Literatura científica selecionada sobre o tema "IA hybride"
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Artigos de revistas sobre o assunto "IA hybride"
Kiefer, Bertrand. "IA – humains – hybrides". Revue Médicale Suisse 19, n.º 827 (2023): 1000. http://dx.doi.org/10.53738/revmed.2023.19.827.1000.
Texto completo da fonteCoeshott, C. M., R. W. Chesnut, R. T. Kubo, S. F. Grammer, D. M. Jenis e H. M. Grey. "Ia-specific mixed leukocyte reactive T cell hybridomas: analysis of their specificity by using purified class II MHC molecules in synthetic membrane system." Journal of Immunology 136, n.º 8 (15 de abril de 1986): 2832–38. http://dx.doi.org/10.4049/jimmunol.136.8.2832.
Texto completo da fonteMaffei, A., L. Scarpellino, M. Bernard, G. Carra, M. Jotterand-Bellomo, J. Guardiola e R. S. Accolla. "Distinct mechanisms regulate MHC class II gene expression in B cells and macrophages." Journal of Immunology 139, n.º 3 (1 de agosto de 1987): 942–48. http://dx.doi.org/10.4049/jimmunol.139.3.942.
Texto completo da fonteGonwa, Thomas A. "HYBRID IA ANTIGENS IN MAN". Transplantation 42, n.º 4 (outubro de 1986): 423–28. http://dx.doi.org/10.1097/00007890-198610000-00019.
Texto completo da fonteAlotaibi, Jameelah S., Yasair S. Al-Faiyz e Saad Shaaban. "Design, Synthesis, and Biological Evaluation of Novel Hydroxamic Acid-Based Organoselenium Hybrids". Pharmaceuticals 16, n.º 3 (28 de fevereiro de 2023): 367. http://dx.doi.org/10.3390/ph16030367.
Texto completo da fonteSt Pierre, Y., e T. H. Watts. "Characterization of the signaling function of MHC class II molecules during antigen presentation by B cells." Journal of Immunology 147, n.º 9 (1 de novembro de 1991): 2875–82. http://dx.doi.org/10.4049/jimmunol.147.9.2875.
Texto completo da fonteWang, Yingxu. "Inference Algebra (IA)". International Journal of Cognitive Informatics and Natural Intelligence 5, n.º 4 (outubro de 2011): 61–82. http://dx.doi.org/10.4018/jcini.2011100105.
Texto completo da fonteCutello, Vincenzo, Georgia Fargetta, Mario Pavone e Rocco A. Scollo. "Optimization Algorithms for Detection of Social Interactions". Algorithms 13, n.º 6 (11 de junho de 2020): 139. http://dx.doi.org/10.3390/a13060139.
Texto completo da fonteWang, Yingxu. "Inference Algebra (IA)". International Journal of Cognitive Informatics and Natural Intelligence 6, n.º 1 (janeiro de 2012): 21–47. http://dx.doi.org/10.4018/jcini.2012010102.
Texto completo da fonteEnns, Charis, Nathan Andrews e J. Andrew Grant. "Security for whom? Analysing hybrid security governance in Africa's extractive sectors". International Affairs 96, n.º 4 (1 de julho de 2020): 995–1013. http://dx.doi.org/10.1093/ia/iiaa090.
Texto completo da fonteTeses / dissertações sobre o assunto "IA hybride"
Benkirane, Fatima Ezzahra. "Integration of contextual knowledge in deep Learning modeling for vision-based scene analysis". Electronic Thesis or Diss., Bourgogne Franche-Comté, 2024. http://www.theses.fr/2024UBFCA002.
