Relevant bibliographies by topics / Artificial intelligence and process

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 process'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Artificial intelligence and process"

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Azmeera, Rajesh. "Robotics Process Automation: Artificial Intelligence with SAP." International Journal of Science and Research (IJSR) 12, no. 11 (2023): 1871–76. http://dx.doi.org/10.21275/sr231126070716.

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Dai, Peng, Mausam, and Daniel Weld. "Artificial Intelligence for Artificial Artificial Intelligence." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 1153–60. http://dx.doi.org/10.1609/aaai.v25i1.8096.

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Crowdsourcing platforms such as Amazon Mechanical Turk have become popular for a wide variety of human intelligence tasks; however, quality control continues to be a significant challenge. Recently, we propose TurKontrol, a theoretical model based on POMDPs to optimize iterative, crowd-sourced workflows. However, they neither describe how to learn the model parameters, nor show its effectiveness in a real crowd-sourced setting. Learning is challenging due to the scale of the model and noisy data: there are hundreds of thousands of workers with high-variance abilities. This paper presents an en
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Thon, Christoph, Benedikt Finke, Arno Kwade, and Carsten Schilde. "Artificial Intelligence in Process Engineering." Advanced Intelligent Systems 3, no. 6 (2021): 2000261. http://dx.doi.org/10.1002/aisy.202000261.

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Cheung, Yen P., and Amanda L. Dowd. "Artificial intelligence in process planning." Computer-Aided Engineering Journal 5, no. 4 (1988): 153. http://dx.doi.org/10.1049/cae.1988.0034.

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Partington, D. "Artificial Intelligence in Process Control." Measurement and Control 21, no. 6 (1988): 177–78. http://dx.doi.org/10.1177/002029408802100605.

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Lenau, Torben, and Leo Alting. "Artificial intelligence for process selection." Journal of Mechanical Working Technology 17 (August 1988): 33–49. http://dx.doi.org/10.1016/0378-3804(88)90006-x.

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Lun, Vernon. "Artificial intelligence in process engineering." Engineering Applications of Artificial Intelligence 4, no. 1 (1991): 74–75. http://dx.doi.org/10.1016/0952-1976(91)90073-f.

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Biondo, Samuel J. "Artificial intelligence and process simulation." ISA Transactions 31, no. 2 (1992): 39–47. http://dx.doi.org/10.1016/0019-0578(92)90027-g.

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Marinchak, Christina McDowell, Edward Forrest, and Bogdan Hoanca. "Artificial Intelligence." International Journal of E-Entrepreneurship and Innovation 8, no. 2 (2018): 14–24. http://dx.doi.org/10.4018/ijeei.2018070102.

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As marketers and consumers simultaneously adopt artificial intelligence (AI) services and applications, the dynamic of the process of exchange between the buyer and seller in the marketplace is being fundamentally altered. This article reviews the emerging patterns of adoption and rates of diffusion of AI applications by both marketers and consumers. On the marketers' side the authors will address the single most defining phenomenon that is affecting the marketer's role and function in the marketing process: the exponential increase in the number, variety and capability of marketing applicatio
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Jung, Ae Ryung. "Artificial Intelligence Decision Making and the infringement of human rights: Focusing on the recruitment process." Korea Association for Corruption Studies 28, no. 1 (2023): 5–30. http://dx.doi.org/10.52663/kcsr.2023.28.1.5.

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The key word in the era of the Fourth Industrial Revolution is definitely AI(Artificial Intelligence). Artificial intelligence's decision based on big data increases the efficiency of work processing by quickly reviewing a large amount of data and reducing time and cost. As a result, artificial intelligence technology has begun to be used in all areas of society, and it is expected to be an anti-corruption policy tool against hiring-related corruption and irregularities that raise doubts about trust and process. However, if there is no proper control over artificial intelligence, artificial in
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Dissertations / Theses on the topic "Artificial intelligence and process"

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Bellman, Markus, and Gustav Göransson. "Intelligent Process Automation : Building the bridge between Robotic Process Automation and Artificial Intelligence." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263090.

