Publication Date: 2012
Communications in Statistics - Theory and Methods (1532415X)41(11)pp. 2000-2013
In this article, we attempt to introduce a discrete analog of the generalized exponential distribution of Gupta and Kundu (1999). This new discrete generalized exponential (DGE(, p)) distribution can be viewed as another generalization of the geometric distribution and it is more flexible in data modeling. We shall first study some basic distributional and moment properties of this family of new distributions. Then, we will reveal their structural properties and applications and also investigate estimation of their parameters. Finally, we shall discuss their convolution properties and arrive at some characterizations in the special cases DGE(2, p) and DGE(3, p). © 2012 Taylor and Francis Group, LLC.
Publication Date: 2012
Statistical Papers (09325026)53(3)pp. 685-696
Skew-symmetric distributions of various types have been the center of attraction by many researchers in the literature. In this article, we shall introduce another more general class of skew distributions, specially related to the Laplace distribution. This new class contains some previously known skew distributions. We shall investigate different characteristics of members of this class such as its moments, thus generalizing a result of Umbach (Stat Probab Lett 76:507-512, 2006), limiting behavior, moment generating function, unimodality and reveal its natural occurrence as the distribution of some order statistics. In addition, we will generalize a result of Aryal and Rao (Nonlinear Anal 63:639-646, 2005) in connection with truncated skew-Laplace distribution and study its certain stochastic orderings. Some illustrative examples are also provided. © 2011 Springer-Verlag.
Publication Date: 2023
Miskolc Mathematical Notes (17872405)24(3)pp. 1117-1126
In this paper, we obtain a generalization of a fixed point theorem given by Popescu [O. Popescu, Comput. Math. Appl., vol. 62, no. 10, pp. 3912–3919, 2011]. An example is also given to support our main result. © (2023) Miskolc University Press
Publication Date: 2013
Communications in Statistics - Theory and Methods (1532415X)42(13)pp. 2324-2334
Skew-symmetric distributions of various types have been the center of attraction by many researchers in the literature. In this article, we will introduce a uni/bimodal generalization of the Azzalini's skew-normal distribution which is indeed an extension of the skew-generalized normal distribution obtained by Arellano-Valle et al. (2004). Our new distribution contains more parameters and thus it is more flexible in data modeling. Indeed, certain univariate case of the so called flexible skew-symmetric distribution of Ma and Genton (2004) is also a particular case of our proposed model. We will first study some basic distributional properties of the new extension, such as its distribution function, limiting behavior and moments. Then, we will investigate some useful results regarding its relation with other known distributions, such as student's t and skew-Cauchy distributions. In addition, we will present certain methods to generate the new distribution and, finally, we shall apply the model to a real data set to illustrate its behavior comparing to some rival models. © Taylor and Francis Group, LLC.
Publication Date: 2017
Canadian Mathematical Bulletin (14964287)60(1)pp. 122-130
We characterize two important notions of amenability and compactness of a locally compact quantum group G in terms of certain homological properties. For this, we show that G is character amenable if and only if it is both amenable and co-amenable. We finally apply our results to Arens regularity problems of the quantum group algebra L1(G). In particular, we improve an interesting result by Hu, Neufang, and Ruan. © 2016 Canadian Mathematical Society.
Publication Date: 2015
Malaysian Journal Of Computer Science (01279084)28(1)pp. 46-58
Image reconstruction is an important part of computed tomography imaging systems, which converts the measured data into images. Because of high computational cost and slow convergence of iterative reconstruction algorithms, these methods are not widely used in practice. In this paper, we propose a hybrid iterative algorithm by combining multigrid method,Tikhonov regularization and Simultaneous Iterative Reconstruction Technique (SIRT) for reconstruction of the computed tomography image that reduces this drawback. To do so, we reduce the time and the volume of computations considerably by finding astable and appropriate starting point. The experimental results indicate that the proposed iterative algorithm has more rapid convergence and reconstructs high quality images in short computational time than the classical ones.
