جستجوی مقالات مرتبط با کلیدواژه « data analysis » در نشریات گروه « ریاضی »

This paper, named IoT-based river water quality monitoring, gives the outcome of quality river water with the advantage of various advanced techniques, the Internet of Things, and Wireless Sensor Networks (WSNs). As part of this, we have made the things to collect the data and transfer the data to the software that we have made. Then, the data we collect using various equipment will be analyzed using the software we have designed. Besides that, we automate the monitoring process with the system's hardware, data visualization, and software. This water quality monitoring system has been a significant issue and can be used digitally, intelligently, and effectively to improve river water quality.

The development of computer technologies and automated learning techniques can make decision-making easier and more efficient. In the field of machine learning, where computers always make decisions or propose suggestions for proper decision-making, there exist many decision-making techniques such as decision trees, neural networks, etc. Flexibility and comprehensibility are one of the advantages of the decision tree model. The decision tree can provide the possible options, goals, financial profit, and information needed for an investment for the managers better than any other tool. The decision tree is one of the most applicable data mining algorithms. On the other hand, crowdfunding in knowledge-based companies is a new financial phenomenon in online financing of innovative projects and knowledge-based businesses that reduces financing costs and problems in addition to changing the nature of the investment. There are four types of crowdfunding in knowledge-based companies namely donation-based, equity-based, lending-based, and reward-based. Reward-based crowdfunding can be considered the most publicly familiar crowdfunding model, where backers will actively participate in the product development process along with investment. Low-cost crowdfunding websites act in the projects as an online mediatory between the initiators and the sponsors. Therefore, the factors affecting the success of crowdfunding were evaluated in this research regarding the initiators' performance and the sponsors' feedback, and the significant attributes were presented in the form of a decision tree structure using the data mining technique. The results reveal that the best performance of initiators is related to the field of direct investment attraction with 92% accuracy of the decision tree with the most important attributes of "number of updates during the investment period" and "number of dynamic technical and tactical analyses".

Nowadays, human resource development is the concept of thought and idea by the organization's staff, and its new concept requires employees to be equipped with qualities and skills that, with full compassion and commitment fulfill their capabilities, energy, expertise and thought to fulfill their mission. Put the organization. This paper is aimed at designing the causal Pattern of Human Resources Development at Islamic Azad University based on the general policies of the administrative system transformation according to the fuzzy Dematel method. In this study, information about 16 experts and professors at Islamic Azad University, forms a statistical sample. The results show that individual, environmental, educational, social, economic and management factors affect human resource development at Islamic Azad University. The fuzzy Dematel-based method has also shown that the training factor with an average effect of 1.65 is the most important factor in this study. On the other hand, environmental factors with an average effect of 0.520 are a factor with the least effect.

The importance of the capital market in economic development is undeniable through the effective management of capital and the optimal allocation of resources. In this study, according to capital market behaviors and research, Statistical Learning (SL) algorithms compared to Artificial Neural Networks (ANN) to analyze time-series data and predict stock prices have been investigated. In studies to compare methods or provide hybrid models, most statistical learning algorithms are limited and examined without the comparison of other algorithms. In this study, to eliminate this shortcoming by implementing and comparing statistical learning algorithms in the two categories of Regression Learner and Classification Learner, the most efficient algorithm has been identified based on the selected shares and based on the presented parameters. The first category (Regression Learner) includes Linear Regression, Interaction Linear Regression, Robust Linear Regression, Stepwise Linear Regression, Fine Tree, Medium Tree, Coarse Tree, Linear Support Vector Machine (SVM), Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Medium Gaussian SVM, Coarse Gaussian SVM, Ensemble Boosted Trees, Ensemble Bagged Trees, Squared Exponential Gaussian Process Regression, Matern 5/2 Gaussian Process Regression, Exponential Gaussian Process Regression, Rational Quadratic Gaussian Process Regression. The second category (Classification Learner) includes Gaussian, Naive Bayes, K-nearest neighbors. The results show that Regression Learner methods are more effective in predicting the price of selected stocks.

In this paper named Internet of Things (IoT) based river water quality monitoring gives the outcome of Quality River of water by the advantage of various advanced techniques (IoT), Wireless Sensor Network (WSN). As part of this we have made the things to collect the data and transfer the data to the software that we have made. Then the data that we collect using various equipment and be analyzed using the software that we have designed. Other than that, the hardware of the system, data visualization, software, we automate the process of monitoring This water quality monitoring system has been the major issue and can be use the digital, intelligent, and effectively gives the quality of river water.

