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Letteratura scientifica selezionata sul tema "Potentiel machine learning"

Autore: Grafiati

Pubblicato: 21 dicembre 2024

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Articoli di riviste sul tema "Potentiel machine learning"

1

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.

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L’apprentissage automatique (ML) est une branche de l’IA qui utilise des données et des algorithmes pour imiter l’apprentissage humain. Intégrant l’informatique, la robotique et les sciences cognitives, il offre des applications transformatrices dans divers domaines. En architecture du patrimoine, le ML analyse les motifs, les styles et les matériaux pour aider à la préservation. Cet Article présente un modèle de classification basé sur le ML pour l’architecture andalouse en Tunisie et en Espagne, comparant des maisons construites par les Morisques expulsés d’Espagne en 1609 à celles de l’Espa

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BOUKHELEF, Faiza. "Investigating Students’ Attitudes Towards Integrating Machine Translation in the EFL Classroom: The case of Google Translate." Langues & Cultures 5, no. 01 (2024): 264–77. http://dx.doi.org/10.62339/jlc.v5i01.243.

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This paper delves into the potential of machine translation tools, with a specific focus on Google Translate, to expand their role beyond traditional translation tasks to enhance language learning in EFL classrooms. While machine translation tools have become integral to translator training programs, their utilization in language education remains limited and understudied. The present study attempts to highlight the promising avenues for innovative pedagogy in language education by incorporating machine translation tools and EFL students’ attitudes towards them. It explores the application of

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Ng, Wenfa. "Evaluating the Potential of Applying Machine Learning Tools to Metabolic Pathway Optimization." Biotechnology and Bioprocessing 2, no. 9 (2021): 01–07. http://dx.doi.org/10.31579/2766-2314/060.

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Successful engineering of a microbial host for efficient production of a target product from a given substrate can be viewed as an extensive optimization task. Such a task involves the selection of high activity enzymes as well as their gene expression regulatory control elements (i.e., promoters and ribosome binding sites). Finally, there is also the need to tune expression of multiple genes along a heterologous pathway to relieve constraints from rate-limiting step and help reduce metabolic burden on cells from unnecessary over-expression of high activity enzymes. While the aforementioned ta

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Datta, 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.

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Recent imaging science and technology discoveries have considered hyperspectral imagery and remote sensing. The current intelligent technologies, such as support vector machines, sparse representations, active learning, extreme learning machines, transfer learning, and deep learning, are typically based on the learning of the machines. These techniques enrich the processing of such three-dimensional, multiple bands, and high-resolution images with their precision and fidelity. This article presents an extensive survey depicting machine-dependent technologies’ contributions and deep learning on

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Srinivasaiah, 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 (2024): 426–29. http://dx.doi.org/10.21275/sr24506012313.

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Kamoun-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 (2024): 191. http://dx.doi.org/10.11591/ijeecs.v35.i1.pp191-202.

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In the realm of healthcare applications leveraging cloud technology, ongoing progress is evident, yet current approaches are rigid and fail to adapt to the dynamic environment, particularly when network and virtual machine (VM) resources undergo modifications mid-execution. Health data is stored and processed in the cloud as virtual resources supported by numerous VMs, necessitating critical optimization of virtual node and data placement to enhance data application processing time. Network security poses a significant challenge in the cloud due to the dynamic nature of the topology, hindering

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Shoureshi, R., D. Swedes, and R. Evans. "Learning Control for Autonomous Machines." Robotica 9, no. 2 (1991): 165–70. http://dx.doi.org/10.1017/s0263574700010201.

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SUMMARYToday's industrial machines and manipulators have no capability to learn by experience. Performance and productivity could be greatly enhanced if a machine could modify its operation based on previous actions. This paper presents a learning control scheme that provides the ability for machines to utilize their past experiences. The objective is to have machines mimic the human learning process as closely as possible. A data base is formulated to provide the machine with experience. An optical infrared distance sensor is developed to inform the machine about objects in its working space.

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Aschepkov, 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.

