Literatura científica selecionada sobre o tema "Arificial Intelligence"
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Artigos de revistas sobre o assunto "Arificial Intelligence"
Kosanović, Nikola. "PORTFOLIO MANAGEMENT AND SYSTEMIC RISK IN THE AGE OF ARIFICIAL INTELLIGENCE". KNOWLEDGE - International Journal 60, n.º 1 (30 de setembro de 2023): 77–81. http://dx.doi.org/10.35120/kij6001077k.
Texto completo da fontePantan, Frans. "CHATGPT DAN ARTIFICIAL INTELLIGENCE: KEKACAUAN ATAU KEBANGUNAN BAGI PENDIDIKAN AGAMA KRISTEN DI ERA POSTMODERN". Diegesis : Jurnal Teologi 8, n.º 1 (28 de fevereiro de 2023): 108–20. http://dx.doi.org/10.46933/dgs.vol8i1108-120.
Texto completo da fonteBudiman, Muhammad Arif, e I. Gusti Agung Widagda. "Fingerprints Image Recognition by Using Perceptron Artificial Neural Network". BULETIN FISIKA 21, n.º 2 (5 de maio de 2020): 37. http://dx.doi.org/10.24843/bf.2020.v21.i02.p01.
Texto completo da fonteSHIYAN, ANNA. "ТHE REVIEW OF ТHE INТERNAТIONAL SCIENТIFIC WORKSHOP “ТHE ТRANSCENDENТAL ТURN IN MODERN PHILOSOPHY — 8: ТRANSCENDENТAL MEТAPHYSICS, EPISТEMOLOGY, TRANSCENDENTAL COGNITIVE SCIENCE AND ARIFICIAL INTELLIGENCE” (April 20–22, 2023, Moscow, Russia)". HORIZON / Fenomenologicheskie issledovanija/ STUDIEN ZUR PHÄNOMENOLOGIE / STUDIES IN PHENOMENOLOGY / ÉTUDES PHÉNOMÉNOLOGIQUES 12, n.º 2 (2023): 570–79. http://dx.doi.org/10.21638/2226-5260-2023-12-2-570-579.
Texto completo da fonte"Special Issue on Arificial Intelligence for Data Fusion". IEEE Aerospace and Electronic Systems Magazine 34, n.º 5 (maio de 2019): 88. http://dx.doi.org/10.1109/maes.2019.2926175.
Texto completo da fonteAasim, Muhammad, Fatma Akin e Seyid Amjad Ali. "Synergizing LED Technology and Hydropriming for Intelligent Modeling and Mathematical Expressions to Optimize Chickpea Germination and Growth Indices". Journal of Plant Growth Regulation, 29 de março de 2024. http://dx.doi.org/10.1007/s00344-024-11269-z.
Texto completo da fonteTeses / dissertações sobre o assunto "Arificial Intelligence"
Lévy, Loup-Noé. "Advanced Clustering and AI-Driven Decision Support Systems for Smart Energy Management". Electronic Thesis or Diss., université Paris-Saclay, 2024. http://www.theses.fr/2024UPASG027.
Texto completo da fonteThis thesis addresses the clustering of complex and heterogeneous energy systems within a Decision Support System (DSS).In chapter 1, we delve into the theory of complex systems and their modeling, recognizing buildings as complex systems, specifically as Sociotechnical Complex Systems. We examine the state of the art of the different agents involved in energy performance within the energy sector, identifying our case study as the Trusted Third Party for Energy Measurement and Performance (TTPEMP.) Given our constraints, we opt to concentrate on the need for a DSS to provide energy recommendations. We compare this system to supervision and recommender systems, highlighting their differences and complementarities and introduce the necessity for explainability in AI-aided decision-making (XAI). Acknowledging the complexity, numerosity, and heterogeneity of buildings managed by the TTPEMP, we argue that clustering serves as a pivotal first step in developing a DSS, enabling tailored recommendations and diagnostics for homogeneous subgroups of buildings. This is presented in Chapter 1.In Chapter 2, we explore DSSs' state of the art, emphasizing the need for governance in semi-automated systems for high-stakes decision-making. We investigate European regulations, highlighting the need for accuracy, reliability, and fairness in our decision system, and identify methodologies to address these needs, such as DevOps methodology and Data Lineage. We propose a DSS architecture that addresses these requirements and the challenges posed by big data, featuring a distributed architecture comprising a data lake for heterogeneous data handling, datamarts for specific data selection and processing, and an ML-Factory populating a model library. Different types of methods are selected for different needs based on the specificities of the data and of the question needing answering.Chapter 3 focuses on clustering as a primary machine learning method in our architecture, essential for identifying homogeneous groups of buildings. Given the combination of numerical, categorical and time series nature of the data describing buildings, we coin the term complex clustering to address this combination of data types. After reviewing the state-of-the-art, we identify the need for dimensionality reduction techniques and the most relevant mixed clustering methods. We also introduce Pretopology as an innovative approach for mixed and complex data clustering. We argue that it allows for greater explainability and interactability in the clustering as it enables Hierarchical clustering and the implementation of logical rules and custom proximity notions. The challenges of evaluating clustering are addressed, and adaptations of numerical clustering to mixed and complex clustering are proposed, taking into account the explainability of the methods.In the datasets and results chapter, we present the public, private, and generated datasets used for experimentation and discuss the clustering results. We analyze the computational performances of algorithms and the quality of clusters obtained on different datasets varying in size, number of clusters, distribution, and number of categorical and numerical parameters. Pretopology and Dimensionality Reduction show promising results compared to state-of-the-art mixed data clustering methods.Finally, we discuss our system's limitations, including the automation limits of the DSS at each step of the data flow. We focus on the critical role of data quality and the challenges in predicting the behavior of complex systems over time. The objectivity of our clustering evaluation methods is challenged due to the absence of ground truth and the reliance on dimensionality reduction to adapt state-of-the-art metrics to complex data. We discuss possible issues regarding the chosen elbow method and future work, such as automation of hyperparameter tuning and continuing the development of the DSS
Livros sobre o assunto "Arificial Intelligence"
Sartor, G. Arificial Intelligence and Law. Legal Philosophy and Legal Theory. Tano Aschehoug, 1993.
Encontre o texto completo da fonteTrabalhos de conferências sobre o assunto "Arificial Intelligence"
Belgiu, G., S. Nanu e I. Silea. "Arificial intelligence in machine tools design based on genetic algorithms application". In 2010 4th International Workshop on Soft Computing Applications (SOFA). IEEE, 2010. http://dx.doi.org/10.1109/sofa.2010.5565623.
Texto completo da fonte