Статьи журнала - International Journal of Mathematical Sciences and Computing
Все статьи: 198
An Efficient Impulse Noise Removal Image Denoising Technique for MRI Brain Images
Статья научная
Image enhancement is an important challenge in medical field. There are various techniques for image enhancement during last two decades. The objective of this paper is to remove impulse noise for MRI brain image. This paper proposed an efficient filter for removing impulse noise. The shape of the filter is changed to diamond. Experiments are conducted for various noise levels. The proposed method is compared with the existing Denoising techniques. The experimental results proved that the proposed filter performed well than the other methods.
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An Overview on Quantum Computing as a Service (QCaaS): Probability or Possibility
Статья научная
Cloud computing is a worldwide classical system. Quantum computing is theoretical concept still in experimental review. Where cloud system is facing vulnerability in security, backup, processing and locality, there quantum computing shows a strong solution to overcome it. Most researchers are optimistic in quantum computing that it will improve cloud system. But to associate physics based subatomic computing system with software based cloud system is not an easy option. Our paper will show all the major advantages and disadvantages of quantum computing in the perspective to integrate it with cloud system. And review some recent progress with some foremost doubtful future aspects of quantum cloud computing. Also we will review the reality of quantum computation and internet system in applied viewpoint until present.
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An efficient genetic algorithm for numerical function optimization with two new crossover operators
Статья научная
Selection criteria, crossover and mutation are three main operators of genetic algorithm’s performance. A lot of work has been done on these operators, but the crossover operator has a vital role in the operation of genetic algorithms. In literature, multiple crossover operators already exist with varying impact on the final results. In this article, we propose two new crossover operators for the genetic algorithms. One of them is based on the natural concept of crossover i.e. the upcoming offspring takes one bit from a parent and next from other parent and continuously takes bits till last one. The other proposed scheme is the extension of two-point crossover with the concept of multiplication rule. These operators are applied for eight benchmark problems in parallel with some traditional crossover operators. Empirical studies show a remarkable performance of the proposed crossover operators.
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An encoding schematic based on coordinate transformations
Статья научная
This paper outlines an encoding schematic that is dependent on simple Cartesian coordinate transformations. Namely, the change of axes and the rotation of axes. A combination of these two is incorporated after turning singular ASCII values into 2D points. This system is based on multiple private keys that can also act as a potential candidate for threshold cryptography. Comprehensive initial testing has been performed on certain parameters by altering their values within a range. Further testing is required for more insights about the system. For now, the list of parameters that amounts to successful decryption is to be noted down for future use with this system.
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An enhanced rough set based feature grouping approach for supervised feature selection
Статья научная
Selection of useful information from a large data collection is an important and challenging problem. Feature selection refers to the problem of selecting relevant features from a given dataset which produces the most predictive outcome as the original features maintain before the selection. Rough set theory (RST) and its extension are the most successful mathematical tools for feature selection from a given dataset. This paper starts with an outline of the fundamental concepts behind the rough set and fuzzy rough set based feature grouping techniques which are related to supervise feature selection. Supervised Quickreduct (QR) and fuzzy-rough feature grouping Quickreduct (FQR) algorithms are highlighted here. Then an enhanced version of FQR method is proposed here which is based on rough set dependency criteria with feature significance measure that select a minimal subset of features. Also, the termination condition of the base method is modified. Experimental studies of the algorithms are carried out on five public domain benchmark datasets available in UCI machine learning repository. JRip and J48 classifier are used to measure the classification accuracy. The performance of the proposed method is found to be satisfactory in comparison with other methods.
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Analysis of Automated Matching of the Semantic Wiki Resources with Elements of Domain Ontologies
Статья научная
Intelligent information systems oriented on the Web open environment need in dynamic and interoperable ontological knowledge bases. We propose an approach for integration of ontological analysis with semantic Wiki resources: domain ontologies are used as a base of semantic markup of the Wiki pages, and this markup becomes the source for improving of these ontologies by new information
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Analysis of Signalling Time of Community Model
Статья научная
Data fusion is generally defined as the application of methods that combines data from multiple sources and collect information in order to get conclusions. This paper analyzes the signalling time of different data fusion filter models available in the literature with the new community model. The signalling time is calculated based on the data transmission time and processing delay. These parameters reduce the signalling burden on master fusion filter and improves throughput. A comparison of signalling time of the existing data fusion models along with the new community model has also been presented in this paper. The results show that our community model incurs improvement with respect to the existing models in terms of signalling time.
