Academic literature on the topic 'Potentiel machine learning'
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Journal articles on the topic "Potentiel machine learning"
Ben Zid, Afef, Asma Najjar, and Imen Hamrouni. "Classification automatique d’emprises au sol de maisons dites « andalouses » à l’aide de modèle de Machine Learning." SHS Web of Conferences 203 (2024): 02001. http://dx.doi.org/10.1051/shsconf/202420302001.
Full textBOUKHELEF, Faiza. "Investigating Students’ Attitudes Towards Integrating Machine Translation in the EFL Classroom: The case of Google Translate." Langues & Cultures 5, no. 01 (June 30, 2024): 264–77. http://dx.doi.org/10.62339/jlc.v5i01.243.
Full textNg, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization." Biotechnology and Bioprocessing 2, no. 9 (November 2, 2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.
Full textDatta, Debaleena, Pradeep Kumar Mallick, Akash Kumar Bhoi, Muhammad Fazal Ijaz, Jana Shafi, and Jaeyoung Choi. "Hyperspectral Image Classification: Potentials, Challenges, and Future Directions." Computational Intelligence and Neuroscience 2022 (April 28, 2022): 1–36. http://dx.doi.org/10.1155/2022/3854635.
Full textSrinivasaiah, Bharath. "The Power of Personalized Healthcare: Harnessing the Potential of Machine Learning in Precision Medicine." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 426–29. http://dx.doi.org/10.21275/sr24506012313.
Full textKamoun-Abid, Ferdaous, Hounaida Frikha, Amel Meddeb-Makhoulf, and Faouzi Zarai. "Automating cloud virtual machines allocation via machine learning." Indonesian Journal of Electrical Engineering and Computer Science 35, no. 1 (July 1, 2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.
Full textShoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (April 1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.
Full textAschepkov, Valeriy. "METHODS OF MACHINE LEARNING IN MODERN METROLOGY." Measuring Equipment and Metrology 85 (2024): 57–60. http://dx.doi.org/10.23939/istcmtm2024.01.057.
Full textLevantesi, Susanna, Andrea Nigri, and Gabriella Piscopo. "Longevity risk management through Machine Learning: state of the art." Insurance Markets and Companies 11, no. 1 (November 25, 2020): 11–20. http://dx.doi.org/10.21511/ins.11(1).2020.02.
Full textShak, Md Shujan, Aftab Uddin, Md Habibur Rahman, Nafis Anjum, Md Nad Vi Al Bony, Murshida Alam, Mohammad Helal, Afrina Khan, Pritom Das, and Tamanna Pervin. "INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE." International Interdisciplinary Business Economics Advancement Journal 05, no. 11 (November 6, 2024): 6–20. http://dx.doi.org/10.55640/business/volume05issue11-02.
Full textDissertations / Theses on the topic "Potentiel machine learning"
Artusi, Xavier. "Interface cerveau machine avec adaptation automatique à l'utilisateur." Phd thesis, Ecole centrale de Nantes, 2012. http://www.theses.fr/2012ECDN0018.
Full textWe 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.
Full textOhlsson, 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.
Full textHu, Jinli. "Potential based prediction markets : a machine learning perspective." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29000.
Full textGustafson, 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.
Full textDel, 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.
Full textUnusual 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.
Full textLundberg, Oscar, Oskar Bjersing, and 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.
Full textSupervisors: 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.
Full textHellsing, Edvin, and 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.
Full textDenna 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%.
Books on the topic "Potentiel machine learning"
Bennaceur, Amel, Reiner Hähnle, and Karl Meinke, eds. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.
Full textPolyakova, Anna, Tat'yana Sergeeva, and Irina Kitaeva. The continuous formation of the stochastic culture of schoolchildren in the context of the digital transformation of general education. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.
Full textTaha, Zahari, Rabiu Muazu Musa, Mohamad Razali Abdullah, and Anwar P.P.Abdul Majeed. Machine Learning in Sports: Identifying Potential Archers. Springer, 2018.
Find full textPumperla, Max, Alex Tellez, and Michal Malohlava. Mastering Machine Learning with Spark 2.x: Harness the potential of machine learning, through spark. Packt Publishing - ebooks Account, 2017.
Find full textQuantum Machine Learning: Unleashing Potential in Science and Industry. Primedia eLaunch LLC, 2023.
Find full textMachine Learning for Dynamic Software Analysis : Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... Papers. Springer, 2018.
Find full textNagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.
Full textAI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
Find full textJaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
Find full textJaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.
Find full textBook chapters on the topic "Potentiel machine learning"
Muazu Musa, Rabiu, Zahari Taha, Anwar P. P. Abdul Majeed, and Mohamad Razali Abdullah. "Psychological Variables in Ascertaining Potential Archers." In Machine Learning in Sports, 21–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_3.
Full textMuazu Musa, Rabiu, Zahari Taha, Anwar P. P. Abdul Majeed, and Mohamad Razali Abdullah. "Psycho-Fitness Parameters in the Identification of High-Potential Archers." In Machine Learning in Sports, 37–44. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_5.
Full textMookambal, M. Adithi, and S. Gokulakrishnan. "Potential Subscriber Detection Using Machine Learning." In Advances in Intelligent Systems and Computing, 389–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_36.
Full textLorena, Ana C., Marinez F. de Siqueira, Renato De Giovanni, André C. P. L. F. de Carvalho, and Ronaldo C. Prati. "Potential Distribution Modelling Using Machine Learning." In New Frontiers in Applied Artificial Intelligence, 255–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69052-8_27.
Full textGastegger, Michael, and Philipp Marquetand. "Molecular Dynamics with Neural Network Potentials." In Machine Learning Meets Quantum Physics, 233–52. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_12.
