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Статті в журналах з теми "Artificial section"
Gond, Pankaj Kumar, Aditya Shukla, Satish Sahani, Neha Gond, and Dr Harvendra Kumar. "Association Rule Mining using FP-Growth and An Innovative Artificial Neural Network Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3407–12. http://dx.doi.org/10.22214/ijraset.2022.43149.
Повний текст джерелаVinayak, Patil Ashish, and S. R. Suryawanshi. "Behaviour of Frp Strengthening of Hysd-I Section With Artificial Degradation." Journal of Advances and Scholarly Researches in Allied Education 15, no. 2 (April 1, 2018): 427–30. http://dx.doi.org/10.29070/15/56859.
Повний текст джерелаYANG, H. S. "Special Section on Artificial Reality and Telexistence." IEICE Transactions on Information and Systems E89-D, no. 1 (January 1, 2006): 9–10. http://dx.doi.org/10.1093/ietisy/e89-d.1.9.
Повний текст джерелаLi, Hairong. "Special Section Introduction: Artificial Intelligence and Advertising." Journal of Advertising 48, no. 4 (August 8, 2019): 333–37. http://dx.doi.org/10.1080/00913367.2019.1654947.
Повний текст джерелаLópez, Beatriz, Clare Martin, and Pau Herrero Viñas. "Special section on artificial intelligence for diabetes." Artificial Intelligence in Medicine 85 (April 2018): 26–27. http://dx.doi.org/10.1016/j.artmed.2017.09.008.
Повний текст джерелаXie, Heng Xing. "Analysis of Water Environmental Quality Using BP Artificial Neural Network in Weihe River Baoji Segment." Applied Mechanics and Materials 401-403 (September 2013): 2147–50. http://dx.doi.org/10.4028/www.scientific.net/amm.401-403.2147.
Повний текст джерелаObeid, H., F. Hillani,, R. Fakih, and K. Mozannar. "Artificial Intelligence: Serving American Security and Chinese Ambitions." Financial Markets, Institutions and Risks 4, no. 3 (2020): 42–52. http://dx.doi.org/10.21272/fmir.4(3).42-52.2020.
Повний текст джерелаWu, Huiling, Bingzheng Wu, Fangping Lai, Peizhong Liu, Guorong Lyu, Shaozheng He, and Jiangfeng Dai. "Application of Artificial Intelligence in Anatomical Structure Recognition of Standard Section of Fetal Heart." Computational and Mathematical Methods in Medicine 2023 (January 24, 2023): 1–13. http://dx.doi.org/10.1155/2023/5650378.
Повний текст джерелаDymitruk, Maria. "The Right to a Fair Trial in Automated Civil Proceedings." Masaryk University Journal of Law and Technology 13, no. 1 (June 30, 2019): 27–44. http://dx.doi.org/10.5817/mujlt2019-1-2.
Повний текст джерелаO'Leary, Daniel E., and John Kingston. "Artificial intelligence in business II: Development, integration and organizational issues." Knowledge Engineering Review 9, no. 1 (March 1994): 1–19. http://dx.doi.org/10.1017/s026988890000655x.
Повний текст джерелаДисертації з теми "Artificial section"
Sanchez, Maguiña Mildred Madeleine, and Feliz Pool Rusbel Vidal. "Optimización de las dimensiones de placas mediante el uso de IA para reducir los costos en edificios de 6 pisos en el distrito de Miraflores." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/652826.
Повний текст джерелаThis article investigates the use of Artificial Neural Networks as a type of Artificial Intelligence in order to reduce the costs of reinforced concrete. For this reason, the use of this type of algorithm was proposed with the objective of optimizing the sections of the shear walls in 6-story buildings without irregularities. Ten different neural networks were configured in order to choose the one that best suits the data used for training. In each algorithm, the width and length of the building; and the distance between maximum span of the X and Y axis were established as input variables. However, the number of hidden layers and the number of neurons in each of them was different. In the training stage, 30 cases with optimized dimensions were used, with this it was obtained that the neuronal network predicts the total length of the shear wall and its thickness with an error of 10%.
Trabajo de investigación
Szames, Esteban Alejandro. "Few group cross section modeling by machine learning for nuclear reactor." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASS134.
