Literatura académica sobre el tema "AI Observer"
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Artículos de revistas sobre el tema "AI Observer"
Straeubig, Michael. "Games, AI and Systems". Eludamos: Journal for Computer Game Culture 10, n.º 1 (21 de abril de 2020): 141–60. http://dx.doi.org/10.7557/23.6176.
Texto completoSalinel, Brandon, Matthew Grudza, Sarah Zeien, Matthew Murphy, Jake Adkins, Corey Jensen, Curt Bay et al. "Comparison of segmentation methods to improve throughput in annotating AI-observer for detecting colorectal cancer." Journal of Clinical Oncology 40, n.º 4_suppl (1 de febrero de 2022): 142. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.142.
Texto completoSmith, Andrew Dennis, Brian C. Allen, Asser Abou Elkassem, Rafah Mresh, Seth T. Lirette, Yujan Shrestha, J. David Giese et al. "Multi-institutional comparative effectiveness of advanced cancer longitudinal imaging response evaluation methods: Current practice versus artificial intelligence-assisted." Journal of Clinical Oncology 38, n.º 15_suppl (20 de mayo de 2020): 2010. http://dx.doi.org/10.1200/jco.2020.38.15_suppl.2010.
Texto completoGürsoy Çoruh, Ayşegül, Bülent Yenigün, Çağlar Uzun, Yusuf Kahya, Emre Utkan Büyükceran, Atilla Elhan, Kaan Orhan y Ayten Kayı Cangır. "A comparison of the fusion model of deep learning neural networks with human observation for lung nodule detection and classification". British Journal of Radiology 94, n.º 1123 (1 de julio de 2021): 20210222. http://dx.doi.org/10.1259/bjr.20210222.
Texto completoM. Lazim, Izzuddin, Abdul Rashid Husain, Nurul Adilla Mohd Subha y Mohd Ariffanan Mohd Basri. "Intelligent Observer-Based Feedback Linearization for Autonomous Quadrotor Control". International Journal of Engineering & Technology 7, n.º 4.35 (30 de noviembre de 2018): 904. http://dx.doi.org/10.14419/ijet.v7i4.35.26280.
Texto completoSalinel, Brandon, Matthew Grudza, Sarah Zeien, Matthew Murphy, Jake Adkins, Corey Jensen, Curt Bay et al. "Ensemble voting decreases false positives in AI second-observer reads for detecting colorectal cancer." Journal of Clinical Oncology 40, n.º 4_suppl (1 de febrero de 2022): 141. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.141.
Texto completoPalm, Christiane, Catherine E. Connolly, Regina Masser, Barbara Padberg Sgier, Eva Karamitopoulou, Quentin Simon, Beata Bode y Marianne Tinguely. "Determining HER2 Status by Artificial Intelligence: An Investigation of Primary, Metastatic, and HER2 Low Breast Tumors". Diagnostics 13, n.º 1 (3 de enero de 2023): 168. http://dx.doi.org/10.3390/diagnostics13010168.
Texto completoAjmera, Pranav, Amit Kharat, Tanveer Gupte, Richa Pant, Viraj Kulkarni, Vinay Duddalwar y Purnachandra Lamghare. "Observer performance evaluation of the feasibility of a deep learning model to detect cardiomegaly on chest radiographs". Acta Radiologica Open 11, n.º 7 (julio de 2022): 205846012211073. http://dx.doi.org/10.1177/20584601221107345.
Texto completoAl-Hammadi, Noora, Palmira Caparrotti, Saju Divakar, Mohamed Riyas, Suparna Halsnad Chandramouli, Rabih Hammoud, Jillian Hayes, Maeve Mc Garry, Satheesh Prasad Paloor y Primoz Petric. "MRI reduces variation of contouring for boost clinical target volume in breast cancer patients without surgical clips in the tumour bed". Radiology and Oncology 51, n.º 2 (24 de mayo de 2017): 160–68. http://dx.doi.org/10.1515/raon-2017-0014.
Texto completoHameed, B. M. Zeeshan, Milap Shah, Nithesh Naik, Sufyan Ibrahim, Bhaskar Somani, Patrick Rice, Naeem Soomro y Bhavan Prasad Rai. "Contemporary application of artificial intelligence in prostate cancer: an i-TRUE study". Therapeutic Advances in Urology 13 (enero de 2021): 175628722098664. http://dx.doi.org/10.1177/1756287220986640.
