Relevant bibliographies by topics / Machine theory of collective intelligence

Contents

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

Academic literature on the topic 'Machine theory of collective intelligence'

Author: Grafiati
Published: 10 December 2022
Last updated: 31 July 2025

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Journal articles on the topic "Machine theory of collective intelligence"

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Canonico, Lorenzo Barberis, Christopher Flathmann, and Nathan McNeese. "The Wisdom of the Market: Using Human Factors to Design Prediction Markets for Collective Intelligence." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 63, no. 1 (2019): 1471–75. http://dx.doi.org/10.1177/1071181319631282.

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There is an ever-growing literature on the power of prediction markets to harness “the wisdom of the crowd” from large groups of people. However, traditional prediction markets are not designed in a human-centered way, often restricting their own potential. This creates the opportunity to implement a cognitive science perspective on how to enhance the collective intelligence of the participants. Thus, we propose a new model for prediction markets that integrates human factors, cognitive science, game theory and machine learning to maximize collective intelligence. We do this by first identifyi
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Dzyaloshinsky, I. M. "Artificial Intelligence: A Humanitarian Perspective." Vestnik NSU. Series: History and Philology 21, no. 6 (2022): 20–29. http://dx.doi.org/10.25205/1818-7919-2022-21-6-20-29.

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The article is devoted to the study of the features of human intelligence and the intelligence of complex computer systems, usually referred to as artificial intelligence (AI). As a hypothesis, a statement was formulated about a significant difference between human and artificial intelligence. Human intelligence is a product of a multi-thousand-year history of the development and interaction of three interrelated processes: 1) the formation and development of the human personality; 2) the formation of complex network relationships between members of the social community; 3) collective activity
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Barberis Canonico, Lorenzo, Nathan J. McNeese, and Chris Duncan. "Machine Learning as Grounded Theory: Human-Centered Interfaces for Social Network Research through Artificial Intelligence." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 1252–56. http://dx.doi.org/10.1177/1541931218621287.

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Internet technologies have created unprecedented opportunities for people to come together and through their collective effort generate large amounts of data about human behavior. With the increased popularity of grounded theory, many researchers have sought to use ever-increasingly large datasets to analyze and draw patterns about social dynamics. However, the data is simply too big to enable a single human to derive effective models for many complex social phenomena. Computational methods offer a unique opportunity to analyze a wide spectrum of sociological events by leveraging the power of
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Kasthurirengan, Arulmozhi. "Analyzing the Role of AI/ML in Optimizing Retail." International Scientific Journal of Engineering and Management 02, no. 05 (2023): 1–8. https://doi.org/10.55041/isjem00692.

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The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources, preferably because of its low-cost advantage as compared to field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: Artificial intelligence (AI) and machine learning (ML) pl
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Mwangi, Jackson. "Analyzing the Role of Artificial Intelligence and Machine Learning in Optimizing Supply Chain Processes in Kenya." International Journal of Supply Chain Management 9, no. 1 (2024): 39–50. http://dx.doi.org/10.47604/ijscm.2322.

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Purpose: The aim of the study was to analyze the role of artificial intelligence and machine learning in optimizing supply chain processes
 Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries.
 Findings: Artificial intelligence (AI) and ma
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Orallo, José Hernández. "Heasuring (machine) intelligence universally: An interdisciplinary challenge." Acta Europeana Systemica 4 (July 13, 2020): 37–40. http://dx.doi.org/10.14428/aes.v4i1.57073.

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Artificial intelligence (Al) is having a deep impact on the way humans work, communicate and enjoy their leisure time. Al systems have been traditionally devised to solve specific tasks, such as playing chess, diagnosing a disease or driving a car. However, more and more Al systems are now being devised to be generally adaptable, and learn to solve a variety of tasks or to assist humans and organisations in their everyday tasks. As a result, an increasing number of robots, bots, avatars and 'smart' devices are enhancing our capabilities as individuals, collectives and humanity as a whole. What
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Petrina, Denis. "THE PROBLEMATIC OF ARTIFICIAL INTELLIGENCE (AI) IN COGNITIVE CAPITALISM: TOWARD POSTHUMANIST CONTOURS OF THE NEW COGNITARIAT." Topos 1, no. 1-2025 (2025): 175–203. https://doi.org/10.61095/815-0047-2025-1-175-203.

