Thematische Bibliographien / Artificial intelligence and machine learning

Inhaltsverzeichnis

  1. Zeitschriftenartikel
  2. Dissertationen
  3. Bücher
  4. Buchteile
  5. Konferenzberichte
  6. Berichte der Organisationen

Auswahl der wissenschaftlichen Literatur zum Thema „Artificial intelligence and machine learning“

Autor: Grafiati
Veröffentlicht am 25. Juni 2021
Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Artificial intelligence and machine learning"

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Григоров, Отто Володимирович, Галина Оттівна Аніщенко, Всеволод Вікторович Стрижак, et al. "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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N. B. Sultangazina, M. A. Ermaganbetova, Zh. B. Akhayeva, and A. B. Zakirova. "Artificial intelligence and machine learning." Bulletin of Toraighyrov University. Physics & Mathematics series, no. 3.2021 (September 27, 2021): 24–33. http://dx.doi.org/10.48081/wcct7602.

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Artificial Intelligence (AI) is an area of ​​research driven by innovation and development that culminates in computers, machines with human-like intelligence characterized by cognitive ability, learnability, adaptability and decision-making ability. The study found that AI is widely adopted and used in education, especially by educational institutions, in various forms. This article reviewed articles by various scientists from different countries. The paper discusses the prospects for the application of artificial intelligence and machine learning technologies in education and in everyday lif
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Srushti, Surendra Naik. "Understanding Artificial Intelligence and Machine Learning." International Journal of Advance and Applied Research S6, no. 22 (2025): 816–20. https://doi.org/10.5281/zenodo.15533444.

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<em>Artificial Intelligence (AI) and Machine Learning (ML) are the driving forces behind many technological advancements that are transforming our world. These fields are transforming how we approach problem-solving, decision-making, and interaction with technology. While AI generally refers to the simulation of human intelligence in machines, ML is a subset that focuses on enabling machines to learn and improve through experience.</em> <em>In this research paper, we will dive into the fundamentals of AI and ML, the various types of machine learning, the applications of AI and ML in various in
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Varma, Naredla Sathwika, Kalvakuntla Sharanya, and Dr D. Shravani. "AI Powered Virtual Assistant." International Journal of Future Engineering Innovations 2, no. 4 (2025): 51–54. https://doi.org/10.54660/ijfei.2025.2.4.51-54.

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Artificial intelligence (AI) and machine learning (ML) are two closely allied fields of computer science. Artificial intelligence (AI) is focused in developing intelligent machines; machine learning (ML) is a subfield of artificial intelligence (AI) aiming to enable machines to learn and grow from data without explicit programming. Machine learning methods enable data analysis and interpretation to identify trends and produce decisions or predictions. Combined artificial intelligence and machine learning has the ability to transform whole sectors, increase output, and improve our quality of li
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Lawlor, Bonnie. "Artificial Intelligence and Machine Learning." Chemistry International 43, no. 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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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 (2019): 26. http://dx.doi.org/10.1097/01.hj.0000554346.30951.8d.

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

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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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Dissertationen zum Thema "Artificial intelligence and machine learning"

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Legkiy, Yaroslav, Ярослав Ярославович Легкий, Bohdan Dubchak, and Богдан Мстиславович Дубчак. "Machine learning &artificial intelligence in aerospace industry." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50479.

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Machine Learning & Artificial Intelligence in Aerospace Industry URL: https://www.axiscades.com/blog-resources/whitepaper/Aerospacewhitepaper.pdf<br>It depicts the artіficіal іntelligence as a signіficant role іn cutting costs, reducing the design cycle tіme, simulation, prototyping, optimizatіon, maіntenance, manufacturіng and updatіng products.<br>Визначено штучний інтелект як значущу роль у скороченні витрат, зменшенні часу циклу проектування, моделюванні, прототипуванні, оптимізації, технічному обслуговуванні, виробництві та модернізації продукції.
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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 algo
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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 ap
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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.<br>This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 113-117).<br>Real estate meets machine learning: real contribution or just hype? Creating and managing the built environment is a complicated task fraught with difficult decisi
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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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<p> 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 15<sup>th</sup> century problem in the 21<sup>st</sup> century. Today, counterfeit checks originating from overseas are the scourge of online dating sites, classifieds forums, and mailboxes throughout the country. Additional frauds including alteratio
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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 cons
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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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<p> 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 kinemat
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Bücher zum Thema "Artificial intelligence and machine learning"

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

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

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D, Sleeman, and Bernsen Niels Ole, eds. Artificial intelligence. L. Erlbaum Associates, 1992.

