Subject: Machine learning / Type: Articles | Search Results

1. Advantages of Synthetic Noise and Machine Learning for Analyzing Radioecological Data Sets

: Shuryak, Igor
: 2017
: Articles
: Machine learningStatisticsRadioactive pollutionMedical radiology

2. A Generalizable Brain-Computer Interface (BCI) Using Machine Learning for Feature Discovery

: Nurse, Ewan S.; Karoly, Philippa J.; Grayden, David B.; Freestone, Dean R.
: 2015
: Articles
: Brain-computer interfacesElectroencephalography--Computer programsMachine learningNeuronsNeural networks (Computer science)Neurosciences

3. An updated Lagrangian LBM–DEM–FEM coupling model for dual-permeability fissured porous media with embedded discontinuities

: Wang, Kun; Sun, WaiChing
: 2019
: Articles
: Civil engineeringMachine learningMechanics--Computer simulationFluid mechanicsPorous materialsPermeability

4. A robust and stable gene selection algorithm based on graph theory and machine learning

: Saha, Subrata; Soliman, Ahmed; Rajasekaran, Sanguthevar
: 2021
: Articles
: Graph theoryMachine learningGenetic algorithmsSupport vector machines

5. Artificial Intelligence’s Fair Use Crisis

: Sobel, Benjamin L. W.
: 2017
: Articles
: Fair use (Copyright)Artificial intelligenceMachine learningTechnology and law

6. Bayesian Transductive Markov Random Fields for Interactive Segmentation in Retinal Disorders

: Lee, Noah; Laine, Andrew F.; Smith, R. Theodore
: 2009
: Articles
: Biomedical engineeringBayesian statistical decision theoryMachine learning

7. Bow-tie signaling in c-di-GMP: Machine learning in a simple biochemical network

: Yan, Jinyuan; Deforet, Maxime; Boyle, Kerry E.; Rahman, Rayees; Liang, Raymond; Okegbe, Chinweike; Dietrich, Lars; Qiu, Weigang; Xavier, Joao B.
: 2017
: Articles
: Computational biologyMachine learningBiofilmsNatural selection

8. Brain-predicted age difference score is related to specific cognitive functions: A multi-site replication analysis

: Boyle, Rory; Jollans, Lee; Rueda-Delgado, Laura M.; Rizzo, Rossella; Yener, Görsev G.; McMorrow, Jason P.; Knight, Silvin P.; Carey, Daniel; Robertson, Ian H.; Emek-Savaş, Derya D.; Stern, Yaakov; Kenny, Rose Anne; Whelan, Robert
: 2020
: Articles
: CognitionBrain--AgingMagnetic resonance imagingBiochemical markersMachine learning

9. Comparative analysis, applications, and interpretation of electronic health record-based stroke phenotyping methods

: Thangaraj, Phyllis M.; Kummer, Benjamin R.; Lorberbaum, Tal; Elkind, Mitchell S. V.; Tatonetti, Nicholas P.
: 2020
: Articles
: Cerebrovascular diseasePhenotypeMachine learningMedical records--Data processing

10. Comprehensive machine learning analysis of Hydra behavior reveals a stable basal behavioral repertoire

: Han, Shuting; Taralova, Ekaterina; Dupre, Christophe; Yuste, Rafael
: 2018
: Articles
: NeurosciencesMachine learningAnimal behaviorBehavioral assessmentBiology

11. Decentralized computation offloading for multi-user mobile edge computing: a deep reinforcement learning approach

: Chen, Zhao; Wang, Xiaodong
: 2020
: Articles
: Deep learning (Machine learning)Machine learningSignal processing

12. Deep learning to represent subgrid processes in climate models

: Rasp, Stephan; Pritchard, Michael S.; Gentine, Pierre
: 2018
: Articles
: Clouds--Mathematical modelsClimatic changes--ModelsClimatology--ResearchMachine learningAtmospheric models

13. Eavesdropping on the Arctic: Automated bioacoustics reveal dynamics in songbird breeding phenology

: Oliver, Ruth Yvonne; Ellis, Daniel P. W.; Chmura, Helen E.; Krause, Jesse S.; Pérez, Jonathan H.; Sweet, Shannan Kathlyn; Gough, Laura; Wingfield, John C.; Boelman, Natalie T.
: 2018
: Articles
: BioacousticsPhenologySongbirds--MigrationAnimal behavior--Climatic factorsMachine learningEcology