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This paper examines the problematic of artificial intelligence (AI) through the lens of contemporary critical theory, offering a dual analytical perspective. On one hand, AI is explored as a symptom of cognitive capitalism, which operates through its key mechanisms, such as modulation, control, and the generation of informational surplus value. On the other hand, the paper raises the issue of exploiting hybrid human-machine labor, emphasizing the need to transcend anthropocentric models and reconceptualize solidarity in a posthumanist framework. In the first section, AI is analyzed as a form o
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Liang, Thow Yick. "The inherent structure and dynamic of intelligent human organizations." Human Systems Management 21, no. 1 (2002): 9–19. http://dx.doi.org/10.3233/hsm-2002-21102.

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As humankind ventures deeper into the intelligence era, a totally re-defined mindset is essential to ensure its continuity. With the emerging new environment, human organizations must behave as intelligent beings, in the same manner as biological entities are competing for survival in an ecological system. They must learn, self-organize, adapt, compete and evolve. Thus, human systems can no longer be like machine. Consequently, the structures and characteristics of the industrial era will have to be dismantled. This shift in paradigm requires all human organizations to re-design their structur
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Kamensky, E. G., A. V. Mayakova, A. Y. Ogurtsova, and M. A. Huseynov. "University 4.0: Contours of Post-Industrial Manifestations In The Optics Of The Triple Helix Model." Proceedings of the Southwest State University. Series: Economics. Sociology. Management 14, no. 1 (2024): 271–82. http://dx.doi.org/10.21869/2223-1552-2024-14-1-271-282.

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Relevance. Universities, as institutional organizations, are subject to the greatest reform in the transforming matrix of goals, values and norms of building the Future. In this regard, the problem of conceptualizing the ultra-new model of the University of the fourth generation (4.0) is the most relevant for science and social sciences.The purpose of this study is to consider and analyze the conceptual foundations reflected in the scientific manifestations of the formation of the fourth generation University model (4.0) as a subject of the “triple helix".Objectives. To analyze the concepts, i
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Liang, Thow Yick. "The new intelligence leadership strategy for iCAS." Human Systems Management 26, no. 2 (2007): 111–22. http://dx.doi.org/10.3233/hsm-2007-26204.

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As humanity becomes more dependent on information and knowledge, the current concepts, theories and practices associated with leadership strategy have to be transformed. Fundamentally, the influence of the knowledge-intensive, fast-changing and more complex environment has initiated a shift in the mindset, strategic thinking, ability and style in the new generation of leaders. In addition, for all categories of human organizations (economics, business, social, education and political) their members are becoming better educated and informed, and consequently they are more sophisticated interact
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Dissertations / Theses on the topic "Machine theory of collective intelligence"

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Ekpe, Bassey. "Theories of collective intelligence and decision-making : towards a viable United Nations intelligence system." Thesis, University of Huddersfield, 2005. http://eprints.hud.ac.uk/id/eprint/7481/.

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The idea of a United Nations (UN) intelligence system is widely misunderstood and debates about it seem to be both misplaced and anecdotal. The lack of a consistent theory on intelligence has fostered the widely held view that such a system is not feasible or incompatible with the UN collective security system. This dissertation takes as its central thesis, the question, of whether an intelligence system is both desirable and feasible within the UN structure. In spite of the fact that no known study has so far engaged with the subject matter at the depth presented in this dissertation, the stu
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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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Gramer, Rachel. "A GENRE OF COLLECTIVE INTELLIGENCE: BLOGS AS INTERTEXTUAL, RECIPROCAL, AND PEDAGOGICAL." Master's thesis, University of Central Florida, 2008. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/2341.

