Teses / dissertações sobre o tema "Potentiel machine learning"
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Artusi, Xavier. "Interface cerveau machine avec adaptation automatique à l'utilisateur". Phd thesis, Ecole centrale de Nantes, 2012. http://www.theses.fr/2012ECDN0018.
Texto completo da fonteWe study a brain computer interface (BCI) to control a prosthesis with thought. The aim of the BCI is to decode the movement desired by the subject from electroencephalographic (EEG) signals. The core of the BCI is a classification algorithm characterized by the choice of signals descriptors and decision rules. The purpose of this thesis is to develop an accurate BCI system, able to improve its performance during its use and to adapt to the user evolutions without requiring multiple learning sessions. We combine two ways to achieve this. The first one is to increase the precision of the decision system by looking for relevant descriptors for the classification. The second one is to include a feedback to the user on the system decision : the idea is to estimate the error of the BCI from evoked brain poten tials, reflecting the emotional state of the patient correlated to the success or failure of the decision taken by the BCI, and to correct the decision system of the BCI accordingly. The main contributions are : we have proposed a method to optimize the feature space based on wavelets for multi-channel EEG signals ; we quantified theoretically the performances of the complete system improved by the detector ; a simulator of the corrected and looped system has been developed to observe the behavior of the overall system and to compare different strategies to update the learning set ; the complete system has been implemented and works online in real conditions
Artusi, Xavier. "Interface Cerveau Machine avec adaptation automatique à l'utilisateur". Phd thesis, Ecole centrale de nantes - ECN, 2012. http://tel.archives-ouvertes.fr/tel-00822833.
Texto completo da fonteOhlsson, Caroline. "Exploring the potential of machine learning : How machine learning can support financial risk management". Thesis, Uppsala universitet, Företagsekonomiska institutionen, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-324684.
Texto completo da fonteHu, Jinli. "Potential based prediction markets : a machine learning perspective". Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29000.
Texto completo da fonteGustafson, Jonas. "Using Machine Learning to Identify Potential Problem Gamblers". Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-163640.
Texto completo da fonteDel, Fré Samuel. "Études théoriques de la photodésorption d'analogues de glaces moléculaires interstellaires : application au monoxyde de carbone". Electronic Thesis or Diss., Université de Lille (2022-....), 2024. http://www.theses.fr/2024ULILR039.
Texto completo da fonteUnusual amounts of gas-phase molecules are detected in the cold regions (around 10 K) of the interstellar medium (ISM), primarily attributed to the non-thermal desorption of molecules from ices deposited on dust grains. In particular, vacuum ultraviolet (VUV) photon-induced desorption (photodesorption) is considered a major desorption pathway in photon-dominated regions of the ISM. Experimental investigations have revealed that in pure carbon monoxide (CO) ices, a ubiquitous species in the ISM, VUV photodesorption can follow an indirect mechanism of desorption induced by electronic transitions (DIET) for photons with energy between 7 and 10 eV. Nevertheless, the understanding of the underlying molecular mechanisms remains a topic of scientific debate. In this astrochemical context, we present a combined theoretical study using ab initio molecular dynamics (AIMD) based on density functional theory (DFT) and machine learning potentials (PML) constructed with artificial neural networks (ANN) to study the final part of the DIET mechanism in amorphous CO ices. Here, a highly vibrationally excited CO molecule (v = 40) at the center of an aggregate initially composed of 50 CO molecules, optimized and then thermalized at 15 K, triggers the indirect desorption of surface molecules. Our theoretical results reveal that the desorption process consists of three fundamental steps, beginning with a mutual attraction between the vibrationally excited molecule and one or two neighboring molecules, activated by CO bond stretching and facilitated by the steric effect of surrounding molecules. This is followed by a sequence of energy transfers initiated by a collision, resulting in the desorption of vibrationally cold CO molecules in 88% of the AIMD trajectories. Additionally, the theoretical distributions of the internal and translational energy of desorbed molecules remarkably match experimental results, supporting the crucial role of vibrational relaxation in the desorption process. Finally, the first PML constructed from AIMD simulations accurately fit the multidimensional potential energy surface of the system, allowing efficient prediction of aggregate energies and atomic forces. Classical molecular dynamics simulations using these potentials are over 1800 times faster than those based on AIMD while offering precision comparable to DFT
Veit, Max David. "Designing a machine learning potential for molecular simulation of liquid alkanes". Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/290295.
Texto completo da fonteLundberg, Oscar, Oskar Bjersing e Martin Eriksson. "Approximation of ab initio potentials of carbon nanomaterials with machine learning". Thesis, Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-62568.
Texto completo da fonteSupervisors: Daniel Hedman and Fredrik Sandin
F7042T - Project in Engineering Physics
DRAGONI, DANIELE. "Energetics and thermodynamics of α-iron from first-principles and machine-learning potentials". Doctoral thesis, École Polytechnique Fédérale de Lausanne, 2016. http://hdl.handle.net/10281/231122.
Texto completo da fonteHellsing, Edvin, e Joel Klingberg. "It’s a Match: Predicting Potential Buyers of Commercial Real Estate Using Machine Learning". Thesis, Uppsala universitet, Institutionen för informatik och media, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-445229.
Texto completo da fonteDenna uppsats har undersökt utvecklingen av och potentiella effekter med ett intelligent beslutsstödssystem (IDSS) för att prediktera potentiella köpare av kommersiella fastigheter. Det övergripande behovet av ett sådant system har identifierats existerar på grund av informtaionsöverflöd, vilket systemet avser att reducera. Genom att förkorta bearbetningstiden av data kan tid allokeras till att skapa förståelse av omvärlden med kollegor. Systemarkitekturen som undersöktes bestod av att gruppera köpare av kommersiella fastigheter i kluster baserat på deras köparegenskaper, och sedan träna en prediktionsmodell på historiska transkationsdata från den svenska fastighetsmarknaden från Lantmäteriet. Prediktionsmodellen tränades på att prediktera vilken av grupperna som mest sannolikt kommer köpa en given fastighet. Tre olika klusteralgoritmer användes och utvärderades för grupperingen, en densitetsbaserad, en centroidbaserad och en hierarkiskt baserad. Den som presterade bäst var var den centroidbaserade (K-means). Tre övervakade maskininlärningsalgoritmer användes och utvärderades för prediktionerna. Dessa var Naive Bayes, Random Forests och Support Vector Machines. Modellen baserad p ̊a Random Forests presterade bäst, med en noggrannhet om 99,9%.
Ntsaluba, Kuselo Ntsika. "AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets". Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31185.
Texto completo da fonteSun, Roger. "Utilisation de méthodes radiomiques pour la prédiction des réponses à l’immunothérapie et combinaisons de radioimmunothérapie chez des patients atteints de cancers Radiomics to Assess Tumor Infiltrating CD8 T-Cells and Response to Anti-PD-1/PD-L1 Immunotherapy in Cancer Patients: An Imaging Biomarker Multi-Cohort Study Imagerie médicale computationnelle (radiomique) et potentiel en immuno-oncologie Radiomics to Predict Outcomes and Abscopal Response of Cancer Patients Treated with Immunotherapy Combined with Radiotherapy Using a Validated Signature of CD8 Cells". Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASL023.
