rating information. Some works use tensor factorization to capture user preference dynamics. Despite the success of these works, the processing and solving the tensor decomposition is hard and usually leads to very high computing costs in practice, especially when the tensor is large and sparse.
In this paper, considering that user preferences change individually over time, and based on the intuition that social influence can affect the users’ preferences in a recommender system, a social recommender system is proposed. In this system, the users’ rating information and social trust information are jointly factorized based on a matrix factorization scheme. Based on this scheme, each users and items is characterized by a sets of features indicating latent factors of the users and items in the system. In addition, it is assumed that user preferences change smoothly, and the user preferences in the current time period depend on his/her preferences in the previous time period. Therefore, the user dynamics are modeled into this framework by learning a transition matrix of user preferences between two consecutive time periods for each individual user. The complexity analysis implies that this system can be scaled to large datasets with millions of users and items. Moreover, the experimental results on a dataset from a popular product review website, Epinions, show that the proposed system performs better than competitive methods in terms of MAE and RMSE.
Mohammad Akafan, Behrouz Minaei, Alireza Bagheri,
Volume 18, Issue 1 (5-2021)
Volume 18, Issue 1 (5-2021)
Abstract
The proposed algorithm in this research is based on the multi-agent particle swarm optimization as a collective intelligence due to the connection between several simple components which enables them to regulate their behavior and relationships with the rest of the group according to certain rules. As a result, self-organizing in collective activities can be seen. Community structure is crucial for many network systems, the algorithm uses a special type of coding to identify the number of communities without any prior knowledge. In this method, the modularity function is used as a fitness function to optimize particle swarm. Several experiments show that the proposed algorithm which is called Multi Agent Particle Swarm is superior compared with other algorithms. This algorithm is capable of detecting nodes in overlapping communities with high accuracy.
The point in using the previously presented PSO algorithms for community detection is that they recognize non-overlapping communities, and this goes back to the representation of genes by these methods, but the use of multi-agent collective intelligence by our algorithm has led to the identification of nodes in overlapping communities.
The results show that the nodes that are shared between a set of agents, these nodes are active nodes that create an overlap in the communities. Our experimental results show that when a member node is more than one community, this node is a good candidate to be selected as the active node, which has led to the creation of overlapping networks.
The point in using the previously presented PSO algorithms for community detection is that they recognize non-overlapping communities, and this goes back to the representation of genes by these methods, but the use of multi-agent collective intelligence by our algorithm has led to the identification of nodes in overlapping communities.
The results show that the nodes that are shared between a set of agents, these nodes are active nodes that create an overlap in the communities. Our experimental results show that when a member node is more than one community, this node is a good candidate to be selected as the active node, which has led to the creation of overlapping networks.
Zoleikha Jahanbakhsh-Nagadeh, Mohammad-Reza Feizi-Derakhshi, Arash Sharifi,
Volume 18, Issue 1 (5-2021)
Volume 18, Issue 1 (5-2021)
Abstract
The rumor is a collective attempt to interpret a vague but attractive situation by using the power of words. Therefore, identifying the rumor language can be helpful in identifying it. The previous research has focused more on the contextual information to reply tweets and less on the content features of the original rumor to address the rumor detection problem. Most of the studies have been in the English language, but more limited work has been done in the Persian language to detect rumors. This study analyzed the content of the original rumor and introduced informative content features to early identify Persian rumors (i.e., when it is published on news media but has not yet spread on social media) on Twitter and Telegram. Therefore, the proposed model is based on physical and non-physical content features in three categories including, lexical, syntactic, and pragmatic. These features are a combination of the common content features along with the proposed new content-based features. Since no social context information is available at the time of posting rumors, the proposed model is independent of propagation-based features and relies on the content-based information of the original rumor. Although in the proposed model, much information (including user information, the user's reaction to the rumor, and propagation structures) are ignored, but helpful content information can be obtained for classification by content analysis of the original rumor.