Повний текст джерелаModern nuclear reactors utilize core calculations that implement a thermo-hydraulic feedback requiring accurate homogenized few-group cross sections.They describe the interactions of neutrons with matter, and are endowed with the properties of smoothness and regularity, steaming from their underling physical phenomena. This thesis is devoted to the modeling of these functions by industry state-of-theart and innovative machine learning techniques. Mathematically, the subject can be defined as the analysis of convenient mapping techniques from one multi-dimensional space to another, conceptualize as the aggregated sum of these functions, whose quantity and domain depends on the simulations objectives. Convenient is intended in terms of computational performance, such as the model’s size, evaluation speed, accuracy, robustness to numerical noise, complexity,etc; always with respect to the engineering modeling objectives that specify the multidimensional spaces of interest. In this thesis, a standard UO₂ PWR fuel assembly is analyzed for three state-variables, burnup,fuel temperature, and boron concentration.Library storage requirements are optimized meeting the evaluation speed and accuracy targets in view of microscopic, macroscopic cross sections and the infinite multiplication factor. Three approximation techniques are studied: The state-of-the-art spline interpolation using computationally convenient B-spline basis, that generate high order local approximations. A full grid is used as usually donein the industry. Kernel methods, that are a very general machine learning framework able to pose in a normed vector space, a large variety of regression or classification problems. Kernel functions can reproduce different function spaces using an unstructured support,which is optimized with pool active learning techniques. The approximations are found through a convex optimization process simplified by the kernel trick. The intrinsic modular character of the method facilitates segregating the modeling phases: function space selection, application of numerical routines and support optimization through active learning. Artificial neural networks which are“model free” universal approximators able Artificial neural networks which are“model free” universal approximators able to approach continuous functions to an arbitrary degree without formulating explicit relations among the variables. With adequate training settings, intrinsically parallelizable multi-output networks minimize storage requirements offering the highest evaluation speed. These strategies are compared to each other and to multi-linear interpolation in a Cartesian grid, the industry standard in core calculations. The data set, the developed tools, and scripts are freely available under aMIT license
Hou, Chuanchuan. "Vibration-based damage identification with enhanced frequency dataset and a cracked beam element model." Thesis, University of Edinburgh, 2016. http://hdl.handle.net/1842/20434.
Повний текст джерелаFatima, Samar. "Mapping artificial intelligence affordances for the public sector." Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235926/1/Samar%2BFatime%2BThesis.pdf.
Повний текст джерелаGomes, Cristina Maria da Silva Ganchinho. "Desafios da adoção de inteligência artificial no sector público." Master's thesis, Instituto Superior de Economia e Gestão, 2020. http://hdl.handle.net/10400.5/21099.
Повний текст джерелаA transformação digital provocada pela introdução da Inteligência Artificial nos negócios, trouxe muitas vantagens competitivas às organizações, através da otimização de processos de trabalho, possibilidade de investimentos em novas áreas de Mercado, entre outros. A evolução tecnológica evidencia cada vez mais o papel da IA nesta crescente transformação digital sendo por isso importante compreender o seu efeito nas organizações e nas pessoas. Este estudo pretende identificar quais são os potenciais desafios que o Setor Publico enfrenta na adoção de tecnologias suportadas em IA. Para tal foi realizada uma investigação, de caracter exploratório, com recurso a entrevistas a gestores da área tecnológica de várias organizações do Sector Público. Foram realizadas cinco entrevistas com questões abertas onde se pretendeu responder a questões relacionadas com a decisão de adoção de IA. Os resultados do estudo demonstram que existe motivação para a adoção destas tecnologias, no entanto existem alguns constrangimentos que influenciam a adoção destas tecnologias, destacamos a falta de recursos humanos e a capacitação dos mesmos como sendo um dos maiores dos desafios.
The digital transformation brought by the introduction of Artificial Intelligence in business provided many competitive advantages to organizations, through the optimization of work processes, the possibility of investments in new business areas among others. Technological evolution increasingly highlights the role of AI in this growing digital transformation and therefore it is important to understand its effect on organizations and in people. This study aims to identify which are the potential challenges that the Public Sector faces in the adoption of technologies supported by AI. To achieve this goal, an exploratory investigation was carried out using interviews with managers in the IT area of distinct business areas of the Public Sector. Five interviews were conducted with open questions about the decision to adopt AI in organizations of the Public Sector. The results show that despite the motivation for the adoption of these technologies, there are some important constraints affecting this adoption, namely the lack of human resources and their training as one of the greatest challenges.
