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Thèses sur le sujet « Generative Artificial Intelligence »

Auteur : Grafiati

Publié le 2 juin 2025

Mis à jour le 31 juillet 2025

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1

Benedetti, Riccardo. "From Artificial Intelligence to Artificial Art: Deep Learning with Generative Adversarial Networks." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18167/.

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Neural Network had a great impact on Artificial Intelligence and nowadays the Deep Learning algorithms are widely used to extract knowledge from huge amount of data. This thesis aims to revisit the evolution of Deep Learning from the origins till the current state-of-art by focusing on a particular prospective. The main question we try to answer is: can AI exhibit artistic abilities comparable to the human ones? Recovering the definition of the Turing Test, we propose a similar formulation of the concept, indeed, we would like to test the machine's ability to exhibit artistic behaviour equiv

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ABUKMEIL, MOHANAD. "UNSUPERVISED GENERATIVE MODELS FOR DATA ANALYSIS AND EXPLAINABLE ARTIFICIAL INTELLIGENCE." Doctoral thesis, Università degli Studi di Milano, 2022. http://hdl.handle.net/2434/889159.

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For more than a century, the methods of learning representation and the exploration of the intrinsic structures of data have developed remarkably and currently include supervised, semi-supervised, and unsupervised methods. However, recent years have witnessed the flourishing of big data, where typical dataset dimensions are high, and the data can come in messy, missing, incomplete, unlabeled, or corrupted forms. Consequently, discovering and learning the hidden structure buried inside such data becomes highly challenging. From this perspective, latent data analysis and dimensionality reductio

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Konuko, Goluck. "Low Bitrate Face Video Compression with Generative Animation Models." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPAST014.

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Cette thèse aborde le défi de réaliser une compression vidéo à ultra-faible débit pour la vidéoconférence, en se concentrant sur la préservation d'une haute qualité visuelle tout en minimisant la bande passante de transmission. Les codecs traditionnels comme HEVC et VVC éprouvent des difficultés à très bas débits, particulièrement pour représenter avec précision les expressions faciales dynamiques, les mouvements de tête et les occlusions, qui sont essentiels pour le réalisme et la précision dans la communication en face à face. Pour surmonter ces limitations, cette recherche développe une mét

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Griffith, Todd W. "A computational theory of generative modeling in scientific reasoning." Diss., Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/8177.

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Navaroli, Nicholas Martin. "Generative Probabilistic Models for Analysis of Communication Event Data with Applications to Email Behavior." Thesis, University of California, Irvine, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3668831.

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<p> Our daily lives increasingly involve interactions with others via different communication channels, such as email, text messaging, and social media. In this context, the ability to analyze and understand our communication patterns is becoming increasingly important. This dissertation focuses on generative probabilistic models for describing different characteristics of communication behavior, focusing primarily on email communication. </p><p> First, we present a two-parameter kernel density estimator for estimating the probability density over recipients of an email (or, more generally,

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Mallik, Mohammed Tariqul Hassan. "Electromagnetic Field Exposure Reconstruction by Artificial Intelligence." Electronic Thesis or Diss., Université de Lille (2022-....), 2023. https://pepite-depot.univ-lille.fr/ToutIDP/EDENGSYS/2023/2023ULILN052.pdf.

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Le sujet de l'exposition aux champs électromagnétiques a fait l'objetd'une grande attention à la lumière du déploiement actuel du réseaucellulaire de cinquième génération (5G). Malgré cela, il reste difficilede reconstituer avec précision le champ électromagnétique dans unerégion donnée, faute de données suffisantes. Les mesures in situ sontd'un grand intérêt, mais leur viabilité est limitée, ce qui renddifficile la compréhension complète de la dynamique du champ. Malgré legrand intérêt des mesures localisées, il existe encore des régions nontestées qui les empêchent de fournir une carte d'exp

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Goodman, Genghis. "A Machine Learning Approach to Artificial Floorplan Generation." UKnowledge, 2019. https://uknowledge.uky.edu/cs_etds/89.

