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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"

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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’Espagne musulmane médiévale. L’objectif est d’identifier les caractéristiques architecturales distinctives. Les données ont été générées à l’aide d’un algorithme DCGAN, et des modèles ML ont atteint des taux de succès de 87,55% avec k-NN et 84,21% avec SVM. Le modèle montre un potentiel pour des applications plus larges en architecture.

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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 machine translation in the context of English language acquisition for non-native speakers. The results demonstrate that students consider machine translation as a useful strategy to learn English, and Google Translate (GT) offers advantages in vocabulary expansion and quick translations. However, its limitations, such as reduced accuracy for longer texts and the inability to process idiomatic expressions, necessitate careful consideration when integrating it into language learning curricula. GT can serve as a supplementary tool to support learners, but it should not replace conventional language learning methods. Ultimately, this research emphasizes the need for cautious guidance and monitoring when utilizing automated translation to ensure effective language learning outcomes, bridging the gap between translation and language education while acknowledging the tool's limitations. Résumé Cet article explore le potentiel des outils de traduction automatique, avec un accent particulier sur Google Translate, en élargissant leur rôle au-delà des tâches de traduction traditionnelles pour améliorer l'apprentissage des langues dans les classes EFL. Bien que les outils de traduction automatique soient devenus une partie intégrante des programmes de formation des traducteurs, leur utilisation dans l'enseignement des langues reste limitée et peu étudiée. La présente étude tente de mettre en évidence les pistes prometteuses pour une pédagogie innovante dans l'enseignement des langues en intégrant les outils de traduction automatique et les attitudes des étudiants EFL à leur égard. Cet article explore l'application de la traduction automatique dans le contexte de l'acquisition de la langue anglaise pour les locuteurs non natifs. Les résultats montrent que les étudiants considèrent la traduction automatique comme une stratégie utile pour apprendre l'anglais, et Google Translate (GT) offre des avantages dans l'expansion du vocabulaire et les traductions rapides. Cependant, ses limites, telles que la précision réduite des textes plus longs et l'incapacité à traiter les expressions idiomatiques, nécessitent une attention particulière lors de leur intégration dans les programmes d'apprentissage des langues. GT peut servir d'outil supplémentaire pour soutenir les apprenants, mais ne devrait pas remplacer les méthodes conventionnelles d'apprentissage des langues. Enfin, cette recherche met l'accent sur la nécessité d'une orientation et d'un suivi prudent dans l'utilisation de la traduction automatisée pour assurer des résultats d'apprentissage linguistique efficaces, combler l'écart entre la traduction et l'éducation linguistique tout en reconnaissant les limites de l'outil.

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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 tasks could be performed through combinatorial experiments, such an approach incurs significant cost, time and effort, which is a handicap that can be relieved by application of modern machine learning tools. Such tools could attempt to predict high activity enzymes from sequence, but they are currently most usefully applied in classifying strong promoters from weaker ones as well as combinatorial tuning of expression of multiple genes. This perspective reviews the application of machine learning tools to aid metabolic pathway optimization through identifying challenges in metabolic engineering that could be overcome with the help of machine learning tools.

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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 landcover classification based on hyperspectral images. The objective of this study is three-fold. First, after reading a large pool of Web of Science (WoS), Scopus, SCI, and SCIE-indexed and SCIE-related articles, we provide a novel approach for review work that is entirely systematic and aids in the inspiration of finding research gaps and developing embedded questions. Second, we emphasize contemporary advances in machine learning (ML) methods for identifying hyperspectral images, with a brief, organized overview and a thorough assessment of the literature involved. Finally, we draw the conclusions to assist researchers in expanding their understanding of the relationship between machine learning and hyperspectral images for future research.

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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 traditional firewalls’ ability to inspect packet contents and leaving the network vulnerable to potential threats. To address this, we propose dividing the cloud topology into zones, each monitored by a controller to oversee individual VMs under firewall protection, a framework termed divided-cloud, aiming to minimize network congestion while strategically placing new VMs. Employing machine learning (ML) techniques, such as decision tree (DT) and linear discriminant analysis (LDA), we achieved improved accuracy rates for adding new controllers, reaching a maximum of 89%, and used the K-neighbours classifier method to determine optimal locations for new VMs, achieving an accuracy of 83%.

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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. A learning control scheme is presented that utilizes the sensory information to enhance machine performance in the next trial. An adaptive scheme is proposed for the modification of learning gain matrices, and is implemented on an industrial robot. Experimental results verify the potentials of the proposed adaptive learning scheme, and illustrate how it can be used for improvement of different manufacturing processes.

