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Добірка наукової літератури з теми "Dynamic machine learning"

Автор: Grafiati

Опубліковано: 22 лютого 2025

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Статті в журналах з теми "Dynamic machine learning"

Tashev, Sarvar Norboboyevich. "DYNAMIC PACKET FILTERING USING MACHINE LEARNING METHODS." American Journal of Applied Science and Technology 4, no. 10 (October 1, 2024): 69–79. http://dx.doi.org/10.37547/ajast/volume04issue10-11.

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With the emergence of the Internet, cyber-attacks and threats have become significant issues. Traditional manual network monitoring and rule-based packet filtering methods have become labor-intensive and less effective in combating attacks. Filtering packets based solely on payload and pattern matching is also inefficient. There is a need for a dynamic model capable of learning packet filtering rules. This article proposes a packet filtering model using Neural Networks. After developing the model classified with training and validation data, it can be utilized to support dynamic packet filtering. The proposed model allows filtering packets not only based on static rules but also considering IP packet attributes and rules learned by the model in advance. The model takes into account payloads and other IP packet attributes for filtering. It can automatically update firewall rules to enhance security.

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Lee, Peiyuan, Zhigang Huang, and Yong Tang. "Trend Prediction Model of Asian Stock Market Volatility Dynamic Relationship Based on Machine Learning." Security and Communication Networks 2022 (October 3, 2022): 1–10. http://dx.doi.org/10.1155/2022/5972698.

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With the rapid development of the global economy and stock market, stock investment has become a common investment method. People’s research on stock forecasting has never stopped. Accurately predicting the dynamic fluctuation of stocks can bring rich investment returns to investors while avoiding investment risks. Machine learning is a relatively important research field in artificial intelligence today, which is mainly used to study how to use machines to simulate human activities. In recent years, with the continuous development of the economy, machine learning under artificial intelligence has developed comprehensively in different fields, and it has been widely used in the field of the financial economy. Machine learning under artificial intelligence is currently widely used in stock market volatility dynamics and related research. This paper applied machine learning to the prediction of the dynamic relationship of Asian stock market volatility and established a model for predicting the dynamic relationship of stock market volatility under machine learning. By using statistical theory, linear support vector machines, generalizable bounds, and other algorithms, it provides the theoretical basis and feasibility analysis for the model. Through investigation and research, this paper found that compared with ordinary forecasting model methods, the stock volatility dynamic trend forecasting model based on machine learning has a relatively complete forecasting effect, and the accuracy of the machine learning forecasting model was up to 52%. The lowest was 39%, the average prediction accuracy was 46.5%, and the accuracy was improved by 16.8%. This showed that the introduction of machine learning prediction models in the dynamic prediction model of Asian stock volatility is relatively successful.

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Chen, Hao, Tianlei Wang, Jiuwen Cao, Pierre-Paul Vidal, and Yimin Yang. "Dynamic Quaternion Extreme Learning Machine." IEEE Transactions on Circuits and Systems II: Express Briefs 68, no. 8 (August 2021): 3012–16. http://dx.doi.org/10.1109/tcsii.2021.3067014.

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Zheng, Li-E., Shrishti Barethiya, Erik Nordquist, and Jianhan Chen. "Machine Learning Generation of Dynamic Protein Conformational Ensembles." Molecules 28, no. 10 (May 12, 2023): 4047. http://dx.doi.org/10.3390/molecules28104047.

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Machine learning has achieved remarkable success across a broad range of scientific and engineering disciplines, particularly its use for predicting native protein structures from sequence information alone. However, biomolecules are inherently dynamic, and there is a pressing need for accurate predictions of dynamic structural ensembles across multiple functional levels. These problems range from the relatively well-defined task of predicting conformational dynamics around the native state of a protein, which traditional molecular dynamics (MD) simulations are particularly adept at handling, to generating large-scale conformational transitions connecting distinct functional states of structured proteins or numerous marginally stable states within the dynamic ensembles of intrinsically disordered proteins. Machine learning has been increasingly applied to learn low-dimensional representations of protein conformational spaces, which can then be used to drive additional MD sampling or directly generate novel conformations. These methods promise to greatly reduce the computational cost of generating dynamic protein ensembles, compared to traditional MD simulations. In this review, we examine recent progress in machine learning approaches towards generative modeling of dynamic protein ensembles and emphasize the crucial importance of integrating advances in machine learning, structural data, and physical principles to achieve these ambitious goals.

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Kumar, K. Bindu, K. R. Remesh Babu, Ramesh Unnikrishnan, and U. Sangeetha. "Dynamic Behaviour Modelling of Magneto-Rheological Fluid Damper Using Machine Learning." Indian Journal Of Science And Technology 16, no. 45 (December 13, 2023): 4233–43. http://dx.doi.org/10.17485/ijst/v16i45.1669.

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Lennie, Matthew, Johannes Steenbuck, Bernd R. Noack, and Christian Oliver Paschereit. "Cartographing dynamic stall with machine learning." Wind Energy Science 5, no. 2 (June 29, 2020): 819–38. http://dx.doi.org/10.5194/wes-5-819-2020.

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Abstract. Once stall has set in, lift collapses, drag increases and then both of these forces will fluctuate strongly. The result is higher fatigue loads and lower energy yield. In dynamic stall, separation first develops from the trailing edge up the leading edge. Eventually the shear layer rolls up, and then a coherent vortex forms and then sheds downstream with its low-pressure core causing a lift overshoot and moment drop. When 50+ experimental cycles of lift or pressure values are averaged, this process appears clear and coherent in flow visualizations. Unfortunately, stall is not one clean process but a broad collection of processes. This means that the analysis of separated flows should be able to detect outliers and analyze cycle-to-cycle variations. Modern data science and machine learning can be used to treat separated flows. In this study, a clustering method based on dynamic time warping is used to find different shedding behaviors. This method captures the fact that secondary and tertiary vorticity vary strongly, and in static stall with surging flow the flow can occasionally reattach. A convolutional neural network was used to extract dynamic stall vorticity convection speeds and phases from pressure data. Finally, bootstrapping was used to provide best practices regarding the number of experimental repetitions required to ensure experimental convergence.