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The process of designing a floorplan is highly iterative and requires extensive human labor. Currently, there are a number of computer programs that aid humans in floorplan design. These programs, however, are limited in their inability to fully automate the creative process. Such automation would allow a professional to quickly generate many possible floorplan solutions, greatly expediting the process. However, automating this creative process is very difficult because of the many implicit and explicit rules a model must learn in order create viable floorplans. In this paper, we propose a met

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Bartocci, John Timothy. "Generating a synthetic dataset for kidney transplantation using generative adversarial networks and categorical logit encoding." Bowling Green State University / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=bgsu1617104572023027.

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Franceschelli, Giorgio. "Generative Deep Learning and Creativity." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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“Non ha la presunzione di originare nulla; può solo fare ciò che noi sappiamo ordinarle di fare”. Così, oltre 150 anni fa, Lady Lovelace commentava la Macchina Analitica di Babbage, l’antenato dei nostri computer. Una frase che, a distanza di tanti anni, suona quasi come una sfida: grazie alla diffusione delle tecniche di Generative Deep Learning e alle ricerche nell’ambito della Computational Creativity, sempre più sforzi sono stati destinati allo smentire l’ormai celebre Obiezione della Lovelace. Proprio a partire da questa, quattro domande formano i capisaldi della Computational Creativity:

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Kalantari, John I. "A general purpose artificial intelligence framework for the analysis of complex biological systems." Diss., University of Iowa, 2017. https://ir.uiowa.edu/etd/5953.

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This thesis encompasses research on Artificial Intelligence in support of automating scientific discovery in the fields of biology and medicine. At the core of this research is the ongoing development of a general-purpose artificial intelligence framework emulating various facets of human-level intelligence necessary for building cross-domain knowledge that may lead to new insights and discoveries. To learn and build models in a data-driven manner, we develop a general-purpose learning framework called Syntactic Nonparametric Analysis of Complex Systems (SYNACX), which uses tools from Bayesian

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Nilsson, Alexander, and Martin Thönners. "A Framework for Generative Product Design Powered by Deep Learning and Artificial Intelligence : Applied on Everyday Products." Thesis, Linköpings universitet, Maskinkonstruktion, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-149454.

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In this master’s thesis we explore the idea of using artificial intelligence in the product design process and seek to develop a conceptual framework for how it can be incorporated to make user customized products more accessible and affordable for everyone. We show how generative deep learning models such as Variational Auto Encoders and Generative Adversarial Networks can be implemented to generate design variations of windows and clarify the general implementation process along with insights from recent research in the field. The proposed framework consists of three parts: (1) A morphologic

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Busatta, Gianluca. "Italian Retrieval-Augmented Generative Question Answering System for Legal Domains." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2022.

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A typical scenario involves a user searching an information about something and obtaining a list of documents from an information retrieval system. The retrieved documents may be more or less relevant and it could happen that the information sought is contained in several documents. This would possibly leave the task of searching the information in different documents to the user. In this thesis, it is has been developed an Italian question answering system for legal domains with a Retrieval-Augmented Generation (RAG) approach that aims to directly satisfy the information need of the user. The

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Delacruz, Gian P. "Using Generative Adversarial Networks to Classify Structural Damage Caused by Earthquakes." DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2158.

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The amount of structural damage image data produced in the aftermath of an earthquake can be staggering. It is challenging for a few human volunteers to efficiently filter and tag these images with meaningful damage information. There are several solution to automate post-earthquake reconnaissance image tagging using Machine Learning (ML) solutions to classify each occurrence of damage per building material and structural member type. ML algorithms are data driven; improving with increased training data. Thanks to the vast amount of data available and advances in computer architectures, ML and

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Haidar, Ahmad. "Responsible Artificial Intelligence : Designing Frameworks for Ethical, Sustainable, and Risk-Aware Practices." Electronic Thesis or Diss., université Paris-Saclay, 2024. https://www.biblio.univ-evry.fr/theses/2024/interne/2024UPASI008.pdf.

