The Experts below are selected from a list of 108171 Experts worldwide ranked by ideXlab platform
Philip T Sager - One of the best experts on this subject based on the ideXlab platform.
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comprehensive in vitro proarrhythmia assay cipa update from a cardiac safety research consortium health and Environmental Sciences institute fda meeting
Therapeutic Innovation & Regulatory Science, 2019Co-Authors: David G Strauss, Rick J Turner, Gary Gintant, Ksenia Blinova, Jose Vicente, Philip T SagerAbstract:A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, ...
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comprehensive in vitro proarrhythmia assay cipa update from a cardiac safety research consortium health and Environmental Sciences institute fda meeting
Therapeutic Innovation & Regulatory Science, 2019Co-Authors: David G Strauss, Rick J Turner, Gary Gintant, Ksenia Blinova, Jose Vicente, Philip T SagerAbstract:A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, clinicians, and regulators from multiple geographic regions to evaluate progress to date in the Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative, a new paradigm to evaluate proarrhythmic risk. Study reports from the 4 different components of the CiPA paradigm (ionic current studies, in silico modeling to generate a Torsade Metric Score, human induced pluripotent stem cell-derived ventricular cardiomyocytes, and clinical ECG assessments including J-Tpeakc) were presented and discussed. This paper provides a high-level summary of the CiPA data presented at the meeting.
Anthony Lehmann - One of the best experts on this subject based on the ideXlab platform.
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grid enabled spatial data infrastructure for Environmental Sciences challenges and opportunities
Future Generation Computer Systems, 2011Co-Authors: Gregory Giuliani, Nicolas Ray, Anthony LehmannAbstract:Spatial Data Infrastructures (SDIs) are being widely used in the Environmental Sciences to share, discover, visualize and retrieve geospatial data through Open Geospatial Consortium (OGC) web services. However, SDIs have limited analytical capabilities, an essential task to turn data into understandable information. Geospatial data are typically processed on desktop computers, but their limited power limits the types of analyses that can be conducted given ever-increasing amounts of high resolution data. With the recently introduced Web Processing Service and the availability of large storage and computing facilities offered by Grid infrastructures, new opportunities are emerging within the Environmental Sciences communities. The enviroGRIDS project, funded by the European Commission ''Seventh Framework Programme'' (EU/FP7), will target these issues.
Andrew D Heap - One of the best experts on this subject based on the ideXlab platform.
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review spatial interpolation methods applied in the Environmental Sciences a review
Environmental Modelling and Software, 2014Co-Authors: Andrew D HeapAbstract:Spatially continuous data of Environmental variables are often required for Environmental Sciences and management. However, information for Environmental variables is usually collected by point sampling, particularly for the mountainous region and deep ocean area. Thus, methods generating such spatially continuous data by using point samples become essential tools. Spatial interpolation methods (SIMs) are, however, often data-specific or even variable-specific. Many factors affect the predictive performance of the methods and previous studies have shown that their effects are not consistent. Hence it is difficult to select an appropriate method for a given dataset. This review aims to provide guidelines and suggestions regarding application of SIMs to Environmental data by comparing the features of the commonly applied methods which fall into three categories, namely: non-geostatistical interpolation methods, geostatistical interpolation methods and combined methods. Factors affecting the performance, including sampling design, sample spatial distribution, data quality, correlation between primary and secondary variables, and interaction among factors, are discussed. A total of 25 commonly applied methods are then classified based on their features to provide an overview of the relationships among them. These features are quantified and then clustered to show similarities among these 25 methods. An easy to use decision tree for selecting an appropriate method from these 25 methods is developed based on data availability, data nature, expected estimation, and features of the method. Finally, a list of software packages for spatial interpolation is provided. Display Omitted Comparison of commonly used spatial interpolation methods in Environmental science.Analysis of factors affecting the performance of spatial interpolation methods.Classification of 25 methods to illustrate their relationship.Guidelines for selecting an appropriate method for a given dataset.A list of software packages for commonly used spatial interpolation methods.
