Journal of Data Science and Modeling

Abstract The purpose of this research is to identify and introduce effective factors in adoption of e-learning based on technology adoption model. Accordingly, by considering the studies conducted in this field, several variables such as computer self-efficacy, content quality, system support, interface design, technology tools and computer anxiety as factors influencing the adoption of e-learning system were extracted and based on them, a conceptual model of research was developed. To measure the model and the relationships between the variables in the model, a questionnaire was designed and provided to users of the electronic education system of Qazvin University of Medical Sciences. The results of the data analysis confirmed the correctness of all hypotheses using the structural equation modeling method, except for the effect of technology tools on the acceptance of the e-learning system. The findings of this study will help university administrators and the professors associated with this system to encourage students to make effective use of the system by creating the necessary background for effective factors.

Abstract The multilinear normal distribution is a widely used tool in the tensor analysis of magnetic resonance imaging (MRI). Diffusion tensor MRI provides a statistical estimate of a symmetric 2nd-order diffusion tensor for each voxel within an imaging volume. In this article, tensor elliptical (TE) distribution is introduced as an extension to the multilinear normal (MLN) distribution. Some properties, including the characteristic function and distribution of affine transformations are given. An integral representation connecting densities of TE and MLN distributions is exhibited that is used in deriving the expectation of any measurable function of a TE variate.

Abstract Interval-valued data are observed as ranges instead of single values and contain richer information than

single-valued data. Meanwhile, interval-valued data are used for interval-valued characteristics. An interval

generalized linear model is proposed for the first time in this research. Then a suitable model is presented to

estimate the parameters of the interval generalized linear model. The two models are provided on the basis of

the interval arithmetic. The estimation procedure of the parameters of the suitable model is as the estimation

procedure of the parameters of the interval generalized linear model. The least-squares (LS) estimation of the

suitable model is developed according to a nice distance in the interval space. The LS estimation is resolved

analytically through a constrained minimization problem. Then some desirable properties of the estimators

are checked. Finally, both the theoretical and the empirical performance of the estimators are investigated.

Abstract In this paper, the probability of failure-free operation until time t, along with the probability of stress-strength, based on progressive censoring data is studied in a family of lifetime distributions. Since the number of data in a progressive censoring scheme is usually reduced, so shrinkage methods have been used to improve the classical estimator. For estimation purposes, the preliminary test and Stein-type shrinkage estimators are proposed and their exact distributional properties are derived. For numerical superiority demonstration of the proposed estimation strategies, some improved bootstrap confidence intervals, are constructed. The theoretical results are illustrated by a real data examples and an extensive simulation study. Simulation shreds of evidence revealed that our proposed shrinkage strategies perform well in the estimation of parameters based on progressive censoring data.

Abstract The aim of this paper is to learn a Bayesian network structure for discrete variables. For this purpose, we introduce a Gibbs sampler method. Each sample represents a Bayesian network. Thus, in the process of Gibbs sampling, we obtain a set of Bayesian networks. For achieving a single graph that represents the best graph fitted on data, we use the mode of burn-in graphs. This means that the most frequent edges of burn-in graphs are considered to indicate the best single graph. The results on the well-known Bayesian networks show that our method has higher accuracy in the task of learning a Bayesian network structure.

Abstract ‎In this paper‎, ‎we consider the problem of parameter estimation in {color{blue} negative binomial mixed model} when it is suspected that some of the fixed parameters may be restricted to a subspace via linear shrinkage‎, ‎{color{blue} preliminary test}‎, ‎shrinkage {color{blue} preliminary test}‎, ‎shrinkage‎, ‎and positive shrinkage estimators along with the unrestricted maximum likelihood and restricted estimators‎. ‎The random effects are considered as nuisance parameters‎. ‎We conduct a Monte Carlo simulation study to evaluate the performance of each estimator in the sense of simulated relative efficiency‎. ‎The results of simulation study reveal that the proposed estimation strategies perform more better than {color{blue} the} maximum likelihood method‎. ‎The proposed estimators are applied to a real dataset to appraise their performance‎.