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L'intelligence artificielle (IA) transforme rapidement le monde, redéfinissant les relations entre technologie et société. Cette thèse explore le besoin essentiel de développer, de gouverner et d'utiliser l'IA et l'IA générative (IAG) de manière responsable et durable. Elle traite des risques éthiques, des lacunes réglementaires et des défis associés aux systèmes d'IA, tout en proposant des cadres concrets pour promouvoir une Intelligence Artificielle Responsable (IAR) et une Innovation Numérique Responsable (INR).La thèse commence par une analyse approfondie de 27 déclarations éthiques mondia

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Kalchbrenner, Nal. "Encoder-decoder neural networks." Thesis, University of Oxford, 2017. http://ora.ox.ac.uk/objects/uuid:d56e48db-008b-4814-bd82-a5d612000de9.

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This thesis introduces the concept of an encoder-decoder neural network and develops architectures for the construction of such networks. Encoder-decoder neural networks are probabilistic conditional generative models of high-dimensional structured items such as natural language utterances and natural images. Encoder-decoder neural networks estimate a probability distribution over structured items belonging to a target set conditioned on structured items belonging to a source set. The distribution over structured items is factorized into a product of tractable conditional distributions over in

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Misino, Eleonora. "Deep Generative Models with Probabilistic Logic Priors." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/24058/.

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Many different extensions of the VAE framework have been introduced in the past. How ever, the vast majority of them focused on pure sub-symbolic approaches that are not sufficient for solving generative tasks that require a form of reasoning. In this thesis, we propose the probabilistic logic VAE (PLVAE), a neuro-symbolic deep generative model that combines the representational power of VAEs with the reasoning ability of probabilistic logic programming. The strength of PLVAE resides in its probabilistic logic prior, which provides an interpretable structure to the latent space that can b

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Chow, Ka Nin. "An embodied cognition approach to the analysis and design of generative and interactive animation." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34695.

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Animation is popularly thought of as a sequence of still images or cartoons that produce an illusion of movement. However, a broader perspective of animation should encompass the diverse kinds of media artifacts imbued with the illusion of life. In many multimedia artifacts today, computational media algorithmically implement expanded illusions of life, which include images not only moving, but also showing reactions to stimuli (reactive animation), transforming according to their own internal rules (autonomous animation), evolving over a period of time (metamorphic animation), or even generat

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Adhikari, Aakriti. "Skin Cancer Detection using Generative Adversarial Networkand an Ensemble of deep Convolutional Neural Networks." University of Toledo / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1574383625473665.

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Flaherty, Drew. "Artistic approaches to machine learning." Thesis, Queensland University of Technology, 2020. https://eprints.qut.edu.au/200191/1/Drew_Flaherty_Thesis.pdf.

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This research is about how Artificial Intelligence and Machine Learning may impact creative practice. The thesis looks at various implementations and models related to the subject from different cultural and technical viewpoints. The project also provides experimental creative outcomes from my personal practice along with a qualitative study into attitudes and perspectives from other creative practitioners.

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Lagerkvist, Love. "Neural Novelty — How Machine Learning Does Interactive Generative Literature." Thesis, Malmö universitet, Fakulteten för kultur och samhälle (KS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:mau:diva-21222.

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Every day, machine learning (ML) and artificial intelligence (AI) embeds itself further into domestic and industrial technologies. Interaction de- signers have historically struggled to engage directly with the subject, facing a shortage of appropriate methods and abstractions. There is a need to find ways though which interaction design practitioners might integrate ML into their work, in order to democratize and diversify the field. This thesis proposes a mode of inquiry that considers the inter- active qualities of what machine learning does, as opposed the tech- nical specifications of wha

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Townsend, Joseph Paul. "Artificial development of neural-symbolic networks." Thesis, University of Exeter, 2014. http://hdl.handle.net/10871/15162.

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Artificial neural networks (ANNs) and logic programs have both been suggested as means of modelling human cognition. While ANNs are adaptable and relatively noise resistant, the information they represent is distributed across various neurons and is therefore difficult to interpret. On the contrary, symbolic systems such as logic programs are interpretable but less adaptable. Human cognition is performed in a network of biological neurons and yet is capable of representing symbols, and therefore an ideal model would combine the strengths of the two approaches. This is the goal of Neural-Symbol

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Feng, Qianli. "Modeling Action Intentionality in Humans and Machines." The Ohio State University, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=osu1616769653536292.

