3,437,613 Hits in 4.4 sec

Test-time Collective Prediction [article]

Celestine Mendler-Dünner, Wenshuo Guo, Stephen Bates, Michael I. Jordan
2021 arXiv   pre-print
In this work, we explore a decentralized mechanism to make collective predictions at test time, leveraging each agent's pre-trained model without relying on external validation, model retraining, or data  ...  An increasingly common setting in machine learning involves multiple parties, each with their own data, who want to jointly make predictions on future test points.  ...  In this work, we instead aim for collective prediction at test time without posing any specific requirement at the training stage.  ... 
arXiv:2106.12012v1 fatcat:b6zu63iwdzahrh3jetbe5qwaam

One-class Collective Anomaly Detection based on Long Short-Term Memory Recurrent Neural Networks [article]

Nga Nguyen Thi, Van Loi Cao, Nhien-An Le-Khac
2018 arXiv   pre-print
Instead of considering each time-step separately, the observation of prediction errors from a certain number of time-steps is now proposed as a new idea for detecting collective anomalies.  ...  The prediction errors of a certain number of the latest time-steps above a threshold will indicate a collective anomaly. The model is evaluated on a time series version of the KDD 1999 dataset.  ...  Results and Discussion The Table 2 shows the collective anomaly prediction of the proposed model on three datasets, n valid , n test and a test .  ... 
arXiv:1802.00324v1 fatcat:g464qpqcxfcdrc3luvzj7bgzoe

Suggested new breakpoints of anti-MERS-CoV antibody ELISA titers: performance analysis of serologic tests

J.-H. Ko, M. A. Müller, H. Seok, G. E. Park, J. Y. Lee, S. Y. Cho, Y. E. Ha, J. Y. Baek, S. H. Kim, J.-M. Kang, Y.-J. Kim, I. J. Jo (+8 others)
2017 European Journal of Clinical Microbiology and Infectious Diseases  
In predicting neutralization activity, ELISA IgG tests showed optimal performance using sera collected after 21 dpoi at cut-off values of OD ratio 0.4 (sensitivity 100% and specificity 100%), and ELISA  ...  Performance of serologic tests improved with delayed sampling time, being maximized after a seroconversion period.  ...  To compare performances of serologic tests depending on serum collection time, each test was evaluated at three different timepoints: (1) regardless of serum collection time, (2) after 14 dpoi (or 21 dpex  ... 
doi:10.1007/s10096-017-3043-3 pmid:28695355 fatcat:hb7kq7ezv5gkzjbj72sym5kdyq

Prediction of maximal heart rate percent during constant intensity efforts on trained subjects

Chams Eddine Guinoubi, Ammar Nbigh, Youssef Grira, Raouf Hammami, Salma Abedelmalek
2012 Open Journal of Internal Medicine  
All test were performed at the same time of day (i.e., 18:00 h).  ...  The results of this study showed that eighteen collective regressions including different independent variables were developed to predict %HRmax.  ...  test and (b) Equation (18) EQ #18 predictors: Time, %vVO 2 max, and Time x% vVO 2 max.  ... 
doi:10.4236/ojim.2012.24031 fatcat:q2uwhxmt5rbxxnp3572k3e4tyi

Novel Method for Measuring the Heat Collection Rate and Heat Loss Coefficient of Water-in-Glass Evacuated Tube Solar Water Heaters Based on Artificial Neural Networks and Support Vector Machine

Zhijian Liu, Hao Li, Xinyu Zhang, Guangya Jin, Kewei Cheng
2015 Energies  
model for the prediction of heat loss coefficient due to their low root mean square (RMS) errors, short training times, and high prediction accuracies (under the tolerances of 30%, 20%, and 10%, respectively  ...  To address this problem, we propose machine learning models including artificial neural networks (ANNs) and support vector machines (SVM) to predict the heat collection rate and heat loss coefficient without  ...  Tables 3 and 4 show the development results for the prediction of heat collection rate and heat loss coefficient, respectively, using the mean RMS error in testing, mean training time, and mean prediction  ... 
doi:10.3390/en8088814 fatcat:rriahgsll5g6tajqscdmj4ap4e

Predicting Cardiopulmonary Response to Incremental Exercise Test

Elena Baralis, Tania Cerquitelli, Silvia Chiusano, Andrea Giordano, Alessandro Mezzani, Davide Susta, Xin Xiao
2015 2015 IEEE 28th International Symposium on Computer-Based Medical Systems  
Each model can be exploited in the real-time stream prediction phase to periodically predict, during the test execution, signal values achievable by the patient.  ...  This paper proposes the Cardiopulmonary Response Prediction (CRP) framework for early predicting the physiological signal values that can be reached during an incremental exercise test.  ...  (ii) When postponing the prediction time, the prediction horizon (i.e., the time interval between the prediction step and the test end) reduces and tends to zero.  ... 
doi:10.1109/cbms.2015.60 dblp:conf/cbms/BaralisCCGMSX15 fatcat:n4o2jssvoveinoolntfeahnc2u

Towards Maximising Openness in Digital Sensitivity Review Using Reviewing Time Predictions [chapter]

Graham McDonald, Craig Macdonald, Iadh Ounis
2018 Lecture Notes in Computer Science  
In this paper, we conduct a user study and use the log data to build models to predict reviewing times for an average sensitivity reviewer.  ...  Moreover, we show that using our reviewing time predictions to select the order that documents are reviewed can markedly increase the ratio of reviewed documents that are released to the public, e.g. +  ...  Generated Test Collection: We use the collected reviews to generate a test collection for developing our models.  ... 
doi:10.1007/978-3-319-76941-7_65 fatcat:kcjr5445tzcddolkuixjet2v3e