Alamatsaz, N.,
Tabatabaei, L.,
Yazdchi, M.,
Payan, H.,
Alamatsaz, N.,
Nasimi, F. Publication Date: 2024
Biomedical Signal Processing and Control (17468108)90
Objective: Electrocardiogram (ECG) is the most frequent and routine diagnostic tool used for monitoring heart electrical signals and evaluating its functionality. The human heart can suffer from a variety of diseases, including cardiac arrhythmias. Arrhythmia is an irregular heart rhythm that in severe cases can lead to stroke and can be diagnosed via ECG recordings. Since early detection of cardiac arrhythmias is of great importance, computerized and automated classification and identification of these abnormal heart signals have received much attention for the past decades. Methods: This paper introduces a light Deep Learning (DL) approach for high accuracy detection of 8 different cardiac arrhythmias and normal rhythms. To employ DL techniques, the ECG signals were preprocessed using resampling and baseline wander removal techniques. The classification was performed using an 11-layer network employing a combination of Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM). Results: In order to evaluate the proposed technique, ECG signals are chosen from the two physionet databases, the MIT-BIH arrhythmia database and the long-term AF database. The proposed DL framework based on the combination of CNN and LSTM showed promising results than most of the state-of-the-art methods. The proposed method reaches the mean diagnostic accuracy of 98.24%. Conclusion: A trained model for arrhythmia classification using diverse ECG signals were successfully developed and tested. Significance: This study presents a lightweight classification technique with high diagnostic accuracy compared to other notable methods, making it a potential candidate for implementation in Holter monitor devices for arrhythmia detection. Finally, we used SHapley Additive exPlanations (SHAP), the most popular Explainable Artificial Intelligence (XAI) method to understand how our model make predictions. The results indicate that those features (ECG samples) that have contributed the most to a prediction are consonant with clinicians’ decisions. Therefore, the use of interpretable models increases the trust of clinicians in AI and thus leads to decreasing the number of misdiagnoses of cardiovascular diseases. © 2023 Elsevier Ltd
Publication Date: 2011
Utilitas Mathematica (03153681)84pp. 105-117
The multiplicative Wiener index, π (G) , is equal to the product of distances between all pairs of vertexes of a (molecular) graph G. In this paper we compute this index for some nanotubes and nanotori by consider them as cartesian product of paths and cycles. Also we compute this index for some composite graphs.
Publication Date: 2021
Computer Networks (13891286)196
In the present article, we propose a virtual machine placement (VMP) algorithm for reducing power consumption in heterogeneous cloud data centers. We propose a novel model for the estimation of power consumption of datacenter's network. The proposed model is employed to estimate power consumption of a Fat-Tree network. It calculates the traffic of each network layer and uses the results to estimate the average power consumption of each switch in the network, which is used for network power calculation. Further, we employ the chemical reaction optimization (CRO) algorithm as a meta-heuristic algorithm to obtain a power-efficient mapping of virtual machines (VMs) to physical machines (PMs). Moreover, two kinds of solution encoding schemes, namely permutation-based and grouping-based encoding schemes, were utilized for representing individuals in CRO. For each encoding scheme, we designed proper operators required by the CRO for manipulating the molecules in search of more optimal solution candidates. Additionally, we modeled VMs with east–west and north–south communications, and PMs with constrained CPU, memory, and bandwidth capacity. Our network power model is integrated into the CRO algorithms to enable the estimation of both PMs and network power consumption. We compared our proposed methods with a number of similar methods. The evaluation results indicate that the proposed methods perform well and the CRO algorithm with the grouping-based encoding outperforms the rest of the methods in terms of power consumption. The evaluation results also show the significance of network power consumption. © 2021 Elsevier B.V.
Publication Date: 2025
Journal of Statistical Theory and Practice (15598616)19(2)
In this paper, another motivation for the well-known quadratic transmuted family of distributions is pointed out and a new relation for the expected value of this family in terms of the Gini index is presented. A bug of the generalized transmuted-G family of distributions Nofal et al. (Commun Stat Theory Methods 46:4119–4136, 2016) is illustrated. In that work, the necessary conditions for the density and distribution functions are not satisfied, for some parameter values. Moreover, a new flexible family of distributions is introduced from a fresh perspective, and their key properties are studied in general forms. As an example, a new high flexible distribution is introduced and some of its important futures such as the moment generating function, moments, order statistics and the stress-strength parameter are investigated. In addition, the parameters of the proposed new distribution are estimated using the maximum likelihood method, and three real data sets are scrutinized to assess the distribution’s adequacy in providing satisfactory fits. © Grace Scientific Publishing 2025.
