Relevant bibliographies by topics / Machine learning potential

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Machine learning potential'

Author: Grafiati
Published: 21 December 2024

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Journal articles on the topic "Machine learning potential"

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Mueller, Tim, Alberto Hernandez, and Chuhong Wang. "Machine learning for interatomic potential models." Journal of Chemical Physics 152, no. 5 (February 7, 2020): 050902. http://dx.doi.org/10.1063/1.5126336.

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

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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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Barbour, Dennis L., and Jan-Willem A. Wasmann. "Performance and Potential of Machine Learning Audiometry." Hearing Journal 74, no. 3 (February 26, 2021): 40,43,44. http://dx.doi.org/10.1097/01.hj.0000737592.24476.88.

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Therrien, Audrey C., Berthié Gouin-Ferland, and Mohammad Mehdi Rahimifar. "Potential of edge machine learning for instrumentation." Applied Optics 61, no. 8 (March 2, 2022): 1930. http://dx.doi.org/10.1364/ao.445798.

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Awan, Kamran H., S. Satish Kumar, and Indu Bharkavi SK. "Potential Role of Machine Learning in Oncology." Journal of Contemporary Dental Practice 20, no. 5 (2019): 529–30. http://dx.doi.org/10.5005/jp-journals-10024-2551.

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Dral, Pavlo O., Alec Owens, Alexey Dral, and Gábor Csányi. "Hierarchical machine learning of potential energy surfaces." Journal of Chemical Physics 152, no. 20 (May 29, 2020): 204110. http://dx.doi.org/10.1063/5.0006498.

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Wu, Yuexiang. "Potential pulsars prediction based on machine learning." Theoretical and Natural Science 12, no. 1 (November 17, 2023): 193–201. http://dx.doi.org/10.54254/2753-8818/12/20230466.

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The search for potential pulsars is a difficult job because of the complex nature of the signals and the vast amounts of data involved. In the last few years, a lot of researchers have tried to use machine learning to deal with complex data. This essay examines how machine learning could help to identify potential pulsars, exploring the various types of algorithms and the challenges and limitations associated with this approach. The essay mainly explored three themes: the training of 5 algorithms for the identification of pulsars, the improvement of 2 algorithms by adjusting parameters, and the simplification of the data to improve the processing speed and performance of the algorithms on prediction. All 5 algorithms reached great accuracy after adjustment and the simplification of the input data can help to boost the prediction time and accuracy for future research about pulsars. The essay highlights the need for further research in this area, as machine learning has demonstrated strong potential for pulsar prediction. By analyzing the results of several previous studies, this essay underscores the importance of machine learning as an approach for predicting potential pulsars and made improvements to the performance of current algorithms by adjusting parameters and simplifying the data.
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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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Zelinska, Snizhana. "Machine learning: technologies and potential application at mining companies." E3S Web of Conferences 166 (2020): 03007. http://dx.doi.org/10.1051/e3sconf/202016603007.

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Implementation of machine learning systems is currently one of the most sought-after spheres of human activities at the interface of information technologies, mathematical analysis and statistics. Machine learning technologies are penetrating into our life through applied software created with the help of artificial intelligence algorithms. It is obvious that machine learning technologies will be developing fast and becoming part of the human information space both in our everyday life and in professional activities. However, building of machine learning systems requires great labour contribution of specialists in the sphere of artificial intelligence and the subject area where this technology is to be applied. The article considers technologies and potential application of machine learning at mining companies. The article describes basic methods of machine learning: unsupervised learning, action learning, semi-supervised machine learning. The criteria are singled out to assess machine learning: operation speed; assessment time; implemented model accuracy; ease of integration; flexible deployment within the subject area; ease of practical application; result visualization. The article describes practical application of machine learning technologies and considers the dispatch system at a mining enterprise (as exemplified by the dispatch system of the mining and transportation complex “Quarry” used to increase efficiency of operating management of enterprise performance; to increase reliability and agility of mining and transportation complex performance records and monitoring. There is also a list of equipment performance data that can be stored in the database and used as a basis for processing by machine learning algorithms and obtaining new knowledge. Application of machine learning technologies in the mining industry is a promising and necessary condition for increasing mining efficiency and ensuring environmental security. Selection of the optimal process flow sheet of mining operations, selection of the optimal complex of stripping and mining equipment, optimal planning of mining operations and mining equipment performance control are some of the tasks where machine learning technologies can be used. However, despite prospectivity of machine learning technologies, this trend still remains understudied and requires further research.
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Sarkar, Soumyadip. "Quantum Machine Learning: A Review." International Journal for Research in Applied Science and Engineering Technology 11, no. 3 (March 31, 2023): 352–54. http://dx.doi.org/10.22214/ijraset.2023.49421.

