Relevant bibliographies by topics / Hybrid Computational Model

Contents

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

Academic literature on the topic 'Hybrid Computational Model'

Author: Grafiati
Published: 4 June 2021
Last updated: 27 July 2025

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Journal articles on the topic "Hybrid Computational Model"

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Chaulwar, Amit. "Sampling Algorithms Combination with Machine Learning for Efficient Safe Trajectory Planning." International Journal of Machine Learning and Computing 11, no. 1 (2021): 1–11. http://dx.doi.org/10.18178/ijmlc.2021.11.1.1007.

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The planning of safe trajectories in critical traffic scenarios using model-based algorithms is a very computationally intensive task. Recently proposed algorithms, namely Hybrid Augmented CL-RRT, Hybrid Augmented CL-RRT+ and GATE-ARRT+, reduce the computation time for safe trajectory planning drastically using a combination of a deep learning algorithm 3D-ConvNet with a vehicle dynamic model. An efficient embedded implementation of these algorithms is required as the vehicle on-board micro-controller resources are limited. This work proposes methodologies for replacing the computationally int
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Wang, Anran, Maruchi Kim, Hao Zhang, and Shyamnath Gollakota. "Hybrid Neural Networks for On-Device Directional Hearing." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 10 (2022): 11421–30. http://dx.doi.org/10.1609/aaai.v36i10.21394.

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On-device directional hearing requires audio source separation from a given direction while achieving stringent human-imperceptible latency requirements. While neural nets can achieve significantly better performance than traditional beamformers, all existing models fall short of supporting low-latency causal inference on computationally-constrained wearables. We present DeepBeam, a hybrid model that combines traditional beamformers with a custom lightweight neural net. The former reduces the computational burden of the latter and also improves its generalizability, while the latter is designe
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Reich, Michael, and Udo Röhr. "A Hybrid Computational Model for Ship Grounding." Ship Technology Research 52, no. 3 (2005): 115–23. http://dx.doi.org/10.1179/str.2005.52.3.004.

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Haidzir, H., Dayang Laila Majid, A. S. M. Rafie, and M. Y. Harmin. "Modal Properties of Hybrid Carbon/Kevlar Composite Thin Plate and Hollow Wing Model." Applied Mechanics and Materials 446-447 (November 2013): 597–601. http://dx.doi.org/10.4028/www.scientific.net/amm.446-447.597.

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In any flutter prediction analysis, modal testing is necessary because flutter, a resonant like vibration occurs at a flutter frequency and adopts a mode shape akin to its structural natural modes. Modal testing can be performed computationally with knowledge of the mechanical properties of the structure. In the present work, computational modal analysis is first performed on a cantilevered hybrid composite thin plate and validated experimentally. Then, the computational procedure is demonstrated on a composite hollow wing model of same material. The concept of hollow wing is explored due to t
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Wang, Xin, Zhi Yu, Le Yang, and Ji Li. "Design and Analysis of a Non-Iterative Estimator for Target Location in Multistatic Sonar Systems with Sensor Position Uncertainties." Mathematics 8, no. 1 (2020): 129. http://dx.doi.org/10.3390/math8010129.

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Target location is the basic application of a multistatic sonar system. Determining the position/velocity vector of a target from the related sonar observations is a nonlinear estimation problem. The presence of possible sensor position uncertainties turns this problem into a more challenging hybrid parameter estimation problem. Conventional gradient-based iterative estimators suffer from the problems of initialization difficulties and local convergence. Even if there is no problem with initialization and convergence, a large computational cost is required in most cases. In view of these drawb
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Kim, Yukyong. "Hybrid Trust Computational Model for M2M Application Services." Journal of Software Assessment and Valuation 16, no. 2 (2020): 53–62. http://dx.doi.org/10.29056/jsav.2020.12.06.

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Escribano, Jorge, Raimon Sunyer, María Teresa Sánchez, Xavier Trepat, Pere Roca-Cusachs, and José Manuel García-Aznar. "A hybrid computational model for collective cell durotaxis." Biomechanics and Modeling in Mechanobiology 17, no. 4 (2018): 1037–52. http://dx.doi.org/10.1007/s10237-018-1010-2.