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Mincolla, Andrea. "Space Systems of Systems Generative Design Using Concurrent MBSE: An Application of ECSS-E-TM-10-25 and the GCD Tool to Copernicus Next Generation." Thesis, KTH, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-286332.

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The Concurrent Design Platform 4 (CDP4®) is a collaborative Model-Based Systems Engineering (MBSE) software tool conceived for architecting complex systems. Nevertheless, there are limitations concerning the manageable number of system options. The upcoming Siemens tool for generative engineering, Simcenter™ Studio, is attempting to overcome this limitation by enabling automatic synthesis and evaluation of architecture variants. The motivation for the Generative Concurrent Design (GCD) project as a collaboration between RHEA, Siemens and OHB is to develop a combined prototype of these two tool

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Ladrón, de Guevara Cortés Rogelio. "Techniques For Estimating the Generative Multifactor Model of Returns in a Statistical Approach to the Arbitrage Pricing Theory. Evidence from the Mexican Stock Exchange." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/386545.

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This dissertation focuses on the estimation of the generative multifactor model of returns on equities, under a statistical approach of the Arbitrage Pricing Theory (APT), in the context of the Mexican Stock Exchange. Therefore, this research takes as frameworks two main issues: (i) the multifactor asset pricing models, specially the statistical risk factors approach, and (ii) the dimension reduction or feature extraction techniques: Principal Component Analysis, Factor Analysis, Independent Component Analysis and Non-linear Principal Component Analysis, utilized to extract the underlying syst

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Ghosh, Aishik. "Simulation of the ATLAS electromagnetic calorimeter using generative adversarial networks and likelihood-free inference of the offshell Higgs boson couplings at the LHC." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASP058.

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Depuis la découverte du boson de Higgs en 2012, les expériences du LHC testent les prévisions du modèle standard avec des mesures de haute précision. Les mesures des couplages du boson de Higgs hors résonance permettront d'éliminer certaines dégénérescences qui ne peuvent pas être résolues avec les mesures sur résonance, comme la sonde de la largeur du boson de Higgs, ce qui pourrait donner des indications pour la nouvelle physique. Une partie de cette thèse se concentre sur la mesure des couplages hors résonance du boson de Higgs produit par la fusion du boson vecteur et se décomposant en qua

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TOMA, ANDREA. "PHY-layer Security in Cognitive Radio Networks through Learning Deep Generative Models: an AI-based approach." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/1003576.

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Recently, Cognitive Radio (CR) has been intended as an intelligent radio endowed with cognition which can be developed by implementing Artificial Intelligence (AI) techniques. Specifically, data-driven Self-Awareness (SA) functionalities, such as detection of spectrum abnormalities, can be effectively implemented as shown by the proposed research. One important application is PHY-layer security since it is essential to establish secure wireless communications against external jamming attacks. In this framework, signals are non-stationary and features from such kind of dynamic spectrum, with m

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Sveding, Jens Jakob. "Unsupervised Image-to-image translation : Taking inspiration from human perception." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-105500.

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Generative Artificial Intelligence is a field of artificial intelligence where systems can learn underlying patterns in previously seen content and generate new content. This thesis explores a generative artificial intelligence technique used for image-toimage translations called Cycle-consistent Adversarial network (CycleGAN), which can translate images from one domain into another. The CycleGAN is a stateof-the-art technique for doing unsupervised image-to-image translations. It uses the concept of cycle-consistency to learn a mapping between image distributions, where the Mean Absolute Erro

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Karlsson, Simon, and Per Welander. "Generative Adversarial Networks for Image-to-Image Translation on Street View and MR Images." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-148475.

