Relevant bibliographies by topics / Artificial Intelligence and Machine Learning (AI/ML)

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 (AI/ML)'

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

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Journal articles on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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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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x, Rajdeep. "Mathematics Model Used in Artificial Intelligence (AI) and Machine Learning (ML)." International Journal of Science and Research (IJSR) 13, no. 12 (2024): 1773–77. https://doi.org/10.21275/sr241227144834.

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Preet, Gandhi. "Artificial Intelligence and Machine Learning: Transforming the Future." International Journal of Advance and Applied Research S6, no. 22 (2025): 1215–17. https://doi.org/10.5281/zenodo.15543059.

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<em>Artificial Intelligence (AI) and Machine Learning (ML) are at the forefront of technological advancements, revolutionizing industries and reshaping human interactions with technology. AI enables machines to simulate human intelligence, while ML allows systems to learn from data and improve decision-making processes. This paper explores the fundamentals of AI and ML, their diverse applications, challenges, ethical considerations, and prospects for future development.</em>
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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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Naina, B. A., and M. D. Manoj. "Artificial Intelligence and Machine Learning Applications." International Journal of Advance and Applied Research 6, no. 25 (2025): 114–16. https://doi.org/10.5281/zenodo.15293993.

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<strong>Abstract:</strong> Automation, predictive analytics, and wise decision-making are some of the ways that artificial intelligence (AI) and machine learning (ML) are revolutionizing various industries.&nbsp; Deep learning, natural language processing, and robotics are all included in AI, whereas machine learning improves performance through experience.&nbsp; In the fields of healthcare, finance, manufacturing, and education, these technologies increase output, precision, and cost effectiveness. &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; AI helps in robotic-assisted
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Anika, Shreya Pawar. "Machine learning (ML) is an artificial intelligence (AI) technique." International Journal of Innovative Science and Research Technology 7, no. 8 (2022): 1422–25. https://doi.org/10.5281/zenodo.7073448.

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Machine learning (ML) is an artificial intelligence (AI) technique that facilitates the improvement of predictability in software applications without requiring explicit programming. Data from the past is used to predict new outcomes using machine learning algorithms. During the course of this paper, we used four machine learning algorithms: Logistic Regression, Support Vector Machine, Decision Tree, and Gradient Boosting. Our chosen algorithms were applied to five datasets from the healthcare domain, in which we were able to predict kidney disease, liver disease, breast cancer predictions, he
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Istamov, Mirjahon Mo'minjon o'g'li Mahkamov Baxtiyor Shuxratovich. "ARTIFICIAL INTELLIGENCE/MACHINE LEARNING IN DIABETES CARE." ilm-fan 1, no. 17 (2023): 75–77. https://doi.org/10.5281/zenodo.8141315.

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Artificial intelligence/Machine learning (AI/ML) is transforming all spheres of our life, including the healthcare system. Application of AI/ML has a potential to vastly enhance the reach of diabetes care thereby making it more efficient. The huge burden of diabetes cases in India represents a unique set of problems, and provides us with a unique opportunity in terms of potential availability of data. Harnessing this data using electronic medical records, by all physicians, can put India at the forefront of research in this area. Application of AI/ML would provide insights to our problems as w
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Kapoor, Madhav. "Probabilistic Machine Learning and Artificial Intelligence." Spectrum of Emerging Sciences 3, no. 2 (2024): 29–36. http://dx.doi.org/10.55878/ses2023-3-2-5.

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Artificial intelligence (AI) and machine learning (ML) have transformed numerous domains by enabling systems to learn from data and make intelligent decisions. Within the field of ML, probabilistic machine learning has gained significant attention due to its ability to capture uncertainty and provide predictions based on probabilities. This essay explores the concept of probabilistic machine learning and its applications in AI. We delve into the fundamentals of machine learning, discuss probabilistic modeling, explore various probabilistic machine learning techniques, highlight the advantages
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Ojedokun, Samson Aderemi. "Artificial Intelligence (AI) and Machine Learning (ML); A Revolutionary Game Changer in Clinical Laboratory Diagnosis." Clinical Pathology & Research Journal 8, no. 1 (2024): 1–4. https://doi.org/10.23880/cprj-16000194.