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Process Automation has the potential to yield great benefits for companies and organizations, especially in the financial services industry where companies are information-intensive and experience rich data flows. This has mostly been done through Robotic Process Automation (RPA), but the increased maturity of Machine Learning algorithms has increased the viability of combining classic RPA with Artificial Intelligence, leading to Intelligent Process Automation (IPA). However, there is a set of challenges embedded in the transition from RPA to IPA. These challenges need to be dealt with in orde
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McBrien, Andrew. "Artificial intelligence methods in process plant layout." Thesis, University of Nottingham, 1994. http://eprints.nottingham.ac.uk/14403/.

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The thesis describes "Plant Layout System" or PLS, an Expert System which automates all aspects of conceptual layout of chemical process plant, from sizing equipment using process data to deriving the equipment items' elevation and plan positions. PLS has been applied to a test process of typical size and complexity and which encompasses a wide range of layout issues and problems. The thesis presents the results of the tests to show that PLS generates layouts that are entirely satisfactory and conventional from an engineering viewpoint. The major advance made during this work is the approach t
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Corea, F. M. Ravindra. "Artificial intelligence in process supervision and control." Thesis, University of Newcastle Upon Tyne, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.336160.

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Cheung, Yen Ping. "Artificial intelligence techniques for assembly process planning." Thesis, University of Warwick, 1991. http://wrap.warwick.ac.uk/79999/.

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Due to current trends in adopting flexible manufacturing philosophies, there has been a growing interest in applying Artificial Intelligence (AI) techniques to implement these manufacturing strategies. This is because conventional computational methods alone are not sufficient to meet these requirements for more flexibility. This research examines the possibility of applying AI techniques to process planning and also addresses the various problems when implementing such techniques. In this project AI planning techniques were reviewed and some of these techniques were adopted and later extended
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Duttala, Satish. "Virtual material processing artificial intelligence based process selection." Ohio : Ohio University, 2002. http://www.ohiolink.edu/etd/view.cgi?ohiou1174590077.

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Gault, Rosemary S. "Intelligent process planning for rapid prototyping." Thesis, Cardiff University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.367509.

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Winsor, Julian. "Process based functionality modelling." Thesis, University of Strathclyde, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.264285.

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Xia, T. A. "The application of artificial intelligence techniques to process identification and control." Thesis, Swansea University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.636701.

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The application of artificial intelligence technique (viz, neural networks, genetic algorithms and fuzzy logic systems) to process identification and control has been investigated with different systems. Neural networks and fuzzy logic systems are able to learn the dynamics of a process by training with a set of data obtained from that process and, subsequently, are able to provide a good predictive performance. Genetic algorithms and evolution strategies are non-gradient-based search schemes which facilitate non-linear system optimisation. Thus, they can be applied to the process industries i
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Silva, R. G. "Cutting tool condition monitoring of the turning process using artificial intelligence." Thesis, University of South Wales, 1997. https://pure.southwales.ac.uk/en/studentthesis/cutting-tool-condition-monitoring-of-the-turning-process-using-artificial-intelligence(25bc91ec-44fd-435b-a55d-06c564f88f35).html.

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This thesis relates to the application of Artificial Intelligence to tool wear monitoring. The main objective is to develop an intelligent condition monitoring system able to detect when a cutting tool is worn out. To accomplish this objective it is proposed to use a combined Expert System and Neural Network able to process data coming from external sensors and combine this with information from the knowledge base and thereafter estimate the wear state of the tool. The novelty of this work is mainly associatedw ith the configurationo f the proposeds ystem. With the combination of sensor-baseid
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Badhe, Y. P. "Process modeling and optimization using artificial intelligence and machine learning formalisms." Thesis(Ph.D.), CSIR-National Chemical Laboratory, Pune, 2008. http://dspace.ncl.res.in:8080/xmlui/handle/20.500.12252/2627.