Texto completo da fonteComputer vision has made an important evolution starting from traditional methods to advanced Deep Learning (DL) models. One of the goals of computer vision tasks is to effectively emulate human perception. The classical process of DL models is completely dependent on visual features, which only reflects how humans visually perceive their surroundings. However, for humans to comprehensively understand their environment, their reasoning not only depends on what they see but also on their pre-acquired knowledge. Addressing this gap is essential as achieving human-like reasoning requires a seamless combination of data-driven and knowledge-driven methods. In this thesis, we propose new approaches to improve the performance of DL models by integrating Knowledge-Based Systems (KBS) within Deep Neural Networks (DNNs). The goal is to empower these networks to make informed decisions by leveraging both visual features and knowledge to emulate human-like visual analysis. These methodologies involve two main axes. First, define the representation of KBS to incorporate useful information for a specific computer vision task. Second, investigate how to integrate this knowledge into DNNs to enhance their performance. To do so, we worked on two main contributions. The first work focuses on monocular depth estimation. Considering humans as an example, they can estimate their distance with respect to seen objects, even using just one eye, based on what is called monocular cues. Our contribution involves integrating these monocular cues as human-like reasoning for monocular depth estimation within DNNs. For this purpose, we investigate the possibility of directly integrating geometric and semantic information into the monocular depth estimation process. We suggest using an ontology model in a DL context to represent the environment as a structured set of concepts linked with semantic relationships. Monocular cues information is extracted through reasoning performed on the proposed ontology and is fed together with the RGB image in a multi-stream way into the DNNs. Our approach is validated and evaluated on widespread benchmark datasets. The second work focuses on panoptic segmentation task that aims to identify and analyze all objects captured in an image. More precisely, we propose a new informed deep learning approach that combines the strengths of DNNs with some additional knowledge about spatial relationships between objects. We have chosen spatial relationships knowledge for this task because it can provide useful cues for resolving ambiguities, distinguishing between overlapping or similar object instances, and capturing the holistic structure of the scene. More precisely, we propose a novel training methodology that integrates knowledge directly into the DNNs optimization process. Our approach includes a process for extracting and representing spatial relationships knowledge, which is incorporated into the training using a specially designed loss function. The performance of the proposed method was also evaluated on various challenging datasets. To validate the effectiveness of the proposed approaches for combining KBS and DNNs regarding different methodologies, we have chosen the urban environment and autonomous vehicles as our main use case application. This domain is particularly interesting because it is a challenging and novel field in continuous development, with significant implications for the safety, comfort and mobility of humans. As a conclusion, the proposed approaches validate that the integration of knowledge-driven and data-driven methods consistently leads to improved results. Integration improves the learning process for DNNs and enhances results of computer vision tasks, providing more accurate predictions. The challenge always lies in choosing the relevant knowledge for each task, representing it in the best structure to leverage meaningful information, and integrating it most optimally into the DNN architecture
Weißenburger, Julius Eric. "Disruption in HR : the impact of Artificial Intelligence and machine learning innovation on recruiting". Master's thesis, 2020. http://hdl.handle.net/10400.14/31314.
Texto completo da fonteO talento é cada vez mais importante para as organizações que utilizam o recrutamento corporativo como uma função contínua e significativa. O recrutamento dos melhores talentos não pode ocorrer onde existem ineficiências, altos custos e falta de inovação. Ao mesmo tempo, a inteligência artificial (IA) e machine learning (ML) estão rompendo indústrias e diferentes áreas de prática de negócios. Essa tecnologia tem o potencial de criar um valor sem precedentes nas funções de recrutamento, impactando positivamente a eficiência, os custos e a adequação dos funcionários. Apesar do rápido desenvolvimento no campo da IA, a literatura acadêmica sobre IA no recrutamento é escassa. Os pesquisadores gostariam que existisse mais trabalho colaborativo entre profissionais e acadêmicos. Esta tese visa abordar essa lacuna, avaliando como a IA e o ML modificam os processos tradicionais de recrutamento e trazem novos resultados potenciais. Ao integrar as experiências de especialistas, executivos e as percepções de possíveis candidatos a emprego, esta tese elucida implicações práticas para a adoção de IA e ML no recrutamento. A tese utiliza coleta de dados qualitativa e quantitativa. Os resultados apresentam oportunidades e também as limitações da IA e ML. Além disso, os efeitos da tecnologia no recrutamento eficiente e válido são avaliados. Isso cria a base para recomendações práticas para as organizações com relação à adoção desta tecnologia. Notavelmente, nos aspectos mais padronizados dos processos de recrutamento, essa tecnologia cria valor na contratação.
Capítulos de livros sobre o assunto "IA hybride"
Alberti, Marco, Evelina Lamma, Fabrizio Riguzzi e Riccardo Zese. "Probabilistic Hybrid Knowledge Bases Under the Distribution Semantics". In AI*IA 2016 Advances in Artificial Intelligence, 364–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49130-1_27.
Texto completo da fonteNapoli, Christian, Giuseppe Pappalardo e Emiliano Tramontana. "A Hybrid Neuro–Wavelet Predictor for QoS Control and Stability". In AI*IA 2013: Advances in Artificial Intelligence, 527–38. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03524-6_45.
Texto completo da fontePiaggio, Maurizio, e Antonio Sgorbissa. "Real-Time Motion Planning in Autonomous Vehicles: A Hybrid Approach". In AI*IA 99: Advances in Artificial Intelligence, 368–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-46238-4_32.
Texto completo da fonteBasili, Roberto, Alessandro Moschitti e Maria Teresa Pazienza. "A Hybrid Approach to Optimize Feature Selection Process in Text Classification". In AI*IA 2001: Advances in Artificial Intelligence, 320–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45411-x_33.