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Gustafsson, Victor. "Artificial intelligence effect on jobs in the financial sector." Thesis, Mittuniversitetet, Avdelningen för ekonomivetenskap och juridik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-34247.
Повний текст джерелаCarbajal, Valverde Giuliana Mayte, and Correa Gianella Alejandra Segura. "Consecuencias positivas y negativas de la inteligencia artificial en el sector hotelero." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2021. http://hdl.handle.net/10757/656854.
Повний текст джерелаTechnology is an essential factor because it transforms, influences, and provides great benefits to humanity; inside it, artificial intelligence stands out, which has been of great help to different areas in recent years. One of them is the hotel sector, which has been implementing artificial intelligence in its services according to market demands. This research work is focused on determining the consequences of artificial intelligence in the hotel sector. It will analyze this technology’s positive and negative aspects and how it is currently being implemented in the hotel industry. This study is based on the literature review of scientific journals and theses. Furthermore, this research uses the integrative methodological approach, which synthesizes and examines the literature to find new definitions and perspectives. In conclusion, the use of artificial intelligence benefits the hotel sector in different ways, for example, automating services, reducing time and costs, among others. In conclusion, artificial intelligence is beneficial for the hotel sector, since it reduces time, reduces costs, and helps to obtain valuable information for decision-making.
Trabajo de investigación
Amaro, Jorge Filipe Montez Vaz Monteiro. "Modelos de avaliação em massa : redes neuronais artificiais aplicadas ao sector imobiliário residencial em Portugal? : estudo de caso na cidade de Lisboa." Master's thesis, Instituto Superior de Economia e Gestão, 2012. http://hdl.handle.net/10400.5/10737.
Повний текст джерелаAs redes neuronais artificiais são uma metodologia alternativa aos modelos tradicionais de previsão. A sua utilização tem-se vindo a massificar, sobretudo nas áreas da medicina, finanças, indústria automóvel e, mais recentemente em modelos de avaliação em massa aplicados ao mercado imobiliário. Este trabalho teve como objectivo fundamental a realização de experiências que utilizassem esta metodologia de previsão não paramétrica (não linear). No estudo de caso apresentado, foram analisadas 2.013 transacções, que ocorreram entre 2007 e 2012, mediadas pela rede de franchisados RE/MAX, relativas ao segmento residencial na cidade de Lisboa. Com esta base de dados, e depois de encontrada a melhor rede neuronal, foi possível obter um erro médio percentual absoluto (MAPE) na ordem dos 19%, em que para cerca de 67% da amostra foi alcançado um erro de estimação abaixo dos 20%. Utilizando esta metodologia, também foi observado que a rede neuronal funciona melhor se eliminados os outliers da amostra, aumentando a sua precisão. Foi ainda experimentada a introdução de variáveis temporais e de localização, tais como o ano de transacção de um determinado imóvel e a sua idade, ou ainda a freguesia onde está inserido, tendo sido obtidos comportamentos positivos no desempenho das redes. Para além da originalidade do tema, é de destacar que foram utilizados para este trabalho, valores reais de transacção relativos ao sector residencial em Portugal, tendo sido verificada uma aproximação do comportamento e da tendência do valor de transacção estimado pela rede, aos verificados pelo mercado.
The artificial neural networks are an alternative approach to traditional forecasting. Its use has been largely used, particularly in the areas of medicine, finance, automotive, and more recently in mass valuation models applied to the real estate market. This work had as main objective conducting experiments that used this methodology to forecast nonparametric (nonlinear). In the case study were analyzed 2013 transactions that occurred between 2007 and 2012, mediated by the network of franchisees RE / MAX, for the residential segment in Lisbon. With this database, and after found the best neural network, it was possible to obtain an mean absolute percent error (MAPE) of around 19%, in which to approximately 67% of the sample was reached estimation error below 20 %. Using this methodology, it was also observed that the neural network works better if the outliers are removed from the sample, increasing its accuracy. It also experienced the introduction of temporal variables and location, such as the year of a particular property transaction and its age, or the parish where it is located, having obtained positive behaviors in network performance. Beyond the originality of the subject, it is worth noting that was used for this work, actual transaction values for the residential sector in Portugal, having been verified an approximation of the behavior and trend of the transaction value estimated by the network, verified by the market.