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Books on the topic "Artificial intelligence and process"

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B, Porto Pazos Ana, Pazos Sierra Alejandro, and Buceta Washington Buno, eds. Advancing artificial intelligence through biological process applications. Medical Information Science Reference, 2008.

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Suh, Sang C., ed. Artificial Intelligence for Design and Process Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-67886-8.

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Giovanni, Sartor, and Branting Karl, eds. Judicial applications of artificial intelligence. Kluwer Academic Publishers, 1998.

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Bhattacharyya, Siddhartha, Jyoti Sekhar Banerjee, and Debashis De, eds. Confluence of Artificial Intelligence and Robotic Process Automation. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8296-5.

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Romero, José Raúl, Inmaculada Medina-Bulo, and Francisco Chicano, eds. Optimising the Software Development Process with Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-9948-2.

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D'Ambrosio, Bruce. Qualitative process theory using linguistic variables. Springer-Verlag, 1989.

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D'Ambrosio, Bruce. Qualitative Process Theory Using Linguistic Variables. Springer New York, 1989.

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Juarez, Jose M., Carlos Fernandez-Llatas, Concha Bielza, et al., eds. Explainable Artificial Intelligence and Process Mining Applications for Healthcare. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-54303-6.

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Forbus, Kenneth D. The qualitative process engine. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 1986.

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S, Gero John, and International Conference on the Applications of Artificial Intelligence in Engineering (3rd : 1988 : Palo Alto, Calif.), eds. Artificial intelligence in engineering: Robotics and processes. Elsevier, 1988.

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Book chapters on the topic "Artificial intelligence and process"

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Frey, Christian M. M., Simon Rauch, Oliver Stritzel, and Moike Buck. "Process-aware Learning." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_6.

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AbstractProcesses in companies are diverse and complex. The production of different products, inter- or intra-company logistic processes, or other serial event sequences within companies have one thing in common: they can be traced on the basis of a documentation of the individual process steps. Usually, companies have domain experts for each department’s processes who use their experience and knowledge to plan and control these steps. However, with increasing complexity and diversity of processes, efficient planning and control is becoming more difficult or even impossible for human decision
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Taulli, Tom. "Robotic Process Automation (RPA)." In Artificial Intelligence Basics. Apress, 2019. http://dx.doi.org/10.1007/978-1-4842-5028-0_5.

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Li, Bin. "Embedded AI Development Process." In Embedded Artificial Intelligence. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-5038-2_9.

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Brown, David C. "Artificial intelligence for design process improvement." In Design process improvement. Springer London, 2005. http://dx.doi.org/10.1007/978-1-84628-061-0_7.

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Choudhary, Raj, and A. Karmel. "Robotic Process Automation." In Artificial Intelligence and Technologies. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6448-9_3.

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Tueschen, Philipp, and Vítor Duarte dos Santos. "Cased Based Reasoning in Business Process Management Design." In Artificial Intelligence in Intelligent Systems. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77445-5_65.

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Strannegård, Claes, Rickard von Haugwitz, Johan Wessberg, and Christian Balkenius. "A Cognitive Architecture Based on Dual Process Theory." In Artificial General Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-39521-5_15.

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Pinzari, Ana, Thomas Baumela, Liliana Andrade, Marcello Coppola, and Frédéric Pétrot. "Power Optimized Wafermap Classification for Semiconductor Process Monitoring." In Embedded Artificial Intelligence. River Publishers, 2023. http://dx.doi.org/10.1201/9781003394440-1.

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Dli, Maksim, Andrey Puchkov, and Tatyana Kakatunova. "Assessment of the Technological Process Condition Based on the Assembly of Deep Recurrent Neural Networks." In Artificial Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59535-7_29.

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Jiang, Wenlan, Chunpu Lv, Tao Zhang, and Huangang Wang. "Phase Partition Based Virtual Metrology for Material Removal Rate Prediction in Chemical Mechanical Planarization Process." In Artificial Intelligence. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93046-2_16.

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Conference papers on the topic "Artificial intelligence and process"

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Rajeev, Nikhil, Suresh Jayaraman, Prajnan Das, and Srividya Varada. "Application of Artificial Intelligence in process simulation tool." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.126215.