Texto completo da fonteMusto, Cataldo, Pasquale Lops, Marco de Gemmis e Giovanni Semeraro. "Feeding a Hybrid Recommendation Framework with Linked Open Data and Graph-Based Features". In AI*IA 2017 Advances in Artificial Intelligence, 229–42. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-70169-1_17.
Texto completo da fonteZhou, P., L. J. Quackenbush, B. Albini e M. B. Zaleski. "Macrophage IA Hybrid Molecule as Product of the Ir-Thy-1 Genes". In H-2 Antigens, 297–304. Boston, MA: Springer US, 1987. http://dx.doi.org/10.1007/978-1-4757-0764-9_29.
Texto completo da fonteKimoto, Masao, B. Beck, M. Shigeta e C. Garrison Fathman. "Functional Characterization Of Hybrid Ia antigens". In Ia Antigens, 81–103. CRC Press, 2019. http://dx.doi.org/10.1201/9781351073332-4.
Texto completo da fonteLafuse, William P., e Chella S. David. "Murine Ia Antigens: Studies Using Hybrid And Mutant Mice". In Ia Antigens, 105–37. CRC Press, 2019. http://dx.doi.org/10.1201/9781351073332-5.
Texto completo da fonteWang, Jing, e Xiang Yi. "A Hybrid Detection Approach for Carbon Emission Intensity Reduction Mechanism Under Environmental Regulations". In Advances in Transdisciplinary Engineering. IOS Press, 2023. http://dx.doi.org/10.3233/atde230302.
Texto completo da fonteTrabalhos de conferências sobre o assunto "IA hybride"
Loia, V., G. Fenza, C. De Maio e S. Salerno. "Hybrid methodologies to foster ontology-based knowledge management platform". In 2013 IEEE Symposium on Intelligent Agents (IA). IEEE, 2013. http://dx.doi.org/10.1109/ia.2013.6595187.
Texto completo da fonteAcampora, Giovanni, e Georgina Cosma. "A hybrid computational intelligence approach for efficiently evaluating customer sentiments in E-commerce reviews". In 2014 IEEE Symposium on Intelligent Agents (IA). IEEE, 2014. http://dx.doi.org/10.1109/ia.2014.7009461.
Texto completo da fonteLee, Ji-Ho, Myeong-Jin Kim e Young-Chai Ko. "IA-based hybrid beamforming design in MIMO interference channel". In 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890113.
Texto completo da fonteDenisenkov, Pavel. "Hybrid C-O-Ne White Dwarfs as Progenitors of Diverse SNe Ia". In XIII Nuclei in the Cosmos. Trieste, Italy: Sissa Medialab, 2015. http://dx.doi.org/10.22323/1.204.0038.
Texto completo da fontePrabakar, D., R. Sindhuja e V. Saminadan. "Hybrid Interference Alignment (IA) Scheme for Improving the Sum-Rate of HetNet Users". In 2019 2nd International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT). IEEE, 2019. http://dx.doi.org/10.1109/icicict46008.2019.8993187.
Texto completo da fonteLi, Yongkui, Lingyan Cao, Yilong Han, Yuchen Shi e Yan Zhang. "Short-Term Electric Load Forecasting with a Hybrid ARIMA, SVR, and IA Methodology". In Construction Research Congress 2020. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784482858.019.
Texto completo da fonteMartin, Ignacio, Tony Markel e J. F. Sanz. "New task on quick charging technology of electric vehicles in IEA IA-HEV (Hybrid and electric vehicles)". In 2013 World Electric Vehicle Symposium and Exhibition (EVS27). IEEE, 2013. http://dx.doi.org/10.1109/evs.2013.6914734.
Texto completo da fonte"Evaluation of a hybrid remote sensing evapotranspiration model for variable rate irrigation management". In 2015 ASABE / IA Irrigation Symposium: Emerging Technologies for Sustainable Irrigation - A Tribute to the Career of Terry Howell, Sr. Conference Proceedings. American Society of Agricultural and Biological Engineers, 2015. http://dx.doi.org/10.13031/irrig.20152142641.
Texto completo da fonteCampbell, Scott, Yuheng Zhang e Pochi Yeh. "Material Limitations in Volume Holographic Copying". In Optical Computing. Washington, D.C.: Optica Publishing Group, 1995. http://dx.doi.org/10.1364/optcomp.1995.omc15.
Texto completo da fonteMucha, Philipp, Amy Robertson, Jason Jonkman e Fabian Wendt. "Hydrodynamic Analysis of a Suspended Cylinder Under Regular Wave Loading Based on Computational Fluid Dynamics". In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95533.
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