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Abstract: Quantum machine learning is an emerging field that aims to leverage the unique properties of quantum computing to accelerate machine learning tasks. In this paper, we review recent advances in quantum machine learning and discuss the potential applications and challenges associated with this technology. Specifically, we examine the current state of quantum machine learning algorithms, including variational quantum algorithms, quantum neural networks, and quantum generative models. We also discuss the challenges associated with practical quantum computing resources, algorithm design, and interdisciplinary collaboration. Furthermore, we highlight the potential applications of quantum machine learning in areas such as drug discovery, speech and image recognition, financial modeling, and many others. We also examine the ethical and societal implications of this technology, including the potential impact on privacy and security. Finally, we discuss future prospects for quantum machine learning, including the potential for quantum-inspired classical algorithms and the development of error correction techniques. We conclude by emphasizing the importance of interdisciplinary collaboration in the continued advancement of this field.
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Dissertations / Theses on the topic "Machine learning potential"

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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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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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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%.
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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Ntsaluba, Kuselo Ntsika. "AI/Machine learning approach to identifying potential statistical arbitrage opportunities with FX and Bitcoin Markets." Master's thesis, Faculty of Commerce, 2019. http://hdl.handle.net/11427/31185.

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In this study, a methodology is presented where a hybrid system combining an evolutionary algorithm with artificial neural networks (ANNs) is designed to make weekly directional change forecasts on the USD by inferring a prediction using closing spot rates of three currency pairs: EUR/USD, GBP/USD and CHF/USD. The forecasts made by the genetically trained ANN are compared to those made by a new variation of the simple moving average (MA) trading strategy, tailored to the methodology, as well as a random model. The same process is then repeated for the three major cryptocurrencies namely: BTC/USD, ETH/USD and XRP/USD. The overall prediction accuracy, uptrend and downtrend prediction accuracy is analyzed for all three methods within the fiat currency as well as the cryptocurrency contexts. The best models are then evaluated in terms of their ability to convert predictive accuracy to a profitable investment given an initial investment. The best model was found to be the hybrid model on the basis of overall prediction accuracy and accrued returns.
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Skabar, Andrew Alojz. "Inductive learning techniques for mineral potential mapping." Thesis, Queensland University of Technology, 2001.

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Syed, Tahir Qasim. "Analysis of the migratory potential of cancerous cells by image preprocessing, segmentation and classification." Thesis, Evry-Val d'Essonne, 2011. http://www.theses.fr/2011EVRY0041/document.