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Process engineers in the Chemical and Oil & Gas industries extensively use process simulation for the design, development, analysis, and optimization of complex systems. This study investigates the integration of Artificial Intelligence (AI) with AVEVATM Process Simulation (APS), a next-generation commercial simulation tool. We propose a framework for a custom chatbot application designed to assist engineers in developing and troubleshooting simulations. This chatbot application utilizes a custom-trained model to transform engineer prompts into standardized queries, facilitating access to
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Dowling, Alexander W. "Artificial Intelligence and Machine Learning for Sustainable Molecular-to-Systems Engineering." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.114705.

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Sustainability encompasses many wicked problems involving complex interdependencies across social, natural, and engineered systems. We argue holistic multiscale modeling and decision-support frameworks are needed to address multifaceted interdisciplinary aspects of these wicked problems. This review highlights three emerging research areas for artificial intelligence (AI) and machine learning (ML) in molecular-to-systems engineering for sustainability: (1) molecular discovery and materials design, (2) automation and self-driving laboratories, (3) process and systems-of-systems optimization. Re
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Masod, Muhammad Yusuf Bin, and Siti Farhana Zakaria. "Application of Artificial Intelligence in Printing Industry: Systematic Review." In 2024 IEEE 12th Conference on Systems, Process & Control (ICSPC). IEEE, 2024. https://doi.org/10.1109/icspc63060.2024.10861924.

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Moghadasi, Negin, Misagh Piran, Rupa S. Valdez, et al. "Process Quality Assurance of Artificial Intelligence in Medical Diagnosis." In 2024 International Conference on Intelligent Systems and Computer Vision (ISCV). IEEE, 2024. http://dx.doi.org/10.1109/iscv60512.2024.10620154.

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Azarov, Vladimir N., and Anatoliy V. Chekmarev. "Process Architecture as a Neural Network and Artificial Intelligence." In 2024 International Conference "Quality Management, Transport and Information Security, Information Technologies" (QM&TIS&IT). IEEE, 2024. https://doi.org/10.1109/qmtisit63393.2024.10762857.

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Sharma, Ayushi, Rakesh Kumar, Deepak Kumar, Purnendu Shekhar Pandey, and Shashank Awasthi. "Simplification Process in Supply Chain Management Through Artificial Intelligence." In 2025 4th International Conference on Sentiment Analysis and Deep Learning (ICSADL). IEEE, 2025. https://doi.org/10.1109/icsadl65848.2025.10933240.

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Rahul and Kamaljeet Kaur. "Artificial Intelligence as a Tool for Graphic's Production Process." In 2024 Second International Conference on Advanced Computing & Communication Technologies (ICACCTech). IEEE, 2024. https://doi.org/10.1109/icacctech65084.2024.00014.

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Erdler, G., S. Klehr, S. von Dosky, and M. Mussler. "P51 - ThinKIsense – Thin Edge Artificial Intelligence for Process Automation." In SMSI 2025. AMA Service GmbH, Von-Münchhausen-Str. 49, 31515 Wunstorf, Germany, 2025. https://doi.org/10.5162/smsi2025/p51.

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D, Vasanthy, and S. Venkatachalam. "Artificial Intelligence and Big Data Applications in Recruitment Process Management." In 2024 4th International Conference on Soft Computing for Security Applications (ICSCSA). IEEE, 2024. https://doi.org/10.1109/icscsa64454.2024.00018.

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Vulpe, Monica-Ioana, and Stelian Stancu. "THE CONTRIBUTION OF ARTIFICIAL INTELLIGENCE IN ROBOTIC PROCESS AUTOMATION DEVELOPMENT." In 17th International Conference on Education and New Learning Technologies. IATED, 2025. https://doi.org/10.21125/edulearn.2025.1037.

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Reports on the topic "Artificial intelligence and process"

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Nduka, Williams. The Significance of Artificial Intelligence in Business Process Management. Iowa State University, 2023. http://dx.doi.org/10.31274/cc-20240624-1492.

