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Open Access Article
1 - Fusion of Learning Automata to Optimize Multi-constraint Problem
Sara Motamed Ali AhmadiThis paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable MoreThis paper aims to introduce an effective classification method of learning for partitioning the data in statistical spaces. The work is based on using multi-constraint partitioning on the stochastic learning automata. Stochastic learning automata with fixed or variable structures are a reinforcement learning method. Having no information about optimized operation, such models try to find an answer to a problem. Converging speed in such algorithms in solving different problems and their route to the answer is so that they produce a proper condition if the answer is obtained. However, despite all tricks to prevent the algorithm involvement with local optimal, the algorithms do not perform well for problems with a lot of spread local optimal points and give no good answer. In this paper, the fusion of stochastic learning automata algorithms has been used to solve given problems and provide a centralized control mechanism. Looking at the results, is found that the recommended algorithm for partitioning constraints and finding optimization problems are suitable in terms of time and speed, and given a large number of samples, yield a learning rate of 97.92%. In addition, the test results clearly indicate increased accuracy and significant efficiency of recommended systems compared with single model systems based on different methods of learning automata. Manuscript profile -
Open Access Article
2 - Fusion Infrared and Visible Images Using Optimal Weights
Mehrnoush Gholampour Hassan Farsi Sajad MohammadzadehImage fusion is a process in which different images recorded by several sensors from one scene are combined to provide a final image with higher quality compared to each individual input image. In fact, combination of different images recorded by different sensors is on MoreImage fusion is a process in which different images recorded by several sensors from one scene are combined to provide a final image with higher quality compared to each individual input image. In fact, combination of different images recorded by different sensors is one of image fusion methods. The fusion is performed based on maintaining useful features and reducing or removing useless features. The aim of fusion has to be clearly specified. In this paper we propose a new method which combines vision and infrared images by weighting average to provide better image quality. The weighting average is performed in gradient domain. The weight of each image depends on its useful features. Since these images are recorded in night vision, the useful features are related to clear scene details. For this reason, object detection is applied on the infrared image and considered as its weight. The vision image is also considered as a complementary of infrared image weight. The averaging is performed in gradient of input images, and final composed image is obtained by Gauss-Seidel method. The quality of resulted image by the proposed algorithm is compared to the obtained images by state-of-the-art algorithms using quantitative and qualitative measures. The obtained results show that the proposed algorithm provides better image quality. Manuscript profile -
Open Access Article
3 - Instance Based Sparse Classifier Fusion for Speaker Verification
Mohammad Hasheminejad Hassan FarsiThis paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers MoreThis paper focuses on the problem of ensemble classification for text-independent speaker verification. Ensemble classification is an efficient method to improve the performance of the classification system. This method gains the advantage of a set of expert classifiers. A speaker verification system gets an input utterance and an identity claim, then verifies the claim in terms of a matching score. This score determines the resemblance of the input utterance and pre-enrolled target speakers. Since there is a variety of information in a speech signal, state-of-the-art speaker verification systems use a set of complementary classifiers to provide a reliable decision about the verification. Such a system receives some scores as input and takes a binary decision: accept or reject the claimed identity. Most of the recent studies on the classifier fusion for speaker verification used a weighted linear combination of the base classifiers. The corresponding weights are estimated using logistic regression. Additional researches have been performed on ensemble classification by adding different regularization terms to the logistic regression formulae. However, there are missing points in this type of ensemble classification, which are the correlation of the base classifiers and the superiority of some base classifiers for each test instance. We address both problems, by an instance based classifier ensemble selection and weight determination method. Our extensive studies on NIST 2004 speaker recognition evaluation (SRE) corpus in terms of EER, minDCF and minCLLR show the effectiveness of the proposed method. Manuscript profile -
Open Access Article
4 - Speech Emotion Recognition Based on Fusion Method