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Analysis of Some Software Reliability Growth Models with Learning Effects
Статья научная
A newly developed software system before its deployment is subjected to vigorous testing so as to minimize the probability of occurrence of failure very soon. Software solutions for safety critical and mission-critical application areas need a much focused level of testing. The testing process is basically carried out to build confidence in the software for its use in real world applications. Thus, reliability of systems is always a matter of concern for us. As we keep on performing the error detection and correction process on our software, the reliability of the system grows. In order to model this growth in the system reliability, many formulations in Software Reliability Growth Models (SRGMs) have been proposed including some based on Non-Homogeneous Poisson Process (NHPP). The role of human learning and experiential pattern gains are being studied and incorporated in such models. The realistic assumptions about human learning behavior and experiential gains of new skill-sets for better detection and correction of faults on software are being incorporated and studied in such models. In this paper, a detailed analysis of some select SRGMs with learning effects is presented based on use of seven data sets. The estimation of parameters and comparative analysis based on goodness of fit using seven data sets are presented. Moreover, model comparisons on the basis of total defects predicted by the select models are also tabulated.
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Analysis of Vascular Pattern Recognition Using Neural Network
Статья научная
Biometric identification using vein patterns is a recent technique. The vein patterns are unique to each individual even in twins and they don't change over age except their size. As veins are beneath the skin it is difficult to forge. BOSPHOROUS hand vein database is used in this work. Hand vein images are uploaded first and key points using Scale Invariant Feature Transform (SIFT) are extracted. Then the neural network is used for training these images. Finally neural network is used for testing these images to check whether the image used for testing matches with the existing database or not. Results are computed like False Acceptation Rate (FAR), False Rejection Rate (FRR), accuracy and error per bit stream.
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Application of Differential Geometry on a Chemical Dynamical Model via Flow Curvature Method
Статья научная
Slow invariant manifolds can contribute major rules in many slow-fast dynamical systems. This slow manifold can be obtained by eliminating the fast mode from the slow-fast system and allows us to reduce the dimension of the system where the asymptotic dynamics of the system occurs on that slow manifold and a low dimensional slow invariant manifold can reduce the computational cost. This article considers a trimolecular chemical dynamical Brusselator model of the mixture of two components that represents a chemical reaction-diffusion system. We convert this system of two-dimensional partial differential equations into four-dimensional ordinary differential equations by considering the new wave variable and obtain a new system of chemical Brusselator flow model. We observe that the onset of the chemical instability does not depend on the flow rate. We particularly study the slow manifold of the four-dimensional Brusselator flow model at zero flow speed. We apply the flow curvature method to the dynamical Brusselator flow model and acquire the analytical equation of the flow curvature manifold. Then we prove the invariance of this slow manifold equation with respect to the flow by using the Darboux invariance theorem. Finally, we find the osculating plane equation by using the flow curvature manifold.
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Статья научная
The article focuses on the application of mathematical methods and models in product - service manufacturing processes in scientific innovative technoparks. The necessity of applying economic-mathematical models and methods in the activity of innovative structures is substantiated. A system of indicators and criteria for assessing the performance and effective management of technoparks has been developed. Based on this system, an information model of the technopark was proposed. A mathematical model of the general management of technoparks is proposed. An econometric model for the innovative product-service manufacturing has been developed. The technique of comparative evaluation of innovative technology parks based on a system of composite indicators and a composite index is proposed. As a result of the experimental implementation of these methods and models, the activity of technoparks.
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Application of the Flow Curvature Method in Lorenz-Haken Model
Статья научная
We consider a recently developed new approach so-called the flow curvature method based on the differential geometry to analyze the Lorenz-Haken model. According to this method, the trajectory curve or flow of any dynamical system of dimension considers as a curve in Euclidean space of dimension . Then the flow curvature or the curvature of the trajectory curve may be computed analytically. The set of points where the flow curvature is null or empty defines the flow curvature manifold. This manifold connected with the dynamical system of any dimension directly describes the analytical equation of the slow invariant manifold incorporated with the same dynamical system. In this article, we apply the flow curvature method for the first time on the three-dimensional Lorenz-Haken model to compute the analytical equation of the slow invariant manifold where we use the Darboux theorem to prove the invariance property of the slow manifold. After that, we determine the osculating plane of the dynamical system and find the relation between flow curvature manifold and osculating plane. Finally, we find the nature of the fixed point stability using flow curvature manifold.
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Asymptotic solutions of a semi-submerged sphere in a liquid under oscillations
Статья научная
One of the most widely used techniques to look into transient behaviour of vibrating systems is the Krylov-Bogoliubov-Mitropolskii (KBM) method, which was developed for obtaining the periodic solutions of second order nonlinear differential systems of small nonlinearities. Later on, this method was studied and modified by numerous scholars to obtain solutions of higher order nonlinear systems. This article modified the method to study the solutions of semi-submerged sphere in a liquid which is floating owing to the gravitational force and the upward pressure of the liquid. This paper suggests that the results obtained for different sets of initial conditions by the modified KBM method correspond well with those obtained by the numerical method.