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Starzyk, J. A., and F. Wang. "Dynamic Probability Estimator for Machine Learning." IEEE Transactions on Neural Networks 15, no. 2 (March 2004): 298–308. http://dx.doi.org/10.1109/tnn.2004.824254.

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Dubach, Christophe, Timothy M. Jones, and Edwin V. Bonilla. "Dynamic microarchitectural adaptation using machine learning." ACM Transactions on Architecture and Code Optimization 10, no. 4 (December 2013): 1–28. http://dx.doi.org/10.1145/2541228.2541238.

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Yadav, Ram Ashish. "Dynamic Playlist Generation using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (May 10, 2024): 1–5. http://dx.doi.org/10.55041/ijsrem32579.

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This study investigates the application of machine learning algorithms for dynamic playlist generation in the context of music recommendation systems. The traditional method of creating playlists frequently depends on immutable standards, like artist or genre, which might not adequately represent the unique and dynamic character of personal preferences. On the other hand, our suggested system makes use of machine learning methods to examine user behavior, preferences, and contextual elements in order to create playlists that are dynamically created and customized to each user’s individual preferences. In order to train machine learning models, the study collects user interaction data, such as listening history, skip patterns, and user feedback. To extract patterns and relationships from the data, a variety of algorithms are used, including hybrid models, content-based filtering, and collaborative filtering. After that, the models are incorporated into a dynamic playlist creation system that can eventually adjust to changing user preferences.Our test findings show how well the suggested strategy works to improve user experience by offering more interesting and customized playlists. With its ability to adjust to changing user preferences and contextual cues, the dynamic playlist generation system provides a smooth and pleasurable music discovery experience. We also talk about possible enhancements, implementation difficulties, and deployment considerations in the real world.This work adds to the ongoing efforts to improve music recommendation systems by demonstrating how machine learning can be used to develop more responsive and intelligent playlist generation systems. The results highlight how crucial customized experiences are in the constantly changing world of digital music consumption. Index Terms—hybrid mode, content-based filtering, intelligent playlist, music

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WANG Peng and MAIMAITINIYAZI Maimaitiabudula. "Quantum Dynamics of Machine Learning." Acta Physica Sinica 74, no. 6 (2025): 0. https://doi.org/10.7498/aps.74.20240999.

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To address the current lack of rigorous theoretical models in the machine learning process, this paper adopts the quantum dynamic method to model the iterative motion process of machine learning based on the principles of first-principles thinking. This approach treats the iterative evolution of algorithms as a physical motion process, defines a generalized objective function in the parameter space of machine learning algorithms, and views the iterative process of machine learning as the process of seeking the optimal value for this generalized objective function. In physical terms, this process corresponds to the system reaching its ground energy state. Since the dynamic equation of a quantum system is the Schrödinger equation, by treating the generalized objective function as the potential energy term in the Schrödinger equation, we can obtain the quantum dynamic equation that describes the iterative process of machine learning. The process of machine learning is thus the process of seeking the ground energy state of the quantum system constrained by a generalized objective function. The quantum dynamic equation for machine learning transforms the iterative process into a time-dependent partial differential equation for precise mathematical representation, allowing for the study of the iterative process of machine learning using physical and mathematical theories. This provides theoretical support for implementing the iterative process of machine learning using quantum computers. To further apply the quantum dynamic equation to explain the iterative process of machine learning on classical computers, the Wick rotation is used to convert the quantum dynamic equation into a thermodynamic equation, demonstrating the convergence of the time evolution process in machine learning. As time approaches infinity, the system will converge to the ground energy state. Since an analytical expression cannot be given for the generalized objective function in the parameter space, Taylor expansion is used to approximate the generalized objective function. Under the zero-order Taylor approximation of the generalized objective function, the quantum dynamic equation and thermodynamic equation for machine learning degrade into the free-particle equation and diffusion equation, respectively. This result indicates that the most basic dynamic processes during the iteration of machine learning on quantum and classical computers are wave packet dispersion and diffusion, respectively. This result explains, from a dynamic perspective, the basic principles of diffusion models that have been successfully applied in the field of generative neural networks in recent years. Diffusion models indirectly realize the thermal diffusion process in the parameter space by adding and removing Gaussian noise to images, thereby optimizing the generalized objective function in the parameter space. The diffusion process is the dynamic process under the zero-order approximation of the generalized objective function. Meanwhile, using the thermodynamic equation of machine learning, we also derived the Softmax and Sigmoid functions commonly used in artificial intelligence. These results show that the quantum dynamic method is an effective theoretical approach for studying the iterative process of machine learning, providing rigorous mathematical and physical models for studying the iterative process of machine learning on both quantum and classical computers.

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Більше джерел

Дисертації з теми "Dynamic machine learning"

Höstklint, Niklas, and Jesper Larsson. "Dynamic Test Case Selection using Machine Learning." Thesis, KTH, Hälsoinformatik och logistik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-296634.