Sara Motamed Saeed Setayeshi Azam Rabiee Arash SharifiSpeech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification meth MoreSpeech emotion signals are the quickest and most neutral method in individuals’ relationships, leading researchers to develop speech emotion signal as a quick and efficient technique to communicate between man and machine. This paper introduces a new classification method using multi-constraints partitioning approach on emotional speech signals. To classify the rate of speech emotion signals, the features vectors are extracted using Mel frequency Cepstrum coefficient (MFCC) and auto correlation function coefficient (ACFC) and a combination of these two models. This study found the way that features’ number and fusion method can impress in the rate of emotional speech recognition. The proposed model has been compared with MLP model of recognition. Results revealed that the proposed algorithm has a powerful capability to identify and explore human emotion. Manuscript profile -
Open Access Article
5 - SGF (Semantic Graphs Fusion): A Knowledge-based Representation of Textual Resources for Text Mining Applications
Morteza Jaderyan Hassan KhotanlouThe proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core component MoreThe proper representation of textual documents has been the greatest challenge in text mining applications. In this paper, a knowledge-based representation model for text documents is introduced. The system works by integrating structured knowledge in the core components of the system. Semantic, lexical, syntactical and structural features are identified by the pre-processing module. The enrichment module is introduced to identify contextually similar concepts and concept maps for improving the representation. The information content of documents and the enriched contents are fused (merged) into the graphical structure of semantic network to form a unified and comprehensive representation of documents. The 20Newsgroup and Reuters-21578 dataset are used for evaluation. The evaluation results suggest that the proposed method exhibits a high level of accuracy, recall and precision. The results also indicate that even when a small portion of information content is available, the proposed method performs well in standard text mining applications. Manuscript profile -
Open Access Article
6 - A Two-Stage Multi-Objective Enhancement for Fused Magnetic Resonance Image and Computed Tomography Brain Images
Leena Chandrashekar A Sreedevi AsundiMagnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a diff MoreMagnetic Resonance Imaging (MRI) and Computed Tomography (CT) are the imaging techniques for detection of Glioblastoma. However, a single imaging modality is never adequate to validate the presence of the tumor. Moreover, each of the imaging techniques represents a different characteristic of the brain. Therefore, experts have to analyze each of the images independently. This requires more expertise by doctors and delays the detection and diagnosis time. Multimodal Image Fusion is a process of generating image of high visual quality, by fusing different images. However, it introduces blocking effect, noise and artifacts in the fused image. Most of the enhancement techniques deal with contrast enhancement, however enhancing the image quality in terms of edges, entropy, peak signal to noise ratio is also significant. Contrast Limited Adaptive Histogram Equalization (CLAHE) is a widely used enhancement technique. The major drawback of the technique is that it only enhances the pixel intensities and also requires selection of operational parameters like clip limit, block size and distribution function. Particle Swarm Optimization (PSO) is an optimization technique used to choose the CLAHE parameters, based on a multi objective fitness function representing entropy and edge information of the image. The proposed technique provides improvement in visual quality of the Laplacian Pyramid fused MRI and CT images. Manuscript profile -
Open Access Article
7 - Hierarchical Weighted Framework for Emotional Distress Detection using Personalized Affective Cues
Nagesh JadhavEmotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distres MoreEmotional distress detection has become a hot topic of research in recent years due to concerns related to mental health and complex nature distress identification. One of the challenging tasks is to use non-invasive technology to understand and detect emotional distress in humans. Personalized affective cues provide a non-invasive approach considering visual, vocal, and verbal cues to recognize the affective state. In this paper, we are proposing a multimodal hierarchical weighted framework to recognize emotional distress. We are utilizing negative emotions to detect the unapparent behavior of the person. To capture facial cues, we have employed hybrid models consisting of a transfer learned residual network and CNN models. Extracted facial cue features are processed and fused at decision using a weighted approach. For audio cues, we employed two different models exploiting the LSTM and CNN capabilities fusing the results at the decision level. For textual cues, we used a BERT transformer to learn extracted features. We have proposed a novel decision level adaptive hierarchical weighted algorithm to fuse the results of the different modalities. The proposed algorithm has been used to detect the emotional distress of a person. Hence, we have proposed a novel algorithm for the detection of emotional distress based on visual, verbal, and vocal cues. Experiments on multiple datasets like FER2013, JAFFE, CK+, RAVDESS, TESS, ISEAR, Emotion Stimulus dataset, and Daily-Dialog dataset demonstrates the effectiveness and usability of the proposed architecture. Experiments on the enterface'05 dataset for distress detection has demonstrated significant results. Manuscript profile -
