Institution
Applied Science Private University
Education•Amman, Jordan•
About: Applied Science Private University is a education organization based out in Amman, Jordan. It is known for research contribution in the topics: Population & Catalysis. The organization has 4124 authors who have published 5299 publications receiving 116167 citations.
Papers published on a yearly basis
Papers
More filters
••
TL;DR: A thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications is provided.
Abstract: Over the past decades, a tremendous amount of research has been done on the use of machine learning for speech processing applications, especially speech recognition. However, in the past few years, research has focused on utilizing deep learning for speech-related applications. This new area of machine learning has yielded far better results when compared to others in a variety of applications including speech, and thus became a very attractive area of research. This paper provides a thorough examination of the different studies that have been conducted since 2006, when deep learning first arose as a new area of machine learning, for speech applications. A thorough statistical analysis is provided in this review which was conducted by extracting specific information from 174 papers published between the years 2006 and 2018. The results provided in this paper shed light on the trends of research in this area as well as bring focus to new research topics.
701 citations
••
TL;DR: The Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases are presented, which predicted the possible ending point of this outbreak will be around June 2020 and compared transmission rates of Canada with Italy and USA.
Abstract: On March 11 th 2020, World Health Organization (WHO) declared the 2019 novel corona virus as global pandemic. Corona virus, also known as COVID-19 was first originated in Wuhan, Hubei province in China around December 2019 and spread out all over the world within few weeks. Based on the public datasets provided by John Hopkins university and Canadian health authority, we have developed a forecasting model of COVID-19 outbreak in Canada using state-of-the-art Deep Learning (DL) models. In this novel research, we evaluated the key features to predict the trends and possible stopping time of the current COVID-19 outbreak in Canada and around the world. In this paper we presented the Long short-term memory (LSTM) networks, a deep learning approach to forecast the future COVID-19 cases. Based on the results of our Long short-term memory (LSTM) network, we predicted the possible ending point of this outbreak will be around June 2020. In addition to that, we compared transmission rates of Canada with Italy and USA. Here we also presented the 2, 4, 6, 8, 10, 12 and 14 th day predictions for 2 successive days. Our forecasts in this paper is based on the available data until March 31, 2020. To the best of our knowledge, this of the few studies to use LSTM networks to forecast the infectious diseases.
673 citations
••
TL;DR: In this article, the effect of fiber alignment and alkalization on the mechanical properties of the composites were measured to observe the effects of fibre alignment and alkylation on fiber properties.
642 citations
••
TL;DR: The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models.
Abstract: This paper provides a discussion of the development and application of Artificial Neural Networks (ANNs) to flow forecasting in two flood-prone UK catchments using real hydrometric data. Given relatively brief calibration data sets it was possible to construct robust models of 15-min flows with six hour lead times for the Rivers Amber and Mole. Comparisons were made between the performance of the ANN and those of conventional flood forecasting systems. The results obtained for validation forecasts were of comparable quality to those obtained from operational systems for the River Amber. The ability of the ANN to cope with missing data and to “learn” from the event currently being forecast in real time makes it an appealing alternative to conventional lumped or semi-distributed flood forecasting models. However, further research is required to determine the optimum ANN training period for a given catchment, season and hydrological contexts.
610 citations
••
TL;DR: In this paper, a host of causes of construction delays in residential projects were identified and classified according to Drewin's Open Conversion System, and most common causes were evaluated by using both, the data collected in a survey conducted to residential projects consultant engineers, contractors, and owners, and interviews with senior professionals in the field.
460 citations
Authors
Showing all 4150 results
Name | H-index | Papers | Citations |
---|---|---|---|
Hua Zhang | 163 | 1503 | 116769 |
Menachem Elimelech | 157 | 547 | 95285 |
Yu Huang | 136 | 1492 | 89209 |
Dmitri Golberg | 129 | 1024 | 61788 |
Andrea Carlo Marini | 123 | 1236 | 72959 |
Dionysios D. Dionysiou | 116 | 675 | 48449 |
Liyuan Han | 114 | 766 | 65277 |
Shunichi Fukuzumi | 111 | 1256 | 52764 |
John A. Stankovic | 109 | 559 | 51329 |
Judea Pearl | 107 | 512 | 83978 |
Feng Wang | 107 | 1136 | 64644 |
O. C. Zienkiewicz | 107 | 455 | 71204 |
Jeffrey I. Zink | 99 | 509 | 42667 |
Kazuhiro Hono | 98 | 878 | 33534 |
Robert W. Boyd | 98 | 1161 | 37321 |