Abstract Due to several reasons as the low resistance of constructed concrete and also change in codes or application of structures, some concrete frames need to be retrofitted. By adding the steel prop and curb to the reinforced concrete, many parameters are changed such as ductility, resistance, and stiffness. This study investigates numerically the impact of adding the prop and curb, slit damper, gusset plate and also prop with a ductile ring on stiffness, resistance, energy dissipation and ductility of RC frames. For this purpose, the effect of the aforementioned methods on the linear and nonlinear moment frame behavior of reinforced concrete under monotonic loads have been numerically investigated using the ABAQUS software. In the present study 12 samples of reinforced frames with one story and one span retrofitted by different methods. The novelty of the paper was using such props and slit damper in RC frames. The results obtained from the modeling showed the retrofitted frame with a ring, slit damper and gusset plate also showed a better behavior in terms of resistance and stiffness compared to the RC frame and the sample with slit damper and prop with a ductile ring as well as compared to the sample with the prop and curb showed more ductility and energy dissipation.

Abstract In this paper, a new adaptive Monte Carlo algorithm is proposed to solve ‎the ‎systems ‎of ‎linear ‎algebraic ‎equations ‎arising ‎from‎ the Black–Scholes model ‎to ‎price‎ European and American options. The proposed algorithm offers several advantages over the conventional and previous adaptive Monte Carlo algorithms. The corresponding properties of the algorithm ‎and ‎Convergence ‎theories‎ are discussed and numerical experiments are presented which demonstrate the computational efficiency of the proposed algorithm.‎‎ The results are also compared with other methods.

Abstract One of the important challenges in Wireless Sensor Networks is to proceeds data transmission in a way that tries to increase the life of the network. One of the main issues is the reduction of latency in the node and energy in the sink nodes. Due to the limited energy of the nodes, data transmission has the largest share in energy consumption, so it is important to design a structure that has the least amount of energy in sending data to the base station. In this paper, we use fuzzy logic and Mamdani method for clustering to solve the challenge and time division multiplexing method to connect the nodes with the header. The proposed clustering is based on the use of the LEACH algorithm, the capability and reliability of which are improved by fuzzy systems, and the particle optimization algorithm is used to optimize the path of the networks. The simulation results show that energy consumption decreases with increasing number of cycles. For example, energy consumption reached 0.9 in the 2000 round and 0.1 in the 5000 round.

Abstract Abstract: so many natural phenomena of determining relationship and the effect of input variables
on response variable in statistical studies may be different from the suggested model that the
researcher selects for his study due to the occupant exists in the structure of data. It may be so
influential on different distributions considered for response variables. The optimal properties of
estimators evaluated and studied for two statistical variables considered for response variable and
input variables in the suggested model. It has been simulated for study and real data has been also
investigated. The results confirmed the superiority of a model which is close to the structure of the
data.

Abstract Deaf people or people with hearing loss have a major problem in everyday communication. There are many applications available in the market to help blind people to interact with the world. Voice-based email and chatting systems are available to communicate with each other by blinds. This helps to interact with persons by blind people. Also, many attempts have been made with Sign Language (SL) translators to solve of communication gap between normal and deaf people and ease communication for deaf people. In this paper, the geometric feature is used as feature extraction for static sign recognition. Support Vector Machine (SVM) classifier is used for training and testing to develop a system using static signs. So, the accuracy result for static signs using the Geometric feature is 62.92\% which needs to be improved by other feature extraction and classifiers.

Abstract In this paper, we will introduce a Bayesian semiparametric model concerned with both constant and coefficients. In Meta-Analysis or Meta-Regression, we usually use a parametric family. However, lately the increasing tendency to use Bayesian nonparametric and semiparametric models, entered this area too. On the other hand, although we have some works on Bayesian nonparametric or semiparametric models, they just focus on intercept and do not pay much attention to regressor coefficient(s). We also would check the efficiency of the proposed model via simulation and give an illustrating example.