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Daveloui, José Fernando. Redefining Teaching with Artificial Intelligence. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/davelouis220254.

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Four years ago, I accepted the fascinating challenge of teaching a Big Data course. The thematic proposal was intimidating, as it presented an approach devoid of "ready-to-use" tools and, instead, proposed a highly technical approach (Kumar et al., 2022). This required meticulous and mechanical practice that consisted of mastering the Linux operating system, configuring virtual machines, and installing more than half a dozen tools through command line, as well as configuring an endless list of system files (.sh, .ini, etc.). These specialized practices demand considerable time and expertise fr
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Reifman, J., T. Y. C. Wei, J. E. Vitela, C. A. Applequist, and T. M. Chasensky. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/224950.

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Moskicheva, Elena. Integration of Artificial Intelligence in Industrial Design: From Generative Models to Intelligent Automation. Intellectual Archive, 2025. https://doi.org/10.32370/iaj.3263.

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This article examines the role of artificial intelligence in transforming industrial design processes. Special attention is given to generative design, the use of AI in CAD environments, and the concept of digital twins. Practical examples of intelligent systems applied in product development are provided, demonstrating increased efficiency, reduced timeframes, and improved quality of design solutions. The paper also discusses future prospects for AI integration and the associated ethical challenges.
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Angulo Gómez-Marañón, Rodrigo. Generative Artificial Intelligence: An Imminent Challenge for Technical and Vocational Higher Education Institutions. Carver University; Universidad Autónoma de Chile, 2025. https://doi.org/10.32457/angulo22025101.

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The emergence of generative artificial intelligence (GAI) across all sectors of society has triggered an institutional reconfiguration process whose full scope we are only beginning to grasp. Technical and vocational higher education institutions (TVHEIs), whose mission is to prepare students for the labor market, face a dual challenge: on the one hand, they must train human capital capable of understanding and using AI in professional contexts; on the other, they must internally adapt to this technology by redesigning teaching, administrative, and outreach processes. Generative AI is no longe
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Marsden, Eric, and Véronique Steyer. Artificial intelligence and safety management: an overview of key challenges. Foundation for an Industrial Safety Culture, 2025. https://doi.org/10.57071/iae290.

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Artificial intelligence based on deep learning, along with big data analysis, has in recent years been the subject of rapid scientific and technological advances. These technologies are increasingly being integrated into various work environments with the aim of enhancing performance and productivity. This dimension of the digital transformation of businesses and regulatory authorities presents both significant opportunities and potential risks for industrial safety management practices. While there are numerous expected benefits, such as the ability to process large volumes of reliability dat
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Shaneyfelt, Wendy, John T. Feddema, and Conrad D. James. A Surety Engineering Framework and Process to Address Ethical Legal and Social Issues for Artificial Intelligence. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1561812.

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Rinaudo, Christina, William Leonard, Jaylen Hopson, Christopher Morey, Robert Hilborn, and Theresa Coumbe. Enabling understanding of artificial intelligence (AI) agent wargaming decisions through visualizations. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48418.

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The process to develop options for military planning course of action (COA) development and analysis relies on human subject matter expertise. Analyzing COAs requires examining several factors and understanding complex interactions and dependencies associated with actions, reactions, proposed counteractions, and multiple reasonable outcomes. In Fiscal Year 2021, the Institute for Systems Engineering Research team completed efforts resulting in a wargaming maritime framework capable of training an artificial intelligence (AI) agent with deep reinforcement learning (DRL) techniques within a mari
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Lee, Sing H. Architecture Studies and System Demonstrations of Optical Parallel Processor for AI (Artificial Intelligence) and NI (Neural Intelligence). Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada203241.

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Lee, Sing H. Architecture Studies and System Demonstrations of Optical Parallel Processor for AI(Artificial Intelligence) and NI(Neural Intelligence). Defense Technical Information Center, 1988. http://dx.doi.org/10.21236/ada195480.

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