Texto completo da fonteWith the advent of immune checkpoint inhibitors, immunotherapy has profoundly changed the therapeutic strategy of many cancers. However, despite constant therapeutic progress and combinations of treatments such as radiotherapy and immunotherapy, the majority of patients treated do not benefit from these treatments. This explains the importance of research into innovative biomarkers of response to immunotherapyComputational medical imaging, known as radiomics, analyzes and translates medical images into quantitative data with the assumption that imaging reflects not only tissue architecture, but also cellular and molecular composition. This allows an in-depth characterization of tumors, with the advantage of being non-invasive allowing evaluation of tumor and its microenvironment, spatial heterogeneity characterization and longitudinal assessment of disease evolution.Here, we evaluated whether a radiomic approach could be used to assess tumor infiltrating lymphocytes and whether it could be associated with the response of patients treated with immunotherapy. In a second step, we evaluated the association of this radiomic signature with clinical response of patients treated with radiotherapy and immunotherapy, and we assessed whether it could be used to assess tumor spatial heterogeneity.The specific challenges raised by high-dimensional imaging data in the development of clinically applicable predictive tools are discussed in this thesis
Syed, Tahir Qasim. "Analysis of the migratory potential of cancerous cells by image preprocessing, segmentation and classification". Thesis, Evry-Val d'Essonne, 2011. http://www.theses.fr/2011EVRY0041/document.
Texto completo da fonteThis thesis is part of a broader research project which aims to analyze the potential migration of cancer cells. As part of this doctorate, we are interested in the use of image processing to count and classify cells present in an image acquired usinga microscope. The partner biologists of this project study the influence of the environment on the migratory behavior of cancer cells from cell cultures grown on different cancer cell lines. The processing of biological images has so far resulted in a significant number of publications, but in the case discussed here, since the protocol for the acquisition of images acquired was not fixed, the challenge was to propose a chain of adaptive processing that does not constrain the biologists in their research. Four steps are detailed in this paper. The first concerns the definition of pre-processing steps to homogenize the conditions of acquisition. The choice to use the image of standard deviations rather than the brightness is one of the results of this first part. The second step is to count the number of cells present in the image. An original filter, the so-called “halo” filter, that reinforces the centre of the cells in order to facilitate counting, has been proposed. A statistical validation step of the centres affords more reliability to the result. The stage of image segmentation, undoubtedly the most difficult, constitutes the third part of this work. This is a matter of extracting images each containing a single cell. The choice of segmentation algorithm was that of the “watershed”, but it was necessary to adapt this algorithm to the context of images included in this study. The proposal to use a map of probabilities as input yielded a segmentation closer to the edges of cells. As against this method leads to an over-segmentation must be reduced in order to move towards the goal: “one region = one cell”. For this algorithm the concept of using a cumulative hierarchy based on mathematical morphology has been developed. It allows the aggregation of adjacent regions by working on a tree representation ofthese regions and their associated level. A comparison of the results obtained by this method with those proposed by other approaches to limit over-segmentation has allowed us to prove the effectiveness of the proposed approach. The final step of this work consists in the classification of cells. Three classes were identified: spread cells (mesenchymal migration), “blebbing” round cells (amoeboid migration) and “smooth” round cells (intermediate stage of the migration modes). On each imagette obtained at the end of the segmentation step, intensity, morphological and textural features were calculated. An initial analysis of these features has allowed us to develop a classification strategy, namely to first separate the round cells from spread cells, and then separate the “smooth” and “blebbing” round cells. For this we divide the parameters into two sets that will be used successively in Two the stages of classification. Several classification algorithms were tested, to retain in the end, the use of two neural networks to obtain over 80% of good classification between long cells and round cells, and nearly 90% of good Classification between “smooth” and “blebbing” round cells
Egieyeh, Samuel Ayodele. "Computational strategies to identify, prioritize and design potential antimalarial agents from natural products". University of the Western Cape, 2015. http://hdl.handle.net/11394/5058.
Texto completo da fonteIntroduction: There is an exigent need to develop novel antimalarial drugs in view of the mounting disease burden and emergent resistance to the presently used drugs against the malarial parasites. A large amount of natural products, especially those used in ethnomedicine for malaria, have shown varying in-vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, the limited resources, high cost, low prospect and the high cost of failure during preclinical and clinical studies might militate against pursue of this mission. Chemoinformatics techniques can simulate and predict essential molecular properties required to characterize compounds thus eliminating the cost of equipment and reagents to conduct essential preclinical studies, especially on compounds that may fail during drug development. Therefore, applying chemoinformatics techniques on natural products with in-vitro antiplasmodial activities may facilitate identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and high likelihood for development into antimalarial drugs. In addition, unique structural features mined from these natural products may be templates to design new potential antimalarial compounds. Method: Four chemoinformatics techniques were applied on a collection of selected natural products with in-vitro antiplasmodial activity (NAA) and currently registered antimalarial drugs (CRAD): molecular property profiling, molecular scaffold analysis, machine learning and design of a virtual compound library. Molecular property profiling included computation of key molecular descriptors, physicochemical properties, molecular similarity analysis, estimation of drug-likeness, in-silico pharmacokinetic profiling and exploration of structure-activity landscape. Analysis of variance was used to assess statistical significant differences in these parameters between NAA and CRAD. Next, molecular scaffold exploration and diversity analyses were performed on three datasets (NAA, CRAD and malarial data from Medicines for Malarial Ventures (MMV)) using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for all the datasets. Thirdly, machine learning approaches were used to build four regression and four classifier models from bioactivity data of NAA using molecular descriptors and molecular fingerprints. Models were built and refined by leave-one-out cross-validation and evaluated with an independent test dataset. Applicability domain (AD), which defines the limit of reliable predictability by the models, was estimated from the training dataset and validated with the test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. Lastly, virtual compound libraries were generated with the unique molecular scaffolds identified from the NAA. The virtual compounds generated were characterized by evaluating selected molecular descriptors, toxicity profile, structural diversity from CRAD and prediction of antiplasmodial activity. Results: From the molecular property profiling, a total of 1040 natural products were selected and a total of 13 molecular descriptors were analyzed. Significant differences were observed between the natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) for at least 11 of the molecular descriptors. Molecular similarity and chemical space analysis identified NAA that were structurally diverse from CRAD. Over 50% of NAA with desirable drug-like properties were identified. However, nearly 70% of NAA were identified as potentially "promiscuous" compounds. Structure-activity landscape analysis highlighted compound pairs that formed "activity cliffs". In all, prioritization strategies for the natural products with in-vitro antiplasmodial activities were proposed. The scaffold exploration and analysis results revealed that CRAD exhibited greater scaffold diversity, followed by NAA and MMV respectively. Unique scaffolds that were not contained in any other compounds in the CRAD datasets were identified in NAA. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which maybe potential bioactive compounds or elucidate the NAA possible synthetic routes. From the machine learning study, the regression and classifier models that were most suitable for NAA were identified as model tree M5P (correlation coefficient = 0.84) and Sequential Minimization Optimization (accuracy = 73.46%) respectively. The test dataset fitted into the applicability domain (AD) defined by the training dataset. The “amine” group was observed to be essential for antimalarial activity in both NAA and MMV dataset but hydroxyl and carbonyl groups may also be relevant in the NAA dataset. The results of the characterization of the virtual compound library showed significant difference (p value < 0.05) between the virtual compound library and currently registered antimalarial drugs in some molecular descriptors (molecular weight, log partition coefficient, hydrogen bond donors and acceptors, polar surface area, shape index, chiral centres, and synthetic feasibility). Tumorigenic and mutagenic substructures were not observed in a large proportion (> 90%) of the virtual compound library. The virtual compound libraries showed sufficient diversity in structures and majority were structurally diverse from currently registered antimalarial drugs. Finally, up to 70% of the virtual compounds were predicted as active antiplasmodial agents. Conclusions:Molecular property profiling of natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) produced a wealth of information that may guide decisions and facilitate antimalarial drug development from natural products and led to a prioritized list of natural products with in-vitro antiplasmodial activities. Molecular scaffold analysis identified unique scaffolds and virtual scaffolds from NAA that possess desirable drug-like properties, which make them ideal starting points for molecular antimalarial drug design. The machine learning study built, evaluated and identified amply accurate regression and classifier accurate models that were used for virtual screening of natural compound libraries to mine possible antimalarial compounds without the expense of bioactivity assays. Finally, a good amount of the virtual compounds generated were structurally diverse from currently registered antimalarial drugs and potentially active antiplasmodial agents. Filtering and optimization may lead to a collection of virtual compounds with unique chemotypes that may be synthesized and added to screening deck against Plasmodium.