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Zong, Jing, Xin Xiong, Jianhua Zhou, Ying Ji, Diao Zhou, and Qi Zhang. "FCAN–XGBoost: A Novel Hybrid Model for EEG Emotion Recognition." Sensors 23, no. 12 (2023): 5680. http://dx.doi.org/10.3390/s23125680.

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In recent years, artificial intelligence (AI) technology has promoted the development of electroencephalogram (EEG) emotion recognition. However, existing methods often overlook the computational cost of EEG emotion recognition, and there is still room for improvement in the accuracy of EEG emotion recognition. In this study, we propose a novel EEG emotion recognition algorithm called FCAN–XGBoost, which is a fusion of two algorithms, FCAN and XGBoost. The FCAN module is a feature attention network (FANet) that we have proposed for the first time, which processes the differential entropy (DE)
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Kumala, Farida Nur, Arnelia Dwi Yasa, and Moh Salimi Sueb. "STEAMER Hybrid Learning Project for Creative and Computational Thinking: Perspectives from Elementary School Teacher Candidates." International Journal of Educational Methodology me-10-2024, me-10-issue-3-august-2024 (2024): 413–29. http://dx.doi.org/10.12973/ijem.10.3.413.

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<p style="text-align:justify">The computing and creative skills of students in Indonesia are still low since the government has not focused on student creativity and computational empowerment programs. This research aims to develop a science, technology, engineering, art, mathematics, and reflection (STEAMER) hybrid learning project model for teachers' creative and computational thinking abilities, as well as analyze elementary school teacher candidates' perceptions of the use of STEAMER hybrid learning model to improve teachers' creative and computational thinking abilities. This resear
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Imanpour, Ali, Robert Tremblay, Martin Leclerc, and Romain Siguier. "Development of a Hybrid Simulation Computational Model for Steel Braced Frames." Key Engineering Materials 763 (February 2018): 609–18. http://dx.doi.org/10.4028/www.scientific.net/kem.763.609.

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Hybrid simulation is an economical structural testing technique in which the critical part of the structure expected to respond in the inelastic range is tested physically whereas the rest of the structure is modelled numerically using a finite element analysis program. The article describes the development of a computational model for the hybrid simulation of the seismic collapse of a steel two-tiered braced frame structure due to column buckling. The column stability response in multi-tiered braced frames is first presented using a pure numerical model of the braced frame studied. The develo
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Dissertations / Theses on the topic "Hybrid Computational Model"

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Narain, Jaya S. M. Massachusetts Institute of Technology. "A hybrid computational and analytical model of irrigation drip emitters." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/111708.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 63-65).<br>This thesis details a hybrid computational and analytical model to predict the performance of inline pressure-compensating (PC) drip emitters. A verified CFD model is used to predict flow behavior through tortuous paths. A method of extracting a pressure scaling parameter from the CFD results for use in an analytical model is presented. Analytical expressions that describe the bending of asymmetric rec
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Vyas, Nilesh. "Quantum cryptography in a hybrid security model." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT049.

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L'extension des fonctionnalités et le dépassement des limitations de performances de QKD nécessitent soit des répéteurs quantiques, soit de nouveaux modèles de sécurité. En étudiant cette dernière option, nous introduisons le modèle de sécurité Quantum Computational Timelock (QCT), en supposant que le cryptage sécurisé informatiquement ne peut être rompu qu'après un temps beaucoup plus long que le temps de cohérence des mémoires quantiques disponibles. Ces deux hypothèses, à savoir la sécurité informatique à court terme et le stockage quantique bruité, ont jusqu'à présent déjà été prises en co
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Huda, Shamsul. "Hybrid training approaches to Hidden Markov Model-based acoustic models for automatic speech recognition." Thesis, University of Ballarat, 2008. http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/39435.

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Jiang, Peng. "A Hybrid Risk Model for Hip Fracture Prediction." Diss., The University of Arizona, 2015. http://hdl.handle.net/10150/579112.