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In the modern world of scientific and technological progress, the requirements for the accuracy and reliability of measurements are becoming increasingly stringent. The rapid development of machine learning (ML) methods opens up perspectives for improving metrological processes and enhancing the quality of measurements. This article explores the potential application of ML methods in metrology, outlining the main types of ML models in automatic instrument calibration, analysis, and prediction of data. Attention is paid to the development of hybrid approaches that combine ML methods with tradit

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Levantesi, Susanna, Andrea Nigri, and Gabriella Piscopo. "Longevity risk management through Machine Learning: state of the art." Insurance Markets and Companies 11, no. 1 (2020): 11–20. http://dx.doi.org/10.21511/ins.11(1).2020.02.

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Longevity risk management is an area of the life insurance business where the use of Artificial Intelligence is still underdeveloped. The paper retraces the main results of the recent actuarial literature on the topic to draw attention to the potential of Machine Learning in predicting mortality and consequently improving the longevity risk quantification and management, with practical implication on the pricing of life products with long-term duration and lifelong guaranteed options embedded in pension contracts or health insurance products. The application of AI methodologies to mortality fo

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Shak, Md Shujan, Aftab Uddin, Md Habibur Rahman, et al. "INNOVATIVE MACHINE LEARNING APPROACHES TO FOSTER FINANCIAL INCLUSION IN MICROFINANCE." International Interdisciplinary Business Economics Advancement Journal 05, no. 11 (2024): 6–20. http://dx.doi.org/10.55640/business/volume05issue11-02.

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This study examines the application of machine learning algorithms to enhance financial inclusion in microfinance, focusing on credit scoring, risk and fraud detection, and customer segmentation. We performed feature engineering and employed models such as Logistic Regression, Decision Trees, Random Forests, Gradient Boosting Machines (XGBoost and LightGBM), Support Vector Machines (SVM), Autoencoders, Isolation Forests, and K-means Clustering. LightGBM achieved the highest accuracy (89.6%) and AUC (0.92) in credit scoring, while Random Forests demonstrated strong performance in both loan appr

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Tesi sul tema "Potentiel machine learning"

1

Artusi, Xavier. "Interface cerveau machine avec adaptation automatique à l'utilisateur." Phd thesis, Ecole centrale de Nantes, 2012. http://www.theses.fr/2012ECDN0018.

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Nous nous intéressons ici à une interface cerveau-machine (BCI, Brain Computer Interface) permettant de commander une prothèse par la pensée. Le rôle du BCI est de décoder à partir de signaux électroencéphalographiques (EEG) le mouvement désiré par le sujet. Le cœur du BCI est un algorithme de classification caractérisé par le choix des descripteurs des signaux et des règles de décision. L’objet de cette thèse est de développer un système BCI précis, capable d’améliorer ses performances en cours d’utilisation et de s’adapter à l’utilisateur sans nécessiter de multiples sessions d’apprentissage

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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.

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Nous nous intéressons ici à une interface cerveau-machine (BCI, Brain Computer Interface) permettant de commander une prothèse par la pensée. Le rôle du BCI est de décoder à partir de signaux électroencéphalographiques (EEG) le mouvement désiré par le sujet. Le coeur du BCI est un algorithme de classification caractérisé par le choix des descripteurs des signaux et des règles de décision. L'objet de cette thèse est de développer un système BCI précis, capable d'améliorer ses performances en cours d'utilisation et de s'adapter à l'utilisateur sans nécessiter de multiples sessions d'apprentissag

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Ohlsson, 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.

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For decades, there have been developments of computer software to support human decision making. Along with the increased complexity of business environments, smart technologies are becoming popular and useful for decision support based on huge amount of information and advanced analysis. The aim of this study was to explore the potential of using machine learning for financial risk management in debt collection, with a purpose of providing a clear description of what possibilities and difficulties there are. The exploration was done from a business perspective in order to complement previous

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Hu, Jinli. "Potential based prediction markets : a machine learning perspective." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29000.

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A prediction market is a special type of market which offers trades for securities associated with future states that are observable at a certain time in the future. Recently, prediction markets have shown the promise of being an abstract framework for designing distributed, scalable and self-incentivized machine learning systems which could then apply to large scale problems. However, existing designs of prediction markets are far from achieving such machine learning goal, due to (1) the limited belief modelling power and also (2) an inadequate understanding of the market dynamics. This work

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Gustafson, 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.