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The first artificial intelligence (AI) program was developed by Christopher Strachey in 1951, though primitive. The term “Artificial Intelligence” was formed by John McCarthy at the Dartmouth Conference in 1956 marking the beginning of modern AI popularity
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Dissertations / Theses on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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Lundin, Lowe. "Artificial Intelligence for Data Center Power Consumption Optimisation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-447627.

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The aim of the project was to implement a machine learning model to optimise the power consumption of Ericsson’s Kista data center. The approach taken was to use a Reinforcement Learning agent trained in a simulation environment based on data specific to the data center. In this manner, the machine learning model could find interactions between parameters, both general and site specific in ways that a sophisticated algorithm designed by a human never could. In this work it was found that a neural network can effectively mimic a real data center and that the Reinforcement Learning policy "TD3"
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Giuliani, Luca. "Extending the Moving Targets Method for Injecting Constraints in Machine Learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23885/.

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Informed Machine Learning is an umbrella term that comprises a set of methodologies in which domain knowledge is injected into a data-driven system in order to improve its level of accuracy, satisfy some external constraint, and in general serve the purposes of explainability and reliability. The said topid has been widely explored in the literature by means of many different techniques. Moving Targets is one such a technique particularly focused on constraint satisfaction: it is based on decomposition and bi-level optimization and proceeds by iteratively refining the target labels through a m
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Klingvall, Emelie. "Artificiell intelligens som ett beslutsstöd inom mammografi : En kvalitativ studie om radiologers perspektiv på icke-tekniska utmaningar." Thesis, Högskolan i Skövde, Institutionen för informationsteknologi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-18768.

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Artificiell intelligence (AI) har blivit vanligare att använda för att stödja människor i deras beslutsfattande. Maskininlärning (ML) är ett delområde inom AI som har börjat användas mer inom hälso-och sjukvården. Patientdata ökar inom vården och ett AI-system kan behandla denna ökade datamängd, vilket vidare kan utveckla ett beslutsstöd som hjälper läkarna. AI-tekniken blir vanligare att använda inom radiologin och specifikt inom mammografin som ett beslutsstöd. Användning av AI-teknik inom mammografin medför fördelar men det finns även utmaningar som inte har något med tekniken att göra.Icke
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Bengtsson, Theodor, and Jonas Hägerlöf. "Stora mängder användardata för produktutveckling : Möjligheter och utmaningar vid integrering av stora mängder användardata i produktutvecklingsprocesser." Thesis, KTH, Integrerad produktutveckling, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297966.

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Teknikutvecklingen har bidragit till ökad mängd användardata företag har tillgång till och väntas fortsätta öka. Företag som integrerar användardata i sina produktutvecklingsprocesser väntas uppnå konkurrensfördelar. Arbetets syfte handlar om att undersöka möjligheter och utmaningar vid integrering av stora mängder användardata. Genom att besvara två frågeställningar fastställer undersökningen arbetets syfte, där även konsekvenser för beslutsfattande behandlas. Arbetsprocessen inleddes med en litteraturstudie som låg till grund för både problematiseringen och syftet som identifierar ett gap i
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Nystad, Marcus, and Lukas Lindblom. "Artificial Intelligence in the Pulp and Paper Industry : Current State and Future Trends." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279574.

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The advancements in Artificial Intelligence (AI) have received large attention in recent years and increased awareness has led to massive societal benefits and new opportunities for industries able to capitalize on these emerging technologies. The pulp and paper industry is going through one of the most considerable transformations into Industry 4.0. Integrating AI technology in the manufacturing process of the pulp and paper industry has shown great potential, but there are uncertainties which direction companies are heading. This study is an investigation of the pulp and paper industry in co
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Olivieri, Emily, and Loredana Isacsson. "Exploring guidelines for human-centred design in the wake of AI capabilities : A qualitative study." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-48072.