14. Empirical assessment of a RGB-D sensor on motion capture and action recognition for construction worker monitoring

: Han, SangUk; Achar, Madhav; Lee, SangHyun; Pena-Mora, Feniosky A.
: 2013
: Articles
: Construction industry--Data processingElectronic monitoring in the workplaceImage convertingMachine learningCivil engineeringEngineering

15. Exploring supervised and unsupervised methods to detect topics in biomedical text

: Lee, Minsuk; Wang, Weiqing; Yu, Hong
: 2006
: Articles
: Natural language processing (Computer science)Cluster analysisInformation storage and retrieval systems--ScienceMachine learningBioinformaticsComputer science

16. Grand Challenges in Remote Sensing Image Analysis and Classification

: Small, Christopher
: 2020
: Articles
: Remote sensingImage analysisClassificationMachine learning

17. Image set for deep learning: field images of maize annotated with disease symptoms

: Wiesner-Hanks, Tyr; Stewart, Ethan L.; Kaczmar, Nicholas; DeChant, Chad; Wu, Harvey; Nelson, Rebecca J.; Lipson, Hod; Gore, Michael A.
: 2018
: Articles
: Computer scienceMachine learningPlant diseasesCornImage processingNeural networks (Computer science)

18. iMEGES: integrated mental-disorder GEnome score by deep neural network for prioritizing the susceptibility genes for mental disorders in personal genomes

: Khan, Atlas; Liu, Qian; Wang, Kai
: 2018
: Articles
: Neural networks (Neurobiology)GenomesMental illness--Risk factorsMachine learning

19. Improving Breast Cancer Survival Analysis through Competition-Based Multidimensional Modeling

: Bilal, Erhan; Dutkowski, Janusz; Guinney, Justin; Jang, In Sock; Logsdon, Benjamin A.; Mecham, Brigham H.; Pandey, Gaurav; Vollan, Hans Kristian Moen; Sauerwine, Benjamin A.; Shimoni, Yishai; Rueda, Oscar M.; Curtis, Christina; Tost, Jorg; Alvarez, Mariano Javier; Kristensen, Vessela N.; Aparicio, Samuel; Børresen-Dale, Anne-Lise; Califano, Andrea; Caldas, Carlos; Friend, Stephen H.; Ideker, Trey; Stolovitzky, Gustavo A.; Schadt, Eric E.; Margolin, Adam A.
: 2013
: Articles
: PrognosisMachine learningBreast--CancerMedicine

20. Interpretable prediction of necrotizing enterocolitis from machine learning analysis of premature infant stool microbiota

: Lin, Yun C.; Salleb-Aouissi, Ansaf; Hooven, Thomas A.
: 2022
: Articles
: Enterocolitis, Neonatal necrotizingMachine learningFeces--Analysis

21. Learning Patterns from Unix Process Execution Traces for Intrusion Detection

: Lee, Wenke; Stolfo, Salvatore; Chan, Philip K.
: 1997
: Articles
: Computer scienceMachine learningIntrusion detection systems (Computer security)

22. Locked Out by Big Data: How Big Data, Algorithms, and Machine Learning May Undermine Housing Justice

: Schneider, Valerie
: 2020
: Articles
: Housing--Law and legislationAlgorithms--ResearchMachine learningDiscrimination

23. Machine learning and data mining in complex genomic data a review on the lessons learned in Genetic Analysis Workshop Nineteen

: Konig, Inke R.; Auerbach, Jonathan Lyle; Gola, Damian; Held, Elizabeth; Holzinger, Emily R.; Legault, Marc Andre; Sun, Rui; Tintle, Nathan; Yang, Hsin Chou
: 2016
: Articles
: Machine learningData miningGenomics

24. Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues

: Xu, Hua; Markatou, Marianthi; Dimova, Rositsa; Liu, Hongfang; Friedman, Carol
: 2006
: Articles
: Machine learningAmbiguityNatural language processing (Computer science)BioinformaticsBiometry

25. Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

: Holtzman, Benjamin K.; Paté, Arthur; Paisley, John W.; Waldhauser, Felix; Repetto, Douglas I.
: 2018
: Articles
: Machine learningEarthquake prediction--Statistical methodsSeismology

26. Modeling aspects of the language of life through transfer-learning protein sequences

: Heinzinger, Michael; Elnaggar, Ahmed; Wang, Yu; Dallago, Christian; Nechaev, Dmitrii; Matthes, Florian; Rost, Burkhard
: 2019
: Articles
: Computational biologyMachine learningAmino acid sequenceTransfer learning (Machine learning)

27. Natural language processing (NLP) tools in extracting biomedical concepts from research articles: a case study on autism spectrum disorder

: Peng, Jacqueline; Zhao, Mengge; Havrilla, James; Liu, Cong; Weng, Chunhua; Guthrie, Whitney; Schultz, Robert; Wang, Kai; Zhou, Yunyun
: 2020
: Articles
: Computational linguisticsMachine learningAutism spectrum disorders

28. Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs

: Christopher, Mark; Belghith, Akram; Bowd, Christopher; Proudfoot, James A.; Goldbaum, Michael H.; Weinreb, Robert N.; Girkin, Christopher A.; Liebmann, Jeffrey M.; Zangwill, Linda M.
: 2018
: Articles
: Glaucoma--DiagnosisFundus oculiDiagnostic imagingMachine learning

29. Personalized Prediction Of Antidepressant V. Placebo Response: Evidence From The Embarc Study

: Webb, Christian A.; Trivedi, Madhukar H.; Cohen, Zachary D.; Dillon, Daniel G.; Fournier, Jay C.; Goer, Franziska; Fava, Maurizio; McGrath, Patrick J.; Weissman, Myrna M.; Parsey, Ramin; Adams, Phil; Trombello, Joseph M.; Cooper, Crystal; Deldin, Patricia; Oquendo, Maria A.; McInnis, Melvin G.; Huys, Quentin; Bruder, Gerard; Kurian, Benji T.; Jha, Manish; DeRubeis, Robert J.; Pizzagalli, Diego A.
: 2019
: Articles
: PsychiatryEpidemiologyAntidepressantsDepression, MentalMachine learning

30. Prospective prediction of PTSD diagnosis in a nationally representative sample using machine learning

: Worthington, Michelle A.; Mandavia, Amar; Richardson-Vejlgaard, Randall
: 2020
: Articles
: Post-traumatic stress disorder--DiagnosisMachine learningBorderline personality disorderDepression, MentalPsychic trauma

31. Pumice Raft Detection Using Machine-Learning on Multispectral Satellite Imagery

: Zheng, Maggie; Mittal, Tushar; Fauria, Kristen E.; Subramaniam, Ajit; Jutzeler, Martin
: 2022
: Articles
: Submarine volcanoesRemote sensingPumiceMachine learningGoogle Earth

32. Quantitative Analysis of Immune Infiltrates in Primary Melanoma.

: Gartrell, Robyn D.; Marks, Douglas K.; Hart, Thomas D.; Li, Gen; Davari, Danielle R.; Wu, Alan; Blake, Zoë; Lu, Yan; Askin. Kayleigh N.; Monod, Anthea; Esancy, Camden L.; Stack, Edward C.; Jia, Dan Tong; Armenta, Paul M.; Fu, Yichun; Izaki, Daisuke; Taback, Bret; Rabadan, Raul; Kaufman, Howard L.; Drake, Charles G.; Horst, Basil; Saenger, Yvonne M.
: 2018
: Articles
: MelanomaMelanoma--HistopathologyMelanoma--Immunological aspectsTumors--Immunological aspectsImmunofluorescenceMachine learning

33. The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies

: Elmas, Abdulkadir; Wang, Xiaodong; Dresch, Jacqueline M.
: 2017
: Articles
: Machine learningNucleotide sequence--Data processingElectrical engineeringComputer science

34. The use of machine learning to understand the relationship between IgE to specific allergens and asthma

: Platts-Mills, Thomas A.; Perzanowski, Matthew S.
: 2018
: Articles
: Machine learningImmunoglobulin EAllergensAsthmaEnvironmental health

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