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Testing code is a vital part at any software producing company, to ensure no faulty code slips through that could have detrimental consequences. At Ericsson, testing code before publishing is a very costly process which can take several hours. Currently, every single test is run for all submitted code. This report aims to address the issue by building a machine learning model that determines which tests need to be run, so that unnecessary tests are left out, saving time and resources. It is however important to find the failures, as having certain failures pass through into production could have all types of economic, environmental and social consequences. The result shows that there is great potential in several different types of models. A Linear Regression model found 92% of all failures within running 25% of all test categories. The linear model however plateaus before finding the final failures. If finding 100% of failures is essential, a Support Vector Regression model proved the most efficient as it was the only model to find 100% of failures within 90% of test categories being run.
Testning av kod är en avgörande del för alla mjukvaruproducerande företag, för att säkerställa att ingen felaktig kod som kan ha skadlig påverkan publiceras. Hos Ericsson är testning av kod innan det ska publiceras en väldigt dyr process som kan ta flera timmar. Vid tiden denna rapport skrivs så körs varenda test för all inlämnad kod. Denna rapport har som mål att lösa/reducera problemet genom att bygga en modell med maskininlärning som avgör vilka tester som ska köras, så onödiga tester lämnas utanför vilket i sin tur sparar tid och resurser. Dock är det viktigt att hitta alla misslyckade tester, eftersom att tillåta dessa passera till produktionen kan innebära alla möjliga olika ekonomiska, miljömässiga och sociala konsekvenser. Resultaten visar att det finns stor potential i flera olika typer av modeller. En linjär regressionsmodell hittade 92% av alla fel inom att 25% av alla test kategorier körts. Den linjära modellen träffar dock en platå innan den hittar de sista felen. Om det är essentiellt att hitta 100% av felen, så visade sig en support vector regressionsmodell vara mest effektiv, då den var den enda modellen som lyckades hitta 100% av alla fel inom att 90% alla test kategorier hade körts.

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Rowe, Michael C. (Michael Charles). "A Machine Learning Method Suitable for Dynamic Domains." Thesis, University of North Texas, 1996. https://digital.library.unt.edu/ark:/67531/metadc278720/.

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The efficacy of a machine learning technique is domain dependent. Some machine learning techniques work very well for certain domains but are ill-suited for other domains. One area that is of real-world concern is the flexibility with which machine learning techniques can adapt to dynamic domains. Currently, there are no known reports of any system that can learn dynamic domains, short of starting over (i.e., re-running the program). Starting over is neither time nor cost efficient for real-world production environments. This dissertation studied a method, referred to as Experience Based Learning (EBL), that attempts to deal with conditions related to learning dynamic domains. EBL is an extension of Instance Based Learning methods. The hypothesis of the study related to this research was that the EBL method would automatically adjust to domain changes and still provide classification accuracy similar to methods that require starting over. To test this hypothesis, twelve widely studied machine learning datasets were used. A dynamic domain was simulated by presenting these datasets in an uninterrupted cycle of train, test, and retrain. The order of the twelve datasets and the order of records within each dataset were randomized to control for order biases in each of ten runs. As a result, these methods provided datasets that represent extreme levels of domain change. Using the above datasets, EBL's mean classification accuracies for each dataset were compared to the published static domain results of other machine learning systems. The results indicated that the EBL's system performance was not statistically different (p>0.30) from the other machine learning methods. These results indicate that the EBL system is able to adjust to an extreme level of domain change and yet produce satisfactory results. This finding supports the use of the EBL method in real-world environments that incur rapid changes to both variables and values.

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Narmack, Kirilll. "Dynamic Speed Adaptation for Curves using Machine Learning." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233545.

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The vehicles of tomorrow will be more sophisticated, intelligent and safe than the vehicles of today. The future is leaning towards fully autonomous vehicles. This degree project provides a data driven solution for a speed adaptation system that can be used to compute a vehicle speed for curves, suitable for the underlying driving style of the driver, road properties and weather conditions. A speed adaptation system for curves aims to compute a vehicle speed suitable for curves that can be used in Advanced Driver Assistance Systems (ADAS) or in Autonomous Driving (AD) applications. This degree project was carried out at Volvo Car Corporation. Literature in the field of speed adaptation systems and factors affecting the vehicle speed in curves was reviewed. Naturalistic driving data was both collected by driving and extracted from Volvo's data base and further processed. A novel speed adaptation system for curves was invented, implemented and evaluated. This speed adaptation system is able to compute a vehicle speed suitable for the underlying driving style of the driver, road properties and weather conditions. Two different artificial neural networks and two mathematical models were used to compute the desired vehicle speed in curves. These methods were compared and evaluated.
Morgondagens fordon kommer att vara mer sofistikerade, intelligenta och säkra än dagens fordon. Framtiden lutar mot fullständigt autonoma fordon. Detta examensarbete tillhandahåller en datadriven lösning för ett hastighetsanpassningssystem som kan beräkna ett fordons hastighet i kurvor som är lämpligt för förarens körstil, vägens egenskaper och rådande väder. Ett hastighetsanpassningssystem för kurvor har som mål att beräkna en fordonshastighet för kurvor som kan användas i Advanced Driver Assistance Systems (ADAS) eller Autonomous Driving (AD) applikationer. Detta examensarbete utfördes på Volvo Car Corporation. Litteratur kring hastighetsanpassningssystem samt faktorer som påverkar ett fordons hastighet i kurvor studerades. Naturalistisk bilkörningsdata samlades genom att köra bil samt extraherades från Volvos databas och bearbetades. Ett nytt hastighetsanpassningssystem uppfanns, implementerades samt utvärderades. Hastighetsanpassningssystemet visade sig vara kapabelt till att beräkna en lämplig fordonshastighet för förarens körstil under rådande väderförhållanden och vägens egenskaper. Två olika artificiella neuronnätverk samt två matematiska modeller användes för att beräkna fordonets hastighet. Dessa metoder jämfördes och utvärderades.

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Sîrbu, Adela-Maria. "Dynamic machine learning for supervised and unsupervised classification." Thesis, Rouen, INSA, 2016. http://www.theses.fr/2016ISAM0002/document.

