Relevant bibliographies by topics / Artificial intelligence and machine learning

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Artificial intelligence and machine learning'

Author: Grafiati
Published: 25 June 2021
Last updated: 2 February 2022

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Journal articles on the topic "Artificial intelligence and machine learning"

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Григоров, Отто Володимирович, Галина Оттівна Аніщенко, Всеволод Вікторович Стрижак, Надія Олександрівна Петренко, Ольга Володимирівна Турчин, Антон Олександрович Окунь, and Олег Ернестович Пономарьов. "Artificial intelligence. Machine learning." Vehicle and Electronics. Innovative Technologies, no. 15 (June 2, 2019): 17. http://dx.doi.org/10.30977/veit.2226-9266.2019.15.0.17.

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Tiwari, Ashutosh. "Artificial Intelligence And Machine Learning Empowering The Mass Medicine." Advanced Materials Letters 10, no. 5 (February 1, 2019): 302. http://dx.doi.org/10.5185/amlett.2019.1005.

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Pedoia, V. "Machine Learning and Artificial Intelligence." Osteoarthritis and Cartilage 28 (April 2020): S16. http://dx.doi.org/10.1016/j.joca.2020.02.010.

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Yakovlev, Igor. "Thesaurus Artificial Intelligence: Machine Learning." Upravlenie Megapolisom, no. 5 (2014): 18–33. http://dx.doi.org/10.14570/issn.2073-2724/um-5-2014/02-yakovlev.

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Wolfgang, Kelly. "Artificial Intelligence and Machine Learning." Hearing Journal 72, no. 3 (March 2019): 26. http://dx.doi.org/10.1097/01.hj.0000554346.30951.8d.

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Mashita, Tomohiro. "Artificial Intelligence and Machine Learning." Journal of The Institute of Image Information and Television Engineers 72, no. 3 (2018): 235–40. http://dx.doi.org/10.3169/itej.72.235.

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Bratko, Ivan. "Machine learning in artificial intelligence." Artificial Intelligence in Engineering 8, no. 3 (January 1993): 159–64. http://dx.doi.org/10.1016/0954-1810(93)90002-w.

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Lawlor, Bonnie. "Artificial Intelligence and Machine Learning." Chemistry International 43, no. 1 (January 1, 2021): 8–13. http://dx.doi.org/10.1515/ci-2021-0103.

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Abstract The uses of Artificial Intelligence (AI) and Machine Learning (ML) are topics of presentations at most conferences today across diverse professional disciplines. Why? The following quote says it all:
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Aryal, Gopi. "Artificial intelligence in surgical pathology." Journal of Pathology of Nepal 9, no. 1 (April 2, 2019): I. http://dx.doi.org/10.3126/jpn.v9i1.23444.

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Artificial intelligence (AI) is machine intelligence that mimics human cognitive function. It denotes the intelligence presented by some artificial entities including computers and robots. In supervised learning, a machine is trained with data that contain pairs of inputs and outputs. In unsupervised learning, machines are given data inputs that are not explicitly programmed.1 Machine learning refines a model that predicts outputs using sample inputs (features) and a feedback loop. It relies heavily on extracting or selecting salient features, which is a combination of art and science (“feature engineering”). A subset of feature learning is deep learning, which harnesses neural networks modeled after the biological nervous system of animals. Deep learning discovers the features from the raw data provided during training. Hidden layers in the artificial neural network represent increasingly more complex features in the data. Convolutional neural network is a type of deep learning commonly used for image analysis.
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Ghahramani, Zoubin. "Probabilistic machine learning and artificial intelligence." Nature 521, no. 7553 (May 2015): 452–59. http://dx.doi.org/10.1038/nature14541.

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Dissertations / Theses on the topic "Artificial intelligence and machine learning"

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林謀楷 and Mau-kai Lam. "Inductive machine learning with bias." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1994. http://hub.hku.hk/bib/B31212426.

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Forsman, Robin, and Jimmy Jönsson. "Artificial intelligence and Machine learning : a diabetic readmission study." Thesis, Högskolan Kristianstad, Avdelningen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hkr:diva-19412.