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Purpose – Artificial Intelligence has seen important growth in the digital area in recent years. Our aim is to explore possible guidelines that make use of AI advances to design good user experiences for digital products. Method – The proposed methods to gather the necessary qualitative data to support our claim involve open-ended interviews with UX/UI Designers working in the industry, in order to gain a deeper understanding of their thoughts and experiences. In addition, a literature review is conducted to identify the knowledge gap and build the base of our new theory. Findings – Our findin
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Ahlm, Kristoffer. "IDENTIFIKATION AV RISKINDIKATORER I FINANSIELL INFORMATION MED HJÄLP AV AI/ML : Ökade möjligheter för myndigheter att förebygga ekonomisk brottslighet." Thesis, Umeå universitet, Institutionen för matematik och matematisk statistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-184818.

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Ekonomisk brottslighet är mer lukrativt jämfört med annan brottslighet som narkotika, häleri och människohandel. Tidiga åtgärder som försvårar att kriminella kan använda företag för brottsliga syften gör att stora kostnader för samhället kan undvikas. En genomgång av litteraturen visade också att det finns stora brister i samarbetet mellan svenska myndigheter för att upptäcka grov ekonomisk brottslighet. Idag uppdagas brotten först ofta efter att en konkurs inletts. I studier har maskininlärningsmodeller prövats för att kunna upptäcka ekonomisk brottslighet och några s
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Mele, Matteo. "Convolutional Neural Networks for the Classification of Olive Oil Geographical Origin." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2020.

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This work proposed a deep learning approach to a multi-class classification problem. In particular, our project goal is to establish whether there is a connection between olive oil molecular composition and its geographical origin. To accomplish this, we implement a method to transform structured data into meaningful images (exploring the existing literature) and developed a fine-tuned Convolutional Neural Network able to perform the classification. We implement a series of tailored techniques to improve the model.
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Busacca, Fabio Antonino. "AI for Resource Allocation and Resource Allocation for AI: a two-fold paradigm at the network edge." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/573371.

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5G-and-beyond and Internet of Things (IoT) technologies are pushing a shift from the classic cloud-centric view of the network to a new edge-centric vision. In such a perspective, the computation, communication and storage resources are moved closer to the user, to the benefit of network responsiveness/latency, and of an improved context-awareness, that is, the ability to tailor the network services to the live user's experience. However, these improvements do not come for free: edge networks are highly constrained, and do not match the resource abundance of their cloud counterparts. In such a
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Shrivastwa, Ritu Ranjan. "Enhancements in Embedded Systems Security using Machine Learning." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT051.

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La liste des appareils connectés (ou IoT) s’allonge avec le temps, de même que leur vulnérabilité face aux attaques ciblées provenant du réseau ou de l’accès physique, communément appelées attaques Cyber Physique (CPS). Alors que les capteurs visant à détecter les attaques, et les techniques d’obscurcissement existent pour contrecarrer et améliorer la sécurité, il est possible de contourner ces contre-mesures avec des équipements et des méthodologies d’attaque sophistiqués, comme le montre la littérature récente. De plus, la conception des systèmes intégrés est soumise aux contraintes de compl
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Books on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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Jacobs, Jan. De kennismachine: Kunstmatige intelligentie en gezond verstand. Veen, 1986.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 15th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2002, Calgary, Canada, May 27-29, 2002 : proceedings. Springer, 2002.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 20th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2007 : Monteal, Canada, May 28-30, 2007 : proceedings. Springer, 2007.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, AI 2006, Québec City, Québec, Canada, June 7-9, 2006 : proceedings. Springer, 2006.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 11th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'96 Toronto, Ontario, Canada, May 21-24, 1996 : proceedings. Springer Verlag, 1996.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI'98 Vancouver, BC, Canada, June 18-20, 1998 : proceedings. Springer, 1998.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 17th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2004, London, Ontario, Canada, May 17-19, 2004 : proceedings. Springer, 2004.

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Canadian Society for Computational Studies of Intelligence. Conference. Advances in artificial intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000, Montéal [sic], Québec, Canada, May 14-17, 2000 : proceedings. Springer, 2000.