Several experiments have been performed on the various combinations of feature sets (i.e., common and proposed content features) to explore the capability of features in distinguishing rumors and non-rumors separately and jointly. To this end, three machine learning algorithms including, Random Forest (RF), AdaBoost, and Support Vector Machine (SVM) have been used as strong classifications to evaluate the accuracy of the proposed model. To achieve the best performance of classification algorithms on the training dataset, it is necessary to use feature selection techniques. In this study, the Sequential Forward Floating Search (SFFS) approach has been used to select valuable features. Also, the statistical results of the t-test on the P-value (<=0.05) demonstrate that most of the new features proposed in this study reveal statistically significant differences between rumor and non-rumor documents. The experimental results are shown the performance of new proposed features to improve the accuracy of the rumor detection. The F-measure of the proposed model to detect Persian rumors on the Twitter dataset was 0.848, on the Kermanshah earthquake dataset was 0.952 and on the Telegram dataset was 0.867, which indicated the ability of the proposed method to identify rumors only by focusing on the content features of the original rumor text. The results of evaluating the proposed model on Twitter rumors show that, despite the short length of Twitter tweets and the extraction of limited content information from tweets, the proposed model can detect Twitter rumors with acceptable accuracy. Hence, the ability of content features to distinguish rumors from non-rumors is proven.
Mina Abbaspour Orangi, Seyyed Alireza Hashemi Golpayegani,
Volume 18, Issue 2 (10-2021)
Volume 18, Issue 2 (10-2021)
Abstract
Trust is one of the most important cornerstones in social networks' discussions. mostly the way that users of these networks trust each other are considered identical, while these users can have different approaches and considerations in trusting others. Meanwhile, users can impress each other and change their trusting patterns in other users. As a result, the mechanism and manner of impressing opinion trust behavior and conditions of behavioral modes changing have a place of importance to be considered. The question is that, how we can consider different behavior of users and their impression in trusting others? In the first step, the main purpose of this paper is to spotlight social networks' different user behavior in trusting other users. For this purpose, the three most important behavioral modes in users trust are considered. In each of these modes, behavioral and functional characteristics of users are the basis of calculating trust, which is based on mental beliefs of them. These modes are named as optimistic, moderate, and pessimistic trusting modes. In optimistic mode, we suppose that users think positively and consider low level of activities and signs in trusting others. Here, negative interactions have little impact on users mind. In moderate mode, we suppose that users are not as optimistic as mode A and consider all the interactions and signs when they want to trust others. Here, any negative action can destroy the trust of users and has a greater impact on users. Finally, in pessimistic mode, we suppose that users are pessimistic and hardly trust someone. In this mode, the interactions that happened more recently have more value than those happened in the past.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
In the next step, the purpose and innovation of this paper is the way that the trust behavior of users spreads. Three different scenarios are considered for the impressing and spreading of nodes behavior, purposely. In each scenario, different states for users and different purposes for diffusion are defined. Next, it is followed by maximizing of impression and finding more impressive agents in diffusing trust behavior through social networks. For this purpose, it's focused on the structure of users social networks, and the most impressive ones are determined through different diffusion scenarios. The findings of this article appear a significant discrepancy in the amount of trust in each of the different behavioral modes, which is more acceptable in the real world. Analyzing test results leads us to the fact that in the presented model, choosing the start node from each community with 48.14 percent in behavior improvement and diffusion speed and the nodes with the highest degree with 37.03 percent in behavior changing has much more reasonable results than usual models.
Hamid Reza Ghaffari, Atena Jalali Mojahed,
Volume 18, Issue 2 (10-2021)
Volume 18, Issue 2 (10-2021)
Abstract
Classification is a machine learning method used to predict a particular sample’s label with the least error. The present study was conducted using label prediction ability with the help of a classifier to create a new feature. Today, there are several feature-extraction methods like principal component analysis (PCA) and independent component analysis (ICA) that are widely used in different fields; however, they all suffer from the high cost of transferring to another space. The purpose of the proposed method was to create a higher distinction between various classes using the new feature in a way that, make the data in the classes closer to each other. As a result, for increasing the efficiency of classifiers, more differentiation is created between the data of various classes. Firstly, the suggested labels for the primary data set were determined using one or more classifiers and added to the primary data set as a new feature. The model was created using a new data set. The new feature for training and testing data sets was provided separately. The tests were performed on 20 standard data sets and the results of the proposed method were compared with those of the two methods described in the related studies. The outputs indicated that the proposed method has significantly improved the classification accuracy. In the second part of the tests, the resolution of the new feature was examined according to two criteria, namely Information Gain and Gini Index, for examining the effectiveness of the proposed method. The results showed that the feature obtained in the proposed method has higher Information Gain and lower Gini Index in most cases, as it has less irregularity. To prevent the increase in data dimensions, the feature with the least Information Gain was replaced with the feature extracted with the most Information Gain. The results of this step showed an increase in efficiency as well.
