Institution
Jordan University of Science and Technology
Education•Irbid, Irbid, Jordan•
About: Jordan University of Science and Technology is a education organization based out in Irbid, Irbid, Jordan. It is known for research contribution in the topics: Population & Medicine. The organization has 7582 authors who have published 13166 publications receiving 298158 citations. The organization is also known as: JUST.
Topics: Population, Medicine, Health care, Heat transfer, Cloud computing
Papers published on a yearly basis
Papers
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TL;DR: Despite the use of radio-opaque sponges and thorough sponge counting, this moribund mishap still occurs and continuous medical training and strict adherence to regulations should reduce the incidence to a minimum.
184 citations
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TL;DR: This work has proposed a classification methodology to classify Focal and Non Focal EEG and found that NNge classifier gave the highest accuracy of 98%, sensitivity of 100% and specificity of 96%, which is the highest comparing to other methods in the literature.
183 citations
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183 citations
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07 Apr 2020TL;DR: One of the key findings of this paper is noticing that oversampling performs better than undersampling for different classifiers and obtains higher scores in different evaluation metrics.
Abstract: Data imbalance in Machine Learning refers to an unequal distribution of classes within a dataset. This issue is encountered mostly in classification tasks in which the distribution of classes or labels in a given dataset is not uniform. The straightforward method to solve this problem is the resampling method by adding records to the minority class or deleting ones from the majority class. In this paper, we have experimented with the two resampling widely adopted techniques: oversampling and undersampling. In order to explore both techniques, we have chosen a public imbalanced dataset from kaggle website Santander Customer Transaction Prediction and have applied a group of well-known machine learning algorithms with different hyperparamters that give best results for both resampling techniques. One of the key findings of this paper is noticing that oversampling performs better than undersampling for different classifiers and obtains higher scores in different evaluation metrics.
182 citations
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TL;DR: The implementation of UbiB breathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error, and can detect apnea with more than 96% accuracy in both the device-on-chest and hands-free scenarios.
Abstract: Monitoring breathing rates and patterns helps in the diagnosis and potential avoidance of various health problems. Current solutions for respiratory monitoring, however, are usually invasive and/or limited to medical facilities. In this paper, we propose a novel respiratory monitoring system, UbiBreathe, based on ubiquitous off-the-shelf WiFi-enabled devices. Our experiments show that the received signal strength (RSS) at a WiFi-enabled device held on a person's chest is affected by the breathing process. This effect extends to scenarios when the person is situated on the line-of-sight (LOS) between the access point and the device, even without holding it. UbiBreathe leverages these changes in the WiFi RSS patterns to enable ubiquitous non-invasive respiratory rate estimation, as well as apnea detection.
We propose the full architecture and design for UbiBreathe, incorporating various modules that help reliably extract the hidden breathing signal from a noisy WiFi RSS. The system handles various challenges such as noise elimination, interfering humans, sudden user movements, as well as detecting abnormal breathing situations. Our implementation of UbiBreathe using off-the-shelf devices in a wide range of environmental conditions shows that it can estimate different breathing rates with less than 1 breaths per minute (bpm) error. In addition, UbiBreathe can detect apnea with more than 96% accuracy in both the device-on-chest and hands-free scenarios. This highlights its suitability for a new class of anywhere respiratory monitoring.
182 citations
Authors
Showing all 7666 results
Name | H-index | Papers | Citations |
---|---|---|---|
Andrew McCallum | 113 | 472 | 78240 |
Yousef Khader | 94 | 586 | 111094 |
Michael P. Jones | 90 | 707 | 29327 |
David S Sanders | 75 | 639 | 23712 |
Nidal Hilal | 72 | 395 | 21524 |
Nagendra P. Shah | 71 | 334 | 19939 |
Jeffrey R. Idle | 70 | 261 | 16237 |
Rahul Sukthankar | 70 | 240 | 28630 |
Matthias Kern | 66 | 332 | 14871 |
David De Cremer | 65 | 297 | 13788 |
Moustafa Youssef | 61 | 299 | 15541 |
Mohammed Farid | 61 | 299 | 15820 |
Rudolf Holze | 58 | 388 | 13761 |
Rich Caruana | 57 | 145 | 26451 |
Eberhardt Herdtweck | 56 | 332 | 10785 |