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a review of comparative studies of spatial interpolation methods in Environmental Sciences performance and impact factors
Ecological Informatics, 2011Co-Authors: Jin Li, Andrew D HeapAbstract:Spatial interpolation methods have been applied to many disciplines. Many factors affect the performance of the methods, but there are no consistent findings about their effects. In this study, we use comparative studies in Environmental Sciences to assess the performance and to quantify the impacts of data properties on the performance. Two new measures are proposed to compare the performance of the methods applied to variables with different units/scales. A total of 53 comparative studies were assessed and the performance of 72 methods/sub-methods compared is analysed. The impacts of sample density, data variation and sampling design on the estimations of 32 methods are quantified using data derived from their application to 80 variables. Inverse distance weighting (IDW), ordinary kriging (OK), and ordinary co-kriging (OCK) are the most frequently used methods. Data variation is a dominant impact factor and has significant effects on the performance of the methods. As the variation increases, the accuracy of all methods decreases and the magnitude of decrease is method dependent. Irregular-spaced sampling design might improve the accuracy of estimation. The effect of sampling density on the performance of the methods is found not to be significant. The implications of these findings are discussed.
William W Hsieh - One of the best experts on this subject based on the ideXlab platform.
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nonlinear regression in Environmental Sciences using extreme learning machines
Environmental Modelling and Software, 2015Co-Authors: Aranildo R Lima, Alex J Cannon, William W HsiehAbstract:The extreme learning machine (ELM), a single-hidden layer feedforward neural network algorithm, was tested on nine Environmental regression problems. The prediction accuracy and computational speed of the ensemble ELM were evaluated against multiple linear regression (MLR) and three nonlinear machine learning (ML) techniques - artificial neural network (ANN), support vector regression and random forest (RF). Simple automated algorithms were used to estimate the parameters (e.g. number of hidden neurons) needed for model training. Scaling the range of the random weights in ELM improved its performance. Excluding large datasets (with large number of cases and predictors), ELM tended to be the fastest among the nonlinear models. For large datasets, RF tended to be the fastest. ANN and ELM had similar skills, but ELM was much faster than ANN except for large datasets. Generally, the tested ML techniques outperformed MLR, but no single method was best for all the nine datasets. We test extreme learning machine (ELM) for nonlinear regression on nine Environmental datasets.We use automated algorithms to estimate the parameters of four nonlinear prediction methods.Scaling the range of the random weights improves the predictions of the ELM ensemble model.Excluding large datasets, ELM tends to be the fastest among the nonlinear models.No single method was best for all the nine datasets.
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nonlinear regression in Environmental Sciences by support vector machines combined with evolutionary strategy
Computers & Geosciences, 2013Co-Authors: Aranildo R Lima, Alex J Cannon, William W HsiehAbstract:A hybrid algorithm combining support vector regression with evolutionary strategy (SVR-ES) is proposed for predictive models in the Environmental Sciences. SVR-ES uses uncorrelated mutation with p step sizes to find the optimal SVR hyper-parameters. Three Environmental forecast datasets used in the WCCI-2006 contest - surface air temperature, precipitation and sulphur dioxide concentration - were tested. We used multiple linear regression (MLR) as benchmark and a variety of machine learning techniques including bootstrap-aggregated ensemble artificial neural network (ANN), SVR-ES, SVR with hyper-parameters given by the Cherkassky-Ma estimate, the M5 regression tree, and random forest (RF). We also tested all techniques using stepwise linear regression (SLR) first to screen out irrelevant predictors. We concluded that SVR-ES is an attractive approach because it tends to outperform the other techniques and can also be implemented in an almost automatic way. The Cherkassky-Ma estimate is a useful approach for minimizing the mean absolute error and saving computational time related to the hyper-parameter search. The ANN and RF are also good options to outperform multiple linear regression (MLR). Finally, the use of SLR for predictor selection can dramatically reduce computational time and often help to enhance accuracy.