Publication Date: 2015
Journal of Applied Statistics (02664763)42(12)pp. 2654-2670
In this paper, a discrete counterpart of the general class of continuous beta-G distributions is introduced. A discrete analog of the beta generalized exponential distribution of Barreto-Souza et al. [2], as an important special case of the proposed class, is studied. This new distribution contains some previously known discrete distributions as well as two new models. The hazard rate function of the new model can be increasing, decreasing, bathtub-shaped and upside-down bathtub. Some distributional and moment properties of the new distribution as well as its order statistics are discussed. Estimation of the parameters is illustrated using the maximum likelihood method and, finally, the model with a real data set is examined. © 2015 Taylor & Francis.
Publication Date: 2017
Communications in Statistics - Theory and Methods (1532415X)46(9)pp. 4296-4310
In this paper, the researchers attempt to introduce a new generalization of the Weibull-geometric distribution. The failure rate function of the new model is found to be increasing, decreasing, upside-down bathtub, and bathtub-shaped. The researchers obtained the new model by compounding Weibull distribution and discrete generalized exponential distribution of a second type, which is a generalization of the geometric distribution. The new introduced model contains some previously known lifetime distributions as well as a new one. Some basic distributional properties and moments of the new model are discussed. Estimation of the parameters is illustrated and the model with two known real data sets is examined. © 2017 Taylor & Francis Group, LLC.
Publication Date: 2016
Communications in Statistics - Theory and Methods (1532415X)45(5)pp. 1575-1575
Publication Date: 2017
RAIRO - Operations Research (28047303)51(4)pp. 921-930
Various reward-risk performance measures and ratios have been considered in reward-risk portfolio selection problems. This paper investigates the optimal portfolio corresponding to the CVaR (STARR) ratio. Considering the LP solvability of CVaR, a method is proposed for detecting the optimal portfolio by using the corresponding Mean-CVaR optimization problem. By applying LP tools, a method is suggested for producing the optimal portfolio as a by-product during the procedure of computing the efficient frontier of the Mean-CVaR problem. © EDP Sciences, ROADEF, SMAI 2017.
Publication Date: 2022
Biomedical Signal Processing and Control (17468108)78
Proposing a practical method for high-performance emotion recognition could facilitate human–computer interaction. Among existing methods, deep learning techniques have improved the performance of emotion recognition systems. In this work, a new multimodal neural design is presented wherein audio and visual data are combined as the input to a hybrid network comprised of a bidirectional long short term memory (BiLSTM) network and two convolutional neural networks (CNNs). The spatial and temporal features extracted from video frames are fused with Mel-Frequency Cepstral Coefficients (MFCCs) and energy features extracted from audio signals and BiLSTM network outputs. Finally, a Softmax classifier is used to classify inputs into the set of target categories. The proposed model is evaluated on Surrey Audio–Visual Expressed Emotion (SAVEE), Ryerson Audio–Visual Database of Emotional Speech and Song (RAVDESS), and Ryerson Multimedia research Lab (RML) databases. Experimental results on these datasets prove the effectiveness of the proposed model where it achieves the accuracy of 99.75%, 94.99%, and 99.23% for the SAVEE, RAVDESS, and RML databases, respectively. Our experimental study reveals that the suggested method is more effective than existing algorithms in adapting to emotion recognition in these datasets. © 2022 Elsevier Ltd
Publication Date: 2012
International Journal of Bio-Inspired Computation (17580374)4(3)pp. 181-195
High volumes of low-level alerts that are generated by intrusion detection systems (IDSs) are serious obstacle for using them effectively. These high volumes of alerts overwhelm system administrators in such a way that they cannot manage and interpret them. Alert correlation is used to reduce the number of alerts and increase their level of abstraction. It selects a group of low-level alerts and converts them into a higher level attack and then produces a high-level alert for them. In this paper, a new artificial immune system-based alert correlation system is presented, named AISAC. It learns the correlation probability between each pair of alert types and uses this knowledge to extract the attack scenarios. AISAC does not need intensive domain knowledge and rule definition efforts. It also does not need to manually update the extracted knowledge. The computational cost of learning algorithm is linear, and the initial learning is done by a very limited general data in offline mode. AISAC is evaluated by DARPA 2000 and net Forensics Honeynet data. Results show that although it uses a relatively simple algorithm, it generates the attack graphs with acceptable accuracy. © 2012 Inderscience Enterprises Ltd.
Publication Date: 2015
Journal of Algebra and its Applications (17936829)14(3)
Let λ be an artin algebra. By letting the Nakayama functor act degree-wise, we define a translation ? in the category of complexes of finitely generated λ-modules, C(mod λ). Then we investigate the existence of almost split sequences in the category C(mod λ). As an application of our results, we see that the full subcategory of D(mod λ) consisting of complexes isomorphic to perfect complexes admits almost split sequences. © World Scientific Publishing Company.