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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 traditional metrological methods for the optimal solution of complex measurement tasks.

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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 forecasts improves both fitting and forecasting of the models traditionally used. In particular, the paper presents the Classification and the Regression Tree framework and the Neural Network algorithm applied to mortality data. The literature results are discussed, focusing on the forecasting performance of the Machine Learning techniques concerning the classical model. Finally, a reflection on both the great potentials of using Machine Learning in longevity management and its drawbacks is offered.

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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 approval (86.7% accuracy) and fraud detection (87.6% accuracy, AUC of 0.88). SVM also performed competitively, and unsupervised methods like Autoencoders and Isolation Forests showed potential for anomaly detection but required further refinement.K-means Clustering excelled in customer segmentation with a silhouette score of 0.72, enabling tailored services based on client demographics. Our findings highlight the significant impact of machine learning on improving credit scoring accuracy, reducing fraud risks, and enhancing customer service delivery in microfinance, thereby promoting financial inclusion for underserved populations. Ethical considerations and model interpretability are crucial, particularly for smaller institutions. This study advocates for the broader adoption of machine learning in the microfinance sector.

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Tesi sul 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.

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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. Nous combinons deux moyens pour y parvenir. Le premier consiste à augmenter la précision du système de décision en recherchant des descripteurs pertinents vis à vis de l’objectif de classification. Le second est d’inclure un retour de l’utilisateur sur le système de décision : l’idée est d’estimer l’erreur du BCI à partir de potentiels cérébraux évoqués, reflétant l’état émotionnel du patient corrélé au succès ou à l’échec de la décision prise par le BCI, et de corriger le système de décision du BCI en conséquence. Les principales contributions de la thèse sont les suivantes : nous avons proposé une méthode d’optimisation de descripteurs à bases d’ondelettes pour des signaux EEG multivoies ; nous avons quantifié théoriquement l’amélioration des performances apportée par le détecteur ; un simulateur du système corrigé et bouclé a été développé pour observer le comportement du système global et comparer différentes stratégies de mise à jour de l’ensemble d’apprentissage ; le système complet a été implémenté et fonctionne en ligne dans des conditions réelles<br>We 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

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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'apprentissage. Nous combinons deux moyens pour y parvenir. Le premier consiste à augmenter la précision du système de décision en recherchant des descripteurs pertinents vis à vis de l'objectif de classification. Le second est d'inclure un retour de l'utilisateur sur le système de décision : l'idée est d'estimer l'erreur du BCI à partir de potentiels cérébraux évoqués, reflétant l'état émotionnel du patient corrélé au succès ou à l'échec de la décision prise par le BCI, et de corriger le système de décision du BCI en conséquence. Les principales contributions de la thèse sont les suivantes : nous avons proposé une méthode d'optimisation de descripteurs à bases d'ondelettes pour des signaux EEG multivoies ; nous avons quantifié théoriquement l'amélioration des performances apportée par le détecteur ; un simulateur du système corrigé et bouclé a été développé pour observer le comportement du système global et comparer différentes stratégies de mise à jour de l'ensemble d'apprentissage ; le système complet a été implémenté et fonctionne en ligne dans des conditions réelles.

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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 research using a computer science approach which centralizes on the development and testing of algorithms. By conducting a case study at Tieto, who provides a market leading debt collection system, data was collected about the process and the findings were analyzed based on machine learning theories. The results showed that machine learning has the potential to improve the predictions for risk assessment through advanced pattern recognition and adapting to changes in the environment. Furthermore, it also has the potential to provide the decision maker with customized suggestions for suitable risk mitigation strategies based on experiences and evaluations of previous strategic decisions. However, the issues related to data availability were concluded as potential difficulties due to the limitations of accessing more data from authorities through an automated process. Moreover, the potential is highly dependent on future laws and regulations for data management which will affect the difficulty of data availability further.

