8 results
Search Results
2. Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning.
- Author
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DeLancey, Evan Ross, Kariyeva, Jahan, Bried, Jason T., and Hird, Jennifer N.
- Subjects
MACHINE learning ,TAIGA ecology ,TAIGAS ,AQUATIC sciences ,PHYSICAL sciences ,EARTH sciences ,ENVIRONMENTAL sciences - Abstract
Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km
2 ) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]- Published
- 2019
- Full Text
- View/download PDF
3. Can co-authorship networks be used to predict author research impact? A machine-learning based analysis within the field of degenerative cervical myelopathy research
- Author
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Noah Grodzinski, Benjamin Davies, Ben Grodzinski, Grodzinski, Ben [0000-0001-8839-4718], and Apollo - University of Cambridge Repository
- Subjects
FOS: Computer and information sciences ,Biomedical Research ,Social connectedness ,Computer science ,International Cooperation ,Field (computer science) ,Machine Learning ,Mathematical and Statistical Techniques ,Knowledge extraction ,Japan ,Medical Laboratory Personnel ,Musculoskeletal System ,Multidisciplinary ,Computer and information sciences ,Artificial neural network ,Applied Mathematics ,Simulation and Modeling ,Statistics ,Opinion leadership ,Neurodegenerative Diseases ,Research Assessment ,Research Personnel ,Physical sciences ,Medicine ,Anatomy ,Network Analysis ,Algorithms ,Network analysis ,Research Article ,Neural Networks ,Science ,FOS: Physical sciences ,Bibliometrics ,Spinal Cord Diseases ,Machine Learning Algorithms ,Artificial Intelligence ,Humans ,Statistical Methods ,Set (psychology) ,Skeleton ,Artificial Neural Networks ,Retrospective Studies ,Computational Neuroscience ,Medicine and health sciences ,Biology and life sciences ,Computational Biology ,Data science ,Spine ,Authorship ,Research and analysis methods ,North America ,Neural Networks, Computer ,Mathematics ,Neck ,Neuroscience ,Forecasting - Abstract
Introduction Degenerative Cervical Myelopathy (DCM) is a common and disabling condition, with a relatively modest research capacity. In order to accelerate knowledge discovery, the AO Spine RECODE-DCM project has recently established the top priorities for DCM research. Uptake of these priorities within the research community will require their effective dissemination, which can be supported by identifying key opinion leaders (KOLs). In this paper, we aim to identify KOLs using artificial intelligence. We produce and explore a DCM co-authorship network, to characterise researchers’ impact within the research field. Methods Through a bibliometric analysis of 1674 scientific papers in the DCM field, a co-authorship network was created. For each author, statistics about their connections to the co-authorship network (and so the nature of their collaboration) were generated. Using these connectedness statistics, a neural network was used to predict H-Index for each author (as a proxy for research impact). The neural network was retrospectively validated on an unseen author set. Results DCM research is regionally clustered, with strong collaboration across some international borders (e.g., North America) but not others (e.g., Western Europe). In retrospective validation, the neural network achieves a correlation coefficient of 0.86 (p Discussion Analysis of the neural network shows that the nature of collaboration strongly impacts an author’s research visibility, and therefore suitability as a KOL. This also suggests greater collaboration within the DCM field could help to improve both individual research visibility and global synergy.
- Published
- 2021
4. Mapping technological innovation dynamics in artificial intelligence domains: Evidence from a global patent analysis
- Author
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Na Liu, Philip Shapira, Xiaoxu Yue, and Jiancheng Guan
- Subjects
Computer and Information Sciences ,China ,Technology ,Asia ,Science ,Social Sciences ,Technology/methods ,Research and Analysis Methods ,Machine Learning ,Geographical Locations ,Patents as Topic ,Machine Learning Algorithms ,Automation ,Japan ,Inventions ,Artificial Intelligence ,Support Vector Machines ,Humans ,Patents ,Language Acquisition ,Multidisciplinary ,Models, Statistical ,Applied Mathematics ,Simulation and Modeling ,Linguistics ,Automation/methods ,United States ,Intellectual Property ,Models, Organizational ,Physical Sciences ,People and Places ,North America ,Medicine ,Law and Legal Sciences ,Commercial Law ,Diffusion of Innovation ,Mathematics ,Algorithms ,Research Article - Abstract
Artificial intelligence (AI) is emerging as a technology at the center of many political, economic, and societal debates. This paper formulates a new AI patent search strategy and applies this to provide a landscape analysis of AI innovation dynamics and technology evolution. The paper uses patent analyses, network analyses, and source path link count algorithms to examine AI spatial and temporal trends, cooperation features, cross-organization knowledge flow and technological routes. Results indicate a growing yet concentrated, non-collaborative and multi-path development and protection profile for AI patenting, with cross-organization knowledge flows based mainly on interorganizational knowledge citation links.