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In modern casinos, personnel exist to advise, or in some cases, order individuals to stop gambling if they are found to be gambling in a destructive way, but what about online gamblers? This thesis evaluated the possibility of using machine learning as a supplement for personnel in real casinos when gambling online. This was done through supervised learning or more specifically, a decision tree algorithm called CART. Studies showed that the majority of problem gamblers would find it helpful to have their behavioral patterns collected to be able to identify their risk of becoming a problem gamb

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Del, 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.

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Des quantités inhabituelles de molécules en phase gazeuse sont détectées dans les régions froides (environ 10 K) du milieu interstellaire (ISM), principalement attribuées à la désorption non thermique de molécules depuis les glaces déposées sur les grains de poussière. En particulier, la désorption induite par les rayons ultraviolets du vide (photodésorption VUV) est considérée comme étant une voie de désorption majoritaire dans les régions de l'ISM dominées par les photons. Les investigations expérimentales ont révélé que dans les glaces pures de monoxyde de carbone (CO), espèce omniprésente

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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.

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Molecular simulation is applied to understanding the behaviour of alkane liquids with the eventual goal of being able to predict the viscosity of an arbitrary alkane mixture from first principles. Such prediction would have numerous scientific and industrial applications, as alkanes are the largest component of fuels, lubricants, and waxes; furthermore, they form the backbones of a myriad of organic compounds. This dissertation details the creation of a potential, a model for how the atoms and molecules in the simulation interact, based on a systematic approximation of the quantum mechanical p

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Lundberg, 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.

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In this work potentials of carbon nanomaterials calculated with Density Functional Theory (DFT) are approximated using an Artificial Neural Network (ANN). Previous work in this field has focused on estimating potential energies of bulk structures. We investigate the possibility to approximate both the potential energies and the forces of periodic carbon nanotubes (CNTs) and fullerenes. The results indicate that for test structures similar to those in the training set the ANN approximates the energies to within 270 meV/atom (< 3.7% error, RMSE 40 meV/atom) and the forces to within 7.5 eV/Å (

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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.

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Iron is a material of fundamental importance in the industrial and economic processes of our society as it is the major constituent of steels. With advances in computational science, much progress has been made in the understanding of the microscopic mechanisms that determine the macroscopic properties of such material at ordinary or extreme conditions. Ab initio quantum mechanical calculations based on density-functional theory (DFT), in particular, proved to be a unique tool for this purpose. Nevertheless, in order to study large enough systems up to length- and time-scales comparable with t

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Hellsing, 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.

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This thesis has explored the development and potential effects of an intelligent decision support system (IDSS) to predict potential buyers for commercial real estate property. The overarching need for an IDSS of this type has been identified exists due to information overload, which the IDSS aims to reduce. By shortening the time needed to process data, time can be allocated to make sense of the environment with colleagues. The system architecture explored consisted of clustering commercial real estate buyers into groups based on their characteristics, and training a prediction model on histo

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Libri sul tema "Potentiel machine learning"

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Bennaceur, Amel, Reiner Hähnle, and Karl Meinke, eds. Machine Learning for Dynamic Software Analysis: Potentials and Limits. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-96562-8.

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Polyakova, 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. INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1876368.

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The material presented in the monograph shows the possibilities of continuous teaching of mathematics at school, namely, the significant potential of modern information and communication technologies, with the help of which it is possible to form elements of stochastic culture among students. Continuity in learning is considered from two positions: procedural and educational-cognitive. In addition, a distinctive feature of the book is the presentation of the digital transformation of general education as a way to overcome the "new digital divide". Methodological features of promising digital t

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Taha, Zahari, Rabiu Muazu Musa, Mohamad Razali Abdullah, and Anwar P.P.Abdul Majeed. Machine Learning in Sports: Identifying Potential Archers. Springer, 2018.

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Pumperla, 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.

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Quantum Machine Learning: Unleashing Potential in Science and Industry. Primedia eLaunch LLC, 2023.

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Machine Learning for Dynamic Software Analysis : Potentials and Limits: International Dagstuhl Seminar 16172, Dagstuhl Castle, Germany, April 24-27, ... Papers. Springer, 2018.