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Ce travail de thèse s’insère dans un projet de recherche plus global dont l’objectif est d’analyser le potentiel migratoire de cellules cancéreuses. Dans le cadre de ce doctorat, on s’intéresse à l’utilisation du traitement des images pour dénombrer et classifier les cellules présentes dans une image acquise via un microscope. Les partenaires biologistes de ce projet étudient l’influence de l’environnement sur le comportement migratoire de cellules cancéreuses à partir de cultures cellulaires pratiquées sur différentes lignées de cellules cancéreuses. Le traitement d’images biologiques a déjà donné lieu `a un nombre important de publications mais, dans le cas abordé ici et dans la mesure où le protocole d’acquisition des images acquises n'était pas figé, le défi a été de proposer une chaîne de traitements adaptatifs ne contraignant pas les biologistes dans leurs travaux de recherche. Quatre étapes sont détaillées dans ce mémoire. La première porte sur la définition des prétraitements permettant d’homogénéiser les conditions d’acquisition. Le choix d’exploiter l’image des écarts-type plutôt que la luminosité est un des résultats issus de cette première partie. La deuxième étape consiste à compter le nombre de cellules présentent dans l’image. Un filtre original, nommé filtre «halo», permettant de renforcer le centre des cellules afin d’en faciliter leur comptage, a été proposé. Une étape de validation statistique de ces centres permet de fiabiliser le résultat obtenu. L’étape de segmentation des images, sans conteste la plus difficile, constitue la troisième partie de ce travail. Il s’agit ici d’extraire des «vignettes», contenant une seule cellule. Le choix de l’algorithme de segmentation a été celui de la «Ligne de Partage des Eaux», mais il a fallu adapter cet algorithme au contexte des images faisant l’objet de cette étude. La proposition d’utiliser une carte de probabilités comme données d’entrée a permis d’obtenir une segmentation au plus près des bords des cellules. Par contre cette méthode entraine une sur-segmentation qu’il faut réduire afin de tendre vers l’objectif : «une région = une cellule». Pour cela un algorithme utilisant un concept de hiérarchie cumulative basée morphologie mathématique a été développée. Il permet d’agréger des régions voisines en travaillant sur une représentation arborescente de ces régions et de leur niveau associé. La comparaison des résultats obtenus par cette méthode à ceux proposés par d’autres approches permettant de limiter la sur-segmentation a permis de prouver l’efficacité de l’approche proposée. L’étape ultime de ce travail consiste dans la classification des cellules. Trois classes ont été définies : cellules allongées (migration mésenchymateuse), cellules rondes «blebbantes» (migration amiboïde) et cellules rondes «lisses» (stade intermédiaire du mode de migration). Sur chaque vignette obtenue à la fin de l’étape de segmentation, des caractéristiques de luminosité, morphologiques et texturales ont été calculées. Une première analyse de ces caractéristiques a permis d’élaborer une stratégie de classification, à savoir séparer dans un premier temps les cellules rondes des cellules allongées, puis séparer les cellules rondes «lisses» des «blebbantes». Pour cela on divise les paramètres en deux jeux qui vont être utilisés successivement dans ces deux étapes de classification. Plusieurs algorithmes de classification ont été testés pour retenir, au final, l’utilisation de deux réseaux de neurones permettant d’obtenir plus de 80% de bonne classification entre cellules longues et cellules rondes, et près de 90% de bonne classification entre cellules rondes «lisses» et «blebbantes»
This thesis is part of a broader research project which aims to analyze the potential migration of cancer cells. As part of this doctorate, we are interested in the use of image processing to count and classify cells present in an image acquired usinga microscope. The partner biologists of this project study the influence of the environment on the migratory behavior of cancer cells from cell cultures grown on different cancer cell lines. The processing of biological images has so far resulted in a significant number of publications, but in the case discussed here, since the protocol for the acquisition of images acquired was not fixed, the challenge was to propose a chain of adaptive processing that does not constrain the biologists in their research. Four steps are detailed in this paper. The first concerns the definition of pre-processing steps to homogenize the conditions of acquisition. The choice to use the image of standard deviations rather than the brightness is one of the results of this first part. The second step is to count the number of cells present in the image. An original filter, the so-called “halo” filter, that reinforces the centre of the cells in order to facilitate counting, has been proposed. A statistical validation step of the centres affords more reliability to the result. The stage of image segmentation, undoubtedly the most difficult, constitutes the third part of this work. This is a matter of extracting images each containing a single cell. The choice of segmentation algorithm was that of the “watershed”, but it was necessary to adapt this algorithm to the context of images included in this study. The proposal to use a map of probabilities as input yielded a segmentation closer to the edges of cells. As against this method leads to an over-segmentation must be reduced in order to move towards the goal: “one region = one cell”. For this algorithm the concept of using a cumulative hierarchy based on mathematical morphology has been developed. It allows the aggregation of adjacent regions by working on a tree representation ofthese regions and their associated level. A comparison of the results obtained by this method with those proposed by other approaches to limit over-segmentation has allowed us to prove the effectiveness of the proposed approach. The final step of this work consists in the classification of cells. Three classes were identified: spread cells (mesenchymal migration), “blebbing” round cells (amoeboid migration) and “smooth” round cells (intermediate stage of the migration modes). On each imagette obtained at the end of the segmentation step, intensity, morphological and textural features were calculated. An initial analysis of these features has allowed us to develop a classification strategy, namely to first separate the round cells from spread cells, and then separate the “smooth” and “blebbing” round cells. For this we divide the parameters into two sets that will be used successively in Two the stages of classification. Several classification algorithms were tested, to retain in the end, the use of two neural networks to obtain over 80% of good classification between long cells and round cells, and nearly 90% of good Classification between “smooth” and “blebbing” round cells
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Egieyeh, Samuel Ayodele. "Computational strategies to identify, prioritize and design potential antimalarial agents from natural products." University of the Western Cape, 2015. http://hdl.handle.net/11394/5058.