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Generative Adversarial Networks (GANs) is a deep learning method that has been developed for synthesizing data. One application for which it can be used for is image-to-image translations. This could prove to be valuable when training deep neural networks for image classification tasks. Two areas where deep learning methods are used are automotive vision systems and medical imaging. Automotive vision systems are expected to handle a broad range of scenarios which demand training data with a high diversity. The scenarios in the medical field are fewer but the problem is instead that it is diffi

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Caillon, Antoine. "Hierarchical temporal learning for multi-instrument and orchestral audio synthesis." Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS115.

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Les progrès récents en matière d'apprentissage automatique ont permis l'émergence de nouveaux types de modèles adaptés à de nombreuses tâches, ce grâce à l'optimisation d'un ensemble de paramètres visant à minimiser une fonction de coût. Parmi ces techniques, les modèles génératifs probabilistes ont permis des avancées notables dans la génération de textes, d'images et de sons. Cependant, la génération de signaux audio musicaux reste un défi. Cela vient de la complexité intrinsèque des signaux audio, une seule seconde d'audio brut comprenant des dizaines de milliers d'échantillons individuels.

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Vassilo, Kyle. "Single Image Super Resolution with Infrared Imagery and Multi-Step Reinforcement Learning." University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1606146042238906.

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Gopinathan, Muraleekrishna. "Toward embodied navigation through vision and language." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2025. https://ro.ecu.edu.au/theses/2894.

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Embodied AI is a challenging but exciting field in which a robot learns to interact with human-living spaces to perform various tasks. This thesis studies the embodied navigation problem in which a robotic agent navigates in a previously unseen indoor environment based on a challenging task. In particular, the Vision-and-Language Navigation (VLN) task requires a robot to navigate based on a descriptive human-language instruction. This thesis aims to improve VLN agents on four key aspects - their understanding of the environment, training via additional data, correcting navigational errors, and

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Ogunoiki, Adebola Oluwaseyi. "Artificial road input data generation tool for vehicle durability assessment using artificial intelligence." Thesis, University of Birmingham, 2015. http://etheses.bham.ac.uk//id/eprint/6156/.

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Vehicle durability assessment in the automotive industry requires a good knowledge of the road load input the vehicle will experience while in service. This research explores the approach of artificial intelligence for predicting the road load input for road load simulation in the CAE environment prior to the development of a vehicle prototype. The multi-body dynamics (MBD) simulation of a quarter vehicle test rig, built with the specification of a commercial SUV, and the full vehicle of the same SUV were modelled and validated in SIMPACK using a simple tyre model developed using the tri-axial

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Kang, Johan, and Sebastian Westskytte. "Diffusion of Cybersecurity Technology - Next Generation, Powered by Artificial Intelligence." Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-246027.

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The cyber world is growing as more information is converted from analogue to digital form. While convenience has been the main driver for this change little effort has been made on securing the data. Data breaches are growing in number and each breach is growing in severity. Combined with regulatory pressure organizations are starting to realize the importance of security. The increased threat level is also driving the security market for more potent solutions and artificial intelligence (AI) have in recent years been implemented to enhance the capabilities of security technologies. The thesi

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Rapoport, Robert S. "The iterative frame : algorithmic video editing, participant observation & the black box." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:8339bcb5-79f2-44d1-b78d-7bd28aa1956e.

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Machine learning is increasingly involved in both our production and consumption of video. One symptom of this is the appearance of automated video editing applications. As this technology spreads rapidly to consumers, the need for substantive research about its social impact grows. To this end, this project maintains a focus on video editing as a microcosm of larger shifts in cultural objects co-authored by artificial intelligence. The window in which this research occurred (2010-2015) saw machine learning move increasingly into the public eye, and with it ethical concerns. What follows is, o

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Xu, Xin. "Interactive hierarchical generate and test search." Thesis, University of Ottawa (Canada), 1991. http://hdl.handle.net/10393/7934.

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Most of the search methods used in AI are inflexible. Interactive search is a new kind of search in which the search system can communicate and cooperate with external agents. There are two kinds of agents: human agents and non-human agents. Through interaction with human agents (man-machine interaction), the search system can make use of the human talent of judging the quality of a solution. Through interaction with non-human agents (machine-machine interaction), the search system can automatically exploit knowledge from its environment. An interactive search system has the ability to take ad

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Gongora, Mario Augusto. "Artificial intelligence tools for path generation and optimisation for mobile robots." Thesis, University of Warwick, 1998. http://wrap.warwick.ac.uk/2830/.