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Nagel, Stefan. Machine Learning in Asset Pricing. Princeton University Press, 2021. http://dx.doi.org/10.23943/princeton/9780691218700.001.0001.

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Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments, techniques from the rapidly advancing field of machine learning (ML) are well-suited for solving prediction problems. Accordingly, ML methods are quickly becoming part of the toolkit in asset pricing research and quantitative investing. This book examines the promises and challenges of ML applications in asset pricing. Asset pricing problems are substantially different from the settings for which ML tool

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AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.

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Jaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.

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Jaswal, Gaurav, Vivek Kanhangad, and Raghavendra Ramachandra. AI and Deep Learning in Biometric Security: Trends, Potential, and Challenges. Taylor & Francis Group, 2020.

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Capitoli di libri sul tema "Potentiel machine learning"

1

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. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_3.

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Muazu 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. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_5.

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Mookambal, M. Adithi, and S. Gokulakrishnan. "Potential Subscriber Detection Using Machine Learning." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_36.

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Lorena, 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. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69052-8_27.

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Gastegger, Michael, and Philipp Marquetand. "Molecular Dynamics with Neural Network Potentials." In Machine Learning Meets Quantum Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_12.

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Aktulga, H., V. Ravindra, A. Grama, and S. Pandit. "Machine Learning Techniques in Reactive Atomistic Simulations." In Lecture Notes in Energy. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-16248-0_2.

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AbstractThis chapter describes recent advances in the use of machine learning techniques in reactive atomistic simulations. In particular, it provides an overview of techniques used in training force fields with closed form potentials, developing machine-learning-based potentials, use of machine learning in accelerating the simulation process, and analytics techniques for drawing insights from simulation results. The chapter covers basic machine learning techniques, training procedures and loss functions, issues of off-line and in-lined training, and associated numerical and algorithmic issues. The chapter highlights key outstanding challenges, promising approaches, and potential future developments. While the chapter relies on reactive atomistic simulations to motivate models and methods, these are more generally applicable to other modeling paradigms for reactive flows.

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Nagabhushan, P., Sanjay Kumar Sonbhadra, Narinder Singh Punn, and Sonali Agarwal. "Towards Machine Learning to Machine Wisdom: A Potential Quest." In Big Data Analytics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-93620-4_19.

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Khine, Myint Swe. "Exploring the Potential of Machine Learning in Educational Research." In Machine Learning in Educational Sciences. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9379-6_1.

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Hellström, Matti, and Jörg Behler. "High-Dimensional Neural Network Potentials for Atomistic Simulations." In Machine Learning Meets Quantum Physics. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-40245-7_13.

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Sharma, Shashi, Soma Kumawat, and Kumkum Garg. "Predicting Student Potential Using Machine Learning Techniques." In Advances in Intelligent Systems and Computing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_40.

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Atti di convegni sul tema "Potentiel machine learning"

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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). IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743375.

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Xing, 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). IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779715.

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Cérin, 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). IEEE, 2019. http://dx.doi.org/10.1109/datacom.2019.00028.

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Jin, 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). IEEE, 2024. http://dx.doi.org/10.1109/icmlca63499.2024.10754493.

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Garg, Swati, Chandra Sekhar, and Lov Kumar. "Unlocking Potential: A Machine Learning Approach to Job Category Prediction." In 2024 IEEE Region 10 Symposium (TENSYMP). IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752119.

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Duan, 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.

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Maia, 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.

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Depression is a major public health problem in Brazil, affecting millions of individuals each year. While the prevalence of depression in Brazil has been well-documented, there is still a need for more accurate and timely predictions of depression trends to improve treatment and prevention strategies. In this study, we explored the potential of machine learning algorithms to forecast depression trends in Brazil using data from the National Health Survey conducted by the Brazilian Institute of Geography and Statistics. We compared the performance of various machine learning models in depression

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Singh, 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.

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Abstract Graphene has been one of the most researched material in the world for the past two decades due to its unique combination of mechanical, thermal and electrical properties. Graphene exists in a stable two dimensional (2D) structure with hexagonal carbon rings. This special 2D structure of graphene enables it to exhibit a wide range of peculiar material properties like high Young’s modulus, high specific strength, and electrical conductivity etc. However, it is extremely challenging and costly to investigate graphene solely based on experimental tests. Atomistic simulations are powerful

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Wang, 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.

