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.

Evolving Support Vector Machines using Whale Optimization Algorithm for spam profiles detection on online social networks in different lingual contexts

Abstract: A new classification approach based Support Vector Machine is proposed for detecting spammers on Twitter.The proposed approach reveals the most influencing features in the process of identifying spammers.Different lingual contexts are studied: Arabic, English, Spanish, and Korean. Detecting spam profiles is considered as one of the most challenging issues in online social networks. The reason is that these profiles are not just a source for unwanted or bad advertisements, but could be a serious threat; as they could initiate malicious activities against other users. Realizing this threat, there is an incremental need for accurate and efficient spam detection models for online social networks. In this paper, a hybrid machine learning model based on Support Vector Machines and one of the recent metaheuristic algorithms called Whale Optimization Algorithm is proposed for the task of identifying spammers in online social networks. The proposed model performs automatic detection of spammers and gives an insight on the most influencing features during the detection process. Moreover, the model is applied and tested on different lingual datasets, where four datasets are collected from Twitter in four languages: Arabic, English, Spanish, and Korean. The experiments and results show that the proposed model outperforms many other algorithms in terms of accuracy, and provides very challenging results in terms of precision, recall, f-measure and AUC. While it also helps in identifying the most influencing features in the detection process.

Understanding the Quality Determinants that Influence the Intention to Use the Mobile Learning Platforms: A Practical Study

Abstract: There is a widespread use of Internet technology in the present times, because of which universities are making investments in Mobile learning to augment their position in the face of extensive competition and also to enhance their students’ learning experience and efficiency. Nonetheless, Mobile Learning Platform are only going to be successful when students show acceptance and adoption of this technology. Our literature review indicates that very few studies have been carried out to show how university students accept and employ Mobile Learning Platform. In addition, it is asserted that behavioral models of technology acceptance are not equally applied in different cultures. The purpose of this study is to develop an extension of Technology Acceptance Model (TAM) by including four more constructs: namely, content quality, service quality, information quality and quality of the system. This is proposed to make it more relevant for the developing countries, like the United Arab Emirates (UAE). An online survey was carried out to obtain the data. A total of 221 students from the UAE took part in this survey. Structural equation modeling was used to determine and test the measurement and structural model. Data analysis was carried out, which showed that ten out of a total of 12 hypotheses are supported. This shows that there is support for the applicability of the extended TAM in the UAE. These outcomes suggest that Mobile Learning Platform should be considered by the policymakers and education developers as being not only a technological solution but also as being new e-learning platform especially for distance learning students.

Prefrontal cortex activation while walking under dual-task conditions in stroke: a multimodal imaging study

Abstract: Background Walking while performing another task (eg, talking) is challenging for many stroke survivors, yet its neural basis are not fully understood. Objective To investigate prefrontal cortex activation and its relationship to gait measures while walking under single-task (ST) and dual-task (DT) conditions (ie, walking while simultaneously performing a cognitive task) in stroke survivors. Methods We acquired near-infrared spectroscopy (NIRS) data from the prefrontal cortex during treadmill walking in ST and DT conditions in chronic stroke survivors and healthy controls. We also acquired functional magnetic resonance imaging (fMRI) and NIRS during simulated walking under these conditions. Results NIRS revealed increased oxygenated hemoglobin concentration in DT-walking compared with ST-walking for both groups. For simulated walking, NIRS showed a significant effect of group and group × task, being greater on both occasions, in stroke survivors. A greater increase in brain activation observed from ST to DT walking/ simulated walking was related to a greater change in motor performance in stroke survivors. fMRI revealed increased activity during DT relative to ST conditions in stroke patients in areas including the inferior temporal gyri, superior frontal gyri and cingulate gyri bilaterally, and the right precentral gyrus. The DT-related increase in fMRI activity correlated with DT-related change in behavior in stroke participants in the bilateral inferior temporal gyrus, left cingulate gyrus, and left frontal pole. Conclusion Our results provide novel evidence that enhanced brain activity changes relate to dual task motor decrements.

A survey on particle swarm optimization with emphasis on engineering and network applications

Abstract: Swarm intelligence is a kind of artificial intelligence that is based on the collective behavior of the decentralized and self-organized systems. This work focuses on reviewing a heuristic global optimization method called particle swarm optimization (PSO). This includes the mathematical representation of PSO in contentious and binary spaces, the evolution and modifications of PSO over the last two decades. We also present a comprehensive taxonomy of heuristic-based optimization algorithms such as genetic algorithms, tabu search, simulated annealing, cross entropy and illustrate the advantages and disadvantages of these algorithms. Furthermore, we present the application of PSO on graphics processing unit and show various applications of PSO in networks.