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Philosophiae Doctor - PhD
Introduction: There is an exigent need to develop novel antimalarial drugs in view of the mounting disease burden and emergent resistance to the presently used drugs against the malarial parasites. A large amount of natural products, especially those used in ethnomedicine for malaria, have shown varying in-vitro antiplasmodial activities. Facilitating antimalarial drug development from this wealth of natural products is an imperative and laudable mission to pursue. However, the limited resources, high cost, low prospect and the high cost of failure during preclinical and clinical studies might militate against pursue of this mission. Chemoinformatics techniques can simulate and predict essential molecular properties required to characterize compounds thus eliminating the cost of equipment and reagents to conduct essential preclinical studies, especially on compounds that may fail during drug development. Therefore, applying chemoinformatics techniques on natural products with in-vitro antiplasmodial activities may facilitate identification and prioritization of these natural products with potential for novel mechanism of action, desirable pharmacokinetics and high likelihood for development into antimalarial drugs. In addition, unique structural features mined from these natural products may be templates to design new potential antimalarial compounds. Method: Four chemoinformatics techniques were applied on a collection of selected natural products with in-vitro antiplasmodial activity (NAA) and currently registered antimalarial drugs (CRAD): molecular property profiling, molecular scaffold analysis, machine learning and design of a virtual compound library. Molecular property profiling included computation of key molecular descriptors, physicochemical properties, molecular similarity analysis, estimation of drug-likeness, in-silico pharmacokinetic profiling and exploration of structure-activity landscape. Analysis of variance was used to assess statistical significant differences in these parameters between NAA and CRAD. Next, molecular scaffold exploration and diversity analyses were performed on three datasets (NAA, CRAD and malarial data from Medicines for Malarial Ventures (MMV)) using scaffold counts and cumulative scaffold frequency plots. Scaffolds from the NAA were compared to those from CRAD and MMV. A Scaffold Tree was also generated for all the datasets. Thirdly, machine learning approaches were used to build four regression and four classifier models from bioactivity data of NAA using molecular descriptors and molecular fingerprints. Models were built and refined by leave-one-out cross-validation and evaluated with an independent test dataset. Applicability domain (AD), which defines the limit of reliable predictability by the models, was estimated from the training dataset and validated with the test dataset. Possible chemical features associated with reported antimalarial activities of the compounds were also extracted. Lastly, virtual compound libraries were generated with the unique molecular scaffolds identified from the NAA. The virtual compounds generated were characterized by evaluating selected molecular descriptors, toxicity profile, structural diversity from CRAD and prediction of antiplasmodial activity. Results: From the molecular property profiling, a total of 1040 natural products were selected and a total of 13 molecular descriptors were analyzed. Significant differences were observed between the natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) for at least 11 of the molecular descriptors. Molecular similarity and chemical space analysis identified NAA that were structurally diverse from CRAD. Over 50% of NAA with desirable drug-like properties were identified. However, nearly 70% of NAA were identified as potentially "promiscuous" compounds. Structure-activity landscape analysis highlighted compound pairs that formed "activity cliffs". In all, prioritization strategies for the natural products with in-vitro antiplasmodial activities were proposed. The scaffold exploration and analysis results revealed that CRAD exhibited greater scaffold diversity, followed by NAA and MMV respectively. Unique scaffolds that were not contained in any other compounds in the CRAD datasets were identified in NAA. The Scaffold Tree showed the preponderance of ring systems in NAA and identified virtual scaffolds, which maybe potential bioactive compounds or elucidate the NAA possible synthetic routes. From the machine learning study, the regression and classifier models that were most suitable for NAA were identified as model tree M5P (correlation coefficient = 0.84) and Sequential Minimization Optimization (accuracy = 73.46%) respectively. The test dataset fitted into the applicability domain (AD) defined by the training dataset. The “amine” group was observed to be essential for antimalarial activity in both NAA and MMV dataset but hydroxyl and carbonyl groups may also be relevant in the NAA dataset. The results of the characterization of the virtual compound library showed significant difference (p value < 0.05) between the virtual compound library and currently registered antimalarial drugs in some molecular descriptors (molecular weight, log partition coefficient, hydrogen bond donors and acceptors, polar surface area, shape index, chiral centres, and synthetic feasibility). Tumorigenic and mutagenic substructures were not observed in a large proportion (> 90%) of the virtual compound library. The virtual compound libraries showed sufficient diversity in structures and majority were structurally diverse from currently registered antimalarial drugs. Finally, up to 70% of the virtual compounds were predicted as active antiplasmodial agents. Conclusions:Molecular property profiling of natural products with in-vitro antiplasmodial activities (NAA) and currently registered antimalarial drugs (CRAD) produced a wealth of information that may guide decisions and facilitate antimalarial drug development from natural products and led to a prioritized list of natural products with in-vitro antiplasmodial activities. Molecular scaffold analysis identified unique scaffolds and virtual scaffolds from NAA that possess desirable drug-like properties, which make them ideal starting points for molecular antimalarial drug design. The machine learning study built, evaluated and identified amply accurate regression and classifier accurate models that were used for virtual screening of natural compound libraries to mine possible antimalarial compounds without the expense of bioactivity assays. Finally, a good amount of the virtual compounds generated were structurally diverse from currently registered antimalarial drugs and potentially active antiplasmodial agents. Filtering and optimization may lead to a collection of virtual compounds with unique chemotypes that may be synthesized and added to screening deck against Plasmodium.
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Nyman, Måns, and Caner Naim Ulug. "Exploring the Potential for Machine Learning Techniques to Aid in Categorizing Electron Trajectories during Magnetic Reconnection." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-279982.