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This thesis investigates the rhetorical features of blogs that lend them dialogic strength as an online genre through the lens of Mikhail Bakhtin's theories of speech genres, utterances, and dialogism. As a relatively new online genre, blogs stem from previous genres (in print and online as well as verbal), but their emergence as a popular form of expression in our current culture demands attention to how blogs also offer us different rhetorical opportunities to meet our changing social exigencies as online subjects in the 21st century. This thesis was inspired by questions about how blogs red
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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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Riedel, Marion, and Tino Schwarze. "Machine Translation (MT) - History, Theory, Problems and Usage." Universitätsbibliothek Chemnitz, 2001. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200100437.

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Gulcehre, Caglar. "Two Approaches For Collective Learning With Language Games." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613109/index.pdf.

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Recent studies in cognitive science indicate that language has an important social function. The structure and knowledge of language emerges from the processes of human communication together with the domain-general cognitive processes. Each individual of a community interacts socially with a limited number of peers. Nevertheless societies are characterized by their stunning global regularities. By dealing with the language as a complex adaptive system, we are able to analyze how languages change and evolve over time. Multi-agent computational simulations assist scientists from different disci
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Shi, Bin. "A Mathematical Framework on Machine Learning: Theory and Application." FIU Digital Commons, 2018. https://digitalcommons.fiu.edu/etd/3876.

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The dissertation addresses the research topics of machine learning outlined below. We developed the theory about traditional first-order algorithms from convex opti- mization and provide new insights in nonconvex objective functions from machine learning. Based on the theory analysis, we designed and developed new algorithms to overcome the difficulty of nonconvex objective and to accelerate the speed to obtain the desired result. In this thesis, we answer the two questions: (1) How to design a step size for gradient descent with random initialization? (2) Can we accelerate the current convex
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Georgescu, Mihai [Verfasser]. "When in doubt ask the crowd : leveraging collective intelligence for improving event detection and machine learning / Mihai Georgescu." Hannover : Technische Informationsbibliothek und Universitätsbibliothek Hannover (TIB), 2015. http://d-nb.info/107359663X/34.

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Ahlberg, Helgee Ernst. "Improving drug discovery decision making using machine learning and graph theory in QSAR modeling." Göteborg : Dept. of Chemistry, University of Gothenburg, 2010. http://gupea.ub.gu.se/dspace/handle/2077/21838.

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He, Fengxiang. "Theoretical Deep Learning." Thesis, The University of Sydney, 2021. https://hdl.handle.net/2123/25674.

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Deep learning has long been criticised as a black-box model for lacking sound theoretical explanation. During the PhD course, I explore and establish theoretical foundations for deep learning. In this thesis, I present my contributions positioned upon existing literature: (1) analysing the generalizability of the neural networks with residual connections via complexity and capacity-based hypothesis complexity measures; (2) modeling stochastic gradient descent (SGD) by stochastic differential equations (SDEs) and their dynamics, and further characterizing the generalizability of deep learning;
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Books on the topic "Machine theory of collective intelligence"

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Wieringa, Roel. Machine intelligence and explanation. Eburon, 1987.

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Bajpai, Manish Kumar, Koushlendra Kumar Singh, and George Giakos, eds. Machine Vision and Augmented Intelligence—Theory and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5078-9.

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Hillis, W. Daniel. The connection machine. M.I.T. Press, 1989.

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Kahraman, Cengiz, and Gülgün Kayakutlu, eds. Energy Management—Collective and Computational Intelligence with Theory and Applications. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-75690-5.

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Najim, K. Learning automata: Theory and applications. Pergamon, 1994.

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Okun, Oleg. Ensembles in Machine Learning Applications. Springer Berlin Heidelberg, 2011.

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AGI Conference (1st 2008 University of Memphis). Artificial general intelligence, 2008: Proceedings of the First AGI Conference. IOS Press, 2008.

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Nadia, Nedjah, and Macedo Mourelle Luiza de, eds. Evolvable machines: Theory & practice. Springer, 2005.

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International Workshop on Implementing Automata (2nd 1997 London, Ont.). Automata implementation: Second International Workshop on Implementing Automata, WIA '97, London, Ontario, Canada, September 18-20, 1997 : revised papers. Springer, 1998.