Open Access Article
8 - Comparison of Structural Equation Model of Automatic Cognitive Processing and Cognitive Fusion among the Students of Payamenoor University and University of Medical Sciences
Hossein Zare Said Talebi Ahmad RastegarThe main aim of this study was to present a structural equation model of cognitive fusion and Automatic Cognitive Processing. The study is correlational. The population was the students of Payamenoor university of Fars province and university of medical sciences. By usi MoreThe main aim of this study was to present a structural equation model of cognitive fusion and Automatic Cognitive Processing. The study is correlational. The population was the students of Payamenoor university of Fars province and university of medical sciences. By using Morgan formula and stratified sampling, 379 university students selected. The research instrument was a Cognitive Fusion Questionnaire (CFQ) of Gillanders et al (2010) and Automatic Cognitive Processing Questionnaire (ACPQ) from Hollon et al (1986) which was assessed by the use of a structural equation model. The direct effect of CFQ on Cognitive Fusion in the whole model (0.314), in Payamenoor University model (0.459) and in University of Medical Sciences model (0.261) is meaningful. The direct effect of cognitive fusion on cognitive defusion in all three models is significant. Finally, the direct effect of ACPQ on low self confidence, personal incompatibility and tendency to change, negative self image and negative expectations and loneliness in the whole model of Payamenoor University and University of Medical Sciences is statistically meaningful (0.05). by surveying the direct effects, it was found out that cognitive fusion has more effects on fusion that cognitive defusion. Also, ACPQ among the four indexes has more effects on personal incompatibility and tendency to change. Manuscript profile -
Open Access Article
9 - Performance Improvement of the Traditional SVM-Based Face Detection Method
M. Roohi G. Mirjalily M. T. SadeghiIn this paper, we propose some ideas to improve the performance of the traditional face detection based on support vector machine (SVM). The traditional SVM-based system for face detection detects faces by exhaustively scanning an image for face-like patterns at any pos MoreIn this paper, we propose some ideas to improve the performance of the traditional face detection based on support vector machine (SVM). The traditional SVM-based system for face detection detects faces by exhaustively scanning an image for face-like patterns at any possible scales. It divides the original image into overlapping sub-images by using a fixed-size cutting window and classifies them using the Support Vector Machine to determine the appropriate class (face or non-face). This approach has not an acceptable detection rate. In this paper to improve the performance, we use cutting windows with different sizes. We fuse the decisions obtained by using different windows. An important issue in the Support Vector Machine classifier is to shift the decision threshold adequately towards the better represented class. In this paper, a novel method is proposed for determining the threshold value adaptively. A post processing algorithm is also presented for reducing the false alarm rate. Experimental results using standard database show that the performance of the proposed SVM-based method is much better than the basic SVM classifier. Manuscript profile -
Open Access Article
10 - A Likelihood Ratio Approach to Information Fusion for Image-Based Fingerprint Verification
M. S. Helfroush M. MohammadpourImage-based fingerprint verification systems have been considered as a parallel method against the minutiae-based approach. This paper proposes a training based fusion method for fingerprint verification, using likelihood ratio (L.R). In this method, the matching scores MoreImage-based fingerprint verification systems have been considered as a parallel method against the minutiae-based approach. This paper proposes a training based fusion method for fingerprint verification, using likelihood ratio (L.R). In this method, the matching scores which are extracted from orientation, spectral and textural features are fused. In order to fuse these image-based features, the likelihood ratio approach has been employed. FVC2000 database has been selected to evaluate the method. Also, the proposed method has been compared to a similar one that uses the simple sum as its fusion system. The comparison results show that the proposed fusion method has made a significant improvement for the accuracy of matching system, so that the equal error rate (ERR) of proposed system has been reduced to 0.14%. Manuscript profile -
Open Access Article
11 - Porosity modeling in Azadegan oil field: a comparative study of Bayesian theory of data fusion, multi layer neural network, and multiple linear regression techniques
عطیه مظاهری طرئی حسین معماریان بهزاد تخم چی بهزاد مشیریPorosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for MorePorosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using these techniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%. Manuscript profile -
Open Access Article
12 - Porosity estimation with data fusion approach (Bayesian theory) in wells of Azadegan oil field, Iran