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Grant, Thomas D. On the path to AI: Law’s prophecies and the conceptual foundations of the machine learning age. Springer Nature, 2020.

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Artificial Intelligence (AI) and Machine Learning (ML) in Human Health and Healthcare. MDPI, 2022. http://dx.doi.org/10.3390/books978-3-0365-3741-2.

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Book chapters on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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Joshi, Ameet V. "Introduction to AI and ML." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-26622-6_1.

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Joshi, Ameet V. "Introduction to AI and ML." In Machine Learning and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12282-8_1.

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Frauendorf, José Luiz, and Érika Almeida de Souza. "Artificial Intelligence (AI) and Machine Learning (ML)." In The Architectural and Technological Revolution of 5G. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10650-7_14.

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Memmert, Daniel, and Leily Bakhtiar. "AI and ML: Definitions and Basics." In Artificial Intelligence and Machine Learning in Sports Science. Springer Berlin Heidelberg, 2025. https://doi.org/10.1007/978-3-662-70155-3_1.

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Guérin, Eric, Orhun Aydin, and Ali Mahdavi-Amiri. "Artificial Intelligence." In Manual of Digital Earth. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_10.

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Abstract In this chapter, we provide an overview of different artificial intelligence (AI) and machine learning (ML) techniques and discuss how these techniques have been employed in managing geospatial data sets as they pertain to Digital Earth. We introduce statistical ML methods that are frequently used in spatial problems and their applications. We discuss generative models, one of the hottest topics in ML, to illustrate the possibility of generating new data sets that can be used to train data analysis methods or to create new possibilities for Digital Earth such as virtual reality or aug
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Myerson, Paul. "Artificial Intelligence (AI), Machine Learning (ML), Blockchain, and Robotics." In The Lean, Smart, Digital Supply Chain. Productivity Press, 2025. https://doi.org/10.4324/9781003372639-21.

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Wittenberg, Thomas, Thomas Lang, Thomas Eixelberger, and Roland Grube. "Acquisition of Semantics for Machine-Learning and Deep-Learning based Applications." In Unlocking Artificial Intelligence. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-64832-8_8.

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AbstractFor the development, training, and validation of machine learning (ML) and deep learning (DL) based methods, such as, e.g., image analysis, prediction of critical events, extraction or reconstruction of information from disrupted data streams, searching similarities in data collections, or planning of procedures, a lot of data is needed. Additionally to this data (images, bio-signals, vital-signs, text records, machine states, trajectories, antenna data, ...) adequate supplementary information about the meaning encoded in the data is required. Only with this additional information – th
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Sharma, Ochin, Raj Gaurang Tiwari, and Heena Wadhwa. "Smart Stock Prediction Techniques Using AI and ML." In Artificial Intelligence and Machine Learning for Smart Community. CRC Press, 2023. http://dx.doi.org/10.1201/9781003409502-6.

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August, Stephanie E., and Audrey Tsaima. "Artificial Intelligence and Machine Learning: An Instructor’s Exoskeleton in the Future of Education." In Innovative Learning Environments in STEM Higher Education. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-58948-6_5.

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AbstractThe role of artificial intelligence in US education is expanding. As education moves toward providing customized learning paths, the use of artificial intelligence (AI) and machine learning (ML) algorithms in learning systems increases. This can be viewed as growing metaphorical exoskeletons for instructors, enabling them to provide a higher level of guidance, feedback, and autonomy to learners. In turn, the instructor gains time to sense student needs and support authentic learning experiences that go beyond what AI and ML can provide. Applications of AI-based education technology sup
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Saxena, Ankur, and Shivani Chandra. "Visualization and Prediction of COVID-19 Using AI and ML." In Artificial Intelligence and Machine Learning in Healthcare. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0811-7_6.

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Conference papers on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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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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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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Umbare, Rupali, Ritesh Patil, Tejas Mukund, Adesh Rathod, and Pratik Rajurkar. "A Smart Healthcare Application using Artificial Intelligence (AI) and Machine Learning (ML)." In 2024 5th International Conference on Image Processing and Capsule Networks (ICIPCN). IEEE, 2024. http://dx.doi.org/10.1109/icipcn63822.2024.00041.