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La direction de recherche que nous abordons dans la thèse est l'application des modèles dynamiques d'apprentissage automatique pour résoudre les problèmes de classification supervisée et non supervisée. Les problèmes particuliers que nous avons décidé d'aborder dans la thèse sont la reconnaissance des piétons (un problème de classification supervisée) et le groupement des données d'expression génétique (un problème de classification non supervisée). Les problèmes abordés sont représentatifs pour les deux principaux types de classification et sont très difficiles, ayant une grande importance dans la vie réelle. La première direction de recherche que nous abordons dans le domaine de la classification non supervisée dynamique est le problème de la classification dynamique des données d'expression génétique. L'expression génétique représente le processus par lequel l'information d'un gène est convertie en produits de gènes fonctionnels : des protéines ou des ARN ayant différents rôles dans la vie d'une cellule. La technologie des micro-réseaux moderne est aujourd'hui utilisée pour détecter expérimentalement les niveaux d'expression de milliers de gènes, dans des conditions différentes et au fil du temps. Une fois que les données d'expression génétique ont été recueillies, l'étape suivante consiste à analyser et à extraire des informations biologiques utiles. L'un des algorithmes les plus populaires traitant de l'analyse des données d'expression génétique est le groupement, qui consiste à diviser un certain ensemble en groupes, où les composants de chaque groupe sont semblables les uns aux autres données. Dans le cas des ensembles de données d'expression génique, chaque gène est représenté par ses valeurs d'expression (caractéristiques), à des points distincts dans le temps, dans les conditions contrôlées. Le processus de regroupement des gènes est à la base des études génomiques qui visent à analyser les fonctions des gènes car il est supposé que les gènes qui sont similaires dans leurs niveaux d'expression sont également relativement similaires en termes de fonction biologique. Le problème que nous abordons dans le sens de la recherche de classification non supervisée dynamique est le regroupement dynamique des données d'expression génique. Dans notre cas, la dynamique à long terme indique que l'ensemble de données ne sont pas statiques, mais elle est sujette à changement. Pourtant, par opposition aux approches progressives de la littérature, où l'ensemble de données est enrichie avec de nouveaux gènes (instances) au cours du processus de regroupement, nos approches abordent les cas lorsque de nouvelles fonctionnalités (niveaux d'expression pour de nouveaux points dans le temps) sont ajoutés à la gènes déjà existants dans l'ensemble de données. À notre connaissance, il n'y a pas d'approches dans la littérature qui traitent le problème de la classification dynamique des données d'expression génétique, définis comme ci-dessus. Dans ce contexte, nous avons introduit trois algorithmes de groupement dynamiques que sont capables de gérer de nouveaux niveaux d'expression génique collectés, en partant d'une partition obtenue précédente, sans la nécessité de ré-exécuter l'algorithme à partir de zéro. L'évaluation expérimentale montre que notre méthode est plus rapide et plus précis que l'application de l'algorithme de classification à partir de zéro sur la fonctionnalité étendue ensemble de données
The research direction we are focusing on in the thesis is applying dynamic machine learning models to salve supervised and unsupervised classification problems. We are living in a dynamic environment, where data is continuously changing and the need to obtain a fast and accurate solution to our problems has become a real necessity. The particular problems that we have decided te approach in the thesis are pedestrian recognition (a supervised classification problem) and clustering of gene expression data (an unsupervised classification. problem). The approached problems are representative for the two main types of classification and are very challenging, having a great importance in real life.The first research direction that we approach in the field of dynamic unsupervised classification is the problem of dynamic clustering of gene expression data. Gene expression represents the process by which the information from a gene is converted into functional gene products: proteins or RNA having different roles in the life of a cell. Modern microarray technology is nowadays used to experimentally detect the levels of expressions of thousand of genes, across different conditions and over time. Once the gene expression data has been gathered, the next step is to analyze it and extract useful biological information. One of the most popular algorithms dealing with the analysis of gene expression data is clustering, which involves partitioning a certain data set in groups, where the components of each group are similar to each other. In the case of gene expression data sets, each gene is represented by its expression values (features), at distinct points in time, under the monitored conditions. The process of gene clustering is at the foundation of genomic studies that aim to analyze the functions of genes because it is assumed that genes that are similar in their expression levels are also relatively similar in terms of biological function.The problem that we address within the dynamic unsupervised classification research direction is the dynamic clustering of gene expression data. In our case, the term dynamic indicates that the data set is not static, but it is subject to change. Still, as opposed to the incremental approaches from the literature, where the data set is enriched with new genes (instances) during the clustering process, our approaches tackle the cases when new features (expression levels for new points in time) are added to the genes already existing in the data set. To our best knowledge, there are no approaches in the literature that deal with the problem of dynamic clustering of gene expression data, defined as above. In this context we introduced three dynamic clustering algorithms which are able to handle new collected gene expression levels, by starting from a previous obtained partition, without the need to re-run the algorithm from scratch. Experimental evaluation shows that our method is faster and more accurate than applying the clustering algorithm from scratch on the feature extended data set

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Boulegane, Dihia. "Machine learning algorithms for dynamic Internet of Things." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAT048.

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Анотація:

La croissance rapide de l’Internet des Objets (IdO) ainsi que la prolifération des capteurs ont donné lieu à diverses sources de données qui génèrent continuellement de grandes quantités de données et à une grande vitesse sous la forme de flux. Ces flux sont essentiels dans le processus de prise de décision dans différents secteurs d’activité et ce grâce aux techniques d’intelligence artificielle et d’apprentissage automatique afin d’extraire des connaissances précieuses et les transformer en actions pertinentes. Par ailleurs, les données sont souvent associées à un indicateur temporel, appelé flux de données temporel qui est défini comme étant une séquence infinie d’observations capturées à intervalles réguliers, mais pas nécessairement. La prévision est une tâche complexe dans le domaine de l’IA et vise à comprendre le processus générant les observations au fil du temps sur la base d’un historique de données afin de prédire le comportement futur. L’apprentissage incremental et adaptatif est le domaine de recherche émergeant dédié à l’analyse des flux de données. La thèse se penche sur les méthodes d’ensemble qui fusionnent de manière dynamique plusieurs modèles prédictifs accomplissant ainsi des résultats compétitifs malgré leur coût élevé en termes de mémoire et de temps de calcul. Nous étudions différentes approches pour estimer la performance de chaque modèle de prévision individuel compris dans l’ensemble en fonction des données en introduisant de nouvelles méthodes basées sur le fenêtrage et le méta-apprentissage. Nous proposons différentes méthodes de sélection qui visent à constituer un comité de modèles précis et divers. Les prédictions de ces modèles sont ensuite pondérées et agrégées. La deuxième partie de la thèse traite de la compression des méthodes d’ensemble qui vise à produire un modèle individuel afin d’imiter le comportement d’un ensemble complexe tout en réduisant son coût. Pour finir, nous présentons ”Real-Time Machine Learning Competition on Data Streams”, dans le cadre de BigDataCup Challenge de la conférence IEEE Big Data 2019 ainsi que la plateforme dédiée SCALAR
With the rapid growth of Internet-of-Things (IoT) devices and sensors, sources that are continuously releasing and curating vast amount of data at high pace in the form of stream. The ubiquitous data streams are essential for data driven decisionmaking in different business sectors using Artificial Intelligence (AI) and Machine Learning (ML) techniques in order to extract valuable knowledge and turn it to appropriate actions. Besides, the data being collected is often associated with a temporal indicator, referred to as temporal data stream that is a potentially infinite sequence of observations captured over time at regular intervals, but not necessarily. Forecasting is a challenging tasks in the field of AI and aims at understanding the process generating the observations over time based on past data in order to accurately predict future behavior. Stream Learning is the emerging research field which focuses on learning from infinite and evolving data streams. The thesis tackles dynamic model combination that achieves competitive results despite their high computational costs in terms of memory and time. We study several approaches to estimate the predictive performance of individual forecasting models according to the data and contribute by introducing novel windowing and meta-learning based methods to cope with evolving data streams. Subsequently, we propose different selection methods that aim at constituting a committee of accurate and diverse models. The predictions of these models are then weighted and aggregated. The second part addresses model compression that aims at building a single model to mimic the behavior of a highly performing and complex ensemble while reducing its complexity. Finally, we present the first streaming competition ”Real-time Machine Learning Competition on Data Streams”, at the IEEE Big Data 2019 conference, using the new SCALAR platform

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Brun, Yuriy 1981. "Software fault identification via dynamic analysis and machine learning." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/17939.

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Анотація:

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2003.
Includes bibliographical references (p. 65-67).
I propose a technique that identifies program properties that may indicate errors. The technique generates machine learning models of run-time program properties known to expose faults, and applies these models to program properties of user-written code to classify and rank properties that may lead the user to errors. I evaluate an implementation of the technique, the Fault Invariant Classifier, that demonstrates the efficacy of the error finding technique. The implementation uses dynamic invariant detection to generate program properties. It uses support vector machine and decision tree learning tools to classify those properties. Given a set of properties produced by the program analysis, some of which are indicative of errors, the technique selects a subset of properties that are most likely to reveal an error. The experimental evaluation over 941,000 lines of code, showed that a user must examine only the 2.2 highest-ranked properties for C programs and 1.7 for Java programs to find a fault-revealing property. The technique increases the relevance (the concentration of properties that reveal errors) by a factor of 50 on average for C programs, and 4.8 for Java programs.
by Yuriy Brun.
M.Eng.

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Emani, Murali Krishna. "Adaptive parallelism mapping in dynamic environments using machine learning." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/10469.

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Modern day hardware platforms are parallel and diverse, ranging from mobiles to data centers. Mainstream parallel applications execute in the same system competing for resources. This resource contention may lead to a drastic degradation in a program’s performance. In addition, the execution environment composed of workloads and hardware resources, is dynamic and unpredictable. Efficient matching of program parallelism to machine parallelism under uncertainty is hard. The mapping policies that determine the optimal allocation of work to threads should anticipate these variations. This thesis proposes solutions to the mapping of parallel programs in dynamic environments. It employs predictive modelling techniques to determine the best degree of parallelism. Firstly, this thesis proposes a machine learning-based model to determine the optimal thread number for a target program co-executing with varying workloads. For this purpose, this offline trained model uses static code features and dynamic runtime information as input. Next, this thesis proposes a novel solution to monitor the proposed offline model and adjust its decisions in response to the environment changes. It develops a second predictive model for determining how the future environment should be, if the current thread prediction was optimal. Depending on how close this prediction was to the actual environment, the predicted thread numbers are adjusted. Furthermore, considering the multitude of potential execution scenarios where no single policy is best suited in all cases, this work proposes an approach based on the idea of mixture of experts. It considers a number of offline experts or mapping policies, each specialized for a given scenario, and learns online the best expert that is optimal for the current execution. When evaluated on highly dynamic executions, these solutions are proven to surpass default, state-of-art adaptive and analytic approaches.

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Dahlberg, Love. "Dynamic algorithm selection for machine learning on time series." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-72576.

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We present a software that can dynamically determine what machine learning algorithm is best to use in a certain situation given predefined traits. The produced software uses ideal conditions to exemplify how such a solution could function. The software is designed to train a selection algorithm that can predict the behavior of the specified testing algorithms to derive which among them is the best. The software is used to summarize and evaluate a collection of selection algorithm predictions to determine which testing algorithm was the best during that entire period. The goal of this project is to provide a prediction evaluation software solution can lead towards a realistic implementation.

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Caceres, Carlos Antonio. "Machine Learning Techniques for Gesture Recognition." Thesis, Virginia Tech, 2014. http://hdl.handle.net/10919/52556.