Skabar, Andrew Alojz. "Inductive learning techniques for mineral potential mapping". Thesis, Queensland University of Technology, 2001.
Encontre o texto completo da fonteGayraud, Nathalie. "Méthodes adaptatives d'apprentissage pour des interfaces cerveau-ordinateur basées sur les potentiels évoqués". Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4231/document.
Texto completo da fonteNon-invasive Brain Computer Interfaces (BCIs) allow a user to control a machine using only their brain activity. The BCI system acquires electroencephalographic (EEG) signals, characterized by a low signal-to-noise ratio and an important variability both across sessions and across users. Typically, the BCI system is calibrated before each use, in a process during which the user has to perform a predefined task. This thesis studies of the sources of this variability, with the aim of exploring, designing, and implementing zero-calibration methods. We review the variability of the event related potentials (ERP), focusing mostly on a late component known as the P300. This allows us to quantify the sources of EEG signal variability. Our solution to tackle this variability is to focus on adaptive machine learning methods. We focus on three transfer learning methods: Riemannian Geometry, Optimal Transport, and Ensemble Learning. We propose a model of the EEG takes variability into account. The parameters resulting from our analyses allow us to calibrate this model in a set of simulations, which we use to evaluate the performance of the aforementioned transfer learning methods. These methods are combined and applied to experimental data. We first propose a classification method based on Optimal Transport. Then, we introduce a separability marker which we use to combine Riemannian Geometry, Optimal Transport and Ensemble Learning. Our results demonstrate that the combination of several transfer learning methods produces a classifier that efficiently handles multiple sources of EEG signal variability
Nyman, Måns, e Caner Naim Ulug. "Exploring the Potential for Machine Learning Techniques to Aid in Categorizing Electron Trajectories during Magnetic Reconnection". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279982.
Texto completo da fonteMagnetisk rekonnektion påverkar rymdvädret som har en direkt påverkan på våra nutida teknologiska system. Således kan fenomenet ge allvarliga konsekvenser för människor. Forskare inom detta fält tror att elektrondynamiken spelar en viktig roll i magnetisk rekonnektion. Magnetisk rekonnektion är ett ämne som har studerats under lång tid men ännu förblir många aspekter av fenomenet outforskade. Under magnetisk rekonnektion kan elektroner accelereras till höga hastigheter. En stor mängd studier har gjorts angående trajektorierna som dessa elektroner uppvisar och forskare som är aktiva inom detta forskningsområde skulle enkelt kunna bestämma vilken sorts trajektoria en specifik elektron uppvisar givet en grafisk illustration av sagda trajektoria. Att försöka göra detta för ett mer realistiskt antal elektroner manuellt är dock ingen enkel eller effektiv uppgift att ta sig an. Genom användning av Maskininlärningstekniker för att försöka kategorisera dessa trajektorier skulle denna process kunna göras mycket mer effektiv. Ännu har dock inga försök att göra detta gjorts. I denna uppsats gjordes ett försök att besvara hur väl vissa Maskinlärningstekniker presterar i detta avseende. Principal component analysis och K-means clustering var huvudmetoderna som användes, applicerade med olika sorters förbehandling av den givna datan. Elbow-metoden användes för att hitta det optimala K-värdet och kompletterades av Self-Organizing Maps. Silhouette coefficient användes för att mäta resultaten av dessa metoder. Förbehandlingsmetoderna First-centering och Mean-centering gav de två högsta siluett-koefficienterna och uppvisade således de bästa kvantitativa resultaten. Inspektion av klustrarna pekade dock på avsaknad av perfekt överlappning, både mellan klasserna som upptäcktes av de tillämpade metoderna samt klasserna som har identifierats i tidigare artiklar inom fysik. Trots detta visade sig Maskininlärningsmetoder besitta viss potential som är värt att utforska i större detalj i framtida studier inom fältet magnetisk rekonnektion.
Zaremba, Wojciech. "Modeling the variability of EEG/MEG data through statistical machine learning". Habilitation à diriger des recherches, Ecole Polytechnique X, 2012. http://tel.archives-ouvertes.fr/tel-00803958.
Texto completo da fonteEvett, Chantal. "What are the Potential Impacts of Big Data, Artificial Intelligence and Machine Learning on the Auditing Profession?" Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1642.
Texto completo da fonteKottorp, Max, e Filip Jäderberg. "Chatbot As a Potential Tool for Businesses : A study on chatbots made in collaboration with Bisnode". Thesis, KTH, Industriell ekonomi och organisation (Inst.), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-210768.
Texto completo da fonteLi, Zhenwei. "On-the-fly machine learning of quantum mechanical forces and its potential applications for large scale molecular dynamics". Thesis, King's College London (University of London), 2014. http://kclpure.kcl.ac.uk/portal/en/theses/onthefly-machine-learning-of-quantum-mechanical-forces-and-its-potential-applications-for-large-scale-molecular-dynamics(2a2f33a6-fa0c-44e3-8689-f4cf3f1c9198).html.