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Coombs, Lori D., Layla M. Coombs, Victoria G. Coombs, and Amanda J. Coombs. "Using Artificial Intelligence (AI) and Machine Learning (ML) for Predicting Credit Card Approvals." In 2024 IEEE Integrated STEM Education Conference (ISEC). IEEE, 2024. http://dx.doi.org/10.1109/isec61299.2024.10665297.

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Khare, Pranav, Sahil Arora, and Sandeep Gupta. "Integration of Artificial Intelligence (AI) and Machine Learning (ML) into Product Roadmap Planning." In 2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP). IEEE, 2024. http://dx.doi.org/10.1109/icecsp61809.2024.10698502.

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Devi, Kavuluri Leela Sai Rasagna, Garnepudi Narasimha Kumar, Potturi Ashok Narayana, Kakani Venkata Ramana, K. Amarendra, and Tirupathi Rao Gullipalli. "Forest Fire Prediction and Management using AI (Artificial Intelligence), ML (Machine Learning) and Deep Learning Techniques." In 2024 8th International Conference on Inventive Systems and Control (ICISC). IEEE, 2024. http://dx.doi.org/10.1109/icisc62624.2024.00062.

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Ho, Jonathan, Xiaoyuan Qi, Yuansheng Ma, et al. "Enhancing multilayer process defect prediction accuracy on an artificial intelligence/machine learning (AI/ML) platform." In DTCO and Computational Patterning IV, edited by Neal V. Lafferty and Harsha Grunes. SPIE, 2025. https://doi.org/10.1117/12.3051562.

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Klein, Mark D., Corey D. Packard, Scott C. Gibbs, Audrey C. Levanen, Logan Canull, and Weston Early. "Multimodal synthetic image generation of terrestrial scenes with humans for AI/ML with MuSES." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, edited by Keith F. Prussing, Kimberly E. Manser, Celso De Melo, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2025. https://doi.org/10.1117/12.3054669.

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Lacava, Steven, Felipe Mercado, Valerio Viti, et al. "Synthetic scene generation for AI/ML training that accounts for system aberrations and environmental effects." In Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications III, edited by Kimberly E. Manser, Celso De Melo, Raghuveer M. Rao, and Christopher L. Howell. SPIE, 2025. https://doi.org/10.1117/12.3053899.

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Appavu, Narenthirakumar. "An Innovative Method for Detecting Brain Tumors that Combines Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning with Artificial Data Augmentation." In 2025 6th International Conference on Mobile Computing and Sustainable Informatics (ICMCSI). IEEE, 2025. https://doi.org/10.1109/icmcsi64620.2025.10883500.

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Reports on the topic "Artificial Intelligence and Machine Learning (AI/ML)"

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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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Ramuhalli, Pradeep, Alex Huning, Askin Guler Yigitoglu, and Abhinav Saxena. Status Report on Regulatory Criteria Applicable to the Use of Artificial Intelligence (AI) and Machine Learning (ML). Office of Scientific and Technical Information (OSTI), 2023. http://dx.doi.org/10.2172/2007715.

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Pasupuleti, Murali Krishna. AI-Driven Automation: Transforming Industry 5.0 withMachine Learning and Advanced Technologies. National Education Services, 2025. https://doi.org/10.62311/nesx/rr225.

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Abstract: This article delves into the transformative role of artificial intelligence (AI) and machine learning (ML) in shaping Industry 5.0, a paradigm centered on human- machine collaboration, sustainability, and resilient industrial ecosystems. Beginning with the evolution from Industry 4.0 to Industry 5.0, it examines core AI technologies, including predictive analytics, natural language processing, and computer vision, which drive advancements in manufacturing, quality control, and adaptive logistics. Key discussions include the integration of collaborative robots (cobots) that enhance hu
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Rodriguez, Simon, Tim Hwang, and Rebecca Gelles. Comparing Corporate and University Publication Activity in AI/ML. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/20200067.