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The maturing of Artificial intelligence provides great opportunities for healthcare, but also comes with new challenges. For Artificial intelligence to be adequate a comprehensive analysis of the data is necessary along with testing the data in multiple algorithms to determine which algorithm is appropriate to use. In this study collection of data has been gathered that consists of patients who have either been readmitted or not readmitted to hospital within 30-days after being admitted. The data has then been analyzed and compared in different algorithms to determine the most appropriate algorithm to use.
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Zhang, Sixiao. "Classifier Privacy in Machine Learning Markets." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1586460332748024.

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Lu, Yibiao. "Statistical methods with application to machine learning and artificial intelligence." Diss., Georgia Institute of Technology, 2012. http://hdl.handle.net/1853/44730.

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This thesis consists of four chapters. Chapter 1 focuses on theoretical results on high-order laplacian-based regularization in function estimation. We studied the iterated laplacian regularization in the context of supervised learning in order to achieve both nice theoretical properties (like thin-plate splines) and good performance over complex region (like soap film smoother). In Chapter 2, we propose an innovative static path-planning algorithm called m-A* within an environment full of obstacles. Theoretically we show that m-A* reduces the number of vertex. In the simulation study, our approach outperforms A* armed with standard L1 heuristic and stronger ones such as True-Distance heuristics (TDH), yielding faster query time, adequate usage of memory and reasonable preprocessing time. Chapter 3 proposes m-LPA* algorithm which extends the m-A* algorithm in the context of dynamic path-planning and achieves better performance compared to the benchmark: lifelong planning A* (LPA*) in terms of robustness and worst-case computational complexity. Employing the same beamlet graphical structure as m-A*, m-LPA* encodes the information of the environment in a hierarchical, multiscale fashion, and therefore it produces a more robust dynamic path-planning algorithm. Chapter 4 focuses on an approach for the prediction of spot electricity spikes via a combination of boosting and wavelet analysis. Extensive numerical experiments show that our approach improved the prediction accuracy compared to those results of support vector machine, thanks to the fact that the gradient boosting trees method inherits the good properties of decision trees such as robustness to the irrelevant covariates, fast computational capability and good interpretation.
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Conway, Jennifer (Jennifer Elizabeth). "Artificial intelligence and machine learning : current applications in real estate." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/120609.

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Thesis: S.M. in Real Estate Development, Massachusetts Institute of Technology, Program in Real Estate Development in conjunction with the Center for Real Estate, 2018.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 113-117).
Real estate meets machine learning: real contribution or just hype? Creating and managing the built environment is a complicated task fraught with difficult decisions, challenging relationships, and a multitude of variables. Today's technology experts are building computers and software that can help resolve many of these challenges, some of them using what is broadly called artificial intelligence and machine learning. This thesis will define machine learning and artificial intelligence for the investor and real estate audience, examine the ways in which these new analytic, predictive, and automating technologies are being used in the real estate industry, and postulate potential future applications and associated challenges. Machine learning and artificial intelligence can and will be used to facilitate real estate investment in myriad ways, spanning all aspects of the real estate profession -- from property management, to investment decisions, to development processes -- transforming real estate into a more efficient and data-driven industry.
by Jennifer Conway.
S.M. in Real Estate Development
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Carlucci, Lorenzo. "Some cognitively-motivated learning paradigms in Algorithmic Learning Theory." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 0.68 Mb., p, 2006. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&res_dat=xri:pqdiss&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&rft_dat=xri:pqdiss:3220797.

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Rose, Lydia M. "Modernizing Check Fraud Detection with Machine Learning." Thesis, Utica College, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=13421455.

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Even as electronic payments and virtual currencies become more popular, checks are still the nearly ubiquitous form of payment for many situations in the United States such as payroll, purchasing a vehicle, paying rent, and hiring a contractor. Fraud has always plagued this form of payment, and this research aimed to capture the scope of this 15th century problem in the 21st century. Today, counterfeit checks originating from overseas are the scourge of online dating sites, classifieds forums, and mailboxes throughout the country. Additional frauds including alteration, theft, and check kiting also exploit checks. Check fraud is causing hundreds of millions in estimated losses to both financial institutions and consumers annually, and the problem is growing. Fraud investigators and financial institutions must be better educated and armed to successfully combat it. This research study collected information on the history of checks, forms of check fraud, victimization, and methods for check fraud prevention and detection. Check fraud is not only a financial issue, but also a social one. Uneducated and otherwise vulnerable consumers are particularly targeted by scammers exploiting this form of fraud. Racial minorities, elderly, mentally ill, and those living in poverty are disproportionately affected by fraud victimization. Financial institutions struggle to strike a balance between educating customers, complying with regulations, and tailoring alerts that are both valuable and fast. Applications of artificial intelligence including machine learning and computer vision have many recent advancements, but financial institution anti-fraud measures have not kept pace. This research concludes that the onus rests on financial institutions to take a modern approach to check fraud, incorporating machine learning into real-time reviews, to adequately protect victims.