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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 is thus motivated by improving and extending current prediction market design in both aspects. This research is focused on potential based prediction markets, that is, prediction markets that are administered by potential (or cost function) based market makers (PMM). To improve the market’s modelling power, we first propose the partially-observable potential based market maker (PoPMM), which generalizes the standard PMM such that it allows securities to be defined and evaluated on future states that are only partially-observable, while also maintaining the key properties of the standard PMM. Next, we complete and extend the theory of generalized exponential families (GEFs), and use GEFs to free the belief models encoded in the PMM/PoPMM from always being in exponential families. To have a better understanding of the market dynamics and its link to model learning, we discuss the market equilibrium and convergence in two main settings: convergence driven by traders, and convergence driven by the market maker. In the former case, we show that a market-wise objective will emerge from the traders’ personal objectives and will be optimized through traders’ selfish behaviours in trading. We then draw intimate links between the convergence result to popular algorithms in convex optimization and machine learning. In the latter case, we augment the PMM with an extra belief model and a bid-ask spread, and model the market dynamics as an optimal control problem. This convergence result requires no specific models on traders, and is suitable for understanding the markets involving less controllable traders.

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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 gambler before their problem started. The collected behavioral features were time spent gambling, the rate of won and lost money and the number of deposits made, all these during a specific period of time. An API was implemented for casino platforms to connect to and give collected data about their users, and to receive responses to notify users about their situation. Unfortunately, there were no platforms available to test this on players gambling live. Therefore a web based survey was implemented to test if the API would work as expected. More studies could be conducted in this area, finding more features to convert for computers to understand and implement into the learning algorithm.

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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 dans l'ISM, la photodésorption VUV peut suivre un mécanisme indirect de désorption induite par transition électronique (DIET) pour les photons dont l'énergie est comprise entre 7 et 10 eV. Néanmoins, la compréhension des mécanismes moléculaires sous-jacents reste un sujet de débat scientifique. Dans ce contexte astrochimique, nous présentons une étude théorique combinée utilisant la dynamique moléculaire ab initio (AIMD) basée sur la théorie de la fonctionnelle de la densité (DFT) et des potentiels machine learning (PML) construits avec des réseaux de neurones artificiels (ANN), afin d'étudier la dernière partie du mécanisme DIET dans les glaces amorphes de CO. Ici, une molécule CO hautement excitée vibrationnellement (v = 40) au centre d'un agrégat composé de 50 molécules de CO, initialement optimisé puis thermalisé à 15 K, déclenche, la désorption indirecte de molécules de surface. Nos résultats théoriques révèlent que le processus de désorption consiste en 3 étapes fondamentales qui commence par une attraction mutuelle entre la molécule excitée vibrationnellement et une ou deux molécules voisines, activée par l'étirement de la liaison CO et favorisée par l'effet stérique des molécules environnantes. Cela est suivi par une séquence de transferts d'énergie initiée par une collision, se concluant en la désorption de molécules CO vibrationnellement froides dans 88% des trajectoires AIMD. De plus, les distributions théoriques de l'énergie interne et translationnelle des molécules désorbées concordent remarquablement avec les résultats expérimentaux, ce qui soutient le rôle crucial de la relaxation vibrationnelle dans le processus de désorption. Enfin, les premiers PML construits à partir des simulations AIMD, sont capables d'ajuster avec précision la surface d'énergie potentielle multidimensionnelle du système, permettant de prédire efficacement les énergies des agrégats et les forces atomiques. Les simulations de dynamique moléculaire classique utilisant ces potentiels sont plus de 1800 fois plus rapides que celles basées sur l'AIMD, tout en offrant des précisions similaires à ceux de la DFT<br>Unusual 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

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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 potential energy surface using machine learning. This approximation has the advantage of producing forces and energies of nearly quantum mechanical accuracy at a tiny fraction of the usual cost. It enables accurate simulation of the large systems and long timescales required for accurate prediction of properties such as the density and viscosity. The approach is developed and tested on methane, the simplest alkane, and investigations are made into potentials for longer, more complex alkanes. The results show that the approach is promising and should be pursued further to create an accurate machine learning potential for the alkanes. It could even be extended to more complex molecular liquids in the future.

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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 (< 73% error, RMSE 1.34 eV/Å) per atom compared with DFT calculations. Furthermore, we investigate how well the ANN approximates the potentials and forces in structures that are combinations of CNTs and fullerenes (capped CNTs) and find that the ANN generalizes the potential energies to within 100 meV/atom (< 1.1% error, RMSE 78 meV/atom) and the forces to within 6 eV/Å (< 60% error, RMSE 0.55 eV/Å) per atom. The ANN approximated potentials and forces are used to geometry optimize CNTs and we observe that the optimized periodic CNTs match DFT calculated structures and energies while the capped CNTs result in comparable energies but incorrect structures compared to DFT calculations. Considering geometry optimization performed with ANN on CNTs the errors lie within 170 meV/atom (< 1.8% error) with an RMSE of 20 meV/atom. For the geometry optimizations of the capped CNTs the errors are within 430 meV/atom (< 5.5% error) with an RMSE of 14 meV/atom. All results are compared with empirical potentials (ReaxFF) and we find that the ANN approximated potentials are more accurate than the best tested empirical potential. This work shows that machine learning may be used to approximate DFT calculations. However, for further applications our conclusion is that the error of the estimated forces must be reduced further. Finally, we investigate the computing time (number of core hours) required and find that the ANN is about two orders of magnitude faster than DFT and three to four orders of magnitude slower than ReaxFF. For the unseen data the ANN is still around 2 orders of magnitude quicker than the DFT but here it is around 4 order of magnitude slower than ReaxFF.<br><p>Supervisors: Daniel Hedman and Fredrik Sandin</p><br>F7042T - Project in Engineering Physics