- Published
- 2021
5. A personalized channel recommendation and scheduling system considering both section video clips and full video clips
- Author
-
SeungGwan Lee and Daeho Lee
- Subjects
Computer science ,Section (typography) ,Video Recording ,Social Sciences ,lcsh:Medicine ,02 engineering and technology ,computer.software_genre ,Geographical locations ,Machine Learning ,Database and Informatics Methods ,Learning and Memory ,0202 electrical engineering, electronic engineering, information engineering ,Data Mining ,Psychology ,Computer Networks ,CLIPS ,lcsh:Science ,Statistical Data ,computer.programming_language ,Multidisciplinary ,Multimedia ,Applied Mathematics ,Simulation and Modeling ,IPTV ,Scheduling system ,Physical Sciences ,Information Retrieval ,020201 artificial intelligence & image processing ,Information Technology ,Algorithms ,Statistics (Mathematics) ,Research Article ,Communication channel ,Computer and Information Sciences ,Schedule ,Minnesota ,Broadcasting ,Research and Analysis Methods ,Computer Communication Networks ,Artificial Intelligence ,Learning ,Humans ,Internet ,business.industry ,Communications Media ,lcsh:R ,Cognitive Psychology ,Biology and Life Sciences ,020207 software engineering ,Models, Theoretical ,United States ,North America ,Cognitive Science ,lcsh:Q ,People and places ,business ,computer ,Mathematics ,Neuroscience - Abstract
With the convergence of various broadcasting systems, the amount of content available in mobile terminals including IPTV has significantly increased. In this paper, we propose a system that enables users to schedule programs considering both section video clips and full video clips based on the user detection method with similar preference. And, since the system constituting the contents can be classified according to the program, the proposed method can store a program desired by the user, and thus create and schedule a kind of individual channel. Experimental results show that the proposed method has a higher prediction accuracy; this is accomplished by comparing existing channel recommendation methods with the program recommendation methods proposed in this paper.
- Published
- 2018
6. Using meta-predictions to identify experts in the crowd when past performance is unknown
- Author
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Marcellin Martinie, Piers D. L. Howe, and Tom Wilkening
- Subjects
Computer science ,Social Sciences ,Surveys ,computer.software_genre ,Geographical locations ,Mathematical and Statistical Techniques ,Sociology ,Psychology ,050207 economics ,Schools ,050208 finance ,Multidisciplinary ,Applied Mathematics ,Simulation and Modeling ,Experimental Design ,Physics ,Statistics ,05 social sciences ,Sports Science ,Research Design ,Physical Sciences ,Medicine ,Algorithms ,Research Article ,Sports ,Leverage (finance) ,Science ,Decision Making ,Research and Analysis Methods ,Machine learning ,Education ,0502 economics and business ,Humans ,Leverage (statistics) ,Statistical Methods ,Sound Waves ,Behavior ,Survey Research ,business.industry ,Probabilistic logic ,Biology and Life Sciences ,Acoustics ,United States ,North America ,Recreation ,Artificial intelligence ,People and places ,business ,computer ,Mathematics ,Forecasting - Abstract
A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified.