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Hip fracture has long been considered as the most serious consequence of osteoporosis, which includes chronic pain, disability, and even death. In the elderly population, a femur fracture is very common. It is assessed that 50% of women aged 50 or older may experience a hip fracture in their remaining life. Hip fracture is among the most common injuries and can lead to substantial morbidity and mortality. In the US alone, over 250,000 hip fractures occur each year and this number is expected to double by the year 2040. Statistics indicate that over 20% of people who experience a hip fracture d
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Yau, Ka-Yen K. "Application of hybrid Computational Fluid Dynamics turbulence model, STRUCT-[epsilon], on heated flow cases." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/123357.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Nuclear Science and Engineering, 2019<br>Cataloged from student-submitted PDF version of thesis.<br>Includes bibliographical references (pages 139-143).<br>Computational Fluid Dynamics (CFD) modeling is a powerful numerical method that can be used to characterize fluid flow, pressure drop, and thermal transient behavior in complex flow geometries. However, with current CFD simulati
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Thatte, Azam. "Multi-scale multi-physics model and hybrid computational framework for predicting dynamics of hydraulic rod seals." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37272.

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Rod seals are one of the most critical components of hydraulic systems. However, the fundamental physics of seal behavior is still poorly understood and the seal designers have virtually no analytical tools with which to predict the behavior of potential seal designs. In pursuit of a comprehensive physics based seal analysis/ design tool, in this work, a multi-scale multi-physics (MSMP) seal model is developed. The model solves the transient problem involving macro-scale viscoelastic deformation mechanics, macro-scale contact, micro-scale two phase fluid mechanics in the sealing zone, micro-sc
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Viademonte, da Rosa Sérgio I. (Sérgio Ivan) 1964. "A hybrid model for intelligent decision support : combining data mining and artificial neural networks." Monash University, School of Information Management and Systems, 2004. http://arrow.monash.edu.au/hdl/1959.1/5159.

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Dai, Xiang. "Nonlinear Dynamics and Vibration of Gear and Bearing Systems using A Finite Element/Contact Mechanics Model and A Hybrid Analytical-Computational Model." Diss., Virginia Tech, 2017. http://hdl.handle.net/10919/78861.

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This work investigates the dynamics and vibration in gear systems, including spur and helical gear pairs, idler gear trains, and planetary gears. The spur gear pairs are analyzed using a finite element/contact mechanics (FE/CM) model. A hybrid analytical-computational (HAC) model is proposed for nonlinear gear dynamics. The HAC predictions are compared with FE/CM results and available experimental data for validation. Chapter ref{{CH:GP_Strain}} investigates the static and dynamic tooth root strains in spur gear pairs using a finite element/contact mechanics approach. Extensive comparisons w
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Ceballos, Andres. "A multiscale model of the neonatal circulatory system following Hybrid Norwood palliation." Master's thesis, University of Central Florida, 2011. http://digital.library.ucf.edu/cdm/ref/collection/ETD/id/4866.

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A system of 32 first-order differential equations is formulated and solved for the LP model using a fourth-order adaptive Runge-Kutta solver. The output pressure and flow waveforms obtained from the LP model are imposed as boundary conditions on the CFD model. Coupling of the two models is done through an iterative process where the parameters in the LP model are adjusted to match the CFD solution. The CFD model domain is a representative HLHS anatomy of an infant after undergoing the Hybrid Norwood procedure and is comprised of the neo-aorta, pulmonary roots, aortic arch with branching arteri
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Stewart, Donal Patrick. "Image analysis and computational modelling of Activity-Dependent Bulk Endocytosis in mammalian central nervous system neurons." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/31468.

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Synaptic vesicle recycling is the reuse of synaptic membrane material and proteins after vesicles have been exocytosed at the pre-synaptic terminal of a neuronal synapse. The discovery of the mechanisms by which recycling operates is a subject of active research. Within small mammalian central nervous system nerve terminals, two studied mechanisms of recovery are clathrin-mediated endocytosis and activity-dependent bulk endocytosis. Research into the comparative kinetics and mechanisms underlying these endocytosis mechanisms commonly involves time-series fluorescence microscopy of in vitro cul
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Books on the topic "Hybrid Computational Model"

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Abd-Alhameed, R. A. Development of Complex Electromagnetic Problems Using FDTD Subgridding in Hybrid Computational Techniques. Nova Science Publishers, Incorporated, 2014.