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The ultimate goal in robotic systems is to develop machines that learn for themselves based on experience. In order to achieve on-line learning some software tools are needed to allow the robots to continually adapt their behaviour in order to constantly optimise their performance. This thesis presents research work focused on path planning for mobile robots with the objective of generating optimal paths for any type of mobile robot in an environment containing any number of static obstacles of any shape. The research specifically recognises that an optimal path can be defined according to sev

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Shafin, Rubayet. "3D Massive MIMO and Artificial Intelligence for Next Generation Wireless Networks." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/97633.

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3-dimensional (3D) massive multiple-input-multiple-output (MIMO)/full dimensional (FD) MIMO and application of artificial intelligence are two main driving forces for next generation wireless systems. This dissertation focuses on aspects of channel estimation and precoding for 3D massive MIMO systems and application of deep reinforcement learning (DRL) for MIMO broadcast beam synthesis. To be specific, downlink (DL) precoding and power allocation strategies are identified for a time-division-duplex (TDD) multi-cell multi-user massive FD-MIMO network. Utilizing channel reciprocity, DL channel s

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Chen, Hsinchun, K. J. Lynch, K. Basu, and Tobun Dorbin Ng. "Generating, Integrating, and Activating Thesauri for Concept-based Document Retrieval." IEEE, 1993. http://hdl.handle.net/10150/105378.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This Blackboard-based design uses a neural-net spreading-activation algorithm to traverse multiple thesauri. Guided by heuristics, the algorithm activates related terms in the thesauri and converges on the most pertinent concepts.

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Chen, Hsinchun, Bruce R. Schatz, Tak Yim, and David Fye. "Automatic Thesaurus Generation for an Electronic Community System." Wiley Periodicals, Inc, 1995. http://hdl.handle.net/10150/105321.

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Artificial Intelligence Lab, Department of MIS, University of Arizona<br>This research reports an algorithmic approach to the automatic generation of thesauri for electronic community systems. The techniques used included term filtering, automatic indexing, and cluster analysis. The testbed for our research was the Worm Community System, which contains a comprehensive library of specialized community data and literature, currently in use by molecular biologists who study the nematode worm C. elegans. The resulting worm thesaurus included 2709 researchers’ names, 798 gene names, 20 experimental

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Tearse, Brandon. "Skald| Exploring Story Generation and Interactive Storytelling by Reconstructing Minstrel." Thesis, University of California, Santa Cruz, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=13423003.

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<p> Within the realm of computational story generation sits Minstrel, a decades old system which was once used to explore the idea that, under the correct conditions, novel stories can be generated by taking an existing story and replacing some of its elements with similar ones found in a different story. This concept would eventually fall within the bounds of a strategy known as Case-Based Reasoning (CBR), in which problems are solved by recalling solutions to past problems (the cases), and mutating the recalled cases in order to create an appropriate solution to the current problem. This dis

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Fehlman, William L. "Classification of non-heat generating outdoor objects in thermal scenes for autonomous robots." W&M ScholarWorks, 2008. https://scholarworks.wm.edu/etd/1539623338.

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We have designed and implemented a physics-based adaptive Bayesian pattern classification model that uses a passive thermal infrared imaging system to automatically characterize non-heat generating objects in unstructured outdoor environments for mobile robots. In the context of this research, non-heat generating objects are defined as objects that are not a source for their own emission of thermal energy, and so exclude people, animals, vehicles, etc. The resulting classification model complements an autonomous bot's situational awareness by providing the ability to classify smaller structure

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Letourneau, Sylvain. "Identification of attribute interactions and generation of globally relevant continuous features in machine learning." Thesis, University of Ottawa (Canada), 2003. http://hdl.handle.net/10393/29029.