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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 those accessible in experiments, interatomic potentials are needed. These are typically based on functional forms driven by physical intuition and fitted on experimental data at zero/low temperature and/or on available first-principles data. Despite their vast success, however, their low flexibility limits their systematic improvement upon database extension. Moreover, their accuracy at intermediate and high temperature remains questionable. In this thesis, we first survey a selection of embedded atom method (EAM) potentials to understand their strengths and limitations in reproducing experimental thermodynamic, vibrational and elastic properties of bcc iron at finite temperature. Our calculations show that, on average, all the potentials rapidly deviate from experiments as temperature is increased. At the same time, they suggest that, despite an anomalous rapid softening of its C44 shear constant, the Mendelev03 parameterization is the most accurate among those considered in this work. As a second step, we compute the same finite-temperature properties from DFT. We verify our plane-wave spin-polarized pseudopotential implementation against selected zero temperature all-electron calculations, thus highlighting the difficulties of the semi-local generalized gradient approximation exchange and correlation functional in describing the electronic properties of iron. On the other hand, we demonstrate that after accounting for the vibrational degrees of freedom, DFT provides a good description of the thermal behavior of thermodynamic and elastic properties of α-iron up to a good fraction of the Curie temperature without the explicit inclusion of magnetic transverse degrees of freedom. Electronic entropy effects are also analyzed and shown to be of secondary importance. Finally, we attempt at generating a set of highly flexible Gaussian approximation potentials (GAP) for bcc iron that retain ab initio accuracy both at zero and finite temperature. To this end, we use a non-linear, non-parametric Gaussian-process regression, and construct a training database of total energies, stresses and forces taken from first-principles molecular dynamics simulations. We cover approximately 105 local atomic environments including pristine and defected bulk systems, and surfaces with different crystallographic orientations. We then validate the different GAP models against DFT data not directly included in the dataset, focusing on the prediction of thermodynamic, vibrational, and elastic properties and of the energetics of bulk defects.

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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 historical transaction data from the Swedish market from the cadastral and land registration authority. The prediction model was trained to predict which out of the cluster groups most likely will buy a given property. For the clustering, three different clustering algorithms were used and evaluated, one density based, one centroid based and one hierarchical based. The best performing clustering model was the centroid based (K-means). For the predictions, three supervised Machine learning algorithms were used and evaluated. The different algorithms used were Naive Bayes, Random Forests and Support Vector Machines. The model based on Random Forests performed the best, with an accuracy of 99.9%.<br>Denna 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%.

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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 technologies (within the framework of teaching students the elements of the probabilistic and statistical line) that contribute to overcoming the "new digital divide": artificial intelligence, the Internet of Things, additive manufacturing, machine learning, blockchain, virtual and augmented reality are described. The solution of the main questions of probability theory and statistics in the 9th grade mathematics course is proposed to be carried out using a distance learning course built in the Moodle distance learning system. The content, structure and methodological features of the implementation of the stochastics course for students of grades 10-11 of a secondary school are based on the use of such tools in the educational process as an online calculator for plotting functions, the Wolfram Alpha service, Google Docs and Google Tables services, the Yaklass remote training, the Banktest website.<url>", interactive module "Galton Board", educational website "Mathematics at school". It will be interesting for students, undergraduates, postgraduates, mathematics teachers, as well as specialists improving their qualifications in the field of pedagogical education.

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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 tools were developed originally. To realize the potential of ML methods, they must be adapted for the specific conditions in asset pricing applications. Economic considerations, such as portfolio optimization, absence of near arbitrage, and investor learning can guide the selection and modification of ML tools. Beginning with a brief survey of basic supervised ML methods, the book discusses the application of these techniques in empirical research in asset pricing and shows how they promise to advance the theoretical modeling of financial markets. The book presents the exciting possibilities of using cutting-edge methods in research on financial asset valuation.