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Bisseling, Rob H. Parallel Scientific Computation. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198788348.001.0001.

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This book explains how to use the bulk synchronous parallel (BSP) model to design and implement parallel algorithms in the areas of scientific computing and big data. Furthermore, it presents a hybrid BSP approach towards new hardware developments such as hierarchical architectures with both shared and distributed memory. The book provides a full treatment of core problems in scientific computing and big data, starting from a high-level problem description, via a sequential solution algorithm to a parallel solution algorithm and an actual parallel program written in the communication library B
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Book chapters on the topic "Hybrid Computational Model"

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Milde, Florian, Michael Bergdorf, and Petros Koumoutsakos. "A Hybrid Model of Sprouting Angiogenesis." In Computational Science – ICCS 2008. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69387-1_19.

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Zhang, Yanxia, and Long Li. "Stability analysis for a diffusive ratio-dependent predator-prey model involving two delays." In Hybrid Computational Intelligent Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003381167-4.

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Jiang, Rui, and Chengxiang Shi. "Analysis and prediction of physical fitness test data of college students based on grey model." In Hybrid Computational Intelligent Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003381167-5.

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Guo, Zaiyi, and Joc Cing Tay. "A Hybrid Agent-Based Model of Chemotaxis." In Computational Science – ICCS 2007. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-72584-8_16.

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Roy, Soumit, Rik Das, Rupal Bhargava, Ayush Gupta, and Sourav De. "Designing of a solution model for global warming and climate change using machine learning and data engineering techniques." In Hybrid Computational Intelligent Systems. CRC Press, 2023. http://dx.doi.org/10.1201/9781003381167-14.

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Merkevicius, Egidijus, Gintautas Garšva, and Stasys Girdzijauskas. "A Hybrid SOM-Altman Model for Bankruptcy Prediction." In Computational Science – ICCS 2006. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758549_53.

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Al-hnaity, Bashar, and Maysam Abbod. "Predicting Financial Time Series Data Using Hybrid Model." In Studies in Computational Intelligence. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-33386-1_2.

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Zabawa, Jacek. "How to Facilitate Hybrid Model Development by Rebuilding the Demographic Simulation Model." In Computational Science – ICCS 2024. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-63759-9_21.

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Agarwal, Akash, Nitin Rakesh, and Nitin Agarwal. "Efficient Module for OHM (Online Hybrid Model)." In Advances in Computer and Computational Sciences. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-3770-2_34.

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Xu, Bo, Ying Wang, and Xuhai Yang. "A Hybrid Model for Navigation Satellite Clock Error Prediction." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35638-4_20.

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Conference papers on the topic "Hybrid Computational Model"

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Wang, Zhaohui, Yongjiang Yu, and Shunfeng Yang. "Improved Model Predictive Control for Hybrid ANPC with Reduced Computational Burden." In 2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia). IEEE, 2024. http://dx.doi.org/10.1109/ipemc-ecceasia60879.2024.10679754.

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Deo, Arpit, Priyasha Gupta, Karnika Deveradi, Kashish Patidar, Ashish Kumawat, and Pankaj Malik. "An Efficient Hybrid Deep Learning Model for Detecting Musculoskeletal Abnormalities." In 2025 International Conference on Computational, Communication and Information Technology (ICCCIT). IEEE, 2025. https://doi.org/10.1109/icccit62592.2025.10927835.

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R, Murugan, Sekar K, Sariga G, Nikhilesh V, Tejashwini J S, and Dheepa T. "Leveraging Hybrid CNN Model for Enhanced Classification on Imbalanced Datasets." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934394.

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D, Selvapandian, Laxmi Raja, Tharageswari K, Senthilkumar VM, Nikitha JC, and Hemalatha M. "Deep-Based Hybrid Model For Enhancing Land Region Prediction Outcomes." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895000.