Sheikhi, R.A.,
Heidari, M.,
Jafari, H.,
Heydarpoor, S.,
Yadollahi, S. Publication Date: 2025
Health In Emergencies And Disasters Quarterly (23454210)10(4)pp. 279-290
Background: Due to the nature of emergency medical services (EMS) and the rush to provide emergency services, ambulance crashes (ACs) occur more frequently and more severely than crashes related to vehicles with similar size and weight characteristics, disrupting the relief process. This research was done to explain the experiences of emergency medical technicians (EMTs) in ACs and provide solutions to prevent and mitigate these accidents. Materials and Methods: This qualitative study used a framework analysis approach and a purposeful sampling method. It involved conducting semi-structured interviews with 18 EMTs who had experiences with ACs. The study utilized the Haddon matrix framework as the basic framework. Data analysis and code extraction were carried out using MAXQDA software, version 10. The codes were extracted using both deductive and inductive methods. Results: According to the Haddon matrix framework, in the host part, factors include personnel health, lack of skills, a staffing shortage, stress and fear, burnout and feeling unsupported. In the agent part, factors include worn-out ambulances, a shortage of them, speed, lights-andsiren use that stabilizes the vehicle, and delays in EMS. In the environment part, factors include public expectations for response times, unsafe roads, unfamiliarity with the roads, inadequate emergency service coverage and root cause analysis. Conclusion: Generally, working in an ambulance can be hazardous. Implementing educational, operational, and engineering strategies can significantly reduce the risk of harm to EMS providers, patients and the public. © 2025 The Author(s).
Bateni, M.,
Baraani, A.,
Ghorbani, A.A.,
Rezaei, A. Publication Date: 2013
International Journal of Innovative Computing, Information and Control (13494198)9(1)pp. 231-255
There are many different approaches to alert correlation such as using correlation rules and prerequisite-consequences, using machine learning and statistical methods and using similarity measures. In this paper, iCorrelator, a new AIS-inspired architecture, is presented. It uses a three-layer architecture that is inspired by three types of responses in the human immune system: the innate immune system's response, the adaptive immune system's primary response, and the adaptive immune system's secondary response. In comparison with other correlators, iCorrelator does not need information about different attacks and their possible relations in order to discover an attack scenario. It uses a very limited number of general rules that are not related to any specific attack scenario. A process of incremental learning is used to encounter new attacks. Therefore, iCorrelator is easy to set up and work dynamically without reconfiguration. As a result of using memory cells and improved alert selection policy, the computational cost of iCorrelator is also acceptable even for online correlation. iCorrelator is evaluated by using the DARPA 2000 dataset and a netForensics honeynet data. The completeness, soundness, false correlation rate and execution time are reported. Results show that iCorrelator is able to extract the attack graphs with acceptable accuracy that is comparable to the best known solutions. © 2013 ICIC International.
Publication Date: 2007
AIP Conference Proceedings (0094243X)971pp. 105-111
In this paper we extend the 2-D directed graphical representation for DNA sequences. The main purpose is to making a directed graph corresponding to a DNA sequence which hasn't any complete coincidence of the edges. To prevent repetition of the edge e we define e→1 by using the outer product of two vectors and some mathematical concepts. Moreover, we have applied this method for some DNA sequences to show the advantage of this method over the some other methods. © 2008 American Institute of Physics.
Publication Date: 2020
Multimedia Tools and Applications (13807501)79(33-34)pp. 24993-25022
In this paper, we propose a novel image encryption scheme based on a hybrid model of DNA computing, chaotic systems and hash functions. The significant advantage of the proposed scheme is high efficiency. The proposed scheme consists of the DNA level permutation and diffusion. In the DNA level permutation, a mapping function based on the logistic map is applied on the DNA image to randomly change the position of elements in the DNA image. In the DNA level diffusion, not only we define two new algebraic DNA operators, called the DNA left-circular shift and DNA right-circular shift, but we also use a variety of DNA operators to diffuse the permutated DNA image with the key DNA image. The experimental results and security analyses indicate that the proposed image encryption scheme not only has good encryption effect and able to resist against the known attacks, but also is sufficiently fast for practical applications. The MATLAB source code of the proposed image encryption scheme is publicly available at the URL: https://github.com/EbrahimZarei64/ImageEncryption. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2019
International Journal of Computer Network and Information Security (20749104)11(9)pp. 9-17
Malware poses one of the most serious threats to computer information systems. The current detection technology of malware has several inherent constraints. Because signature-based traditional techniques embedded in commercial antiviruses are not capable of detecting new and obfuscated malware, machine learning algorithms are applied in identifing patterns of malware behavior through features extracted from programs. There, a method is presented for detecting malware based on the features extracted from the PE header and section table PE files. The packed files are detected and then unpacke them. The PE file features are extracted and their static features are selected from PE header and section tables through forward selection method. The files are classified into malware files and clean files throughs different classification methods. The best results are obtained through DT classifier with an accuracy of 98.26%. The results of the experiments consist of 971 executable files containing 761 malware and 210 clean files with an accuracy of 98.26%. © 2019 MECS.