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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"

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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 trends, including decision trees, random forests, support vector machines, and neural networks. Additionally, we aimed to identify key risk factors for depression trends in Brazil, including age, gender, income, education, and marital status. These findings have important implications for public health policies and mental healthcare in Brazil. Our study provides insights into the use of machine learning algorithms to predict and prevent depression trends and highlights the potential of data-driven approaches to improve mental health outcomes in Brazil.

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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 computational techniques for characterizing materials at small length and time scales with a fraction of cost relative to experimental testing. High fidelity atomistic simulations like Density Functional Theory (DFT) simulations, and ab initio molecular dynamic simulations have higher accuracy in predicting 2D material properties but are computationally expensive. Classic molecular dynamics (MD) simulations adopt empirical interatomic potentials which drastically reduce the computational time but has lower simulation accuracy. To bridge the gap between these two type of simulation techniques, a new artificial neural network potential is developed, for graphene in this study, to enable the characterization of 2D materials using classic MD simulations with a comparable accuracy of first principles simulation. This is expected to accelerate the discovery and design of novel graphene based functional materials. In the present study mechanical and thermal properties of graphene are investigated using the machine learning potentials by conducting MD simulations. To validate the accuracy of machine learning potentials mechanical properties such as Young’s modulus, ultimate tensile strength and thermal properties such as coefficient of thermal expansion and lattice parameter are evaluated for graphene and compared with existing literature.

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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 simulations in essence, but with a high computational cost and largely limited simulation size. Recently machine learning potentials become a trend to interpolate the potential energy surface based on artificial neural networks (ANNs) with reference datasets from first principle calculations. Although machine learning potentials have been developed for many material systems including 2D materials, there is limited work published regarding structural defects. Using graphene as a model material, we demonstrate a new machine learning potential for 2D materials with defects. The reference energies are generated by DFT calculations. ANN with two hidden layers is used for the training, which has been demonstrated to be suitable for developing machine learning potential in our previous work. For ANN input, the atomic structures are coded using perturbation-invariant representations. After training, the weight and bias parameters are exported as our potential and then imported into LAMMPS software to conduct MD simulations. It is expected that our work provides fundamental support on investigating defective graphene and understanding defect effects to develop structure-property relationship. This will also promote the development of machine learning based simulation tools for the study and design of complex materials.

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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 approaches can uncover new insights into the variation in responses to interventions, and possible causal mechanisms, which in turn can highlight potential areas that policy and planning can focus on. This paper describes these studies using machine learning methods, gives an overview of the common aims and methodological approaches used in impact evaluations, and highlights some lessons and important caveats.

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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 a normal part of technology transformations; however, this report will provide suggestions for effective stakeholder engagement.

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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 challenges in defining adequately safe responses for all possible situations and an acceptable level of residual risk, which is then compounded by the reliance on training data.</div><div class="htmlview paragraph"><b>The Path to Safe Machine Learning for Automotive Applications</b> discusses the challenges involved in the application of ML to safety-critical vehicle functions and provides a set of recommendations within the context of current and upcoming safety standards. In summary, the potential of ML will only be unlocked for safety-related functions if the inevitable uncertainties associated with both the specification and performance of the trained models can be sufficiently well understood and controlled within the application-specific context.</div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

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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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Abstract (sommario):

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 the European Center for Medium-Range Weather Forecasts to predict future GWL values for a desired period of time. The results presented in this report indicate that the ML method has the potential to predict both short-term (4-hourly) as well as daily variations in GWL several days into the future for the chosen study region, thus alleviating the need for employing sophisticated process-based numerical models with complicated model structure configurations.

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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 probit model (OPM) were evaluated as statistical models, while random forest (RF) and XGBoost were evaluated as ML models. For DL, multi-layer perceptron (MLP) and TabNet were evaluated. The performance of these models varied across severity levels, with property damage only (PDO) predictions performing the best and severe injury predictions performing the worst. The TabNet model performed best in predicting severe injury and PDO crashes, while RF was the most effective in predicting moderate injury crashes. However, all models struggled with severe injury classification, indicating the potential need for model refinement and exploration of other techniques. Hence, the choice of model depends on the specific application and the relative costs of false negatives and false positives. This conclusion underscores the need for further research in this area to improve the prediction accuracy of severe and moderate injury incidents, ultimately improving available data that can be used to increase road safety.

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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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