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Augmented Apriori by Simulating Map-Reduce
Статья научная
Association rule mining is a data mining technique which is used to identify decision-making patterns by analyzing datasets. Many association rule mining techniques exist to find various relationships among itemsets. The techniques proposed in the literature are processed using non-distributed platform in which the entire dataset is sustained till all transactions are processed and the transactions are scanned sequentially. They require more space and are time consuming techniques when large amounts of data are considered. An efficient technique is needed to find association rules from big data set to minimize the space as well as time. Thus, this paper aims to enhance the efficiency of association rule mining of big transaction database both in terms of memory and speed by processing the big transaction database as distributed file system in Map-Reduce framework. The proposed method organizes the transactions into clusters and the clusters are distributed among many parallel processors in a distributed platform. This distribution makes the clusters to be processed simultaneously to find itemsets which enhances the performance both in memory and speed. Then, frequent itemsets are discovered using minimum support threshold. Associations are generated from frequent itemsets and finally interesting rules are found using minimum confidence threshold. The efficiency of the proposed method is enhanced in a noticeably higher level both in terms of memory and speed.
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BRAINSEG – Brain Structures Segmentation Pipeline Using Open Source Tools
Статья научная
Structure segmentation is often the first step in the diagnosis and treatment of various diseases. Because of the variations in the various brain structures and overlapping structures, segmenting brain structures is a very crucial step. Though a lot of research had been done in this area, still it is a challenging field. Using prior knowledge about the spatial relationships among structures, called as atlases, the structures with dissimilarities can be segmented efficiently. Multiple atlases prove a better one when compared to single atlas, especially when there are dissimilarities in the structures. In this paper, we proposed a pipeline for segmenting brain structures using open source tools. We test our pipeline for segmenting brain structures in MRI using the publicly available data provided by MIDAS.
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Bayesian Parameter Inference of Explosive Yields Using Markov Chain Monte Carlo Techniques
Статья научная
A Bayesian parameter inference problem is conducted to estimate the explosive yield of the first atomic explosion at Trinity in New Mexico. The first of its kind, the study advances understanding of fireball dynamics and provides an improved method for the determination of explosive yield. Using fireball radius-time data taken from archival film footage of the explosion and a physical model for the expansion characteristics of the resulting fireball, a yield estimate is made. Bayesian results from the Markov chain indicate that the estimated parameters are consistent with previous calculation except for the critical parameter that modifies the independent time variable. This unique result finds that this parameter deviates in a statistically significant way from previous predictions. Use of the Bayesian parameter estimates computed is found to greatly improve the ability of the fireball model to predict the observed data. In addition, parameter correlations are computed from the Markov chain and discussed. As a result, the method used increases basic understanding of fireball dynamics and provides an improved method for the determination of explosive yields.
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Bayesian approach to generalized normal distribution under non-informative and informative priors
Статья научная
The generalized Normal distribution is obtained from normal distribution by adding a shape parameter to it. This paper is based on the estimation of the shape and scale parameter of generalized Normal distribution by using the maximum likelihood estimation and Bayesian estimation method via Lindley approximation method under Jeffreys prior and informative priors. The objective of this paper is to see which is the suitable prior for the shape and scale parameter of generalized Normal distribution. Simulation study with varying sample sizes, based on MSE, is conducted in R-software for data analysis.
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Статья научная
This study describes the Bayesian approach as an alternative approach for estimating time axes parameters and the periodogram (power spectrum) associated with sinusoidal model when the white noise (sigma) is known or unknown. The conventional method of estimating the time axes parameters and the periodogram has been via the Schuster method that relies solely on Maximum Likelihood Estimation (MLE). The Bayesian alternative approach proposed in this work, on the other hand, adopted the Maximum A Posteriori (MAP) via the Markov Chain Monte Carlo (MCMC) in order to checkmate the problem of re-parameterization and over- parameterization associated with MLE in the conventional practice. The rates of heartbeat variability at exactly an hour and two hours after birth of one thousand eight hundred (1800) newly born babies in a state hospital were recorded and subjected to both the Bayesian approach and Schuster approach for inferences. The periodogram estimates, exactly an hour and two hours of after birth, were estimated to be 0.7395 and 0.7549, respectively - and it was deduced that rates of heartbeat (frequency) variability moderated and stabilized the pulse among the babies after two hours of birth. In addition, MAP mean estimates of the parameters approximately equals to the true mean of estimates when round up to curb the problem of re-parameterization and over- parameterization that do affect Schuster method via MLE.
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Статья научная
The present study is concerned with the estimation of Inverse Exponential distribution using various Bayesian approximation techniques like normal approximation, Tierney and Kadane (T-K) Approximation. Different informative and non-informative priors are used to obtain the Baye’s estimate of Inverse Exponential distribution under different approximation techniques. A simulation study has also been conducted for comparison of Baye’s estimates obtained under different approximation using different priors.
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Bayesian normal and T-K approximations for shape parameter of Type-I Dagum distribution
Статья научная
Dagum distribution is a statistical distribution used closely for fitting income and wealth distributions. This distribution has wide application in fields like reliability theory survival analysis, actuarial sciences, and meteorological data. In this article, we obtained Bayes estimators for the shape parameter of Dagum distribution using approximation techniques like normal and T-K approximations. Moreover different informative priors have been considered and a simulation study and three real data sets have been considered to study the efficiency of obtained results.
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