Texto completoLibros sobre el tema "AI Observer"
Enemark, Christian, ed. Ethics of Drone Strikes. Edinburgh University Press, 2021. http://dx.doi.org/10.3366/edinburgh/9781474483575.001.0001.
Texto completoRUNCAN, PATRICIA. Copilărie, consiliere și parentalitate cu impact. Vol. 1. Ediție revizuită. Seria AUTENTIC. EDITURA DE VEST, 2021. http://dx.doi.org/10.51820/autentic.2021.vol.1.editie_revizuita.
Texto completoRuncan, Patricia. Copilărie și parentalitate cu impact. Editura de Vest, 2020. http://dx.doi.org/10.51820/autentic.2020.vol.1.
Texto completoCapítulos de libros sobre el tema "AI Observer"
Cowie, Roddy, Dearbhaile Bradley y Mark Livingstone. "Using observer-controlled movement and expectations of regularity to recover tridimensional structure". En AI and Cognitive Science ’90, 178–92. London: Springer London, 1991. http://dx.doi.org/10.1007/978-1-4471-3542-5_12.
Texto completoMaruyama, Yoshihiro. "AI, Quantum Information, and External Semantic Realism: Searle’s Observer-Relativity and Chinese Room, Revisited". En Fundamental Issues of Artificial Intelligence, 115–27. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-26485-1_8.
Texto completoMamun, Shamim Al, Mohammad Eusuf Daud, Mufti Mahmud, M. Shamim Kaiser y Andre Luis Debiaso Rossi. "ALO: AI for Least Observed People". En Applied Intelligence and Informatics, 306–17. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-82269-9_24.
Texto completoBragaglia, Stefano, Federico Chesani, Paola Mello, Marco Montali y Davide Sottara. "Fuzzy Conformance Checking of Observed Behaviour with Expectations". En AI*IA 2011: Artificial Intelligence Around Man and Beyond, 80–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23954-0_10.
Texto completoMusiolik, Grzegorz. "Predictability of AI Decisions". En Analyzing Future Applications of AI, Sensors, and Robotics in Society, 17–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-3499-1.ch002.
Texto completoNatale, Simone. "How to Dispel Magic". En Deceitful Media, 33–49. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190080365.003.0003.
Texto completoCharles, Darryl, Colin Fyfe, Daniel Livingstone y Stephen McGlinchey. "Ant Colony Optimisation". En Biologically Inspired Artificial Intelligence for Computer Games, 180–201. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59140-646-4.ch011.
Texto completoSmith, Gary. "If You Torture the Data Long Enough". En The AI Delusion. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198824305.003.0008.
Texto completoDasgupta, Tirthankar, Abir Naskar, Lipika Dey y Mohammad Shakir. "A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text". En Towards a Knowledge-Aware AI. IOS Press, 2022. http://dx.doi.org/10.3233/ssw220021.
Texto completoPandey, Avinash Kumar, Varsha Singh y Sachin Jain. "Study and comparative analysis of perturb and observe (P&O) and fuzzy logic based PV-MPPT algorithms". En Applications of AI and IOT in Renewable Energy, 193–209. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-91699-8.00011-5.
Texto completoActas de conferencias sobre el tema "AI Observer"
Barnett, Alina J., Vaibhav Sharma, Neel Gajjar, Jerry D. Fang, Fides Schwartz, Chaofan Chen, Joseph Y. Lo y Cynthia Rudin. "Interpretable deep learning models for better clinician-AI communication in clinical mammography". En Image Perception, Observer Performance, and Technology Assessment, editado por Claudia R. Mello-Thoms y Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612372.
Texto completoGiger, Maryellen L. "Towards understanding perception in the latest era of AI in medical imaging (Conference Presentation)". En Image Perception, Observer Performance, and Technology Assessment, editado por Frank W. Samuelson y Sian Taylor-Phillips. SPIE, 2020. http://dx.doi.org/10.1117/12.2556704.
Texto completoŠerić, Ljiljana, Darko Stipaničev y Damir Krstinić. "ML/AI in Intelligent Forest Fire Observer Network". En 3rd EAI International Conference on Management of Manufacturing Systems. EAI, 2018. http://dx.doi.org/10.4108/eai.6-11-2018.2279681.