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Based on news coverage alone, it can seem as if corporations dominate the research on artificial intelligence and machine learning when compared to the work of universities and academia. Authors Simon Rodriguez, Tim Hwang and Rebecca Gelles analyze the data over the past decade of research publications and find that, in fact, universities are the more dominant producers of AI papers. They also find that while corporations do tend to generate more citations to the work they publish in the field, these “high performing” papers are most frequently cross-collaborations with university labs
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Pasupuleti, Murali Krishna. Mathematical Modeling for Machine Learning: Theory, Simulation, and Scientific Computing. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv125.

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Abstract Mathematical modeling serves as a fundamental framework for advancing machine learning (ML) and artificial intelligence (AI) by integrating theoretical, computational, and simulation-based approaches. This research explores how numerical optimization, differential equations, variational inference, and scientific computing contribute to the development of scalable, interpretable, and efficient AI systems. Key topics include convex and non-convex optimization, physics-informed machine learning (PIML), partial differential equation (PDE)-constrained AI, and Bayesian modeling for uncertai
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Niles, Kenneth, Emily Leathers, Joe Tom, et al. Leveraging artificial intelligence and machine learning (AI/ML) for levee culvert Inspections in USACE Flood Control Systems (FCS). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49210.

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Levee inspections are essential in preventing flooding within populated regions. Risk assessments of structures are performed to identify potential failure modes to maintain the safety and health of the structure. The data collection and defect coding parts of the inspection process can be labor-intensive and time-consuming. The integration of machine learning (ML) and artificial intelligence (AI) techniques may increase accuracy of assessments and reduce time and cost. To develop a foundation for a fully autonomous inspection process, this research investigates methods to gather information f
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Alharbi, Shuaa S., and Haifa F. Alhasson. Toward the Identification of Applications of Artificial Intelligence for Dental Image Detection: Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.11.0023.

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Review question / Objective: The purpose of this systematic review is to understand and compare the current applications of machine learning in the care of dental patients. This will enable us to assess their diagnostic and prognostic accuracy. As part of the study, we will identify areas of development for ML applications in the dental care field. In addition, we will suggest improvements to research methodology that will facilitate the implementation of ML technologies in services and improve clinical treatment guidelines based on the results of future studies. Condition being studied: This
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Marra de Artiñano, Ignacio, Franco Riottini Depetris, and Christian Volpe Martincus. Automatic Product Classification in International Trade: Machine Learning and Large Language Models. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0005012.

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Accurately classifying products is essential in international trade. Virtually all countries categorize products into tariff lines using the Harmonized System (HS) nomenclature for both statistical and duty collection purposes. In this paper, we apply and assess several different algorithms to automatically classify products based on text descriptions. To do so, we use agricultural product descriptions from several public agencies, including customs authorities and the United States Department of Agriculture (USDA). We find that while traditional machine learning (ML) models tend to perform we
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Shukla, Indu, Rajeev Agrawal, Kelly Ervin, and Jonathan Boone. AI on digital twin of facility captured by reality scans. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/47850.

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The power of artificial intelligence (AI) coupled with optimization algorithms can be linked to data-rich digital twin models to perform predictive analysis to make better informed decisions about installation operations and quality of life for the warfighters. In the current research, we developed AI connected lifecycle building information models through the creation of a data informed smart digital twin of one of US Army Corps of Engineers (USACE) buildings as our test case. Digital twin (DT) technology involves creating a virtual representation of a physical entity. Digital twin is created
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Nowinska, Agnieszka Urszula, and Gisele Msann. AI disruption in chartering in Danish Shipping. Aalborg University Open Publishing, 2025. https://doi.org/10.54337/aau.bk2_2025.

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Our research highlights the current state and trends of artificial intelligence (AI) adoption in Denmark’s chartering, particularly in the dry bulk and tanker segments. Companies in the dry bulk sector are leading AI adoption, with the tanker segment closely following and adoption rates in our sample appear higher than national averages reported by consultancies. Most firms are in either the experimental phase or transitioning toward more integrated AI systems, often opting for hybrid models that allow them to maintain internal control over key processes. Factors such as company size and matur
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