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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. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-76640-5.

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Leiva, Luis A., Cédric Pruski, Réka Markovich, Amro Najjar, and Christoph Schommer, eds. Artificial Intelligence and Machine Learning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-93842-0.

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Joshi, Ameet V. Machine Learning and Artificial Intelligence. 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, et al., eds. Artificial Intelligence and Machine Learning. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65154-1.

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Joshi, Ameet V. Machine Learning and Artificial Intelligence. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-12282-8.

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Tanwar, Rohit, Surbhi Bhatia, Varun Sapra, and Neelu Jyoti Ahuja. Artificial Intelligence and Machine Learning. CRC Press, 2023. http://dx.doi.org/10.1201/9781003388319.

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Soliman, Khalid S., ed. Artificial intelligence and Machine Learning. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-62843-6.

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Buchteile zum Thema "Artificial intelligence and machine learning"

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

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Dix, Alan, and Janet Finlay. "Machine Learning." In Artificial Intelligence, 2nd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003082880-7.

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

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Nyamawe, Ally S., Mohamedi M. Mjahidi, Noe E. Nnko, Salim A. Diwani, Godbless G. Minja, and Kulwa Malyango. "Artificial general intelligence." In Practical Machine Learning. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003486817-7.

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

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

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

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Singh, Rajesh, Anita Gehlot, Mahesh Kumar Prajapat, and Bhupendra Singh. "Machine Learning." In Artificial Intelligence in Agriculture. CRC Press, 2021. http://dx.doi.org/10.1201/9781003245759-4.

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Karl, Florian, Janek Thomas, Jannes Elstner, Ralf Gross, and Bernd Bischl. "Automated Machine Learning." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_1.

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AbstractIn the past few years automated machine learning (AutoML) has gained a lot of traction in the data science and machine learning community. AutoML aims at reducing the partly repetitive work of data scientists and enabling domain experts to construct machine learning pipelines without extensive knowledge in data science. This chapter presents a comprehensive review of the current leading AutoML methods and sets AutoML in an industrial context. To this extent we present the typical components of an AutoML system, give an overview over the stateof-the-art and highlight challenges to indus
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Roy, Radhika Ranjan. "Supervised Machine Learning." In Networked Artificial Intelligence. Auerbach Publications, 2024. http://dx.doi.org/10.1201/9781003499466-9.

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Konferenzberichte zum Thema "Artificial intelligence and machine learning"

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Umamaheswari, M. S., S. Nareshkumar, and D. Sherwin Jaya Durai. "Artificial Intelligence in Mathematics and Machine Learning." In 2024 2nd International Conference on Computing and Data Analytics (ICCDA). IEEE, 2024. https://doi.org/10.1109/iccda64887.2024.10867358.

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Nishane, Om, and Vinod Ajankar. "Pharmacy Management System using Artificial Intelligence." In 2025 International Conference on Machine Learning and Autonomous Systems (ICMLAS). IEEE, 2025. https://doi.org/10.1109/icmlas64557.2025.10968850.

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Chen, Xinjia. "Predicate credibility logic for artificial intelligence." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VII, edited by Peter J. Schwartz and Myron E. Hohil. SPIE, 2025. https://doi.org/10.1117/12.3052667.

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Bhatnagar, Gunjan, Alpika Verma, Nilanjan Chakraborty, Suman, Ashwini Kumar, and Aman Mittal. "Legal Considerations for Artificial Intelligence and Machine Learning." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725972.

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Tremblay, Christine. "Artificial Intelligence and Machine Learning in Optical Networking [Tutorial]." In Photonic Networks and Devices. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/networks.2024.nem4c.1.

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In this tutorial, we explore various applications of artificial intelligence (AI) and machine learning (ML) methods aimed at improving the performance, operations, and reliability of optical networks, as well as simplifying their management.
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Meng, Yang. "Exploration of Game Artificial Intelligence: Key Technologies and Case Analysis." In International Conference on Data Analysis and Machine Learning. SCITEPRESS - Science and Technology Publications, 2024. https://doi.org/10.5220/0013517500004619.