Mohammad Reza Esmaeili, Seyed Hamid Zahiri, Seyed Mohammad Razavi,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
Digital transformers are considered as one of the digital circuits being widely used in signal and data processing systems, audio and video processing, medical signal processing as well as telecommunication systems. Transforms such as Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT) and Fast Fourier Transform (FFT) are among the ones being commonly used in this area. As an illustration, the DCT is employed in compressing the images. Moreover, the FFT can be utilized in separating the signal spectrum in signal processing systems as fast as possible. The DWT is used in separating the signal spectrum in a variety of applications from signal processing to telecommunication systems, as well.
In order to build a VLSI circuit, several steps have to be taken from chip design to final construction. The first step in the synthesis of the integrated circuits is called high-level synthesis (HLS), in which a structural characteristic is obtained from a behavioral or algorithmic description. The resulting structural characteristic is equivalent to the one being considered in the behavioral description and it somehow represents the method for implementing the behavioral description as a result several structural descriptions could be implementable for each behavioral description. Therefore, depending on the intended use, the characteristic will be selected that outperforms the others. The main purpose of the HLS is to optimize the power consumption, the chip occupied area and delayed and is fulfilled by selecting the appropriate number of operating units and how they are implemented to the operators. This is generally accomplished through a graph analysis called the data flow graph (DFG) which is a graphical representation of the type and how the operators connect. In the DFG, each node is equivalent to an operator while the edges represent the relationship between these operators.
Experience has proved that if the level of design optimization is high, in addition to higher efficiency, the design time will be lower, which is why the researchers are far more interested in optimization at higher levels of design than the lower levels. The complex, extensive, and discrete nature of the HLS problems have been ranked them among the most complex problems in VLSI circuits engineering. Bearing this mind, using meta-heuristic and Swarm intelligence methods to solve high-level synthesis projects seems to be a favored option. In this paper, a heuristic method called Moth-Flame Optimization (MFO) has been used to solve the HLS problem in the design of digital transformer to find the optimal response. The MFO is a population-based heuristic algorithm that optimizes the problems using the laws of nature. The leading notion behind the MFO algorithm inspired from the moths’ movements and their instinctive navigation during the night. In the MFO algorithm, the moths are like chromosomes in the GA and like the particles in the PSO algorithm. In order to compare and prove the efficiency of the proposed method, it was applied on the test data with the GA-based method separately but with the same initial conditions. The comparative results along with the results of the GA-based method demonstrated that the proposed method exhibits a higher ability to provide the appropriate hardware structure and high-level synthesis of various types of transformers. Another outstanding feature of the proposed method is its high speed of finding an optimal response with an average of more than 20% greater than the GA based method.
Reza Kayvan Shokooh, Majid Okhovvat, Meisam Raees Danaee,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
The matched filter in the radar receiver is only adapted to the transmitted signal version and its output will be wasted due to non-matching with the received signal from the environment. The sidelobes amplitude of the matched filter output in pulse compression radars are depended on the transmitted coded waveforms that extended as much as the length of the code on both sides of the target location. In order to detect a weak target in vicinity of strong target, the sidelobes of the matched filter output resulting from the strong target masked the weak target and didn’t detect its. Generally, the radar dynamic range is defined by the maximum power ratio to the minimum detectable power that is depended on the level of the threshold and the sidelobe levels. Adaptive algorithms suppress the sidelobe levels to noise level with condition of maintain the range resolution and therefore increase the dynamic range. In this paper, an improved algorithm (in terms of computational cost and Doppler robustness) is proposed based on the minimum mean square error (MMSE) estimator denoted as Flexible Filter Length-Adaptive Pulse Compression Repair (FFL-APCR), which filter length depends on the length of transmitted code. It is also shown that the length of the code is influenced by determining the asymptotic peak sidelobe level and the dynamics range. In addition, the influence of the high-speed target on main lobe broadening and the performance degradation of adaptive filters is investigated. Finally, extending of radar dynamic range with the proposed FFL-APCR algorithm is shown in various conditions and its performance evaluated by mean square error criteria.
Where return signals coincide with the transmission of a pulse, pulse eclipsing can occur which results in detection performance loss. The mismatches (Doppler phase shift and pulse eclipsing) degrades performance of sidelobes suppression algorithms. The FFL-APCR algorithm suppresses range sidelobes by using a smaller filter length and reduces the computational cost. Consequently, this algorithm should be computationally efficient (real-time) to enable the practical application of RMMSE.