Full textAktulga, H., V. Ravindra, A. Grama, and S. Pandit. "Machine Learning Techniques in Reactive Atomistic Simulations." In Lecture Notes in Energy, 15–52. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_2.
Full textNagabhushan, P., Sanjay Kumar Sonbhadra, Narinder Singh Punn, and Sonali Agarwal. "Towards Machine Learning to Machine Wisdom: A Potential Quest." In Big Data Analytics, 261–75. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_19.
Full textKhine, Myint Swe. "Exploring the Potential of Machine Learning in Educational Research." In Machine Learning in Educational Sciences, 3–8. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9379-6_1.
Full textHellström, Matti, and Jörg Behler. "High-Dimensional Neural Network Potentials for Atomistic Simulations." In Machine Learning Meets Quantum Physics, 253–75. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_13.
Full textSharma, Shashi, Soma Kumawat, and Kumkum Garg. "Predicting Student Potential Using Machine Learning Techniques." In Advances in Intelligent Systems and Computing, 485–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_40.
Full textConference papers on the topic "Potentiel machine learning"
S, Thanigaivelu P., Priyanka Dash, Sravan Kumar G, S. Viveka, Vijayasri Nidadavolu, and V. Gautham. "Investigating the Potential of Self-Supervised Learning in Adversarial Machine Learning." In 2024 International Conference on Advances in Computing Research on Science Engineering and Technology (ACROSET), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743375.
Full textXing, Shuaifei, Hankiz Yilahun, and Askar Hamdulla. "Enhancing Knowledge Graph Completion by Extracting Potential Positive Examples." In 2024 IEEE 5th International Conference on Pattern Recognition and Machine Learning (PRML), 177–83. IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779715.
Full textCérin, Christophe, Walid Saad, Congfeng Jiang, and Emna Mekni. "Where are the optimization potential of machine learning kernels?" In 2019 IEEE 5th International Conference on Big Data Intelligence and Computing (DATACOM), 130–36. IEEE, 2019. http://dx.doi.org/10.1109/datacom.2019.00028.
Full textJin, Bolai. "Unlocking the Potential of Raw Images for Object Detection with YOLOv8 and BOT-SORT Techniques." In 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA), 252–57. IEEE, 2024. http://dx.doi.org/10.1109/icmlca63499.2024.10754493.
Full textGarg, Swati, Chandra Sekhar, and Lov Kumar. "Unlocking Potential: A Machine Learning Approach to Job Category Prediction." In 2024 IEEE Region 10 Symposium (TENSYMP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752119.
Full textDuan, Dongliang, Weifeng Liu, Pengwen Chen, Murali Rao, and Jose C. Principe. "Variance and Bias Analysis of Information Potential and Symmetric Information Potential." In 2007 IEEE Workshop on Machine Learning for Signal Processing. IEEE, 2007. http://dx.doi.org/10.1109/mlsp.2007.4414339.
Full textMaia, Carlos D., Cristiane N. Nobre, Marco Paulo S. Gomes, and Luis E. Zárate. "Using Machine Learning to identify profiles of individuals with depression." In Symposium on Knowledge Discovery, Mining and Learning. Sociedade Brasileira de Computação - SBC, 2023. http://dx.doi.org/10.5753/kdmile.2023.232945.
Full textSingh, Akash, and Yumeng Li. "Machine Learning Potentials for Graphene." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95341.
Full textWang, Jia, Xiao-bei Wu, and Zhi-liang Xu. "Decentralized Formation Control and Obstacles Avoidance Based on Potential Field Method." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.258457.
Full textSun, Shijie, Akash Singh, and Yumeng Li. "Machine Learning Accelerated Atomistic Simulations for 2D Materials With Defects." In ASME 2023 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/imece2023-113427.
Full textReports on the topic "Potentiel machine learning"
Lundquist, Sheng. Exploring the Potential of Sparse Coding for Machine Learning. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.7484.
Full textMusser, Micah, and Ashton Garriott. Machine Learning and Cybersecurity: Hype and Reality. Center for Security and Emerging Technology, June 2021. http://dx.doi.org/10.51593/2020ca004.
Full textLewin, Alex, Karla Diaz-Ordaz, Chris Bonell, James Hargreaves, and Edoardo Masset. Machine learning for impact evaluation in CEDIL-funded studies: an ex ante lesson learning paper. Centre for Excellence and Development Impact and Learning (CEDIL), April 2023. http://dx.doi.org/10.51744/llp3.
Full textUlissi, Zachary. Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials. Office of Scientific and Technical Information (OSTI), August 2022. http://dx.doi.org/10.2172/2324766.
Full textNickerson, Jeffrey, Kalle Lyytinen, and John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, April 2022. http://dx.doi.org/10.4271/epr2022009.
Full textSmith, Justin, Nicholas Lubbers, Aidan Thompson, and Kipton Barros. Simple and efficient algorithms for training machine learning potentials to force data. Office of Scientific and Technical Information (OSTI), June 2020. http://dx.doi.org/10.2172/1763572.
Full textBurton, Simon. The Path to Safe Machine Learning for Automotive Applications. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, October 2023. http://dx.doi.org/10.4271/epr2023023.
Full textDutta, Sourav, Anna Wagner, Theadora Hall, and Nawa Raj Pradhan. Data-driven modeling of groundwater level using machine learning. Engineer Research and Development Center (U.S.), May 2024. http://dx.doi.org/10.21079/11681/48452.
Full textOgunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, December 2023. http://dx.doi.org/10.31979/mti.2023.2320.
Full textTaylor, Michael, and Nicholas Lubbers. IMS Rapid Response 2024 Summary Report: A Machine Learning Potential for the Periodic Table. Office of Scientific and Technical Information (OSTI), October 2024. http://dx.doi.org/10.2172/2460463.
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