Publication Date: 2014
Journal Of Medical Signals And Sensors (22287477)4(1)pp. 72-83
Improving the quality of medical images at pre- and post-surgery operations are necessary for beginning and speeding up the recovery process. Partial differential equations-based models have become a powerful and well-known tool in different areas of image processing such as denoising, multiscale image analysis, edge detection and other fields of image processing and computer vision. In this paper, an algorithm for medical image denoising using anisotropic diffusion filter with a convenient stopping criterion is presented. In this regard, the current paper introduces two strategies: utilizing the efficient explicit method due to its advantages with presenting impressive software technique to effectively solve the anisotropic diffusion filter which is mathematically unstable, proposing an automatic stopping criterion, that takes into consideration just input image, as opposed to other stopping criteria, besides the quality of denoised image, easiness and time. Various medical images are examined to confirm the claim.
Publication Date: 2021
Numerical Algorithms (10171398)88(1)pp. 67-91
This paper introduces an adaptive collocation method to solve retarded and neutral delay differential equations (RDDEs and NDDEs) with constant or time-dependent delays. The delays are allowed to be small or become vanishing during the integration. We determine the convergence properties of the proposed method for neutral equations with solutions in appropriate Sobolev spaces. It is shown that the proposed scheme enjoys the spectral accuracy. Numerical results show that the proposed method can be implemented in an efficient and accurate manner for a wide range of RDDE and NDDE model problems. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.
Publication Date: 2019
Simulation Modelling Practice and Theory (1569190X)91pp. 102-122
Memory footprint is a metric for quantifying data reuse in memory trace. It can also be used to approximate cache performance, especially in shared cache systems. Memory footprint is acquired through memory footprint analysis (FPA). However, its main limitation is that, for a memory trace of n accesses, the all-window FPA algorithm requires O(n3) time. Therefore, in this paper, we propose an analytical algorithm for FPA, whereby the average footprints are calculated in O(n2). The proposed algorithm can also be employed for window distribution analysis. Moreover, we propose a framework to enable the application of FPA to GPU kernels and model the performance of L1 cache memories. The results of experimental evaluations indicate that our proposed framework functions 1.55X slower than the Xiang's formula, as a fast average FPA method, while it can also be utilized for window distribution analysis. In the context of FPA-based cache performance estimation, the experimental results indicate a fair correlation between the estimated L1 miss rates and those of the native GPU executions. On average, the proposed framework has 23.8% error in the estimation of L1 cache miss rates. Further, our algorithm runs 125X slower than the reuse distance analysis (RDA) when analyzing a single kernel. However, the proposed method outperforms RDA in modeling shared caches and multiple kernel executions in GPUs. © 2018 Elsevier B.V.
Publication Date: 2024
Journal Of The Iranian Statistical Society (17264057)23(1)pp. 99-115
This paper examines a novel extension of the geometric distribution characterized by two parameters, that is not created based on discretizing existing continuous models. This model, due to its analytical form of the cumulative distribution function and simple structure, can be of interest from mathematical perspectives, particularly in cases where the analysis of stochastic orders is desired. In addition, it is a suitable candidate for analyzing monotone hazard rate discrete data, in view of the fact that its hazard rate function exhibits monotonicity in both increasing and decreasing directions. Additionally, the behavior of the survival function of residual lifetime is briefly addressed. The parameters of the distribution are estimated using the maximum likelihood method, and a real-world data set is scrutinized to assess the distribution's adequacy in providing satisfactory fits. © (2024), (Iranian Statistical Society). All rights reserved.