Texto completo da fonteBALESTRUCCI, ALESSANDRO. "Potential target audience of misinformation on Social Media: Credulous Users". Doctoral thesis, Gran Sasso Science Institute, 2020. http://hdl.handle.net/20.500.12571/14754.
Texto completo da fonteHagward, Anders. "Using Git Commit History for Change Prediction : An empirical study on the predictive potential of file-level logical coupling". Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-172998.
Texto completo da fonteDe senaste åren har en ny generation av distribuerade versionshanteringssystem tagit plats där tidigare centraliserade sådana huserat. I spetsen för dessa nya system går ett system vid namn Git. Vi undersöker potentialen i att nyttja versionshistorik från Git i syftet att förutspå filer som ofta redigeras ihop. I synnerhet synar vi Gits heuristik för att detektera när en fil flyttats eller bytt namn, någonting som torde vara användbart för att bibehålla historiken för en sådan fil, och mäter dess inverkan på prediktionsprestandan. Genom att applicera en datautvinningsalgoritm på fem populära GitHubprojekt extraherar vi logisk koppling – beroenden mellan filer som inte nödvändigtvis är detekterbara medelst statisk analys – på vilken vi baserar vår prediktion. Därtill utreder vi huruvida vissa Gitcommits är bättre lämpade för prediktion än andra; vi definierar en buggfixcommit som en commit som löser en eller flera buggar i den tillhörande buggdatabasen, och jämför deras prediktionsprestanda. Medan våra resultat ej kan påvisa några större prestandamässiga skillnader när flytt- och namnbytesinformationen ignorerades, indikerar de att extrahera koppling från, och prediktera på, enbart bugfixcommits kan leda till förutsägelser som är både mer precisa och mångtaliga.
Ioannides, Charalambos. "Investigating the potential of machine learning techniques for feedback-based coverage-directed test genreation in simulation-based digital design verification". Thesis, University of Bristol, 2013. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.618315.
Texto completo da fonteLeo, Stephen. "Potential of remote and proximal sensing, publicly available datasets and machine learning for site-specific management in Australian irrigated cotton systems". Thesis, Queensland University of Technology, 2022. https://eprints.qut.edu.au/235383/1/Stephen%2BLeo%2BThesis%281%29.pdf.
Texto completo da fonteJahangiri, Arash. "Investigating Violation Behavior at Intersections using Intelligent Transportation Systems: A Feasibility Analysis on Vehicle/Bicycle-to-Infrastructure Communications as a Potential Countermeasure". Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/76729.
Texto completo da fontePh. D.
Sepp, Löfgren Nicholas. "Accelerating bulk material property prediction using machine learning potentials for molecular dynamics : predicting physical properties of bulk Aluminium and Silicon". Thesis, Linköpings universitet, Teoretisk Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-179894.
Texto completo da fonteChowdhury, Ziaul Islam, e Iskanter Bensenousi. "Evaluation of different machine learning models for the prediction of electric or hybrid vehicle buyers and identification of the characteristics of the buyers in the EU". Thesis, Blekinge Tekniska Högskola, Institutionen för industriell ekonomi, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-20712.
Texto completo da fonteZanghieri, Marcello. "sEMG-based hand gesture recognition with deep learning". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/18112/.
Texto completo da fonteDuggan, Kieran Eamon. "A supervised machine-learning method for detecting steady-state visually evoked potentials for use in brain computer interfaces: A comparative assessment". Master's thesis, University of Cape Town, 2017. http://hdl.handle.net/11427/27335.
Texto completo da fonteÖsterberg, Viktor. "Using Machine Learning to Develop a Quantum-Accurate Inter-Atomic Potential for Large Scale Molecular Dynamics Simulations of Iron under Earth’s Core Conditions". Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298848.
Texto completo da fonteMätningar av järns smälttemperatur under påfrestningar jämförbara med desom tros gälla i jordens kärna överensstämmer ej. För att försöka bestämma stabiliteten av relevanta gitter krävs simulationer av enorma mängder partiklar. I denna tes tränas en maskininlärd modell att återge resultat från Täthetsfunktionalteori. Olika maskininlärningsmodeller jämförs. Den tränade modellen används sedan i Molekyldynamik-simulationer av relevanta gitter som är förstora för Täthetsfunktionalteori.
Elhashmi, Rodwan. "Comprehensive Study Toward Energy Opportunity for Buildings Considering Potentials for Using Geothermal and Predicting Chiller Demand". University of Dayton / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1589332482268134.
Texto completo da fonteBotros, Andrew Computer Science & Engineering Faculty of Engineering UNSW. "The application of machine intelligence to cochlear implant fitting and the analysis of the auditory nerve response". Awarded By:University of New South Wales. Computer Science & Engineering, 2010. http://handle.unsw.edu.au/1959.4/44707.
Texto completo da fonteRaphel, Fabien. "Mathematical modelling and learning of biomedical signals for safety pharmacology". Thesis, Sorbonne université, 2022. http://www.theses.fr/2022SORUS116.
Texto completo da fonteAs a branch of pharmacology, cardiac safety pharmacology aims at investigating compound side effects on the cardiac system at therapeutic doses. These investigations, made through in silico, in vitro and in vivo experiments, allow to select/reject a compound at each step of the drug development process. A large subdomain of cardiac safety pharmacology is devoted to the study of the electrical activity of cardiac cells based on in silico and in vitro assays. This electrical activity is the consequence of polarised structure exchanges (mainly ions) between the extracellular and intracellular medium. A modification of the ionic exchanges induces changes in the electrical activity of the cardiac cell which can be pathological (e.g. by generating arrhythmia). Strong knowledges of these electrical signals are therefore essential to prevent risk of lethal events. Patch-clamp techniques are the most common methods to record the electrical activity of a cardiac cell. Although these electrical signals are well known, they are slow and tedious to perform, and therefore, expansive. A recent alternative is to consider microelectrode array (MEA) devices. Originally developped for neurons studies, its extension to cardiac cells allows a high throughput screening which was not possible with patch-clamp techniques. It consists of a plate with wells in which cardiac cells (forming a tissue) cover some electrodes. Therefore, the extension of these devices to cardiac cells allow to record the electrical activity of the cells at a tissue level (before and after compound addition into the wells). As a new signal, many studies have to be done to understand how ionic exchanges induce this recorded electrical activity, and, finally, to proceed the selection/rejection of a compound. Despite these signals are still not well known, recent studies have shown promising results in the consideration of MEA into cardiac safety pharmacology. The automation of the compound selection/rejection is still challenging and far from industrial applications, which is the final goal of this manuscript. Mathematically, the selection/rejection process can be seen as a binary classification problem. As in any supervised classification (and machine learning tasks, more generally), an input has to be defined. In our case, time series of the cardiac electrical activities are possibly long (minutes or hours) with a high sampling rate (∼ kHz) leading to an input living in a high-dimensional space (hundreds, thousands or even more). Moreover the number of available data is still low (at most hundreds). This critical regime named high dimension/low sample size make the context challenging. The aim of this manuscript is to provide a systematic strategy to select/reject compounds in an automated way, under the following constraints:• Deal with high dimension/low sample size regime. • No assumptions on the data distributions. • Exploit in silico models to improve the classification performances. • No or few parameters to tune. The first part of the manuscript is devoted to the context, followed by the description of the patch-clamp and MEA technologies. This part ends by the description of action potential and field potential models to perform in silico experiments. In a second part, two methodological aspects are developped, trying to comply, at best, with the constraints of the industrial application. The first one describes a double greedy goal-oriented strategy to reduce the input space based on a score function related to the classification success rate. Comparisons with classical dimension reduction methods such as PCA and PLS (with default parameters) are performed, showing that the proposed method led to better results. The second method consists in the construction of an augmented training set based on a reservoir of simulations, by considering the Hausdorff distance between sets and the maximisation of same score function as in the first method. The proposed strategy [...]