Where return signals coincide with the transmission of a pulse, pulse eclipsing can occur which results in detection performance loss. The mismatches (Doppler phase shift and pulse eclipsing) degrades performance of sidelobes suppression algorithms. The FFL-APCR algorithm suppresses range sidelobes by using a smaller filter length and reduces the computational cost. Consequently, this algorithm should be computationally efficient (real-time) to enable the practical application of RMMSE.
Mohsen Moradi, Samad Nejatian, Hamid Parvin, Karamolla Bagherifard, Vahideh Rezaei,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
In the real world, we face some complex and important problems that should be optimized, most of the real-world problems are dynamic. Solving dynamic optimization problems are very difficult due to possible changes in the location of the optimal solution. In dynamic environments, we are faced challenges when the environment changes. To respond to these changes in the environment, any change can be considered as the input of a new optimization problem that should be solved from the beginning, which is not suitable because it is time consuming. One technique for improving optimization and learning in dynamic environments is by using information from the past. By using solutions from previous environments, it is often easier to find promising solutions in a new environment. A common way to maintain and exploit information from the past is the use of memory, where solutions are stored periodically and can be retrieved and refined at the time that the environment changes. Memory can help search respond quickly and efficiently to change in a dynamic problem. Given that a memory has a finite size, if one wishes to store new information in the memory, one of the existing entries must be discarded. The mechanism used to decide whether the candidate entry should be included in the memory or not, and if so, which of the old entries should be replaced it, is called the replacement strategy. This paper explores ways to improve memory for optimization and learning in dynamic environments. In this paper, a memory with clustering and new replacement strategy for storing and restoring memory solutions has been used to enhance memory performance. The evolutionary algorithms that have been presented so far have the problem of rebuilding populations when multiple populations converge to an optimum. For this reason, we proposed algorithm with exclution mechanism that have the ability to explore the environment (Exploration) and extraction (Explitation). Thus, an optimization algorithm is required to solve the problems in dynamic environments well. In this paper, a novel collective optimization algorithm, namely the Clustering and Memory-based Parent-Child Swarm Algorithm (CMPCS), is presented. This method relies on both individual and group behavior. The proposed CMPCS method has been tested on the moving peaks benchmark (MPB). The MPB is a good Benchmark to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMPCS method compared to the other state-of-the-art methods in solving the dynamic optimization problems.
Ghanbar Azarnia, Mohammad Ali Tinati, Tohid Yousefi Rezaii,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
Recently, the problem of compressive sensing (CS) has attracted lots of attention in the area of signal processing. So, much of the research in this field is being carried out in this issue. One of the applications where CS could be used is wireless sensor networks (WSNs). The structure of WSNs consists of many low power wireless sensors. This requires that any improved algorithm for this application must be optimized in terms of energy consumption. In other words, the computational complexity of algorithms must be as low as possible and should require minimal interaction between the sensors. For such networks, CS has been used in data gathering and data persistence scenario, in order to minimize the total number of transmissions and consequently minimize the network energy consumption and to save the storage by distributing the traffic load and storage throughout the network. In these applications, the compression stage of CS is performed in sensor nodes, whereas the recovering duty is done in the fusion center (FC) unit in a centralized manner. In some applications, there is no FC unit and the recovering duty must be performed in sensor nodes in a cooperative and distributed manner which we have focused on in this paper. Indeed, the notable algorithm for this purpose is distributed least absolute shrinkage and selection operation (D-LASSO) algorithm which is based on diffusion cooperation structure. This algorithm that compete to the state-of-the-art CS algorithms has a major disadvantage; it involves matrix inversion that may be computationally demanding for sufficiently large matrices. On this basis, in this paper, we have proposed a distributed CS recovery algorithm for the WSNs with a bi-directional incremental mode of cooperation. Actually, we have proposed a comprehensive distributed framework for the recovery of sparse signals in WSNs. Here, we applied this comprehensive structure to three problems with different constraints which results in three completely distributed solutions named as distributed bi-directional incremental basis pursuit (DBIBP), distributed bi-directional incremental noise-aware basis pursuit (DBINBP) and distributed bi-directional incremental regularized least squares (DBIRLS). The proposed algorithms solely involve linear combinations of vectors and soft thresholding operations. Hence, the computational load is significantly reduced in each sensor. In the proposed method each iteration consists of two phases; clockwise and anti-clockwise phases. At each iteration, in anti-clockwise phase, each node receives the local estimate from its previous neighbor and updates an auxiliary variable. Then in the clockwise phase, each node receives the updated auxiliary variable from its next neighbors to update the local estimate. On the other hand, information exchange in two directions in an incremental manner which we called it bi-directional incremental structure. In an incremental strategy, information flows in a sequential manner from one node to the adjacent node. Unlike the diffusion structure (like as D-LASSO) where each node communicates with all of their neighbors, the incremental mode of cooperation requires the least amount of communication and power. The low computational complexity and better steady state performance are the important features of the proposed methods.