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Datasets found in real world applications of machine learning are often characterized by low-level attributes with important interactions among them. Such interactions may increase the complexity of the learning task by limiting the usefulness of the attributes to dispersed regions of the representation space. In such cases, we say that the attributes are locally relevant. To obtain adequate performance with locally relevant attributes, the learning algorithm must be able to analyse the interacting attributes simultaneously and fit an appropriate model for the type of interactions observed. Th

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Ben, ameur Ayoub. "Artificial intelligence for resource allocation in multi-tenant edge computing." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAS019.

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Dans cette thèse, nous considérons le Edge Computing (EC) comme un environnement multi-tenant où les Opérateurs Réseau (NOs) possèdent des ressources en périphérie déployées dans les stations de base, les bureaux centraux et/ou les boîtiers intelligents, les virtualisent, et permettent aux Fournisseurs de Services tiers (SPs) - ou tenants - de distribuer une partie de leurs applications en périphérie afin de répondre aux demandes des utilisateurs. Les SPs aux besoins hétérogènes coexistent en périphérie, allant des Communications Ultra-Fiables à Latence Ultra-Basse (URLLC) pour le contrôle des

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Bagnall, A. J. "Modelling the UK market in electricity generation with autonomous adaptive agents." Thesis, University of East Anglia, 2000. https://ueaeprints.uea.ac.uk/21583/.

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The modern trend in electricity industries around the world is towards privatisation. Increased competition, it is argued, will ultimately benefit the consumer. However, the particular nature of electricity generation and supply means strong regulation of a privatised market will always be necessary. In establishing a privatised industry, decisions need to be made about the mechanisms governing the requirements to meet demand, to maintain the viability of the network and to ensure generators are paid correctly for power generated. Unfortunately, it is unclear what processes to use to achieve t

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An, Hongyu. "Powering Next-Generation Artificial Intelligence by Designing Three-dimensional High-Performance Neuromorphic Computing System with Memristors." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/101838.

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Human brains can complete numerous intelligent tasks, such as pattern recognition, reasoning, control and movement, with remarkable energy efficiency (20 W). In contrast, a typical computer only recognizes 1,000 different objects but consumes about 250 W power [1]. This performance significant differences stem from the intrinsic different structures of human brains and digital computers. The latest discoveries in neuroscience indicate the capabilities of human brains are attributed to three unique features: (1) neural network structure; (2) spike-based signal representation; (3) synaptic plast

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Merz, Evan X. "Method for simulating creativity to generate sound collages from documents on the web." Thesis, University of California, Santa Cruz, 2014. http://pqdtopen.proquest.com/#viewpdf?dispub=3609658.

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<p> To create algorithmic art with documents available on the internet, artists must discover strategies for organizing those documents. In this project I used a graph structure based on Melissa Schilling's model of cognitive insight to reorganize sounds on the web using aural and lexical relationships. I was then able to generate music with these graphs using several different activation strategies. In section one I introduce my goals for this project. In section two I review other approaches to this problem and art that has influenced my approach. In section three I demonstrate techniques f

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Boyd, Richard Victor 1942. "PLAN GENERATION AND PROLOG (LOGIC, DECLARATIVE, WARPLAN)." Thesis, The University of Arizona, 1986. http://hdl.handle.net/10150/291278.

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Parker, Johne' Michelle. "A methodology for generating physically accurate synthetic images for machine vision applications." Thesis, Georgia Institute of Technology, 1992. http://hdl.handle.net/1853/18384.

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O'Neill, Brian. "A computational model of suspense for the augmentation of intelligent story generation." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/50416.

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In this dissertation, I present Dramatis, a computational human behavior model of suspense based on Gerrig and Bernardo's de nition of suspense. In this model, readers traverse a search space on behalf of the protagonist, searching for an escape from some oncoming negative outcome. As the quality or quantity of escapes available to the protagonist decreases, the level of suspense felt by the audience increases. The major components of Dramatis are a model of reader salience, used to determine what elements of the story are foregrounded in the reader's mind, and an algorithm for determining the

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Thanguturi, Naren. "Automatic News Generation System based on Natural Language." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1525973404437239.

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