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Sun, 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.

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Abstract 2D materials generally show very different physical and chemical properties from 3D materials, which provide them promising applications in cutting-edge technology areas like aerospace, energy storage and electronics. To better understand and illustrate their unique properties, current research heavily relies on atomistic simulations, while successful simulations require the high reliability of interatomic interaction potentials that empirical potentials cannot provide. The ab initio calculations, for example density functional theory (DFT), are able to conduct high-fidelity simulatio

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Rapporti di organizzazioni sul tema "Potentiel machine learning"

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Lundquist, Sheng. Exploring the Potential of Sparse Coding for Machine Learning. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7484.

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Musser, Micah, and Ashton Garriott. Machine Learning and Cybersecurity: Hype and Reality. Center for Security and Emerging Technology, 2021. http://dx.doi.org/10.51593/2020ca004.

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Cybersecurity operators have increasingly relied on machine learning to address a rising number of threats. But will machine learning give them a decisive advantage or just help them keep pace with attackers? This report explores the history of machine learning in cybersecurity and the potential it has for transforming cyber defense in the near future.

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Lewin, 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), 2023. http://dx.doi.org/10.51744/llp3.

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The Centre of Excellence for Development Impact and Learning (CEDIL) has recently funded several studies that use machine learning methods to enhance the inferences made from impact evaluations. These studies focus on assessing the impact of complex development interventions, which can be expected to have impacts in different domains, possibly over an extended period of time. These studiestherefore involve study participants being followed up at multiple time-points after the intervention, and typically collect large numbers of variables at each follow-up. The hope is that machine learning app

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Ulissi, Zachary. Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials. Office of Scientific and Technical Information (OSTI), 2022. http://dx.doi.org/10.2172/2324766.

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Nickerson, Jeffrey, Kalle Lyytinen, and John L. King. Automated Vehicles: A Human/Machine Co-learning Perspective. SAE International, 2022. http://dx.doi.org/10.4271/epr2022009.

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Automated vehicles (AVs)—and the automated driving systems (ADSs) that enable them—are increasing in prevalence but remain far from ubiquitous. Progress has occurred in spurts, followed by lulls, while the motor transportation system learns to design, deploy, and regulate AVs. Automated Vehicles: A Human/Machine Co-learning Experience focuses on how engineers, regulators, and road users are all learning about a technology that has the potential to transform society. Those engaged in the design of ADSs and AVs may find it useful to consider that the spurts and lulls and stakeholder tussles are

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Smith, 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), 2020. http://dx.doi.org/10.2172/1763572.

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Burton, Simon. The Path to Safe Machine Learning for Automotive Applications. SAE International, 2023. http://dx.doi.org/10.4271/epr2023023.

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<div class="section abstract"><div class="htmlview paragraph">Recent rapid advancement in machine learning (ML) technologies have unlocked the potential for realizing advanced vehicle functions that were previously not feasible using traditional approaches to software development. One prominent example is the area of automated driving. However, there is much discussion regarding whether ML-based vehicle functions can be engineered to be acceptably safe, with concerns related to the inherent difficulty and ambiguity of the tasks to which the technology is applied. This leads to chal

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Dutta, 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.), 2024. http://dx.doi.org/10.21079/11681/48452.

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This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at

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Ogunbire, Abimbola, Panick Kalambay, Hardik Gajera, and Srinivas Pulugurtha. Deep Learning, Machine Learning, or Statistical Models for Weather-related Crash Severity Prediction. Mineta Transportation Institute, 2023. http://dx.doi.org/10.31979/mti.2023.2320.

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Nearly 5,000 people are killed and more than 418,000 are injured in weather-related traffic incidents each year. Assessments of the effectiveness of statistical models applied to crash severity prediction compared to machine learning (ML) and deep learning techniques (DL) help researchers and practitioners know what models are most effective under specific conditions. Given the class imbalance in crash data, the synthetic minority over-sampling technique for nominal (SMOTE-N) data was employed to generate synthetic samples for the minority class. The ordered logit model (OLM) and the ordered p

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Taylor, 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), 2024. http://dx.doi.org/10.2172/2460463.

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