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Townsend, Larry. "Wireless Sensor Network Clustering with Machine Learning." Diss., NSUWorks, 2018. https://nsuworks.nova.edu/gscis_etd/1042.

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Wireless sensor networks (WSNs) are useful in situations where a low-cost network needs to be set up quickly and no fixed network infrastructure exists. Typical applications are for military exercises and emergency rescue operations. Due to the nature of a wireless network, there is no fixed routing or intrusion detection and these tasks must be done by the individual network nodes. The nodes of a WSN are mobile devices and rely on battery power to function. Due the limited power resources available to the devices and the tasks each node must perform, methods to decrease the overall power consumption of WSN nodes are an active research area. This research investigated using genetic algorithms and graph algorithms to determine a clustering arrangement of wireless nodes that would reduce WSN power consumption and thereby prolong the lifetime of the network. The WSN nodes were partitioned into clusters and a node elected from each cluster to act as a cluster head. The cluster head managed routing tasks for the cluster, thereby reducing the overall WSN power usage. The clustering configuration was determined via genetic algorithm and graph algorithms. The fitness function for the genetic algorithm was based on the energy used by the nodes. It was found that the genetic algorithm was able to cluster the nodes in a near-optimal configuration for energy efficiency. Chromosome repair was also developed and implemented. Two different repair methods were found to be successful in producing near-optimal solutions and reducing the time to reach the solution versus a standard genetic algorithm. It was also found the repair methods were able to implement gateway nodes and energy balance to further reduce network energy consumption.
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Abdul-hadi, Omar. "Machine Learning Applications to Robot Control." Thesis, University of California, Berkeley, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10817183.

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Control of robot manipulators can be greatly improved with the use of velocity and torque feedforward control. However, the effectiveness of feedforward control greatly relies on the accuracy of the model. In this study, kinematics and dynamics analysis is performed on a six axis arm, a Delta2 robot, and a Delta3 robot. Velocity feedforward calculation is performed using the traditional means of using the kinematics solution for velocity. However, a neural network is used to model the torque feedforward equations. For each of these mechanisms, we first solve the forward and inverse kinematics transformations. We then derive a dynamic model. Later, unlike traditional methods of obtaining the dynamics parameters of the dynamics model, the dynamics model is used to infer dependencies between the input and output variables for neural network torque estimation. The neural network is trained with joint positions, velocities, and accelerations as inputs, and joint torques as outputs. After training is complete, the neural network is used to estimate the feedforward torque effort. Additionally, an investigation is done on the use of neural networks for deriving the inverse kinematics solution of a six axis arm. Although the neural network demonstrated outstanding ability to model complex mathematical equations, the inverse kinematics solution was not accurate enough for practical use.

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Cox, Michael Thomas. "Introspective multistrategy learning : constructing a learning strategy under reasoning failure." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/10074.

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Books on the topic "Artificial intelligence and machine learning"

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BOOKS, Editors of TIME-LIFE. Artificial intelligence. Edited by Time-Life Books. Alexandra, Va: Time-Life Books, 1991.

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Joshi, Ameet V. Machine Learning and Artificial Intelligence. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-26622-6.

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Bogaerts, Bart, Gianluca Bontempi, Pierre Geurts, Nick Harley, Bertrand Lebichot, Tom Lenaerts, and Gilles Louppe, eds. Artificial Intelligence and Machine Learning. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65154-1.

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Baratchi, Mitra, Lu Cao, Walter A. Kosters, Jefrey Lijffijt, Jan N. van Rijn, and Frank W. Takes, eds. Artificial Intelligence and Machine Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76640-5.

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Furukawa, Kōichi. Machine intelligence 14: Applied machine intelligence. Oxford: Clarendon Press, 2002.

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On machine intelligence. 2nd ed. Chichester: E. Horwood, 1986.

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Artificial Intelligence and Intelligent Systems. Oxford: Oxford University Press, 2005.