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Gadah, Abdulgader Ramadan, Suhaila Isaak, and Abdul-Malik H. Y. Saad. "Accelerating CNN Inference: Hybrid Model Pruning for Reduced Computational Demands and High Accuracy." In 2024 IEEE 22nd Student Conference on Research and Development (SCOReD). IEEE, 2024. https://doi.org/10.1109/scored64708.2024.10872661.

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Abdelfatah, Omar Sami, Yehia A. Eldrainy, Ali I. Shehata, and Ahmed S. Shehata. "A Computational Fluid Dynamics Model for Tornado Wind Turbine." In The 12th International Conference on Fracture Fatigue and Wear. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-rcglq6.

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The increasing demand for clean and renewable energy has led researchers to focus on the development of vertical-axis wind turbines. This paper aims to compare the flow field and performance of the Tornado wind turbine with those of Savonius, Darrieus, and hybrid wind turbines at different tip speed ratios. A two-dimensional, incompressible, turbulent, and unsteady flow model was created using ANSYS Fluent 21 and verified through grid independence studies. The Tornado wind turbine demonstrates enhanced aerodynamic efficiency and reduced negative torque. The results show that the Tornado model
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Kaur, Gaganjot, Meenu Gupta, and Rakesh Kumar. "Electroencephalography-based Human Emotion Detection using Hybrid ABC-F-LSTM Model." In 2025 International Conference on Intelligent Systems and Computational Networks (ICISCN). IEEE, 2025. https://doi.org/10.1109/iciscn64258.2025.10934515.

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Mohammed, Abed Jawad, Hithaifa Alani, Mohammed M. Jaafar, Nashwan Adnan Othman, and Ali Ali Saber Mohammed. "Designing a Hybrid Instructional Model Centered on a Mobile Learning Platform." In 2024 International Conference on Emerging Research in Computational Science (ICERCS). IEEE, 2024. https://doi.org/10.1109/icercs63125.2024.10895402.

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George, Jossy, Kamal Upreti, Ramesh Chandra Poonia, and Bosco Paul Alapatt. "A Hybrid Evolutionary Deep Learning Model Integrating Multi-Modal Data for Optimizing Ovarian Cancer Diagnosis." In 2024 3rd International Conference on Computational Modelling, Simulation and Optimization (ICCMSO). IEEE, 2024. http://dx.doi.org/10.1109/iccmso61761.2024.00023.

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Xue, Jiaxing, Jean Gao, and Liping Tang. "A hybrid computational model for phagocyte transmigration." In 2008 8th IEEE International Conference on Bioinformatics and BioEngineering. IEEE, 2008. http://dx.doi.org/10.1109/bibe.2008.4696731.

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Reports on the topic "Hybrid Computational Model"

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Pasupuleti, Murali Krishna. Decision Theory and Model-Based AI: Probabilistic Learning, Inference, and Explainability. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv525.

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Abstract Decision theory and model-based AI provide the foundation for probabilistic learning, optimal inference, and explainable decision-making, enabling AI systems to reason under uncertainty, optimize long-term outcomes, and provide interpretable predictions. This research explores Bayesian inference, probabilistic graphical models, reinforcement learning (RL), and causal inference, analyzing their role in AI-driven decision systems across various domains, including healthcare, finance, robotics, and autonomous systems. The study contrasts model-based and model-free approaches in decision-
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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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Zheng, Jinhui, Matteo Ciantia, and Jonathan Knappett. On the efficiency of coupled discrete-continuum modelling analyses of cemented materials. University of Dundee, 2021. http://dx.doi.org/10.20933/100001236.

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Computational load of discrete element modelling (DEM) simulations is known to increase with the number of particles. To improve the computational efficiency hybrid methods using continuous elements in the far-field, have been developed to decrease the number of discrete particles required for the model. In the present work, the performance of using such coupling methods is investigated. In particular, the coupled wall method, known as the “wall-zone” method when coupling DEM and the continuum Finite Differences Method (FDM) using the Itasca commercial codes PFC and FLAC respectively, is here
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Pasupuleti, Murali Krishna. Optimal Control and Reinforcement Learning: Theory, Algorithms, and Robotics Applications. National Education Services, 2025. https://doi.org/10.62311/nesx/rriv225.

