The organizational perspective of Process Mining is a valuable technique that allows knowing the social network of an organization. To do so, it provides means to make an evaluation of networks by mapping and analyzing relationships among people, teams, departments or even entire organizations. There are many tools available in the market for social network analysis. However, for a long time th...

Processes in sport like motions or games are influenced by communication, interaction, adaptation, and spontaneous decisions. Therefore, on the one hand, those processes are often fuzzy and unpredictable and so have not extensively been dealt with, yet. On the other hand, most of those processes structurally are roughly determined by intention, rules, and context conditions and so can be classi...

One of the most advanced and complex multi-criteria decision-making methods is the analytic network process (ANP). This method supports modelling dependencies and feedback between elements in the network. For this reason, the ANP is one of the most appropriate methods for making decisions in fields that are characterised by existing dependencies of higher-level elements on lower level elements....

A Bayesian Network based mathematical model has been used for modelling Extreme Programming software development process. The model is capable of predicting the expected finish time and the expected defect rate for each XP release. Therefore, it can be used to determine the success/failure of any XP Project. The model takes into account the effect of three XP practices, namely: Pair Programming...

This paper considers the dynamics of edges in a network. The Dynamic Bond Percolation (DBP) process models, through stochastic local rules, the dependence of an edge (a, b) in a network on the states of its neighboring edges. Unlike previous models, DBP does not assume statistical independence between different edges. In applications, this means for example that failures of transmission lines i...

in this article, the effect of operating conditions, such as temperature, gas hourly space velocity (ghsv), ch4/o2 ratio and diluents gas (mol% n2) on ethylene production by oxidative coupling of methane (ocm) in a fixed bed reactor at atmospheric pressure was studied over mn/na2wo4/sio2 catalyst. based on the properties of neural networks, an artificial neural network was used for model develo...

This paper presents the Markov decision process extraction network, which is a data-efficient, automatic state estimation approach for discrete-time reinforcement learning (RL) based on recurrent neural networks. The architecture is designed to model the minimal relevant dynamics of an environment, capable of condensing large sets of continuous observables to a compact state representation and ...

Consider observing a collection of discrete events within a network that reflect how network nodes influence one another. Such data are common in spike trains recorded from biological neural networks, interactions within a social network, and a variety of other settings. Data of this form may be modeled as self-exciting point processes, in which the likelihood of future events depends on the pa...

The aim of this paper is to contribute to the development of the Internationalization Process Model (IP model) through the use of a resource network perspective, based on industrial network theory. According to the IP model, firms internationalize through an incremental path characterized by small steps that are dependent on the firms and the networks resource combination. In this paper, it is ...

The present paper presented a methodology for prioritizing the innovative and entrepreneurial indicators using Multi Criteria Decision Making (MCDM) and Artificial Neural Networks (ANNs), taking into account three individual, organizational and cultural dimensions simultaneously in decision making procedure. This methodology has two main advantages: first, the speed of operation in the accounti...