Mendieta, Retuerto Carlos Andres. "El uso del Chatbot con respecto a la satisfacción del cliente en empresas del sector financiero en Lima Metropolitana." Bachelor's thesis, Universidad Peruana de Ciencias Aplicadas (UPC), 2020. http://hdl.handle.net/10757/651915.
Повний текст джерелаTechnology has been evolving in recent years. Artificial intelligence has managed to enter markets around the world as a new technology and a new way of seeing the world. That is why, in this study, the dimensions of the chatbot are explored, which are three: Information quality, system quality and service quality in relation to customer satisfaction in the financial system in Metropolitan Lima. This study is divided into two parts: a qualitative and a quantitative study. In the qualitative part, where the opinion of three experts of the subject was sought and, likewise, three focus groups were carried out. With respect to the quantitative study, a sample of 250 people was used. The data analyzed suggest that there is a relationship between the dimensions of the chatbot and customer satisfaction, but this relationship is very low negative.
Trabajo de investigación
Kloub, Maha, and Annika Gerigoorian. "A Cross-Sectional Technology Acceptance Study of an AI CAD System in a Breast Screening Unit." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299859.
Повний текст джерелаI januari 2021, implementerades ett artificiellt intelligent datorstött detektionssystem som ska upptäcka bröstcancer på Capio S:t Görans sjukhus i Stockholm. Användning av AI CAD för att upptäcka bröstcancer är lovande, men det kan endast bli en framgångsrik implementering om det accepteras av de som använder systemet. Denna studie undersöker och utvärderar de initiala faktorer som är avgörande för användaracceptansen av AI CAD hos radiologipersonal genom att utgå från den senaste versionen av teknologiacceptansmodellen – Technology Acceptance Model 3 (TAM3). Ett frågeformulär utformades i enlighet med modellen och distribuerades till 28 yrkesverksamma på S:t Görans mammografiavdelning. Den kvantitativa data som samlades in från enkäten analyserades med hjälp av det statistiska verktyget SPSS. De empiriska resultaten visade att radiologipersonalens avsikt att använda AI CAD påverkades direkt av den upplevda användbarheten av systemet och indirekt av personalens upplevda syn på hur enkelt systemet är att använda, att systemet är relevant för personalens jobb samt att systemet kan höja personalens image. Dessutom bekräftade studien att den subjektiva normen påverkar systemets image. Slutligen upptäcktes två nya associationer, i vilken TAM3 inte antar. Dessa påträffades mellan image och den beteendemässiga intentionen till att använda systemet samt mellan jobbrelevansen och den beteendemässiga intentionen till att använda systemet. Organisationsstöd, systemrelaterade aktiviteter samt information och kommunikation med personal är interventioner som föreslås baserat på resultaten i denna studie som mammografiavdelningen på S:t Görans sjukhus bör utnyttja för att öka acceptansen av AI CAD systemet.
Книги з теми "Artificial section"
O'Dell, S. L. Fifth Conference on Artificial Intelligence for Space Applications: Proceedings of a conference sponsored by the University of Alabama in Huntsville, the IEEE Computer Society/Huntsville Chapter, the AIAA Alabama-Mississippi Section, and the National Aeronautics and Space Administration and held in Huntsville, Alabama, May 22-23, 1990. Huntsville, Ala: George C. Marshall Space Flight Center, 1990.
Знайти повний текст джерелаUnited States. President (1989-1993 : Bush). Approving exports for the AUSSAT and FREJA projects: Communication from the President of the United States transmitting his justification for waiving legislative prohibitions on approval of United States-origin exports to China for the AUSSAT communication and FREJA scientific satellite projects, pursuant to Public Law 101-246, section 902(b)(2) (104 Stat. 85). Washington: U.S. G.P.O., 1991.
Знайти повний текст джерелаAhmed, S. E. (Syed Ejaz), 1957- editor of compilation, ed. Perspectives on big data analysis: Methodologies and applications : International Workshop on Perspectives on High-Dimensional Data Anlaysis II, May 30-June 1, 2012, Centre de Recherches Mathématiques, University de Montréal, Montréal, Québec, Canada. Providence, Rhode Island: American Mathematical Society, 2014.
Знайти повний текст джерелаChuvikov, Dmitriy. Models and algorithms for reconstruction and examination of emergency events of road accidents based on logical artificial intelligence. 2nd ed. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1220729.
Повний текст джерелаKsenofontov, Boris. Biological wastewater treatment. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1013710.