Dragoni, Laurent. "Tri de potentiels d'action sur des données neurophysiologiques massives : stratégie d’ensemble actif par fenêtre glissante pour l’estimation de modèles convolutionnels en grande dimension". Thesis, Université Côte d'Azur, 2022. http://www.theses.fr/2022COAZ4016.
Texto completo da fonteIn the nervous system, cells called neurons are specialized in the communication of information. Through the generation and propagation of electrical currents named action potentials, neurons are able to transmit information in the body. Given the importance of the neurons, in order to better understand the functioning of the nervous system, a wide range of methods have been proposed for studying those cells. In this thesis, we focus on the analysis of signals which have been recorded by electrodes, and more specifically, tetrodes and multi-electrode arrays (MEA). Since those devices usually record the activity of a set of neurons, the recorded signals are often a mixture of the activity of several neurons. In order to gain more knowledge from this type of data, a crucial pre-processing step called spike sorting is required to separate the activity of each neuron. Nowadays, the general procedure for spike sorting consists in a three steps procedure: thresholding, feature extraction and clustering. Unfortunately this methodology requires a large number of manual operations. Moreover, it becomes even more difficult when treating massive volumes of data, especially MEA recordings which also tend to feature more neuronal synchronizations. In this thesis, we present a spike sorting strategy allowing the analysis of large volumes of data and which requires few manual operations. This strategy makes use of a convolutional model which aims at breaking down the recorded signals as temporal convolutions between two factors: neuron activations and action potential shapes. The estimation of these two factors is usually treated through alternative optimization. Being the most difficult task, we only focus here on the estimation of the activations, assuming that the action potential shapes are known. Estimating the activations is traditionally referred to convolutional sparse coding. The well-known Lasso estimator features interesting mathematical properties for the resolution of such problem. However its computation remains challenging on high dimensional problems. We propose an algorithm based of the working set strategy in order to compute efficiently the Lasso. This algorithm takes advantage of the particular structure of the problem, derived from biological properties, by using temporal sliding windows, allowing it to scale in high dimension. Furthermore, we adapt theoretical results about the Lasso to show that, under reasonable assumptions, our estimator recovers the support of the true activation vector with high probability. We also propose models for both the spatial distribution and activation times of the neurons which allow us to quantify the size of our problem and deduce the theoretical complexity of our algorithm. In particular, we obtain a quasi-linear complexity with respect to the size of the recorded signal. Finally we present numerical results illustrating both the theoretical results and the performances of our approach
Schmidt, Eric. "Atomistic modelling of precipitation in Ni-base superalloys". Thesis, University of Cambridge, 2019. https://www.repository.cam.ac.uk/handle/1810/275131.
Texto completo da fonteAli, Mahammed Ali. "Studie av artificiell intelligens för ökad resurseffektivitet inom produktionsplanering : En studie med fokus på hur nuvarande samt potentiella implementeringar av artificiell intelligens inom produktionsplanering kan öka resurseffektiviteten hos ett tillverkande företag". Thesis, KTH, Maskinkonstruktion (Inst.), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299735.
Texto completo da fonteThe global introduction of Industry 4.0 has brought with it changes within industry. The indirect consequence of Industry 4.0 being artificial intelligence. The idea of AI is as old as the invention of computers with Alan Turing the father of computer science stating the first description of AI. His thought was that if a machine could be mistaken for a human then the machine was intelligent. The thought being that machine never could outperform humans back then. Now in modern times we have witnessed great feats made by intelligent algorithms where they outperform humans in various fields. For AI to be implemented in industry the most innovative buisness it has to adapt to the workings of indutrial processes. Systematic approach and standardization being two values that strongly represents industries. During the last decade global initiative and investment in innovation of industry. Has led to global competitors such as Germany creating Industry 4.0, The United States creating Smart Manufacturing Leadership Coalition, China introducing their plan called China 2025 and EU with Factories for the future. This paper is a reaction of these enormous investments made into Industry 4.0. The objective of this paper is to examine how AI can help manufacturing enterprises increase their resource efficiency within production planning. Since this field of science stillbeing in its infancy this paper will base its result on interviews made with companies as ABB and Scania. However this field needs more work.
Naderi, Darbaghshahi Saeid [Verfasser]. "Exploring the potential of machine learning methods and selection signature analyses for the estimation of genomic breeding values, the estimation of SNP effects and the identification of possible candidate genes in dairy cattle / Saeid Naderi Darbaghshahi". Gießen : Universitätsbibliothek, 2019. http://d-nb.info/1177678365/34.
Texto completo da fonteNgoungue, Langue Cédric Gacial. "Détection, caractéristiques et prédictibilité des évènements à potentiels forts impacts humains sur les villes ouest-africaines : cas des vagues de chaleur". Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASJ021.