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Classification of human movement is a large field of interest to Human-Machine Interface researchers. The reason for this lies in the large emphasis humans place on gestures while communicating with each other and while interacting with machines. Such gestures can be digitized in a number of ways, including both passive methods, such as cameras, and active methods, such as wearable sensors. While passive methods might be the ideal, they are not always feasible, especially when dealing in unstructured environments. Instead, wearable sensors have gained interest as a method of gesture classification, especially in the upper limbs. Lower arm movements are made up of a combination of multiple electrical signals known as Motor Unit Action Potentials (MUAPs). These signals can be recorded from surface electrodes placed on the surface of the skin, and used for prosthetic control, sign language recognition, human machine interface, and a myriad of other applications. In order to move a step closer to these goal applications, this thesis compares three different machine learning tools, which include Hidden Markov Models (HMMs), Support Vector Machines (SVMs), and Dynamic Time Warping (DTW), to recognize a number of different gestures classes. It further contrasts the applicability of these tools to noisy data in the form of the Ninapro dataset, a benchmarking tool put forth by a conglomerate of universities. Using this dataset as a basis, this work paves a path for the analysis required to optimize each of the three classifiers. Ultimately, care is taken to compare the three classifiers for their utility against noisy data, and a comparison is made against classification results put forth by other researchers in the field. The outcome of this work is 90+ % recognition of individual gestures from the Ninapro dataset whilst using two of the three distinct classifiers. Comparison against previous works by other researchers shows these results to outperform all other thus far. Through further work with these tools, an end user might control a robotic or prosthetic arm, or translate sign language, or perhaps simply interact with a computer.
Master of Science

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Botlani-Esfahani, Mohsen. "Modeling of Dynamic Allostery in Proteins Enabled by Machine Learning." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6804.

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Regulation of protein activity is essential for normal cell functionality. Many proteins are regulated allosterically, that is, with spatial gaps between stimulation and active sites. Biological stimuli that regulate proteins allosterically include, for example, ions and small molecules, post-translational modifications, and intensive state-variables like temperature and pH. These effectors can not only switch activities on-and-off, but also fine-tune activities. Understanding the underpinnings of allostery, that is, how signals are propagated between distant sites, and how transmitted signals manifest themselves into regulation of protein activity, has been one of the central foci of biology for over 50 years. Today, the importance of such studies goes beyond basic pedagogical interests as bioengineers seek design features to control protein function for myriad purposes, including design of nano-biosensors, drug delivery vehicles, synthetic cells and organic-synthetic interfaces. The current phenomenological view of allostery is that signaling and activity control occur via effector-induced changes in protein conformational ensembles. If the structures of two states of a protein differ from each other significantly, then thermal fluctuations can be neglected and an atomically detailed model of regulation can be constructed in terms of how their minimum-energy structures differ between states. However, when the minimum-energy structures of states differ from each other only marginally and the difference is comparable to thermal fluctuations, then a mechanistic model cannot be constructed solely on the basis of differences in protein structure. Understanding the mechanism of dynamic allostery requires not only assessment of high-dimensional conformational ensembles of the various individual states, including inactive, transition and active states, but also relationships between them. This challenge faces many diverse protein families, including G-protein coupled receptors, immune cell receptors, heat shock proteins, nuclear transcription factors and viral attachment proteins, whose mechanisms, despite numerous studies, remain poorly understood. This dissertation deals with the development of new methods that significantly boost the applicability of molecular simulation techniques to probe dynamic allostery in these proteins. Specifically, it deals with two different methods, one to obtain quantitative estimates for subtle differences between conformational ensembles, and the other to relate conformational ensemble differences to allosteric signal communication. Both methods are enabled by a new application of the mathematical framework of machine learning. These methods are applied to (a) identify specific effects of employed force fields on conformational ensembles, (b) compare multiple ensembles against each other for determination of common signaling pathways induced by different effectors, (c) identify the effects of point mutations on conformational ensemble shifts in proteins, and (d) understand the mechanism of dynamic allostery in a PDZ domain. These diverse applications essentially demonstrate the generality of the developed approaches, and specifically set the foundation for future studies on PDZ domains and viral attachment proteins.

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Книги з теми "Dynamic machine learning"

Gultekin, San. Dynamic Machine Learning with Least Square Objectives. [New York, N.Y.?]: [publisher not identified], 2019.

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

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IEEE, International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.

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Hinders, Mark K. Intelligent Feature Selection for Machine Learning Using the Dynamic Wavelet Fingerprint. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-49395-0.

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IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning (1st 2007 Honolulu, Hawaii). 2007 IEEE Symposium on Approximate Dynamic Programming and Reinforcement Learning: Honolulu, HI, 1-5 April 2007. Piscataway, NJ: IEEE, 2007.

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Achmad, Widodo, ed. Introduction of intelligent machine fault diagnosis and prognosis. New York: Nova Science Publishers, 2009.

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Russell, David W. The BOXES Methodology: Black Box Dynamic Control. London: Springer London, 2012.

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Hayes-Roth, Barbara. An architecture for adaptive intelligent systems. Stanford, Calif: Stanford University, Dept. of Computer Science, 1993.

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Duriez, Thomas, Steven L. Brunton, and Bernd R. Noack. Machine Learning Control – Taming Nonlinear Dynamics and Turbulence. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-40624-4.

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Частини книг з теми "Dynamic machine learning"

Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Programming." In Encyclopedia of Machine Learning, 298–308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_237.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Systems." In Encyclopedia of Machine Learning, 308. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_239.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Bayesian Network." In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_234.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Decision Networks." In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_235.

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Webb, Geoffrey I., Johannes Fürnkranz, Johannes Fürnkranz, Johannes Fürnkranz, Geoffrey Hinton, Claude Sammut, Joerg Sander, et al. "Dynamic Memory Model." In Encyclopedia of Machine Learning, 298. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_236.

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Kakas, Antonis C., David Cohn, Sanjoy Dasgupta, Andrew G. Barto, Gail A. Carpenter, Stephen Grossberg, Geoffrey I. Webb, et al. "Approximate Dynamic Programming." In Encyclopedia of Machine Learning, 39. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_26.

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Webb, Geoffrey I., Eamonn Keogh, Risto Miikkulainen, Risto Miikkulainen, and Michele Sebag. "Neuro-Dynamic Programming." In Encyclopedia of Machine Learning, 716. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_588.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Relational Dynamic Programming." In Encyclopedia of Machine Learning, 851. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_718.