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Ramanna, Sheela. Emerging Paradigms in Machine Learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Artificial intelligence: How machines think. New York, N.Y: Baen Books, 1985.

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Saxena, Ankur, and Shivani Chandra. Artificial Intelligence and Machine Learning in Healthcare. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0811-7.

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Book chapters on the topic "Artificial intelligence and machine learning"

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Michalewicz, Zbigniew. "Machine Learning." In Artificial Intelligence, 215–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-662-02830-8_13.

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Vermeulen, Andreas François. "Industrialized Artificial Intelligence." In Industrial Machine Learning, 533–56. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5316-8_14.

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Taulli, Tom. "Machine Learning." In Artificial Intelligence Basics, 39–67. Berkeley, CA: Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5028-0_3.

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Chowdhary, K. R. "Machine Learning." In Fundamentals of Artificial Intelligence, 375–413. New Delhi: Springer India, 2020. http://dx.doi.org/10.1007/978-81-322-3972-7_13.

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Akerkar, Rajendra. "Machine Learning." In Artificial Intelligence for Business, 19–32. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-97436-1_2.

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Aggarwal, Charu C. "Machine Learning: The Inductive View." In Artificial Intelligence, 167–210. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72357-6_6.

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Lee, Raymond S. T. "Machine Learning." In Artificial Intelligence in Daily Life, 41–70. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7695-9_3.

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Kumar, Rajul, Karan Sehgal, and Ankit Lal Meena. "Artificial Intelligence." In Artificial Intelligence, Machine Learning, and Data Science Technologies, 155–71. New York: CRC Press, 2021. http://dx.doi.org/10.1201/9781003153405-8.

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Kumar, Rohit. "Artificial Intelligence—Basics." In Machine Learning and Cognition in Enterprises, 33–49. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-3069-5_3.

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Maria, Italia Joseph, and T. Devi. "Machine Learning." In Artificial Intelligence Theory, Models, and Applications, 241–80. Boca Raton: Auerbach Publications, 2021. http://dx.doi.org/10.1201/9781003175865-14.

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Conference papers on the topic "Artificial intelligence and machine learning"

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Villamil, Valentina, Rochelle Deloria, and Gregor Wolbring. "Artificial intelligence and machine learning." In REHAB 2019: 5th Workshop on ICTs for improving Patients Rehabilitation Research Techniques. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3364138.3364158.

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Ongsulee, Pariwat. "Artificial intelligence, machine learning and deep learning." In 2017 15th International Conference on ICT and Knowledge Engineering (ICT&KE). IEEE, 2017. http://dx.doi.org/10.1109/ictke.2017.8259629.

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Leduc, Jean-Pierre. "Sensor networks and artificial intelligence for real time motion analysis." In Applications of Machine Learning, edited by Michael E. Zelinski, Tarek M. Taha, Jonathan Howe, Abdul A. Awwal, and Khan M. Iftekharuddin. SPIE, 2019. http://dx.doi.org/10.1117/12.2529424.

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Sclavounos, Paul D., and Yu Ma. "Artificial Intelligence Machine Learning in Marine Hydrodynamics." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77599.

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Artificial Intelligence (AI) Support Vector Machine (SVM) learning algorithms have enjoyed rapid growth in recent years with applications in a wide range of disciplines often with impressive results. The present paper introduces this machine learning technology to the field of marine hydrodynamics for the study of complex potential and viscous flow problems. Examples considered include the forecasting of the seastate elevations and vessel responses using their past time records as “explanatory variables” or “features” and the development of a nonlinear model for the roll restoring, added moment of inertia and viscous damping using the vessel response kinematics from free decay tests as “features”. A key innovation of AI-SVM kernel algorithms is that the nonlinear dependence of the dependent variable on the “features” is embedded into the SVM kernel and its selection plays a key role in the performance of the algorithms. The kernel selection is discussed and its relation to the physics of the marine hydrodynamic flows considered in the present paper is addressed.
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Pandey, Anand, Pragyadeep Manglik, and Punit Taluja. "Pollution Control Machine Using Artificial Intelligence And Machine Learning." In 2019 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE). IEEE, 2019. http://dx.doi.org/10.1109/iccike47802.2019.9004288.

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"SECTION 3: Artificial Intelligence and Machine Learning." In 2020 10th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2020. http://dx.doi.org/10.1109/acit49673.2020.9208952.

