University of Jordan

About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.

Assessment of development of the learning organization concept in Jordanian industrial companies

Abstract: Purpose – The purpose of this research is to examine the impact on organizational performance of the application of management and human resource practices, and to attempt to outline key elements and assess development of the learning organization (LO) concept in Jordan.Design/methodology/approach – The tool described in this article assesses relationships between LO practices and financial and operational performance measures. The empirical research aims at deconstructing the LO formation through the development and validation of a conceptual model. A total of 41 companies belonging to large industrial sectors in Jordan participated in a survey by responding to a research questionnaire.Findings – The outcomes of the study indicate that the LO concept can be explored in Jordanian industry using eight constructs. These constructs were found to be strongly correlated. In general, this study identifies basic steps in the process of transformation into a learning organization in Jordan.Originality/value – The...

Time-varying hierarchical chains of salps with random weight networks for feature selection

Abstract: Feature selection (FS) is considered asone of the most common and challenging tasks in MachineLearning. FScanbeconsideredasanoptimizationproblemthatrequiresanefficient optimization algorithm to find its optimal set of features. This paper proposes a wrapper FS method that combines a time-varying number of leaders and followers binary Salp Swarm Algorithm (called TVBSSA) with Random Weight Network (RWN). In this approach, the TVBSSA is used as a search strategy, while RWN is utilized as an induction algorithm. The objective function is formulated in a manner to aggregate three objectives: maximizing the classification accuracy, maximizing the reduction rate of the selected features, and minimizing the complexity of generated RWN models. To assess the performance of the proposed approach, 20 well-known UCI datasets and a number of existing FS methods are employed. The comparative results show the ability of the proposed approach in outperforming similar algorithms in the literature and its merits to be used in systems that require FS.Faris, H., Heidari, A. A., Al-Zoubi, A. M., Mafarja, M., Aljarah, I., Eshtay, M., & Mirjalili, S. (2020). Time-varying hierarchical chains of salps with random weight networks for feature selection. Expert Systems with Applications, 140. https://doi.org/10.1016/j.eswa.2019.112898