Texto completoTao, Xuetong, Ziba Gandomkar, Tong Li, Warren M. Reed y Patrick C. Brennan. "Varying performance levels for diagnosing mammographic images depending on reader nationality have AI and educational implications". En Image Perception, Observer Performance, and Technology Assessment, editado por Claudia R. Mello-Thoms y Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2611342.
Texto completoWhitney, Heather M., Karen Drukker, Hiroyuki Abe y Maryellen L. Giger. "Case-based repeatability and operating point variability of AI: breast lesion classification based on deep transfer learning". En Image Perception, Observer Performance, and Technology Assessment, editado por Claudia R. Mello-Thoms y Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612405.
Texto completoPershin, Ilya, Maksim Kholiavchenko, Bulat Maksudov, Tamerlan Mustafaev y Bulat Ibragimov. "AI-based analysis of radiologist’s eye movements for fatigue estimation: a pilot study on chest X-rays". En Image Perception, Observer Performance, and Technology Assessment, editado por Claudia R. Mello-Thoms y Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2612760.
Texto completoByrd, Darrin, Dennis Bontempi, Hao Yang, Hugo Aerts, Binsheng Zhao, Andriy Fedorov, Lawrence Schwartz, Tavis Allison, Chaya Moscowitz y Paul E. Kinahan. "Using virtual clinical trials to determine the accuracy of AI-based quantitative imaging biomarkers in oncology trials using standard-of-care CT". En Image Perception, Observer Performance, and Technology Assessment, editado por Claudia R. Mello-Thoms y Sian Taylor-Phillips. SPIE, 2022. http://dx.doi.org/10.1117/12.2610980.
Texto completoKammardi Shashiprakash, Avinash, Brendon Lutnick, Brandon Ginley, Darshana Govind, Nicholas Lucarelli, Kuang-Yu Jen, Avi Z. Rosenberg et al. "A distributed system improves inter-observer and AI concordance in annotating interstitial fibrosis and tubular atrophy". En Digital and Computational Pathology, editado por John E. Tomaszewski y Aaron D. Ward. SPIE, 2021. http://dx.doi.org/10.1117/12.2581789.
Texto completoZhang, Qiang, Konrad Werys, Elena Lukaschuk, Iulia Popescu, Evan Hann, Stefan Neubauer, Vanessa M Ferreira y Stefan K Piechnik. "3 Train the Ai like a human observer: deep learning with visualisation and guidance on attention in cardiac T1 mapping". En British Society of Cardiovascular Magnetic Resonance 2019 annual meeting, March 26 – 27th, Oxford UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2019. http://dx.doi.org/10.1136/heartjnl-2019-bscmr.3.
Texto completoZhang, Qiang, Konrad Werys, Elena Lukaschuk, Iulia Popescu, Evan Hann, Stefan Neubauer, Vanessa M Ferreira y Stefan K Piechnik. "9 Train the Ai like a human observer: deep learning with visualisation and guidance on attention in cardiac T1 mapping". En British Society of Cardiovascular Magnetic Resonance 2019 annual meeting, March 26 – 27th, Oxford UK. BMJ Publishing Group Ltd and British Cardiovascular Society, 2019. http://dx.doi.org/10.1136/heartjnl-2019-bscmr.9.
Texto completoInformes sobre el tema "AI Observer"
Roschelle, Jeremy, James Lester y Judi Fusco. AI and the Future of Learning: Expert Panel Report. Digital Promise, noviembre de 2020. http://dx.doi.org/10.51388/20.500.12265/106.
Texto completoBorrett, Veronica, Melissa Hanham, Gunnar Jeremias, Jonathan Forman, James Revill, John Borrie, Crister Åstot et al. Science and Technology for WMD Compliance Monitoring and Investigations. The United Nations Institute for Disarmament Research, diciembre de 2020. http://dx.doi.org/10.37559/wmd/20/wmdce11.
Texto completoDaudelin, Francois, Lina Taing, Lucy Chen, Claudia Abreu Lopes, Adeniyi Francis Fagbamigbe y Hamid Mehmood. Mapping WASH-related disease risk: A review of risk concepts and methods. United Nations University Institute for Water, Environment and Health, diciembre de 2021. http://dx.doi.org/10.53328/uxuo4751.
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