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Martin, Eric, Samuel Kaski, Fei Zheng, Geoffrey I. Webb, Xiaojin Zhu, Ion Muslea, Kai Ming Ting, et al. "Symbolic Dynamic Programming." In Encyclopedia of Machine Learning, 946–54. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_806.

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Buhmann, M. D., Prem Melville, Vikas Sindhwani, Novi Quadrianto, Wray L. Buntine, Luís Torgo, Xinhua Zhang, et al. "Real-Time Dynamic Programming." In Encyclopedia of Machine Learning, 829. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-0-387-30164-8_701.

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Тези доповідей конференцій з теми "Dynamic machine learning"

Lin, Yu Chi, and Po-Wen Chi. "Adaptive Machine Learning Model for Dynamic Field Selection." In 2024 19th Asia Joint Conference on Information Security (AsiaJCIS), 151–56. IEEE, 2024. http://dx.doi.org/10.1109/asiajcis64263.2024.00032.

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Guo, Ben, Ming-yan Wang, and Jian Zhang. "A Dynamic Fuzzy Neural Networks Controller for Dynamic Load Simulator." In 2006 International Conference on Machine Learning and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icmlc.2006.259042.

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Ying Gui, Xue-Qin Zhu, and Wen-Lin Song. "The Tracking Dynamical Particle Swarm Optimizer for dynamic environments." In 2008 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2008. http://dx.doi.org/10.1109/icmlc.2008.4621020.

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Peleshchak, Ivan, Diana Koshtura, Mykhailo Luchkevych, and Volodymyr Tymchuk. "Classification of Dynamic Objects Using a Multilayer Perceptron." In Machine Learning Workshop at CoLInS 2024. CoLInS, 2024. http://dx.doi.org/10.31110/colins/2024-1/014.

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Gaitang Wang and Ping Li. "Dynamic Adaboost ensemble extreme learning machine." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579726.

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Dhiman, Gaurav, and Tajana Rosing. "Dynamic Power Management Using Machine Learning." In 2006 IEEE/ACM International Conference on Computer Aided Design. IEEE, 2006. http://dx.doi.org/10.1109/iccad.2006.320115.

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Dhiman, Gaurav, and Tajana Simunic Rosing. "Dynamic power management using machine learning." In the 2006 IEEE/ACM international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1233501.1233656.

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Chebrolu, Chandan Sai, Chung-Horng Lung, and Samuel A. Ajila. "Dynamic Packet Filtering Using Machine Learning." In 2022 IEEE 23rd International Conference on Information Reuse and Integration for Data Science (IRI). IEEE, 2022. http://dx.doi.org/10.1109/iri54793.2022.00053.

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Zhang, Yingying, and Yue Shi. "Constructing Dynamic Honeypot Using Machine Learning." In ICCSIE 2023: 8th International Conference on Cyber Security and Information Engineering. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3617184.3618056.

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Min, Fan, Qi-He Liu, Hong-Bin Cai, and Zhong-Jian Bai. "Dynamic Discretization: A Combination Approach." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370785.

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Звіти організацій з теми "Dynamic machine learning"

Sgroi, Michael Jacobson. Dynamic and System Agnostic Malware Detection Via Machine Learning. Ames (Iowa): Iowa State University, January 2018. http://dx.doi.org/10.31274/cc-20240624-572.

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Gonzalez Pibernat, Gabriel, and Miguel Mascaró Portells. Dynamic structure of single-layer neural networks. Fundación Avanza, May 2023. http://dx.doi.org/10.60096/fundacionavanza/2392022.

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This article examines the practical applications of single hidden layer neural networks in machine learning and artificial intelligence. They have been used in diverse fields, such as finance, medicine, and autonomous vehicles, due to their simplicit

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Ao, Tommy, Brendan Donohoe, Carianne Martinez, Marcus Knudson, Dane Morgan, Mark Rodriguez, and James Lane. LDRD 226360 Final Project Report: Simulated X-ray Diffraction and Machine Learning for Optimizing Dynamic Experiment Analysis. Office of Scientific and Technical Information (OSTI), October 2022. http://dx.doi.org/10.2172/1891594.

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Bailey Bond, Robert, Pu Ren, James Fong, Hao Sun, and Jerome F. Hajjar. Physics-informed Machine Learning Framework for Seismic Fragility Analysis of Steel Structures. Northeastern University, August 2024. http://dx.doi.org/10.17760/d20680141.

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The seismic assessment of structures is a critical step to increase community resilience under earthquake hazards. This research aims to develop a Physics-reinforced Machine Learning (PrML) paradigm for metamodeling of nonlinear structures under seismic hazards using artificial intelligence. Structural metamodeling, a reduced-fidelity surrogate model to a more complex structural model, enables more efficient performance-based design and analysis, optimizing structural designs and ease the computational effort for reliability fragility analysis, leading to globally efficient designs while maintaining required levels of accuracy. The growing availability of high-performance computing has improved this analysis by providing the ability to evaluate higher order numerical models. However, more complex models of the seismic response of various civil structures demand increasing amounts of computing power. In addition, computational cost greatly increases with numerous iterations to account for optimization and stochastic loading (e.g., Monte Carlo simulations or Incremental Dynamic Analysis). To address the large computational burden, simpler models are desired for seismic assessment with fragility analysis. Physics reinforced Machine Learning integrates physics knowledge (e.g., scientific principles, laws of physics) into the traditional machine learning architectures, offering physically bounded, interpretable models that require less data than traditional methods. This research introduces a PrML framework to develop fragility curves using the combination of neural networks of domain knowledge. The first aim involves clustering and selecting ground motions for nonlinear response analysis of archetype buildings, ensuring that selected ground motions will include as few ground motions as possible while still expressing all the key representative events the structure will probabilistically experience in its lifetime. The second aim constructs structural PrML metamodels to capture the nonlinear behavior of these buildings utilizing the nonlinear Equation of Motion (EOM). Embedding physical principles, like the general form of the EOM, into the learning process will inform the system to stay within known physical bounds, resulting in interpretable results, robust inferencing, and the capability of dealing with incomplete and scarce data. The third and final aim applies the metamodels to probabilistic seismic response prediction, fragility analysis, and seismic performance factor development. The efficiency and accuracy of this approach are evaluated against existing physics-based fragility analysis methods.