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1964-, Auer Peter, Meir Ron, and LINK (Online service), eds. Learning theory: 18th annual conference on learning theory, COLT 2005, Bertinoro, Italy, June 27-30 : proceedings. Springer, 2005.

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Book chapters on the topic "Machine theory of collective intelligence"

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Paaß, Gerhard, and Sven Giesselbach. "Foundation Models for Text Generation." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-23190-2_6.

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AbstractThis chapter discusses Foundation Models for Text Generation. This includes systems for Document Retrieval, which accept a query and return an ordered list of text documents from a document collection, often evaluating the similarity of embeddings to retrieve relevant text passages. Question Answering systems are given a natural language question and must provide an answer, usually in natural language. Machine Translation models take a text in one language and translate it into another language. Text Summarization systems receive a long document and generate a short summary covering th
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Perennou, Loïc, and Raja Chiky. "Applying Supervised Machine Learning to Predict Virtual Machine Runtime for a Non-hyperscale Cloud Provider." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_58.

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Psathas, Anastasios Panagiotis, Antonios Papaleonidas, and Lazaros Iliadis. "Machine Learning Modeling of Human Activity Using PPG Signals." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_42.

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Rahman, Shadikur, Syeda Sumbul Hossain, Saiful Islam, Mazharul Islam Chowdhury, Fatama Binta Rafiq, and Khalid Been Md Badruzzaman. "Context-Based News Headlines Analysis Using Machine Learning Approach." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_15.

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Wolpert, David. "Theory of Collective Intelligence." In Collectives and the Design of Complex Systems. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4419-8909-3_2.

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Cupek, Rafal, Łukasz Gólczyński, and Adam Ziebinski. "An OPC UA Machine Learning Server for Automated Guided Vehicle." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_19.

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Petrynski, Kacper, Robert Szost, Iwona Pozniak-Koszalka, Leszek Koszalka, and Andrzej Kasprzak. "Single Machine Weighted Tardiness Problem: An Algorithm and Experimentation System." In Computational Collective Intelligence. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98446-9_4.

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Tukeyev, Ualsher, Dina Amirova, Aidana Karibayeva, Aida Sundetova, and Balzhan Abduali. "Combined Technology of Lexical Selection in Rule-Based Machine Translation." In Computational Collective Intelligence. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-67077-5_47.

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Mussiraliyeva, Shynar, Milana Bolatbek, Batyrkhan Omarov, and Kalamkas Bagitova. "Detection of Extremist Ideation on Social Media Using Machine Learning Techniques." In Computational Collective Intelligence. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63007-2_58.

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Mansurova, Madina, Vladimir Barakhnin, Yerzhan Khibatkhanuly, and Ilya Pastushkov. "Named Entity Extraction from Semi-structured Data Using Machine Learning Algorithms." In Computational Collective Intelligence. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28374-2_6.

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Conference papers on the topic "Machine theory of collective intelligence"

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Ke, Daijia, Akbar Agniputra, Zhaoyan Feng, Ilin Wu, Ilaria DiCarlo, and Annarita Papeschi. "Cartographies of Immersive Fractality: An exploration of collective emotive responses in urban settings through Machine Learning." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.519.

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Wu, Xiao-Kun, Tian-Fang Zhao, and Wei-Neng Chen. "Enhancing Cross-Community Diffusion Through Swarm Intelligence." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751714.

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Mahlich, Christopher, Tobias Vente, and Joeran Beel. "From theory to practice: implementing and evaluating e-fold cross-validation." In International Conference on Artificial Intelligence and Machine Learning Research (CAIMLR 2024), edited by Ankush Ghosh, Haiquan Zhao, Chee Wei Tan, and Jun Liu. SPIE, 2025. https://doi.org/10.1117/12.3058568.