Повний текст джерела1974-, Zomorodian Afra J., ed. Advances in applied and computational topology: American Mathematical Society Short Course on Computational Topology, January 4-5, 2011, New Orleans, Louisiana. Providence, R.I: American Mathematical Society, 2012.
Знайти повний текст джерелаIndoor Gardening/Artificial Lighting, Terrariums, Hanging Baskets, and Plant Section (Family Library of Home Improvement). Xs Books, 1985.
Знайти повний текст джерелаPetrov, R. V. Immunology: Cell Interactions, Myelopeptides Artificial Immunogens (Soviet Medical Reviews Section D, Immunology Reviews, Vol 1). Routledge, 1987.
Знайти повний текст джерела[Artificial fruit sections]. Italy: Biella, 1989.
Знайти повний текст джерелаThe World Market for Artificial Monofilament of At Least 67 Decitex with Cross-Section Dimension Not Exceeding 1 mm and Artificial Textile Strip or Straw ... 5 mm Wide: A 2004 Global Trade Perspective. Icon Group International, Inc., 2005.
Знайти повний текст джерелаЧастини книг з теми "Artificial section"
Bachmeier, Andreas S. J. L. "Experimental Section." In Metalloenzymes as Inspirational Electrocatalysts for Artificial Photosynthesis, 213–33. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47069-6_8.
Повний текст джерелаÖzcan, Fatma, V. S. Subrahmanian, and Leana Golubchik. "Optimal Agent Section." In KI 2001: Advances in Artificial Intelligence, 2–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45422-5_2.
Повний текст джерелаSobhy, M. A., A. Y. Abdelaziz, M. Ezzat, W. Elkhattam, Anamika Yadav, and Bhupendra Kumar. "Artificial Bee Colony Optimization Algorithm for Fault Section Estimation." In Advances in Intelligent Systems and Computing, 127–39. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3174-8_12.
Повний текст джерелаChen, Chih-Wei, Nai-Wen Chang, Yung-Chun Chang, and Hong-Jie Dai. "Section Heading Recognition in Electronic Health Records Using Conditional Random Fields." In Technologies and Applications of Artificial Intelligence, 47–55. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-13987-6_5.
Повний текст джерелаBoubakri, Akram, Saber Dakhli, Fethi Choubani, Tan Hoa Vuong, and Jacques David. "Coding metasurface for radar cross section reduction." In Innovative and Intelligent Technology-Based Services for Smart Environments – Smart Sensing and Artificial Intelligence, 110–16. London: CRC Press, 2021. http://dx.doi.org/10.1201/9781003181545-17.
Повний текст джерелаStahl, Bernd Carsten, Doris Schroeder, and Rowena Rodrigues. "The Ethics of Artificial Intelligence: An Introduction." In Ethics of Artificial Intelligence, 1–7. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-17040-9_1.
Повний текст джерелаde Oliveira, Karen Rezende Caino, Rodrigo Hartstein Salim, André Darós Filomena, Mariana Resener, and Arturo Suman Bretas. "Unbalanced Underground Distribution Systems Fault Detection and Section Estimation." In Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence, 1054–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74205-0_109.
Повний текст джерелаCapali, Veli. "Improve or Approximation of Nuclear Reaction Cross Section Data Using Artificial Neural Network." In Artificial Intelligence and Applied Mathematics in Engineering Problems, 935–39. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36178-5_82.
Повний текст джерелаDollhopf, Sherry, HÉctor Ayala-Del-RÍo, Syed Hashsham, and James Tiedje. "Section 7 update: Multivariate statistical methods and artificial neural networks for analysis of microbial community molecular fingerprints." In Molecular Microbial Ecology Manual, 3349–87. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-2177-0_706.
Повний текст джерелаTeixeira, Roselito A., Antônio P. Braga, Rodney R. Saldanha, Ricardo H. C. Takahashi, and Talles H. Medeiros. "The Usage of Golden Section in Calculating the Efficient Solution in Artificial Neural Networks Training by Multi-objective Optimization." In Lecture Notes in Computer Science, 289–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74690-4_30.
Повний текст джерелаТези доповідей конференцій з теми "Artificial section"
"Section 9: Artificial Intelligence Systems." In 2022 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS). IEEE, 2022. http://dx.doi.org/10.1109/itqmis56172.2022.9976877.