- Published
- 2020
7. Windowed persistent homology: A topological signal processing algorithm applied to clinical obesity data
- Author
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Kayvan Najarian, Amy E. Rothberg, Harm Derksen, Craig Biwer, Heidi B. IglayReger, and Charles F. Burant
- Subjects
Michigan ,Computer science ,Physiology ,Economics ,Entropy ,Social Sciences ,Health Care Sector ,lcsh:Medicine ,02 engineering and technology ,Overweight ,computer.software_genre ,01 natural sciences ,Geographical locations ,Weight loss ,0202 electrical engineering, electronic engineering, information engineering ,Medicine and Health Sciences ,Entropy (energy dispersal) ,lcsh:Science ,education.field_of_study ,Multidisciplinary ,Entropy (statistical thermodynamics) ,Applied Mathematics ,Simulation and Modeling ,Physics ,Hausdorff space ,Signal Processing, Computer-Assisted ,Physiological Parameters ,Physical Sciences ,Signal processing algorithms ,Engineering and Technology ,Thermodynamics ,medicine.symptom ,Algorithms ,Research Article ,Population ,Machine learning ,Research and Analysis Methods ,Entropy (classical thermodynamics) ,Health Economics ,Weight Loss ,medicine ,Entropy (information theory) ,Humans ,Obesity ,0101 mathematics ,education ,Entropy (arrow of time) ,Persistent homology ,business.industry ,010102 general mathematics ,Body Weight ,lcsh:R ,Biology and Life Sciences ,020206 networking & telecommunications ,medicine.disease ,United States ,Health Care ,Hausdorff distance ,Signal Processing ,North America ,lcsh:Q ,Artificial intelligence ,People and places ,Electronics ,Accelerometers ,business ,computer ,Mathematics ,Entropy (order and disorder) - Abstract
Overweight and obesity are highly prevalent in the population of the United States, affecting roughly 2/3 of Americans. These diseases, along with their associated conditions, are a major burden on the healthcare industry in terms of both dollars spent and effort expended. Volitional weight loss is attempted by many, but weight regain is common. The ability to predict which patients will lose weight and successfully maintain the loss versus those prone to regain weight would help ease this burden by allowing clinicians the ability to skip treatments likely to be ineffective. In this paper we introduce a new windowed approach to the persistent homology signal processing algorithm that, when paired with a modified, semimetric version of the Hausdorff distance, can differentiate the two groups where other commonly used methods fail. The novel approach is tested on accelerometer data gathered from an ongoing study at the University of Michigan. While most standard approaches to signal processing show no difference between the two groups, windowed persistent homology and the modified Hausdorff semimetric show a clear separation. This has significant implications for clinical decision making and patient care.
- Published
- 2017
8. Large-scale probabilistic identification of boreal peatlands using Google Earth Engine, open-access satellite data, and machine learning
- Author
-
Jahan Kariyeva, Jennifer N. Hird, Evan R. DeLancey, and Jason T. Bried
- Subjects
Topography ,Earth observation ,Peat ,010504 meteorology & atmospheric sciences ,Earth, Planet ,0211 other engineering and technologies ,Marine and Aquatic Sciences ,02 engineering and technology ,computer.software_genre ,01 natural sciences ,Geographical locations ,Alberta ,Ecosystem services ,Machine Learning ,Remote Sensing ,Bogs ,Taiga ,Satellite imagery ,Lidar ,Multidisciplinary ,Applied Mathematics ,Simulation and Modeling ,Biodiversity ,Physical Sciences ,Medicine ,Engineering and Technology ,Algorithms ,Research Article ,Freshwater Environments ,Conservation of Natural Resources ,Canada ,Computer and Information Sciences ,Science ,Climate Change ,Climate change ,Research and Analysis Methods ,Machine learning ,Machine Learning Algorithms ,Surface Water ,Artificial Intelligence ,Resource management ,Ecosystem ,Fens ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Landforms ,Radar ,business.industry ,Ecology and Environmental Sciences ,Aquatic Environments ,Geomorphology ,Carbon ,Boreal ,Wetlands ,North America ,Earth Sciences ,Environmental science ,Artificial intelligence ,Hydrology ,People and places ,Scale (map) ,business ,computer ,Mathematics - Abstract
Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands-a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage-in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management.
- Published
- 2019
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