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Czajkowski, Michael F., Aysha S. Khan, and Jinhong Guo. "Detecting and evaluating deception with artificial intelligence assistants." In Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications VII, edited by Peter J. Schwartz and Myron E. Hohil. SPIE, 2025. https://doi.org/10.1117/12.3052798.

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Cirucci, Nick. "Leveraging artificial intelligence and machine learning for lens design." In Optical Modeling and Performance Predictions XIV, edited by Mark A. Kahan and Catherine Merrill. SPIE, 2024. http://dx.doi.org/10.1117/12.3029783.

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Liu, Xin, Yi Ren, Guodong Qi, Yujing Li, and Rui Fan. "Artificial Intelligence Digital Audit System Under Machine Learning Technology." In 2024 3rd International Conference on Artificial Intelligence and Autonomous Robot Systems (AIARS). IEEE, 2024. http://dx.doi.org/10.1109/aiars63200.2024.00139.

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K.Priya, Sathish Kumar James, A. Aalan Babu, Sumalatha A, S.Sharanyaa, and SVS Prasad. "Artificial Intelligence and Machine Learning: Revolutionizing Data-Driven Decisions." In 2024 5th IEEE Global Conference for Advancement in Technology (GCAT). IEEE, 2024. https://doi.org/10.1109/gcat62922.2024.10924120.

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Berichte der Organisationen zum Thema "Artificial intelligence and machine learning"

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

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Randerson, James, Efi Georgiou, Padhraic Smyth, et al. Machine learning and artificial intelligence for wildfire prediction. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1769739.

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JOSI, Editor. Artificial Intelligence and Machine Learning: Transforming Industrial Optimization. Industrial Engineering Department, Faculty of Engineering, Universitas Andalas, 2025. https://doi.org/10.25077/03032025.

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The integration of artificial intelligence (AI) and machine learning (ML) into industrial systems is rapidly reshaping the way industries operate, optimize, and innovate. As industries grow more complex and data-driven, the ability to harness AI and ML technologies offers unprecedented efficiency, precision, and adaptability. In the pursuit of optimization, these technologies are no longer just experimental tools but have become essential drivers of transformation.
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Vecherin, Sergey, Jacob Desmond, Taylor Hodgdon, et al. Artificial intelligence and machine learning for autonomous military vehicles. Engineer Research and Development Center (U.S.), 2020. http://dx.doi.org/10.21079/11681/37943.

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Arnold, David, Jafar Saniie, and Alexander Heifetz. Homomorphic Encryption for Machine Learning and Artificial Intelligence Applications. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/1886256.

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Chen, Yousu, Xiaoyuan Fan, Renke Huang, Qiuhua Huang, Ang Li, and Kishan Prudhvi Guddanti. Artificial Intelligence/Machine Learning Technology in Power System Applications. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2340760.

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Laplante, Phillip. Workshop on Assured Autonomy, Artificial Intelligence and Machine Learning. National Institute of Standards and Technology, 2024. https://doi.org/10.6028/nist.ir.8555.

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McKenzie, Taylor, Kelsey Abel, John Flory, Andjelka Kelic, Marilee Orr, and Raymond Reilly. Application of Artificial Intelligence/Machine Learning to Operations Research. Office of Scientific and Technical Information (OSTI), 2025. https://doi.org/10.2172/2516868.

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Latorre, Lucia, Valentín Muro, Eduardo Rego, Mariana Gutierrez, Ignacio Cerrato, and Jose Daniel Zarate. Tech Report Artificial Intelligence. Inter-American Development Bank, 2024. http://dx.doi.org/10.18235/0013015.

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This report provides a comprehensive overview of AI, from its fundamentals to its practical applications, covering topics such as its definition, evolution, and implementation. It also delves into various applications, such as machine learning, natural language processing, computer vision, and generative AI, providing specific examples and use cases across sectors like healthcare, logistics, environment, and security.
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Milgrom, Paul, and Steven Tadelis. How Artificial Intelligence and Machine Learning Can Impact Market Design. National Bureau of Economic Research, 2018. http://dx.doi.org/10.3386/w24282.

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