Texto completo da fonteHeat waves (HWs) are a real threat to humans and their environment. Due to climate change, heat waves will become more frequent and more intense. Climatic conditions in West Africa make the region more vulnerable to heat waves. West African cities are highly populated centers, and when it comes to the impact of heat waves on human activities, it's important to study these events at these scales. This study aims to monitor heat waves in major West African cities and evaluate their predictability in subseasonal to seasonal forecast models. The first part of this work focuses on monitoring heat waves in fifteen cities over West Africa located in coastal and continental regions. Three sources of uncertainty encountered in the heat wave detection process were identified: the first related to reanalysis data, the second to the choice of threshold used to define a heat wave, and the last to the methodology adopted. The inter-annual variability of heat waves in the different regions highlighted particularly hot years with a high frequency of heat wave events for all the three indicators AT, T2m,Tw: 1998, 2005, 2010, 2016, 2019 and 2020, mostly corresponding to El Nino years. The GU region has been more affected by heat waves over the past decade (2012-2020) than the CONT and ATL regions. However, the most persistent and intense heat waves occurred in the CONT region. An increase in the frequency, duration and intensity of heat waves has been observed over the last decade (2012-2020), probably due to global warming acting on extreme events." In the second part of this study, we focused on the predictability aspect of heat waves. A preliminary study of the predictability of heat waves has been carried out for the period 2001-2020 using subseasonal to seasonal forecast models from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the United Kingdom Meteorological Office (UKMO). The forecast models perform better than a reference climatology, particularly for short-term forecasts (up to two weeks) in all the three regions. Nighttime heatwaves are more predictable than daytime heatwaves. According to the FAR values, only 15 to 30% of the predicted heatwave days by the models are actually observed in the reanalyses, respectively for lead weeks 5 and 2. This suggests that the models overestimate the duration of heat waves compared with ERA5 reanalysis. ECMWF issues fewer false alarms than UKMO for short-term forecasts. Although the models show skills to detect heat waves compared to a reference climatology, their ability to forecast the intensity of events remains weak even for a short lead time. The predictability of heat waves was performed using machine learning methods. The BRF model demonstrated better heat wave detection skills than subseasonal forecast models in all the three regions. The BRF model considerably improves heat wave detection in forecast models, but on the other hand it generates a high rate of false alarms. The predictability of heat waves using large-scale predictors such as the Saharan Heat Low (SHL) was investigated using two seasonal forecast models: the fifth version of the European Center Seasonal Forecast Model "SEAS5" and the seventh version of the Météo-France Seasonal Forecast Model "MF7". The models show skills on the representation of the mean seasonal cycle of the SHL and capture some characteristics of its inter-annual variability, such as the warming trend observed during the 2010s. SEAS5 makes a more realistic representation of the climatic trend of the SHL compared to MF7. Using bias correction techniques, the results highlight the capacity of the models to represent the intra-seasonal variability of the SHL. Bias correction helps to improve the Continuous Ranked Probability Score (CRPS), but the skills of the model remain low for lead times beyond one month
Talevi, Luca. "Sviluppo e test di un sistema BCI SSVEP-based". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2016. http://amslaurea.unibo.it/11636/.
Texto completo da fonteMichielan, Lisa. "Advance Methodologies in Linear and Nonlinear Quantitative Structure-Activity Relationships (QSARs): from Drug Design to In Silico Toxicology Applications". Doctoral thesis, Università degli studi di Padova, 2010. http://hdl.handle.net/11577/3422242.
Texto completo da fonteNuove strategie computazionali vengono continuamente richieste dall'industria farmaceutica per assistere, migliorare e velocizzare il processo di scoperta dei farmaci. In questo scenario la chemoinformatica fornisce affidabili strumenti matematici per ottenere relazioni quantitative struttura-attività (QSAR), in grado di descrivere la correlazione tra descrittori molecolari e vari profili sperimentali dei composti. Negli ultimi anni approcci non lineari di machine learning hanno dimostrato una notevole capacità predittiva in diverse applicazioni QSAR, confermando la loro superiorità sulle tradizionali metodologie lineari. E' stata evidenziata particolarmente la praticabilità dell'approccio di classificazione nel risolvere compiti complessi. Inoltre, l'introduzione del concetto di autocorrelazione in chimica permette il confronto strutturale delle molecole attraverso l'uso di una rappresentazione vettoriale di lunghezza fissa che serve da efficace descrittore molecolare. Nella presente tesi abbiamo studiato approfonditamente l'ampia applicabilità e le potenzialità delle strategie QSAR non lineari, soprattutto in combinazione con i descrittori autocorrelati potenziale elettrostatico molecolare proiettato sulla superficie molecolare. Il nostro intento si articola in sei differenti casi studio, che si concentrano su problemi cruciali nei campi della farmacodinamica, farmacocinetica e tossicologia. Il primo caso studio considera la valutazione di una proprietà fisico-chimica, l'energia libera di solvatazione acquosa, che è strettamente connessa con il profilo farmacocinetico e la tossicità dei composti chimici. La nostra discussione in farmacodinamica riguarda la predizione di potenza e selettività di antagonisti del recettore adenosinico umano (hAR). La famiglia del recettore adenosinico appartiene alla famiglia A di GPCR (recettori accoppiati a proteine G), che include quattro diversi sottotipi, cui ci si riferisce come A1, A2A, A2B e A3, ampiamente distribuiti nei tessuti. Si differenziano sia per profilo farmacologico che per effettore cui sono accoppiati. Le intense sintesi esplorativa e valutazione farmacologica hanno lo scopo di scoprire ligandi potenti e selettivi per ogni sottotipo del recettore adenosinico. Nella presente tesi abbiamo considerato diversi derivati pirazolo-triazolo-pirimidinici e xantinici, studiati come promettenti antagonisti del recettore adenosinico. Quindi, un secondo caso studio si focalizza sul confronto e l'applicabilità in parallelo di modelli lineari e non lineari per predire l'affinità di legame di antagonisti del recettore adenosinico A2A umano e trovare un consenso nei risultati di predizione. Gli studi successivi valutano la predizione sia della selettività che dell'affinità di legame ai sottotipi A2AR e A3R combinando strategie di classificazione e regressione, per studiare infine il completo spettro di potenza del recettore adenosinico e il profilo di selettività per i sottotipi hAR mediante l'applicazione di un approccio di classificazione multilabel. Nel campo della farmacocinetica, e più specificamente nella predizione del metabolismo, è coinvolto l'uso di strategie di classificazione multi- e single-label per analizzare la specificità di isoforma di substrati del citocromo P450. I risultati conducono all'identificazione della metodologia appropriata per interpretare la reale informazione sul metabolismo, caratterizzata da xenobiotici potenzialmente trasformati da multiple isoforme del citocromo P450. Come caso studio finale, presentiamo un'indagine in tossicologia computazionale. Le recenti iniziative regolatorie dovute al REACH (Registration, Evaluation, Authorization and Restriction of Chemicals) richiedono l'accertamento ecotossicologico e del rischio dei composti chimici per la sicurezza. La maggiorparte dei correnti protocolli di valutazione è basata su costosi esperimenti animali. Così, gli strumenti chemoinformatici sono caldamente raccomandati per facilitare la caratterizzazione della tossicità di sostanze chimiche. Noi descriviamo una nuova strategia integrata per predire la tossicità acquatica acuta attraverso la combinazione di entrambi i comportamenti tossicocinetico e tossicodinamico dei composti chimici, utilizzando un metodo di classificazione machine learning. L'obbiettivo è assegnare i composti chimici a diversi livelli di tossicità acquatica acuta, fornendo un'appropriata risposta alle nuove esigenze regolatorie. Come validazione preliminare del nostro approccio, due modelli tossicocinetico e tossicodinamico sono stati applicati in serie per esaminare sia il rischio di tossicità acquatica che il modo d'azione di un set di sostanze chimiche con informazione tossicodinamica sconosciuta o incerta, valutandone il potenziale rischio ecologico ed il meccanismo tossico.
Hoffmann, Guillaume. "Mise au point de nouveaux descripteurs théoriques pour la réactivité chimique Can molecular and atomic descriptors predict the electrophilicity of Michael acceptors? On the influence of dynamical effects on reactivity descriptors Predicting experimental electrophilicities from quantum and topological descriptors : a machine learning approach Electrophilicity indices and halogen bonds : some new alternatives to the molecular electrostatic potential". Thesis, Normandie, 2020. http://www.theses.fr/2020NORMR042.