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Bielski, Jessica, Ozan Karaali, Viktor Eisenstadt, Christoph Langenhan, and Frank Petzold. "Sequencing the Architectural Design Process for Artificial Intelligence - A design-theory-based framework for machine learning approaches." In eCAADe 2024: Data-Driven Intelligence. eCAADe, 2024. http://dx.doi.org/10.52842/conf.ecaade.2024.1.449.

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Chen, Xin. "Construal Level Theory and Maslow's Hierarchy with Machine Learning for Enhanced Consumer Demand Analysis." In International Conference on Engineering Management, Information Technology and Intelligence. SCITEPRESS - Science and Technology Publications, 2024. http://dx.doi.org/10.5220/0012922800004508.

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Li, Jian-Yu, Zhao-Kun Shao, Zi-Jia Wang, et al. "A Performance Investigation of Genetic Algorithm with Different Evolutionary Operators." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751720.

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Xie, Linpeng, Changjun Zhou, and Riheng Jia. "DNA-Encoded Design of Arithmetic Optimization Algorithm Fused With Whales." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751713.

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Bai, Yu, Guang-Xu Shen, Bowen Fu, et al. "Orthogonal Design Assisted Particle Swarm Optimization." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751715.

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Wang, Hu-Long, Qiang Yang, Xu-Dong Gao, and Zhen-Yu Lu. "Population-Level Hybridization between Roulette Wheel Selection and Tournament Selection for Particle Swarm Optimization." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751699.

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Alkhathami, Meshari, Yong-Feng Ge, and Hua Wang. "A Variation-Based Genetic Algorithm for Privacy-Preserving Data Publishing." In 2024 11th International Conference on Machine Intelligence Theory and Applications (MiTA). IEEE, 2024. http://dx.doi.org/10.1109/mita60795.2024.10751708.

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Reports on the topic "Machine theory of collective intelligence"

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Engel, David, and Thomas Malone. Measuring Collective Intelligence in Human-Machine Systems. Defense Technical Information Center, 2013. http://dx.doi.org/10.21236/ada602979.

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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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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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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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Pasupuleti, Murali Krishna. Quantum Cognition: Modeling Decision-Making with Quantum Theory. National Education Services, 2025. https://doi.org/10.62311/nesx/rrvi225.

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Abstract Quantum cognition applies quantum probability theory and mathematical principles from quantum mechanics to model human decision-making, reasoning, and cognitive processes beyond the constraints of classical probability models. Traditional decision theories, such as expected utility theory and Bayesian inference, struggle to explain context-dependent reasoning, preference reversals, order effects, and cognitive biases observed in human behavior. By incorporating superposition, interference, and entanglement, quantum cognitive models offer a probabilistic framework that better accounts
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Alexander, Serena, Bo Yang, Owen Hussey, and Derek Hicks. Examining the Externalities of Highway Capacity Expansions in California: An Analysis of Land Use and Land Cover (LULC) Using Remote Sensing Technology. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2251.

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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt
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Kulhandjian, Hovannes. AI-Based Bridge and Road Inspection Framework Using Drones. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2226.

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There are over 590,000 bridges dispersed across the roadway network that stretches across the United States alone. Each bridge with a length of 20 feet or greater must be inspected at least once every 24 months, according to the Federal Highway Act (FHWA) of 1968. This research developed an artificial intelligence (AI)-based framework for bridge and road inspection using drones with multiple sensors collecting capabilities. It is not sufficient to conduct inspections of bridges and roads using cameras alone, so the research team utilized an infrared (IR) camera along with a high-resolution opt
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Goodwin, Sarah, Yigal Attali, Geoffrey LaFlair, et al. Duolingo English Test - Writing Construct. Duolingo, 2023. http://dx.doi.org/10.46999/arxn5612.

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Abstract:
Assessments, especially those used for high-stakes decision making, draw on evidence-based frameworks. Such frameworks inform every aspect of the testing process, from development to results reporting. The frameworks that language assessment professionals use draw on theory in language learning, assessment design, and measurement and psychometrics in order to provide underpinnings for the evaluation of language skills including speaking, writing, reading, and listening. This paper focuses on the construct, or underlying trait, of writing ability. The paper conceptualizes the writing construct
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