Ms. Serveh Lotfi, Mitra Mirzarezaee, Mehdi Hosseinzadeh, Vahid Seydi,
Volume 18, Issue 3 (12-2021)
Volume 18, Issue 3 (12-2021)
Abstract
Today, online social media with numerous users from ordinary citizens to top government officials, organizations, artists and celebrities, etc. is one of the most important platforms for sharing information and communication. These media provide users with quick and easy access to information so that the content of shared posts has the potential to reach millions of users in a matter of seconds. Twitter is one of the most popular and practical/used online social networks for spreading information, which, while being reliable, can also, be a source for spreading unrealistic and deceptive rumors as a result can have irreversible effects on individuals and society.
Recently, several studies have been conducted in the field of rumor detection and verify using models based on deep learning and machine learning methods. Previous research into rumor detection has focused more on linguistic, user, and structural features. Concerning structural features, they examined the retweet propagation graph. However, in this study, unlike the previous studies, new structural features of the reply tree and user graph in extracting rumored conversations were extracted and analyzed from different aspects.
In this study, the effectiveness of new structural features related to reply tree and user graph in detecting rumored conversations in Twitter events were evaluated from different aspects. First, the structural features of the reply tree and user graph were extracted at different time intervals, and important features in these intervals were identified using the Sequential Forward Selection approach. To evaluate the usefulness of valuable new structural features, these features have been compared with consideration of linguistic and user-specific features. Experiments have shown that combining new structural features with linguistic and user-specific features increases the accuracy of the rumor detection classification. Therefore, a rumor classification algorithm based on new structural, linguistic, and user-specific features in rumor conversation detection was proposed. This algorithm performs better than the basic methods and detects rumored conversations with greater accuracy. In addition, due to the importance of the source tweet user in conversations, this user was examined and analyzed from different aspects. The results showed that most rumored conversations were started by a small number of users. Rumors can be prevented by early identification of these users on Twitter events.
Recently, several studies have been conducted in the field of rumor detection and verify using models based on deep learning and machine learning methods. Previous research into rumor detection has focused more on linguistic, user, and structural features. Concerning structural features, they examined the retweet propagation graph. However, in this study, unlike the previous studies, new structural features of the reply tree and user graph in extracting rumored conversations were extracted and analyzed from different aspects.
In this study, the effectiveness of new structural features related to reply tree and user graph in detecting rumored conversations in Twitter events were evaluated from different aspects. First, the structural features of the reply tree and user graph were extracted at different time intervals, and important features in these intervals were identified using the Sequential Forward Selection approach. To evaluate the usefulness of valuable new structural features, these features have been compared with consideration of linguistic and user-specific features. Experiments have shown that combining new structural features with linguistic and user-specific features increases the accuracy of the rumor detection classification. Therefore, a rumor classification algorithm based on new structural, linguistic, and user-specific features in rumor conversation detection was proposed. This algorithm performs better than the basic methods and detects rumored conversations with greater accuracy. In addition, due to the importance of the source tweet user in conversations, this user was examined and analyzed from different aspects. The results showed that most rumored conversations were started by a small number of users. Rumors can be prevented by early identification of these users on Twitter events.