Texto completo da fonteThe study of global, local and non-local reactivity descriptors of a reactive system is of paramount importance in order to understand the reactivity of all chemical processes during a reaction. The goal of this thesis was then to develop new reactivity descriptors, as well as prediction models based on them, in order to study chemical reactivity. The main theoretical methods used were the Conceptual Density Functional Theory (CDFT) and Quantum Theory of “Atoms in Molecules” (QTAIM), which are both based on electron density. Our field of study is mainly within the framework of the Mayr experimental scale, which allows, through kinetic measurements a classification of molecules in order of reactivity. In the first part, great advances were made during this thesis with respect to the theoretical prediction of experimental electrophilicity of Michael acceptors. Then in a second step, we looked at the application of reactivity descriptors on the chemical bond, and in particular the halogen bond. Finally, a part of synthesis carried out during the course of this thesis is presented, by proposing a new way of synthesis of vinylogous iminium cations
Skyner, Rachael Elaine. "Hydrate crystal structures, radial distribution functions, and computing solubility". Thesis, University of St Andrews, 2017. http://hdl.handle.net/10023/11746.
Texto completo da fonteOliver, Gelabert Antoni. "Desarrollo y aceleración hardware de metodologías de descripción y comparación de compuestos orgánicos". Doctoral thesis, Universitat de les Illes Balears, 2018. http://hdl.handle.net/10803/462902.
Texto completo da fonteIntroducció El creixement accelerat de les dades en la societat actual i l'arribada de la tecnologia del transistor als límits físics exigeix la proposta de metodologies per al processament eficient de dades. Contingut Aquesta tesi doctoral, de caràcter transdisciplinària i a mig camí entre els camps de l'enginyeria electrònica i la química computacional presenta solucions optimitzades en maquinari i en programari per tal d’accelerar el processament de bases de dades moleculars. En primer lloc es proposa i s'estudia el funcionament de blocs digitals que implementen funcions de lògica polsant estocàstica aplicades a tasques de reconeixement d'objectes. En concret es proposen i analitzen dissenys específics per a la construcció de generadors de nombres aleatoris (RNG) com a sistemes bàsics per al funcionament dels sistemes de computació estocàstics implementats en dispositius programables com les Field Programable Gate Array (FPGA). En segon lloc es proposen i avaluen un conjunt reduït de descriptors moleculars especialment orientats a la caracterització de compostos orgànics. Aquests descriptors reuneixen la informació sobre la distribució de càrrega molecular i les energies electroestàtiques. Les bases de dades generades amb aquests descriptors s’han processat emprant sistemes de computació convencionals en programari i mitjançant sistemes basats en computació estocàstica implementats en maquinari programable. Finalment es proposen optimitzacions per al càlcul del potencial electroestàtic molecular (MEP) calculat mitjançant la teoria del funcional de la densitat (DFT) i dels punts d’interacció que se’n deriven (SSIP). Conclusions Per una banda, els resultats obtinguts posen de manifest la importància de la uniformitat del RNG en el període d’avaluació per a poder implementar sistemes de computació estocàstics d’alta fiabilitat. A més, els RNG proposats presenten una font d’aleatorietat aperiòdica que minimitza les correlacions entre senyals, fent-los adequats per a la implementació de sistemes de computació estocàstica. Per una altra banda, el conjunt de descriptors moleculars proposats PED, han demostrat obtenir molts bons resultats en comparació amb els mètodes presents a la literatura. Aquest fet ha estat discutit mitjançant l’anàlisi dels paràmetres Area Under The Curve (AUC) i Enrichment Factor (EF) de les curves Receiving Operating Characteristic (ROC) analitzades. A més, s’ha mostrat com l’eficàcia dels descriptors augmenta de manera significativa quan s’implementen en sistemes de classificació amb aprenentatge supervisat com les finestres de Parzen, fent-los adequats per a la construcció d’un sistema de predicció de dianes terapèutiques eficient. En aquesta tesi doctoral, a més, s’ha trobat que els MEP calculats mitjançant la teoria DFT i el conjunt de bases B3LYP/6-31*G en la superfície amb densitat electrònica 0,01 au correlacionen bé amb dades experimentals possiblement a causa de la contribució més gran de les propietats electroestàtiques locals reflectides en el MEP. Les parametritzacions proposades en funció del tipus d’hibridació atòmica han contribuït també a la millora dels resultats. Els càlculs realitzats en aquestes superfícies suposen un guany en un factor cinc en la velocitat de processament del MEP. Donat l’acceptable ajust a les dades experimentals del mètode proposat per al càlcul del MEP aproximat i dels SSIP que se’n deriven, aquest procediment es pot emprar per obtenir els SSIP en bases de dades moleculars extenses i en macromolècules (com ara proteïnes) d’una manera molt ràpida (ja que la velocitat de processament obtinguda arriba fins als cinc mil àtoms per segon amb un sol processador). Les tècniques proposades en aquesta tesi doctoral resulten d’interès donades les nombroses aplicacions que tenen els SSIP com per exemple, en el cribratge virtual de cocristalls o en la predicció d’energies lliures en dissolució.
Introduction Because of the generalized data growth in the nowadays digital era and due to the fact that we are possibly living on the last days of the Moore’s law, there exists a good reason for being focused on the development of technical solutions for efficient data processing. Contents In this transdisciplinary thesis between electronic engineering and computational chemistry, it's shown optimal solutions in hardware and software for molecular database processing. On the first hand, there's proposed and studied a set of stochastic computing systems in order to implement ultrafast pattern recognition applications. Specially, it’s proposed and analyzed specific digital designs in order to create digital Random Number Generators (RNG) as a base for stochastic functions. The digital platform used to generate the results is a Field Programmable Gate Array (FPGA). On the second hand, there's proposed and evaluated a set of molecular descriptors in order to create a compact molecular database. The proposed descriptors gather charge and molecular geometry information and they have been used as a database both in software conventional computing and in hardware stochastic computing. Finally, there's a proposed a set of optimizations for Molecular Electrostatic Potential (MEP) and Surface Site Interaction Points (SSIP). Conclusions Firstly, the results show the relevance of the uniformity of the RNG within the evaluation period in order to implement high precision stochastic computing systems. In addition, the proposed RNG have an aperiodic behavior which avoid some potential correlations between stochastic signals. This property makes the proposed RNG suitable for implementation of stochastic computing systems. Secondly, the proposed molecular descriptors PED have demonstrated to provide good results in comparison with other methods that are present in the literature. This has been discussed by the use of Area Under the Curve (AUC) and Enrichment Factor (EF) of averaged Receiving Operating Characteristic (ROC) curves. Furthermore, the performance of the proposed descriptors gets increased when they are implemented in supervised machine learning algorithms making them appropriate for therapeutic target predictions. Thirdly, the efficient molecular database characterization and the usage of stochastic computing circuitry can be used together in order to implement ultrafast information processing systems. On the other hand, in this thesis, it has been found that the MEP calculated by using DFT and B3LYP/6-31*G basis at 0.01 au density surface level has good correlation with experimental data. This fact may be due to the important contribution of local electrostatics and the refinement performed by the parameterization of the MEP as a function of the orbital atom type. Additionally, the proposed calculation over 0.01 au is five times faster than the calculation over 0.002 au. Finally, due to acceptable agreement between experimental data and theoretical results obtained by using the proposed calculation for MEP and SSIP, the proposed method is suitable for being applied in order to quickly process big molecular databases and macromolecules (the processing speed can achieve five thousand molecules per second using a single processor). The proposed techniques have special interest with the purpose of finding the SSIP because the big number of applications they have as for instance in virtual cocrystal screening and calculation of free energies in solution.