رویا خضرلو هادی کرمانشاهیPorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by dif MorePorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by different factors, such as temperature, pressure, fluid type, clay content and the and amount of hydrocarbons. One of the best, and yet most practical ways to reduce the amount of uncertainty in porosity measurement is using various sources of data and data fusion techniques. Data fusion increase certainty and confidence and reduce risk and error in decision making. In this research, the porosity is estimated in 4 wells of Azadegan oil field, with data fusion method (Bayesian theory). To check the ability of generalization of the method, the porosity was also estimated in one other well of this field. A maximum of 7 input variables were used to estimate porosity in this new approach. The results showed that data fusion technique is more powerfull than traditional tecniques for porosity estimation. According to the results, this method has higher credibility than traditional techniques that show 0.7 to 0.8 regressions with log data but data fusion technique showed solidarity over 0.9 with log data. Manuscript profile -
Open Access Article
13 - Porosity estimation with data fusion approach (Bayesian theory) in wells of Azadegan oil field, Iran
عطیه مظاهری طرئی Hoseyn Memarian Behzad Tokhmchi Behzad MoshiriPorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by differ MorePorosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs, containes some errors and uncertainty. This porosity is influenced by different factors, such as temperature, pressure, fluid type, clay content and the and amount of hydrocarbons. One of the best, and yet most practical ways to reduce the amount of uncertainty in porosity measurement is using various sources of data and data fusion techniques. Data fusion increase certainty and confidence and reduce risk and error in decision making. In this research, the porosity is estimated in 4 wells of Azadegan oil field, with data fusion method (Bayesian theory). To check the ability of generalization of the method, the porosity was also estimated in one other well of this field. A maximum of 7 input variables were used to estimate porosity in this new approach. The results showed that data fusion technique is more powerfull than traditional tecniques for porosity estimation. According to the results, this method has higher credibility than traditional techniques that show 0.7 to 0.8 regressions with log data but data fusion technique showed solidarity over 0.9 with log data. Manuscript profile -
Open Access Article
14 - Persian Stance Detection Based On Multi-Classifier Fusion
Mojgan Farhoodi Abbas Toloie Eshlaghy<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Stance detection More<p style="text-align: left;"><span style="font-size: 12.0pt; font-family: 'Times New Roman',serif; mso-fareast-font-family: 'Times New Roman'; mso-bidi-font-family: Nazanin; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: FA;">Stance detection (also known as stance classification, stance prediction, and stance analysis) is a recent research topic that has become an emerging paradigm of the importance of opinion-mining. The purpose of stance detection is to identify the author's viewpoint toward a specific target, which has become a key component of applications such as fake news detection, claim validation, argument search, etc. In this paper, we applied three approaches including machine learning, deep learning and transfer learning for Persian stance detection. Then we proposed a framework of multi-classifier fusion for getting final decision on output results. We used a weighted majority voting method based on the accuracy of the classifiers to combine their results. The experimental results showed the performance of the proposed multi-classifier fusion method is better than individual classifiers.</span></p> Manuscript profile -
Open Access Article
15 - Crystallinity of polymers determined by differential scanning calorimetry (II)
Mina AlizadehaghdamDifferential scanning calorimetry (DSC) is widely used to determine the crystallinity of semicrystalline polymers. In the two-phase model, the measured heat of fusion is compared to the melting enthalpy of a completely crystalline polymer to get the crystallinity degree MoreDifferential scanning calorimetry (DSC) is widely used to determine the crystallinity of semicrystalline polymers. In the two-phase model, the measured heat of fusion is compared to the melting enthalpy of a completely crystalline polymer to get the crystallinity degree. Fusion heat of a polymeric sample is identified by area under the melting endotherm and a baseline. A correct baseline is heat capacity of the semicrystalline sample. It varies with both temperature and crystallinity and is difficult to evaluate. Enthalpy of a process is a state-function quantity and is independent of the process path. In polymer melting, temperature increase and fusion process occur simultaneously. This makes evaluation of the fusion heat challenging. Herein, alternative paths are supposed in which temperature increase and fusion process occur separately and sequentially. This leads to a convenient enthalpy evaluation. Two alternative paths can be defined: first, polymer melts at a constant temperature which is followed by temperature increase of the melt; second, polymer temperature increases without any change in crystallinity degree which is followed by polymer melting at a constant temperature. Lastly, an enthalpy deficiency due to the amorphous-crystalline interface and an excess enthalpy due to the defects present in crystalline regions are investigated how to affect the crystallinity. Manuscript profile
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