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Magnetic reconnection determines the space weather which has a direct impact on our contemporary technological systems. As such, the phenomenon has serious ramifications on humans. Magnetic reconnection is a topic which has been studied for a long time, yet still many aspects surrounding the phenomenon remain unexplored. Scientists within the field believe that the electron dynamics play an important role in magnetic reconnection. During magnetic reconnection, electrons can be accelerated to high velocities. A large number of studies have been made regarding the trajectories that these electrons exhibit and researchers in this field could easily point out what type of trajectory a specific electron exhibits given a plot of said trajectory. Attempting to do this for a more realistic number of electrons manually is however not an easy or efficient task to take on. By using Machine Learning techniques to attempt to categorize these trajectories, this process could be sped up immensely. Yet to date there has been no attempt at this. In this thesis, an attempt to answer how certain Machine Learning techniques perform in this matter was made. Principal component analysis and K-means clustering were the main methods applied after using different preprocessing methods on the given data set. The Elbow method was employed to find the optimal K-value and was complemented by Self-Organizing Maps. Silhouette coefficient was used to measure the performance of the methods. The First-centering and Mean-centering preprocessing methods yielded the two highest silhouette coefficients, thus displaying the best quantitative performances. However, inspection of the clusters pointed to a lack of perfect overlap between the classes detected by employed techniques and the classes identified in previous physics articles. Nevertheless, Machine Learning methods proved to possess certain potential that is worth exploring in greater detail in future studies in the field of magnetic reconnection.
Magnetisk rekonnektion påverkar rymdvädret som har en direkt påverkan på våra nutida teknologiska system. Således kan fenomenet ge allvarliga konsekvenser för människor. Forskare inom detta fält tror att elektrondynamiken spelar en viktig roll i magnetisk rekonnektion. Magnetisk rekonnektion är ett ämne som har studerats under lång tid men ännu förblir många aspekter av fenomenet outforskade. Under magnetisk rekonnektion kan elektroner accelereras till höga hastigheter. En stor mängd studier har gjorts angående trajektorierna som dessa elektroner uppvisar och forskare som är aktiva inom detta forskningsområde skulle enkelt kunna bestämma vilken sorts trajektoria en specifik elektron uppvisar givet en grafisk illustration av sagda trajektoria. Att försöka göra detta för ett mer realistiskt antal elektroner manuellt är dock ingen enkel eller effektiv uppgift att ta sig an. Genom användning av Maskininlärningstekniker för att försöka kategorisera dessa trajektorier skulle denna process kunna göras mycket mer effektiv. Ännu har dock inga försök att göra detta gjorts. I denna uppsats gjordes ett försök att besvara hur väl vissa Maskinlärningstekniker presterar i detta avseende. Principal component analysis och K-means clustering var huvudmetoderna som användes, applicerade med olika sorters förbehandling av den givna datan. Elbow-metoden användes för att hitta det optimala K-värdet och kompletterades av Self-Organizing Maps. Silhouette coefficient användes för att mäta resultaten av dessa metoder. Förbehandlingsmetoderna First-centering och Mean-centering gav de två högsta siluett-koefficienterna och uppvisade således de bästa kvantitativa resultaten. Inspektion av klustrarna pekade dock på avsaknad av perfekt överlappning, både mellan klasserna som upptäcktes av de tillämpade metoderna samt klasserna som har identifierats i tidigare artiklar inom fysik. Trots detta visade sig Maskininlärningsmetoder besitta viss potential som är värt att utforska i större detalj i framtida studier inom fältet magnetisk rekonnektion.
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Books on the topic "Machine learning potential"