Zemzemi, Nejib. "Étude théorique et numérique de l'activité électrique du cœur: Applications aux électrocardiogrammes". Phd thesis, Université Paris Sud - Paris XI, 2009. http://tel.archives-ouvertes.fr/tel-00470375.
Texto completo da fonteRothe, Tom. "Machine Learning Potentials - State of the research and potential applications for carbon nanostructures". 2019. https://monarch.qucosa.de/id/qucosa%3A35780.
Texto completo da fonteTurgeon, Stéphanie. "L’analyse appliquée du comportement en autisme et ses enjeux : une évaluation du potentiel de la technologie pour améliorer la pratique et la recherche". Thesis, 2021. http://hdl.handle.net/1866/25604.
Texto completo da fonteAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by significant deficits in social communication and social interactions and by the presence of restricted and repetitive behaviors or interests. Empirical evidence suggests that interventions based on applied behavior analysis (ABA) are the most effective for treating individuals with ASD. Nevertheless, interventions based on behavior analysis present some issues. In particular, intervention services are hard to access, knowledge about the underlying factors of the effectiveness of interventions is lacking and divergent perceptions about of ABA hamper the adoption of the science. This dissertation includes three studies in which technology is used to better understand or improve these issues regarding ABA. As part of my first study, the effects of a fully self-guided interactive web training (IWT) developed for teaching parents of children with ASD ABA-derived strategies to reduce their child's challenging behaviors were evaluated using a randomized waitlist trial. The results of this study support the effectiveness of the IWT for increasing the frequency of parents’ use of behavioral interventions as well as for reducing the frequency and severity of their child’s challenging behaviors. In contrast, no significant difference was observed for the measurement of parenting practices. Ethical and practical consideration regarding the dissemination of fully self-guided online trainings are discussed. The second study of my doctoral thesis aimed to show how to use machine learning algorithms to predict individuals who were most likely to improve following an intervention. Specifically, a demonstration of how to implement four machine learning algorithms to predict the participants from my first study who were the most likely to report a decrease in their child's iv challenging behaviors. This study argues that machine learning algorithms can be used with small samples to support clinicians’ and researchers’ decision making. The third study of my dissertation aimed to quantify the information about ABA published on four subforums of an internet forum; an online resource often used by families to identify potential interventions for their child. This goal was achieved through the use of a data mining procedure. The analyses showed that parents who visited the forum were exposed to a significant proportion of messages that disapproved of ABA for individuals with ASD or that inaccurately described its underlying principles, methods, procedures, or interventions. Together, the studies carried out as part of my doctoral dissertation highlight the benefits of technology to support assessments, interventions, and knowledge gains or transfer within psychosocial practices. As highlighted in the three studies of this dissertation, each of the tools used presents limitations and should therefore be used to support clinicians and researchers, and should not replace their interventions and clinical judgment. Future studies should continue to focus on the effectiveness of technological tools and on the underlying factors that will promote their use. Finally, researchers must reflect on the ethical considerations related to use of technology when working with humans.
Nascimento, Matheus Lopes do. "Investigation of Geothermal Potential Zones with Machine Learning in Mainland Portugal". Master's thesis, 2022. http://hdl.handle.net/10362/134617.
Texto completo da fonteEarth’s internal heat is explored to produce electricity or used directly in industrial processes or residencies. It is considered to be renewable and cleaner than fossil fuels and has great importance to pursue environmental goals. The exploration phase of geothermal resources is complex and expensive. It requires field surveys, geological, geophysical and geochemical analysis, as well as drilling campaigns. Geospatial data and technologies have been used to target promising sites for further investigations, and helped reduce costs while also pointed to important criteria data related to geothermal potential. Machine learning is a data driven set of technologies that has been successfully used to model environmental parameters, and in the field of geothermal energy it has been used to predict thermal properties of the surface and subsurface. Random Forests and Extreme Gradient Boosting are ensemble machine learning algorithms that have been extensively used in environmental and geological sciences, and have been demonstrated to perform well when predicting thermal properties. This study investigated a methodology that coupled GIS and ML to predict two crucial parameters in geothermal exploration throughout Mainland Portugal: Geothermal gradient and surface Heat flow density. Training data consisted in different types of wells drilled in the study area where the two labels were measured. It was provided by Portugal’s Geology and Energy Laboratory. Features were all publicly available and consisted in geological, hydrogeological, geophysical, weather and terrain data. Data were aggregated in two grids with two spatial resolutions. The results between the two algorithms have been compared and discussed. The most important features that contributed to the models were identified and their relationships with the outputs discussed. The models and the prediction maps over the study area showed the location of zones with higher geothermal gradient and surface heat flow density and can be used to aid geothermal exploration and provide insights for geothermal modelling.
Hughes, Zak E., J. C. R. Thacker, A. L. Wilson e P. L. A. Popelier. "Description of Potential Energy Surfaces of Molecules using FFLUX Machine Learning Models". 2018. http://hdl.handle.net/10454/16776.
Texto completo da fonteA new type of model, FFLUX, to describe the interaction between atoms has been developed as an alternative to traditional force fields. FFLUX models are constructed from applying the kriging machine learning method to the topological energy partitioning method, Interacting Quantum Atoms (IQA). The effect of varying parameters in the construction of the FFLUX models is analyzed, with the most dominant effects found to be the structure of the molecule and the number of conformations used to build the model. Using these models the optimization of a variety of small organic molecules is performed, with sub kJ mol-1 accuracy in the energy of the optimized molecules. The FFLUX models are also evaluated in terms of their performance in describing the potential energy surfaces (PESs) associated with specific degrees of freedoms within molecules. While the accurate description of PESs presents greater challenges than individual minima, FFLUX models are able to achieve errors of <2.5 kJ mol-1 across the full C-C-C-C dihedral PES of n-butane, indicating the future possibilities of the technique.
The full text will be available at the end of the publisher's embargo, 4th Dec 2019.
Grey, Talora Bryn. "One step at a time: analysis of neural responses during multi-state tasks". Thesis, 2020. http://hdl.handle.net/1828/11695.
Texto completo da fonteGraduate