Institution
University of Jordan
Education•Amman, Jordan•
About: University of Jordan is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Health care. The organization has 7796 authors who have published 13764 publications receiving 213526 citations.
Papers published on a yearly basis
Papers
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TL;DR: MTA cement provides a better seal than amalgam and EBA cement when used as retrograde filling, but the extrapolation of this result into a clinical practice may be questionable.
Abstract: Objective To compare apical microleakage of MTA following reverse retrograde root filling with that following amalgam and EBA retrofilling. Design Prospective random control trial. Setting It was conducted at the University of Jordan in 1998. Materials and methods The root canals of 79 extracted teeth were instrumented and obturated with vertically condensed gutta-percha. Each tooth was apically resected and the apex was prepared ultrasonically to 3 mm depth and the root surface isolated with nail varnish. Teeth were divided randomly into three groups of 25 teeth each. First group was retrofilled with amalgam, second group with EBA and the third group with MTA. Following immersion in 1% methylene blue dye for 72 hours, the roots were sectioned and the depth of dye penetration was evaluated by a stereomicroscope at x10 magnification. Interventions Super EBA is a reinforced zinc oxide cement based on a mixture of 32% eugenol and 68% ethoxy benzoic acid (EBA). MTA is a mineral trioxide aggregate cement (MTA) based on a mixture of sterile water. Main outcome measures The sealing effectiveness of the retrograde filling materials used in this study was determined by their ability to inhibit dye penetration. Results 56% of the group filled with amalgam and 20% of the group filled with EBA showed dye leakage beyond the retrofilling material whereas the MTA group showed none, two samples from MTA group were eliminated because of their fractured roots. The chi-squared test revealed a statistically significant difference among all three groups (P < 0.05). Conclusion MTA cement provides a better seal than amalgam and EBA cement when used as retrograde filling, but the extrapolation of this result into a clinical practice may be questionable.
176 citations
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TL;DR: The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
Abstract: To help individuals or companies make a systematic and more accurate decisions, sentiment analysis (SA) is used to evaluate the polarity of reviews. In SA, feature selection phase is an important phase for machine learning classifiers specifically when the datasets used in training is huge. Whale Optimization Algorithm (WOA) is one of the recent metaheuristic optimization algorithm that mimics the whale hunting mechanism. However, WOA suffers from the same problem faced by many other optimization algorithms and tend to fall in local optima. To overcome these problems, two improvements for WOA algorithm are proposed in this paper. The first improvement includes using Elite Opposition-Based Learning (EOBL) at initialization phase of WOA. The second improvement involves the incorporation of evolutionary operators from Differential Evolution algorithm at the end of each WOA iteration including mutation, crossover, and selection operators. In addition, we also used Information Gain (IG) as a filter features selection technique with WOA using Support Vector Machine (SVM) classifier to reduce the search space explored by WOA. To verify our proposed approach, four Arabic benchmark datasets for sentiment analysis are used since there are only a few studies in sentiment analysis conducted for Arabic language as compared to English. The proposed algorithm is compared with six well-known optimization algorithms and two deep learning algorithms. The comprehensive experiments results show that the proposed algorithm outperforms all other algorithms in terms of sentiment analysis classification accuracy through finding the best solutions, while its also minimizes the number of selected features.
176 citations
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Ton Duc Thang University1, Flinders University2, Khulna University3, Broad Institute4, North South University5, University of Hyderabad6, Khyber Medical University7, Polish Academy of Sciences8, Selçuk University9, University of Catania10, Iuliu Hațieganu University of Medicine and Pharmacy11, Medical University of Łódź12, University of Hong Kong13, Mansoura University14, University of Macau15, Sungkyunkwan University16, Narsee Monjee Institute of Management Studies17, King Abdulaziz University18, University of Jordan19, Central University, India20, University of Vienna21
TL;DR: A systematic review of reported biological activities of phytol, including phytanic acid (PA), along with their underlying mechanism of action, is presented.
175 citations
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Centers for Disease Control and Prevention1, Wake Forest University2, University of California, Davis3, Food and Drug Administration4, Radboud University Nijmegen5, University of South Carolina6, University of Jordan7, Rutgers University8, University of Oulu9, University of Basel10, University Hospitals of Cleveland11
174 citations
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TL;DR: The ages of customary production, acquisition, and mastery of Arabic consonants were similar to those for English but with notable exceptions that have implications for description of phonological acquisition.
Abstract: This normative study of the acquisition of consonants of Arabic as spoken in Jordan answered 4 questions: (1) What percentage of children at each of 9 age levels produced each consonant correctly? ...
173 citations
Authors
Showing all 7905 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yousef Khader | 94 | 586 | 111094 |
Crispian Scully | 86 | 917 | 33404 |
Debra K. Moser | 85 | 558 | 27188 |
Pierre Thibault | 77 | 332 | 17741 |
Ali H. Nayfeh | 71 | 618 | 31111 |
Harold S. Margolis | 71 | 199 | 26719 |
Gerrit Hoogenboom | 69 | 560 | 24151 |
Shaher Momani | 64 | 301 | 13680 |
Robert McDonald | 62 | 577 | 17531 |
Kaarle Hämeri | 58 | 175 | 10969 |
James E. Maynard | 56 | 141 | 9158 |
E. Richard Moxon | 54 | 176 | 10395 |
Liam G Heaney | 53 | 234 | 8556 |
Stephen C. Hadler | 52 | 148 | 11458 |
Nicholas H. Oberlies | 52 | 262 | 9683 |