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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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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. ru: 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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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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U.S. Air Force Enlisted Classification and Reclassification: Potential Improvements Using Machine Learning and Optimization Models. RAND Corporation, 2022. http://dx.doi.org/10.7249/rr-a284-1.

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Book chapters on the topic "Machine learning potential"

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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, 21–27. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_3.

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Mookambal, M. Adithi, and S. Gokulakrishnan. "Potential Subscriber Detection Using Machine Learning." In Advances in Intelligent Systems and Computing, 389–96. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-51859-2_36.

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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, 255–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-69052-8_27.

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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, 37–44. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2592-2_5.

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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, 261–75. Cham: 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, 3–8. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-9379-6_1.

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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, 485–95. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-2594-7_40.

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Hu, Gebiao, Zhichi Lin, Zheng Guo, Ruiqing Xu, and Xiao Zhang. "Research on Potential Threat Identification Algorithm for Electric UAV Network Communication." In Machine Learning for Cyber Security, 649–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-20096-0_49.

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Muazu Musa, Rabiu, Anwar P. P. Abdul Majeed, Norlaila Azura Kosni, and Mohamad Razali Abdullah. "Physical Fitness Parameters in the Identification of High-Potential Sepak Takraw Players." In Machine Learning in Team Sports, 41–48. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3219-1_5.