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Hovakimyan, Naira, Hunmin Kim, Wenbin Wan, and Chuyuan Tao. Safe Operation of Connected Vehicles in Complex and Unforeseen Environments. Illinois Center for Transportation, August 2022. http://dx.doi.org/10.36501/0197-9191/22-016.

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Autonomous vehicles (AVs) have a great potential to transform the way we live and work, significantly reducing traffic accidents and harmful emissions on the one hand and enhancing travel efficiency and fuel economy on the other. Nevertheless, the safe and efficient control of AVs is still challenging because AVs operate in dynamic environments with unforeseen challenges. This project aimed to advance the state-of-the-art by designing a proactive/reactive adaptation and learning architecture for connected vehicles, unifying techniques in spatiotemporal data fusion, machine learning, and robust adaptive control. By leveraging data shared over a cloud network available to all entities, vehicles proactively adapted to new environments on the proactive level, thus coping with large-scale environmental changes. On the reactive level, control-barrier-function-based robust adaptive control with machine learning improved the performance around nominal models, providing performance and control certificates. The proposed research shaped a robust foundation for autonomous driving on cloud-connected highways of the future.

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Perdigão, Rui A. P., and Julia Hall. Augmented Post-Quantum Synergistic Manifold Intelligence for Complex System Dynamics and Coevolutionary Multi-Hazards. Synergistic Manifolds, December 2024. https://doi.org/10.46337/241211.

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This work seeks to unveil system dynamic predictability beyond existing data and model horizons, and beyond methodological paradigms in classical physics, machine learning and artificial intelligence, in order to increase awareness and preparedness to predict and tackle complexity and multi-hazards in a changing world, one where the coevolution among humans and nature has been reshaping the structure and functioning of our planet and the multiscale multidomain interactions within. For this purpose, we design and implement our next-generation Information Physical suite of Augmented Post-Quantum Synergistic Manifold Intelligence, harnessing the inherently physical nature of information from quantum fundamentals to augmented system dynamic intelligence for high-order complex system dynamics, including non-ergodic coevolution and emergence. Firstly, in the general physics of complex systems, from high-order quantum entanglement structures to fluid dynamical systems. Secondly, in tackling the emergence of new typologies of multi-hazards in coevolutionary multiscale multidomain spatiotemporal Earth System Dynamics, with special relevance to unprecedented challenges stemming from disruptive emerging features and in coevolutionary and synergistic interplay. These developments bring out a robust ability to predict the emergence of novel features and synergies elusive to prior record data or classical model structures, providing an augmented system dynamic intelligence to discern the complexity at play, unveiling extra predictability, spatiotemporal resolution and lead. Seamlessly integrated with our QITES and QuASI constellations, our latest advances empower researchers, practitioners and decision makers with augmented information capabilities crucial to avoid surprises and to improve readiness for novel structural dynamic features arising in our environment, in ways that are elusive to the classical systems theories, formulations, and products. In doing so, our novel solutions pave new avenues for strengthening predictability, preparedness and resilience.

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Muñoz-Martínez, Jonathan Alexander, David Orozco, and Mario A. Ramos-Veloza. Tweeting Inflation: Real-Time measures of Inflation Perception in Colombia. Banco de la República, November 2023. http://dx.doi.org/10.32468/be.1256.

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This study follows a novel approach proposed by Angelico et al. (2022) of using Twitter to measure inflation perception in in real-time in Colombia. By applying machine learning techniques, we implement two real-time indicators of inflation perception and show that both exhibit a similar dynamic pattern to that of inflation and inflation expectations for the sample period January 2015 to March 2023. Our interpretation of these results is that they suggest that our indicators are closely linked to the underlying factors driving inflation perception. Overall, this approach provides a valuable instrument to gauge public sentiment towards inflation and complements the traditional inflation expectation measures used in the inflation–targeting framework.

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Lewis, James, Aldo Romero, Oleg Prozhdo, and Marcus Hanwell. Machine-Learning for Excited-State Dynamics. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1848053.

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Ків, Арнольд Юхимович, Володимир Миколайович Соловйов, Сергій Олексійович Семеріков, Hanna B. Danylchuk, Liubov O. Kibalnyk, Andriy V. Matviychuk, Andrii M. Striuk, et al. Machine learning for prediction of emergent economy dynamics. Криворізький державний педагогічний університет, December 2021. http://dx.doi.org/10.31812/123456789/6973.

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This is an introductory text to a collection of selected papers and revised from the M3E2 2021: 9th International Conference on Monitoring, Modeling & Management of Emergent Economy, which held in Odessa National University of Economics, Odessa, Ukraine, on the May 26-28, 2021. It consists of introduction, conference review and some observations about the event and its future.

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Jääskeläinen, Emmihenna. Construction of reliable albedo time series. Finnish Meteorological Institute, September 2023. http://dx.doi.org/10.35614/isbn.9789523361782.

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A reliable satellite-based black-sky albedo time series is a crucial part of detecting changes in the climate. This thesis studies the solutions to several uncertainties impairing the quality of the black-sky albedo time series. These solutions include creating a long dynamic aerosol optical depth time series for enhancing the removal of atmospheric effects, a method to fill missing data to improve spatial and temporal coverage, and creating a function to correctly model the diurnal variation of melting snow albedo. Mathematical methods are the center pieces of the solutions found in this thesis. Creating a melting snow albedo function and the construction of an aerosol optical depth time series lean on a linear regression approach, whereas the process to fill missing values is based on gradient boosting, a machine learning method that is in turn based on decision trees. These methods reflect the basic nature of these problems as well as the need to take into account the large amounts of satellite-based data and computational resources available.

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