List of Articles خرد جمعی

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  1 - Evaluating the crowdsourcing key factors and its impact on project success
  Mir Hamid Taghavi Alireza Aliahmadi Mohammad ali shafia   Ali Bonyadi Naeini
  Crowdsourcing is one of the most important processes fundamentally changing the way that businesses, governments and humanitarian organizations look at the internet and mobile market. This new paradigm has root in participation under the collective wisdom and is emergin More

  Crowdsourcing is one of the most important processes fundamentally changing the way that businesses, governments and humanitarian organizations look at the internet and mobile market. This new paradigm has root in participation under the collective wisdom and is emerging as the new business model to bring together the amateurs and professionals seeking to spend less time and, as a result, less cost to run the tasks. The objective of this study is to investigate the factors influencing crowdsourcing and its impact on project success in "Padideh Shandiz Company”. In order to conduct variables confirmatory factor analysis, the structural equation modeling based on partial least square method using the smart PLS 2 software was used. To this end, 200 managers and experts in Padideh Shandiz enjoying a good deal of knowledge in operational and research activities in crowdsourcing field were randomly selected and evaluated through questionnaire and interview. The results of the study revealed that (1) the participants have the greatest impact on crowdsourcing (2) crowdsourcing significantly impacts on the businesses performance and project success and (3) the customers’ satisfaction indicator serves as the most important factor in explaining the changes in project success. Manuscript profile
• Open Access Article
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  2 - A Hybrid-Based Feature Selection Method for High-Dimensional Data Using Ensemble Methods
  A. Rouhi H. Nezamabadi-pour
  Nowadays, with the advent and proliferation of high-dimensional data, the process of feature selection plays an important role in the domain of machine learning and more specifically in the classification task. Dealing with high-dimensional data, e.g. microarrays, is as More

  Nowadays, with the advent and proliferation of high-dimensional data, the process of feature selection plays an important role in the domain of machine learning and more specifically in the classification task. Dealing with high-dimensional data, e.g. microarrays, is associated with problems such as increased presence of redundant and irrelevant features, which leads to decreased classification accuracy, increased computational cost, and the curse of dimensionality. In this paper, a hybrid method using ensemble methods for feature selection of high dimensional data, is proposed. In the proposed method, in the first stage, a filter method reduces the dimensionality of features and then, in the second stage, two state-of-the-art wrapper methods run on the subset of reduced features using the ensemble technique. The proposed method is benchmarked using 8 microarray datasets. The comparison results with several state-of-the-art feature selection methods confirm the effectiveness of the proposed approach. Manuscript profile