Identifying important variables for predicting travel time of freeway with non-recurrent congestion with neural networks

Chi-Sen Li, Mu-Chen Chen
2012 Neural computing & applications (Print)  
Hence, how to improve the prediction capability of longdistance travel time in the case of non-recurrent congestion is an important issue that must be overcome in the field of travel time prediction.  ...  Furthermore, the historical travel time inferred from the original data of electronic toll collection (ETC) system is also used as the input variable, and the actual travel time inferred from ETC is used  ...  of performance measures on usage of historical travel time collected by ETC test with the performance of using time variable as the input variable (Experiment 5.2).For investigating the prediction capabilities  ... 
doi:10.1007/s00521-012-1114-z fatcat:ygcyqym7bfaovpe455vxypqvpa

Research on Cloud Hard Disk Capacity Prediction Scheme Based on Time Series Model

Zizhen Yuan, Chengyu Wen, Xiaoli Zhang
2019 International Journal of Computer Applications Technology and Research  
After data processing, the ARIMA time series model is built. Then the capacity of the disk in the future is predicted.  ...  OpenStack is a cloud operating system, but it does not provide the predictive functionality for cloud disk usage of the virtual machines running on it.  ...  Then use these data to predict the disk capacity by the ARIMA time series model , and the prediction values for the next period of time are obtained, but its predictions for long periods of time are not  ... 
doi:10.7753/ijcatr0801.1006 fatcat:4lcqo4a3dzcurmm5nsyqsn4q2u

Early prediction of acquiring acute kidney injury for older inpatients using most effective laboratory test results

Yi-Shian Chen, Che-Yi Chou, Arbee L.P. Chen
2020 BMC Medical Informatics and Decision Making  
The most effective (laboratory test, type) pairs found for different prediction times are slightly different.  ...  However, Blood Urea Nitrogen (BUN) is found the most effective (laboratory test, type) pair for most prediction times.  ...  The funding bodies played no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.  ... 
doi:10.1186/s12911-020-1050-2 pmid:32079533 pmcid:PMC7032003 fatcat:kiqigpm7mfgopdu5j52vpik5pi

Some factors influencing quality of spontaneous or induced sputum for inflammatory cell analysis

M.L. Bartoli, E. Bacci, S. Cianchetti, F.L. Dente, A. Di Franco, B. Vagaggini, M. De Santis, E. Masino, P.L. Paggiaro
2016 Monaldi Archives for Chest Disease  
We also monitored changes in the quality in patients who repeated sputum collection several times, comparing those whose first sample was adequate with those whose first sample was inadequate.  ...  predict the quality of the sputum samples obtained in a large group of asthmatic subjects. Methods.  ...  sputum in subjects who repeat-ed the test several times, can be used as predicting factors of sputum adequacy.  ... 
doi:10.4081/monaldi.2007.493 pmid:17695690 fatcat:36ddt2tjbncwxehxxqpdod5skm

Alumina Concentration Detection Based on the Kernel Extreme Learning Machine

Sen Zhang, Tao Zhang, Yixin Yin, Wendong Xiao
2017 Sensors  
Li et al. [5] proposed a new fuzzy expert control method based on smart identification, multi-control mode, and decision making mechanisms to achieve alumina concentration prediction and real time control  ...  The learning speed and accuracy of these networks are, in general, far slower and cannot meet the requirements of real time detection.  ...  Author Contributions: Sen Zhang proposed the idea of the soft-sensor model for alumina concentration prediction based on the KELM.  ... 
doi:10.3390/s17092002 pmid:28862685 pmcid:PMC5620724 fatcat:3k3uaz6clbbuffhqvyvse3g7zy

ARIMA Model-Based Web Services Trustworthiness Evaluation and Prediction [chapter]

Meng Li, Zhebang Hua, Junfeng Zhao, Yanzhen Zou, Bing Xie
2012 Lecture Notes in Computer Science  
As most Web services are delivered by third parties over unreliable Internet and are late bound at run-time, it is reasonable and useful to evaluate and predict the trustworthiness of Web services.  ...  First, we evaluate Web services trustworthiness with comprehensive trustworthy evidences collected from the Internet on a regular basis.  ...  Test: Our method use sample Autocovariance Function (ACF) [12] to test whether a times series is a stationary series. (2) Differencing: If the series is identified as non-stationary in stationary test  ... 
doi:10.1007/978-3-642-34321-6_51 fatcat:eepplzavt5bpxeiwcatvvtni2e

Page 49 of American Society of Civil Engineers. Collected Journals Vol. 114, Issue 1 [page]

1989 American Society of Civil Engineers. Collected Journals  
Box Test RESULTS The successful prediction of the time to instability for the trench contain- ing the 218 mm diameter pipe from data collected using the 7.9 mm diameter heating rod represents a test of  ...  Included are the predictions of time to instability determined by the Hartley and Black method based on the results gathered from the scaled model tests.  ... 

MAV Stabilization using Machine Learning and Onboard Sensors [article]

Jason Yosinski, Cooper Bills
2012 arXiv   pre-print
This predicted drift will allow the MAV to adjust it's flightpath to maintain a desired course.  ...  In this research, we explore using machine learning to predict the drift (flight path errors) of an MAV while executing a desired flight path.  ...  To test our tracking system and to assist with data collection, we constructed a simple PD controller to center the drone in real-time.  ... 
arXiv:1202.4465v1 fatcat:o7rwgmeopzchjjatptle7k64su
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