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Lowell, J., and P. Szafian. "Fault detection from 3D seismic data using Artificial Intelligence." In Second EAGE Workshop on Machine Learning. European Association of Geoscientists & Engineers, 2021. http://dx.doi.org/10.3997/2214-4609.202132013.

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Freeman, Sean. "Artificial intelligence for emergency management." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications II, edited by Tien Pham, Latasha Solomon, and Katie Rainey. SPIE, 2020. http://dx.doi.org/10.1117/12.2561636.

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Gupta, Anitya, Akhilesh Kumar, and Vinayak Bhushan. "World of intelligence defense object detection–machine learning (artificial intelligence)." In INTERNATIONAL CONFERENCE ON ELECTRICAL, ELECTRONICS, MATERIALS AND APPLIED SCIENCE. Author(s), 2018. http://dx.doi.org/10.1063/1.5032027.

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Meng, Kevin, Cheng Shi, and Yu Meng. "Vehicle Action Prediction Using Artificial Intelligence." In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2018. http://dx.doi.org/10.1109/icmla.2018.00200.

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Reports on the topic "Artificial intelligence and machine learning"

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Byrd, Lexie, Curtis Smith, Ross Kunz, Nancy Lybeck, Ronald Boring, Humberto Garcia, Victor Walker, et al. Big Data, Machine Learning, Artificial Intelligence [PowerPoint]. Office of Scientific and Technical Information (OSTI), May 2020. http://dx.doi.org/10.2172/1617329.

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Randerson, James, Efi Georgiou, Padhraic Smyth, Yang Chen, Shane Coffield, Casey Graff, Stijn Hantson, et al. Machine learning and artificial intelligence for wildfire prediction. Office of Scientific and Technical Information (OSTI), April 2021. http://dx.doi.org/10.2172/1769739.

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Vecherin, Sergey, Jacob Desmond, Taylor Hodgdon, Jordan Bates, Michael Parker, James Lever, Garett Hoch, Mark Bodie, and Sally Shoop. Artificial intelligence and machine learning for autonomous military vehicles. Engineer Research and Development Center (U.S.), August 2020. http://dx.doi.org/10.21079/11681/37943.

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Milgrom, Paul, and Steven Tadelis. How Artificial Intelligence and Machine Learning Can Impact Market Design. Cambridge, MA: National Bureau of Economic Research, February 2018. http://dx.doi.org/10.3386/w24282.

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Baker, Nathan, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, et al. Brochure on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), December 2018. http://dx.doi.org/10.2172/1484362.

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Aboaba, A., Y. Martinez, S. Mohaghegh, M. Shahnam, C. Guenther, and Y. Liu. Smart Proxy Modeling Application of Artificial Intelligence & Machine Learning in Computational Fluid Dynamics. Office of Scientific and Technical Information (OSTI), July 2020. http://dx.doi.org/10.2172/1642460.

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Baker, Nathan, Frank Alexander, Timo Bremer, Aric Hagberg, Yannis Kevrekidis, Habib Najm, Manish Parashar, et al. Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), February 2019. http://dx.doi.org/10.2172/1478744.

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Ratner, Daniel, Bobby Sumpter, Frank Alexander, Jay Jay Billings, Ryan Coffee, Sarah Cousineau, Peter Denes, et al. BES Roundtable on Producing and Managing Large Scientific Data with Artificial Intelligence and Machine Learning. Office of Scientific and Technical Information (OSTI), October 2019. http://dx.doi.org/10.2172/1630823.

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Ali, Alee. From the Starship Enterprise to Los Alamos National Laboratory Artificial Intelligence and Machine Learning in the NSRC. Office of Scientific and Technical Information (OSTI), January 2021. http://dx.doi.org/10.2172/1760557.

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Daniels, Matthew, Autumn Toney, Melissa Flagg, and Charles Yang. Machine Intelligence for Scientific Discovery and Engineering Invention. Center for Security and Emerging Technology, May 2021. http://dx.doi.org/10.51593/20200099.

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The advantages of nations depend in part on their access to new inventions—and modern applications of artificial intelligence can help accelerate the creation of new inventions in the years ahead. This data brief is a first step toward understanding how modern AI and machine learning have begun accelerating growth across a wide array of science and engineering disciplines in recent years.
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