Повний текст джерела"Section 9. Artificial Intelligence Systems." In 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS). IEEE, 2021. http://dx.doi.org/10.1109/itqmis53292.2021.9642888.
Повний текст джерела"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.
Повний текст джерелаK, Kalaivani, Yaswin Vikhaash R R, Mani Bharathi P A A, Kuttambakam Bhavana C H, and Mahalaxmi R. "An Artificial Eye for Blind People." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9752999.
Повний текст джерела"SECTION 5: Artificial Intelligence and Cognitive Systems 2022." In 2022 12th International Conference on Advanced Computer Information Technologies (ACIT). IEEE, 2022. http://dx.doi.org/10.1109/acit54803.2022.9913077.
Повний текст джерелаMalhotra, Neil, Ram Suri, Puru Verma, and Rajesh Kumar. "Smart Artificial Intelligence Based Online Proctoring System." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9753313.
Повний текст джерелаSoni, Kanishka, and Yasha Hasija. "Artificial Intelligence Assisted Drug Research and Development." In 2022 IEEE Delhi Section Conference (DELCON). IEEE, 2022. http://dx.doi.org/10.1109/delcon54057.2022.9753179.
Повний текст джерелаTaheri, Amir, and Ali Hooshmandkoochi. "Optimum Selection of Artificial Lift System for Iranian Heavy Oil Fields." In SPE Western Regional/AAPG Pacific Section/GSA Cordilleran Section Joint Meeting. Society of Petroleum Engineers, 2006. http://dx.doi.org/10.2118/99912-ms.
Повний текст джерелаBhartiya, Namrata, Namrata Jangid, Sheetal Jannu, Purvika Shukla, and Radhika Chapaneri. "Artificial Neural Network Based University Chatbot System." In 2019 IEEE Bombay Section Signature Conference (IBSSC). IEEE, 2019. http://dx.doi.org/10.1109/ibssc47189.2019.8973095.
Повний текст джерелаAlarfaj, Malik K., Abdulazeez Abdulraheem, and Yasser R. Busaleh. "Estimating Dewpoint Pressure Using Artificial Intelligence." In SPE Saudi Arabia section Young Professionals Technical Symposium. Society of Petroleum Engineers, 2012. http://dx.doi.org/10.2118/160919-ms.
Повний текст джерелаЗвіти організацій з теми "Artificial section"
Rinta-Kahila, Tapani, Ida Someh, Marta Indulska, and I. Ryan. Building Artificial Intelligence capability in the public sector. Brisbane, Australia; Canberra, Australia: The University of Queensland; SAP Institute for Digital Government, January 2021. http://dx.doi.org/10.14264/91738b9.
Повний текст джерелаAgarwal, Smisha, Madhu Jalan, Holly C. Wilcox, Ritu Sharma, Rachel Hill, Emily Pantalone, Johannes Thrul, Jacob C. Rainey, and Karen A. Robinson. Evaluation of Mental Health Mobile Applications. Agency for Healthcare Research and Quality (AHRQ), May 2022. http://dx.doi.org/10.23970/ahrqepctb41.
Повний текст джерелаArnold, Zachary, Roxanne Heston, Remco Zwetsloot, and Tina Huang. Immigration Policy and the U.S. AI Sector. Center for Security and Emerging Technology, September 2019. http://dx.doi.org/10.51593/20190009.
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Повний текст джерелаRudd, Ian. Leveraging Artificial Intelligence and Robotics to Improve Mental Health. Intellectual Archive, July 2022. http://dx.doi.org/10.32370/iaj.2710.
Повний текст джерелаArnold, Zachary, Rebecca Gelles, and Ilya Rahkovsky. Identifying AI-Related Companies: A Conceptual Outline and Proof of Concept. Center for Security and Emerging Technology, July 2020. http://dx.doi.org/10.51593/20200018.
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Повний текст джерелаSolovyanenko, Nina I. Legal features of innovative (digital) entrepreneurship in the agricultural and food sector. DOI CODE, 2021. http://dx.doi.org/10.18411/0131-5226-2021-70008.
Повний текст джерелаAbuhamad, Grace, Cory Salveson, Lilia Stubrin, and Carlos Braga. Abierta configuration options Inteligencia artificial en el sector de maquinaria agrícola de Argentina: diagnóstico de madurez y recomendaciones de política para acelerar la adopción. Inter-American Development Bank, February 2022. http://dx.doi.org/10.18235/0004000.
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