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Yang, Xianhai, Huihui Liu, Rebecca Kusko, and Huixiao Hong. "ED Profiler: Machine Learning Tool for Screening Potential Endocrine-Disrupting Chemicals." In Machine Learning and Deep Learning in Computational Toxicology, 243–62. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-20730-3_10.

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Conference papers on the topic "Machine learning potential"

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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), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/acroset62108.2024.10743375.

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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), 130–36. IEEE, 2019. http://dx.doi.org/10.1109/datacom.2019.00028.

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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), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752119.

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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), 177–83. IEEE, 2024. https://doi.org/10.1109/prml62565.2024.10779715.

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Patil, Sachin C., Sairam Madasu, Krishna J. Rolla, Ketan Gupta, and N. Yuvaraj. "Examining the Potential of Machine Learning in Reducing Prescription Drug Costs." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10724434.

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P, Dinusha, Subha Sreekumar, and Lijiya A. "Detection of Potential Specific Learning Disabilities in Children through Handwriting Analysis Using Machine Learning." In 2024 IEEE Region 10 Symposium (TENSYMP), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/tensymp61132.2024.10752261.

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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), 252–57. IEEE, 2024. http://dx.doi.org/10.1109/icmlca63499.2024.10754493.

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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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Balyakin, I. A., and A. A. Rempel. "Machine learning interatomic potential for molten TiZrHfNb." In THE VII INTERNATIONAL YOUNG RESEARCHERS’ CONFERENCE – PHYSICS, TECHNOLOGY, INNOVATIONS (PTI-2020). AIP Publishing, 2020. http://dx.doi.org/10.1063/5.0032302.

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Aliod, Carles. "Machine learning the C5H5 potential energy surface." In Proposed for presentation at the Unimolecular reactions Faraday Discussion held June 22-24, 2022 in Oxford, United Kingdom. US DOE, 2022. http://dx.doi.org/10.2172/2003611.

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Reports on the topic "Machine learning potential"

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

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

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

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

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Burton, Simon. The Path to Safe Machine Learning for Automotive Applications. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, October 2023. http://dx.doi.org/10.4271/epr2023023.

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

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This US Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory engineering technical note (CHETN) documents a preliminary study on the use of specialized machine learning (ML) methods to model the variations in groundwater level (GWL) with time. This approach uses historical groundwater observation data at seven gage locations in Wyoming, USA, available from the USGS database and historical data on several relevant meteorological variables obtained from the ERA5 reanalysis dataset produced by the Copernicus Climate Change Service (usually referred to as C3S) at 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, December 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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Alonso-Robisco, Andrés, José Manuel Carbó, and José Manuel Carbó. Machine Learning methods in climate finance: a systematic review. Madrid: Banco de España, February 2023. http://dx.doi.org/10.53479/29594.

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Preventing the materialization of climate change is one of the main challenges of our time. The involvement of the financial sector is a fundamental pillar in this task, which has led to the emergence of a new field in the literature, climate finance. In turn, the use of Machine Learning (ML) as a tool to analyze climate finance is on the rise, due to the need to use big data to collect new climate-related information and model complex non-linear relationships. Considering the proliferation of articles in this field, and the potential for the use of ML, we propose a review of the academic literature to assess how ML is enabling climate finance to scale up. The main contribution of this paper is to provide a structure of application domains in a highly fragmented research field, aiming to spur further innovative work from ML experts. To pursue this objective, first we perform a systematic search of three scientific databases to assemble a corpus of relevant studies. Using topic modeling (Latent Dirichlet Allocation) we uncover representative thematic clusters. This allows us to statistically identify seven granular areas where ML is playing a significant role in climate finance literature: natural hazards, biodiversity, agricultural risk, carbon markets, energy economics, ESG factors & investing, and climate data. Second, we perform an analysis highlighting publication trends; and thirdly, we show a breakdown of ML methods applied by research area.
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de Luis, Mercedes, Emilio Rodríguez, and Diego Torres. Machine learning applied to active fixed-income portfolio management: a Lasso logit approach. Madrid: Banco de España, September 2023. http://dx.doi.org/10.53479/33560.

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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced.
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