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Abstract: Optimal control and reinforcement learning (RL) are foundational techniques for intelligent decision-making in robotics, automation, and AI-driven control systems. This research explores the theoretical principles, computational algorithms, and real-world applications of optimal control and reinforcement learning, emphasizing their convergence for scalable and adaptive robotic automation. Key topics include dynamic programming, Hamilton-Jacobi-Bellman (HJB) equations, policy optimization, model-based RL, actor-critic methods, and deep RL architectures. The study also examines traject
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Dietterich, Thomas G., and Prasad Tadepalli. Hybrid Computational Models for Skill Acquisition. Defense Technical Information Center, 1998. http://dx.doi.org/10.21236/ada353324.

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Wu, Yingjie, Selim Gunay, and Khalid Mosalam. Hybrid Simulations for the Seismic Evaluation of Resilient Highway Bridge Systems. Pacific Earthquake Engineering Research Center, University of California, Berkeley, CA, 2020. http://dx.doi.org/10.55461/ytgv8834.

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Bridges often serve as key links in local and national transportation networks. Bridge closures can result in severe costs, not only in the form of repair or replacement, but also in the form of economic losses related to medium- and long-term interruption of businesses and disruption to surrounding communities. In addition, continuous functionality of bridges is very important after any seismic event for emergency response and recovery purposes. Considering the importance of these structures, the associated structural design philosophy is shifting from collapse prevention to maintaining funct
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Bajwa, Abdullah, Tim Kroeger, and Timothy Jacobs. PR-457-17201-R04 Residual Gas Fraction Estimation Based on Measured Engine Parameters - Phase IV. Pipeline Research Council International, Inc. (PRCI), 2021. http://dx.doi.org/10.55274/r0012176.

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Based on experimental characterization of the scavenging behavior of a cross-scavenged, piston-aspirated, two-stroke, natural gas engine in phase III of the current project, a computationally inexpensive simple scavenging model was improved in this phase. Experimental results using fast nondispersive infrared (NDIR) CO2 measurements from the cylinder and the exhaust, as well as experiments using unburned fuel pre-mixed in the scavenging chamber as a tracer for short-circuiting during scavenging, were used in this phase to validate the improved model. The model represents the fundamental phenom
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Hough, Patricia Diane, Genetha Anne Gray, Joseph Pete Jr Castro, .), and Anthony Andrew Giunta. Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions. Office of Scientific and Technical Information (OSTI), 2006. http://dx.doi.org/10.2172/877137.

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Pasupuleti, Murali Krishna. Quantum-Enhanced Machine Learning: Harnessing Quantum Computing for Next-Generation AI Systems. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv125.

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Abstract Quantum-enhanced machine learning (QML) represents a paradigm shift in artificial intelligence by integrating quantum computing principles to solve complex computational problems more efficiently than classical methods. By leveraging quantum superposition, entanglement, and parallelism, QML has the potential to accelerate deep learning training, optimize combinatorial problems, and enhance feature selection in high-dimensional spaces. This research explores foundational quantum computing concepts relevant to AI, including quantum circuits, variational quantum algorithms, and quantum k
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Lutz, Carsten, Carlos Areces, Ian Horrocks, and Ulrike Sattler. Keys, Nominals, and Concrete Domains. Technische Universität Dresden, 2002. http://dx.doi.org/10.25368/2022.122.

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Many description logics (DLs) combine knowledge representation on an abstract, logical level with an interface to 'concrete' domains such as numbers and strings with built-in predicates such as &lt;, +, and prefix-of. These hybrid DLs have turned out to be quite useful for reasoning about conceptual models of information systems, and as the basis for expressive ontology languages. We propose to further extend such DLs with key constraints that allow the expression of statements like 'US citizens are uniquely identified by their social security number'. Based on this idea, we introduce a number
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