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machine learning methods in the Environmental Sciences neural networks and kernels
2009Co-Authors: William W HsiehAbstract:Machine learning methods originated from artificial intelligence and are now used in various fields in Environmental Sciences today. This is the first single-authored textbook providing a unified treatment of machine learning methods and their applications in the Environmental Sciences. Due to their powerful nonlinear modeling capability, machine learning methods today are used in satellite data processing, general circulation models(GCM), weather and climate prediction, air quality forecasting, analysis and modeling of Environmental data, oceanographic and hydrological forecasting, ecological modeling, and monitoring of snow, ice and forests. The book includes end-of-chapter review questions and an appendix listing web sites for downloading computer code and data sources. A resources website containing datasets for exercises, and password-protected solutions are available. The book is suitable for first-year graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in Environmental Sciences interested in applying these new methods to their own work. Preface Excerpt Machine learning is a major subfield in computational intelligence (also called artificial intelligence). Its main objective is to use computational methods to extract information from data. Neural network methods, generally regarded as forming the first wave of breakthrough in machine learning, became popular in the late 1980s, while kernel methods arrived in a second wave in the second half of the 1990s. This is the first single-authored textbook to give a unified treatment of machine learning methods and their applications in the Environmental Sciences. Machine learning methods began to infiltrate the Environmental Sciences in the 1990s. Today, thanks to their powerful nonlinear modeling capability, they are no longer an exotic fringe species, as they are heavily used in satellite data processing, in general circulation models (GCM), in weather and climate prediction, air quality forecasting, analysis and modeling of Environmental data, oceanographic and hydrological forecasting, ecological modeling, and in the monitoring of snow, ice and forests, etc. This book presents machine learning methods and their applications in the Environmental Sciences (including satellite remote sensing, atmospheric science, climate science, oceanography, hydrology and ecology), written at a level suitable for beginning graduate students and advanced undergraduates. It is also valuable for researchers and practitioners in Environmental Sciences interested in applying these new methods to their own work. Chapters 1-3, intended mainly as background material for students, cover the standard statistical methods used in Environmental Sciences. The machine learning methods of chapters 4-12 provide powerful nonlinear generalizations for many of these standard linear statistical methods. End-of-chapter review questions are included, allowing readers to develop their problem-solving skills and monitor their understanding of the material presented. An appendix lists websites available for downloading computer code and data sources. A resources website is available containing datasets for exercises, and additional material to keep the book completely up-to-date. About the Author WILLIAM W. HSIEH is a Professor in the Department of Earth and Ocean Sciences and in the Department of Physics and Astronomy, as well as Chair of the Atmospheric Science Programme, at the University of British Columbia. He is internationally known for his pioneering work in developing and applying machine learning methods in Environmental Sciences. He has published over 80 peer-reviewed journal publications covering areas of climate variability, machine learning, oceanography, atmospheric science and hydrology.
David G Strauss - One of the best experts on this subject based on the ideXlab platform.
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comprehensive in vitro proarrhythmia assay cipa update from a cardiac safety research consortium health and Environmental Sciences institute fda meeting
Therapeutic Innovation & Regulatory Science, 2019Co-Authors: David G Strauss, Rick J Turner, Gary Gintant, Ksenia Blinova, Jose Vicente, Philip T SagerAbstract:A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, ...
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comprehensive in vitro proarrhythmia assay cipa update from a cardiac safety research consortium health and Environmental Sciences institute fda meeting
Therapeutic Innovation & Regulatory Science, 2019Co-Authors: David G Strauss, Rick J Turner, Gary Gintant, Ksenia Blinova, Jose Vicente, Philip T SagerAbstract:A Cardiac Safety Research Consortium / Health and Environmental Sciences Institute / FDA-sponsored Think Tank Meeting was convened in Washington, DC, on May 21, 2018, to bring together scientists, clinicians, and regulators from multiple geographic regions to evaluate progress to date in the Comprehensive In Vitro Proarrhythmia Assay (CiPA) Initiative, a new paradigm to evaluate proarrhythmic risk. Study reports from the 4 different components of the CiPA paradigm (ionic current studies, in silico modeling to generate a Torsade Metric Score, human induced pluripotent stem cell-derived ventricular cardiomyocytes, and clinical ECG assessments including J-Tpeakc) were presented and discussed. This paper provides a high-level summary of the CiPA data presented at the meeting.