Seyed Mohammad Reza Jalalian Shahri, Hadi Hadizadeh, Morteza Khademi Darah, Abbas Ebrahimi Moghadam,
Volume 18, Issue 4 (3-2022)
Volume 18, Issue 4 (3-2022)
Abstract
In this paper we describe a novel noise-robust texture classification method using joint multiscale local binary pattern. The first step in texture classification is to describe the texture by extracting different features. So far, several methods have been developed for this topic, one of the most popular ones is Local Binary Pattern (LBP) method and its variants such as Completed Local Binary Pattern, Extended Local Binary Pattern, Local Temporary Pattern, Local Contrast Pattern, etc. In order to extract the features of a texture in different scales, the LBP method can be implemented in a multi-scale framework. For this purpose, the extracted feature vectors at different scales are usually concatenated together to produce the final feature vector with a longer length. But such a scheme has two main shortcomings. First, the LBP method is very sensitive to noise, hence by adding noise to a texture image, its feature vectors may change significantly. Second, by increasing the number of the scales, the length of the final feature vector is increased accordingly. This action increases the classification process time, and it may reduce the classification accuracy. To mitigate these shortcomings, this paper presents a method based on multiscale LBP, which has a better resistance against white Gaussian noise, while the length of its final feature vector is equal to the length of the final feature vector produced by the original LBP method. To implement the proposed method, we used 17 circular binary masks that contain 8 directed first-order masks, 8 directed second-order masks and 1 undirected mask. These masks have positive and negative weightes and each group of these masks have different radius which after convolution with input image extract features in different scales. Experiments were performed on four test groups of Outex database. Experimental results show that the proposed method is superior to the existing state-of-the-art methods. The complexity of proposed method is also analyzed. The results show that in this method, despite obtaining excellent classification accuracy, the complexity of the method has not changed much and even its complexity is less than some of the existing state-of-the-art methods.
Sepehr Ebrahimi Mood, Mohammad Masoud Javidi, Mohammad Reza Khosravi,
Volume 18, Issue 4 (3-2022)
Volume 18, Issue 4 (3-2022)
Abstract
In the past decades, vehicle routing problem (VRP) has gained considerable attention for its applications in industry, military, and transportation applications. Vehicle routing problem with simultaneous pickup and delivery is an extension of the VRP. This problem is an NP-hard problem; hence finding the best solution for this problem which is using exact method, take inappropriate time, and these methods are not useful in real-world applications. Using meta-heuristic algorithms for calculating and computing the solutions for NP-hard problems is a common method to contrast this challenge.
The objective function defined for this problem, is a constrained objective function. In previous algorithms, the penalty method was used as constraint handling technique to define the objective function. Determining the value of parameters and penalty coefficient is not easy in these methods. Moreover, the optimal number of vehicles was not considered in the previous algorithms. So, the user should guess number of vehicles and compare the result with other values for this variable.
In this paper, a novel objective function is defined to solve the vehicle routing problem with simultaneous pickup and delivery. This method can find the vehicle routes such that increases the performance of the vehicles and decreases the processes’ costs of transportation. in addition, the optimal number of vehicle in this problem can be calculated using this objective function. Finding the best solution for this optimization problems is an NP-hard and meta-heuristic methods can be used to estimate good solutions for this problem.
Then, a constrained version of gravitational search algorithm is proposed. In this method, a fuzzy logic controller is used to calculate the value of the parameters and control the abilities of the algorithm, automatically. Using this controller can balance the exploration and exploitation abilities in the gravitational search algorithm and improve the performance of the algorithm. This new version of gravitational search algorithm is used to find a good solution for the predefined objective function. The proposed method is evaluated on some standard benchmark test functions and problems. The experimental results show that the proposed method outperforms the state-of-the-art methods, despite the simplicity of implementation.
The objective function defined for this problem, is a constrained objective function. In previous algorithms, the penalty method was used as constraint handling technique to define the objective function. Determining the value of parameters and penalty coefficient is not easy in these methods. Moreover, the optimal number of vehicles was not considered in the previous algorithms. So, the user should guess number of vehicles and compare the result with other values for this variable.
In this paper, a novel objective function is defined to solve the vehicle routing problem with simultaneous pickup and delivery. This method can find the vehicle routes such that increases the performance of the vehicles and decreases the processes’ costs of transportation. in addition, the optimal number of vehicle in this problem can be calculated using this objective function. Finding the best solution for this optimization problems is an NP-hard and meta-heuristic methods can be used to estimate good solutions for this problem.
Then, a constrained version of gravitational search algorithm is proposed. In this method, a fuzzy logic controller is used to calculate the value of the parameters and control the abilities of the algorithm, automatically. Using this controller can balance the exploration and exploitation abilities in the gravitational search algorithm and improve the performance of the algorithm. This new version of gravitational search algorithm is used to find a good solution for the predefined objective function. The proposed method is evaluated on some standard benchmark test functions and problems. The experimental results show that the proposed method outperforms the state-of-the-art methods, despite the simplicity of implementation.
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