The power version of the modified Lindley distribution is introduced in this paper, offering a new two-parameter lifetime distribution. As a main interest, it provides a motivated alternative to the Weibull and power Lindley distributions. We discuss its main characteristics and properties, including shapes of the probability density and hazard rate functions, incomplete moments, crude moments, variance, skewness, kurtosis and order statistics. Then, a statistical study of the model is developed. The parameters are estimated by the maximum likelihood method. A simulation study examines the numerical comportment of the bias and mean square error of the maximum likelihood estimates of the parameters. Application of the new model to three data sets is presented, showing that the model has a better fit behavior in comparison to some other well-known lifetime models, including the Weibull and power Lindley models.

Statistical modeling is constantly in demand for simple and flexible probability distributions. We are helping to meet this demand by proposing a new candidate extending the standard Ailamujia distribution, called the power Ailamujia distribution. The idea is to extend the adaptability of the  Ailamujia distribution  through the use of the power transform, introducing a new shape parameter in its definition. In particular, the new parameter is able to produce original non-monotonic shapes for the main functions that are desirable for data fitting  purposes. Its interest is also shown through results about stochastic orders, quantile function, moments (raw, incomplete and probability weighted), stress-strength parameter and Tsallis entropy. New classes of distributions based on the power Ailamujia distribution are also presented. Then, we investigate the  corresponding statistical model to analyze two kinds of data:  complete data and data in presence of censorship.  In particular, a goodness-of-fit statistical test allowing the processing of right-censored data is developed. The potential of the new model is demonstrated by its application  to four data sets, two being related to the Covid-19 pandemic.

In this paper, we analyze the covid-19 data set in two ways, The first one depends on the calculation of correlation coefficient via classical mathematical representation. And the second way of analysis depends on modern technique which is associated with copula function concepts and its relationship to measures of association. Afterwards, we compare the obtained results to decide far which is better in an analysis of the examined dataset.

This study is concerned with generating odd distribution by combining Rayleigh distribution to uniform distribution, denoted by (RUOD). We drive the distribution function (df), its probability density function (pdf), and we discuss some other properties like the survival function, moments and graphs of such functions. Also, we estimate the parameters model of the generated distribution RUOD by using the maximum likelihood method to find the approximate values that fit the desired distribution. Eventually, we analyze some data set by the generated RUOD and compare the results upon the goodness-of-fit measures with respect to Rayleigh distribution to decide the best distribution that fits the data set.

Communities in social networks form with different purposes and play a significant role in interpersonal interactions. Analysis of virtual communities indicates a more precise understanding of the behaviours and desires of individuals in social networks. In this paper, new measures have been proposed for analyzing implicit and explicit communities in Online Social Networks (OSNs). The measures of “potential value of the community members” and “value of the community messages”, which are used for calculating the measure of “community value” are among the most important measures introduced in this paper. Another measure introduced is “user influence rate” in a community, which represents the contribution of a person in creating value in a community. To provide a sound dataset, we collected the information from several real implicit communities in Twitter based on different hashtags. Finally, the suggested measures have been analyzed and compared statistically and behaviourally across different communities. The results of this research well indicate the importance and practicality of the measures introduced in Community analysis of Twitter.

This paper is devoted to the study of the Sin-G class of distributions and one of its special member. We first explore the mathematical properties of the Sin-G class, giving the cumulative and probability density functions and their expansions, quantile function, moments, moment generating function, reliability parameter, R'enyi entropy and order statistics. Then, we focus our attention on the special member defined with the Inverse Weibull distribution as baseline,  denoted by SinIW. The mathematical and practical aspects of the SinIW distribution are investigated.  In order to illustrate the usefulness of the SinIW model, an application to real life data set is carried out.

The problem of the sample variance computation for epistemic inter-val-valued data is, in general, NP-hard. Therefore, known efficient algorithms for computing variance require strong restrictions on admissible intervals like the no-subset property or heavy limitations on the number of possible intersections between intervals. A new asymptotic algorithm for computing the upper bound of the sample variance in a feasible time is proposed. Conditions required for its application with finite samples are discussed and some properties of the algorithm are also given. It appears that our new algorithm could be effectively applied in definitely more situations than methods used so far.