In this paper we propose a framework for automated forecasting of energy-related time series using open access data from European Network of Transmission System Operators for Electricity (ENTSO-E). The framework provides forecasts for various European countries using publicly available historical data only. Our solutio…
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40 results for “open-access”
Hierarchical NMF organizes COVID-19 literature into a searchable tree.
problem Organizing and searching for relevant COVID-19 research.
method Hierarchical nonnegative matrix factorization.
result Identified 8 major and 52 subtopics related to COVID-19.
Study creates open-access wildfire dataset for Russia.
problem Data scarcity for comprehensive Eurasian wildfire research.
method Machine learning for exploratory data analysis and predictive modeling.
result Identified key environmental factors influencing fire behavior.
Prototype real-world RL environment for robotics training.
problem Lack of common real-world RL benchmark.
method Developed OffWorld Gym with open access to real-world robotics environments.
result Baseline results in navigation task on real and simulated terrain.
Forbidden knowledge in machine learning -- Reflections on the limits of research and publicationcs.LG
Paper discusses ethical norms for machine learning to prevent misuse.
problem Harmful misuse of machine learning applications.
method Proposes review parameters for ethical framework.
result Ethical guidelines for sharing sensitive machine learning information.
Generative model learns functional vector fields for pharmacokinetics.
problem Generating accurate virtual cohorts and forecasting patient trajectories without manual tuning.
method Prior-Fitted Functional Flows model, learning functional vector fields conditioned on sparse, irregular data.
result State-of-the-art predictive accuracy on real-world datasets.
We explicitly test if the reliability of credit ratings depends on the total number of admissible states. We analyse open access credit rating data and show that the effect of the number of states in the dynamical properties of ratings change with time, thus giving supportive evidence that the ideal number of admissibl…
We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous genera…
3D dataset for intracranial aneurysms aids deep learning applications.
problem Lack of 3D medical datasets for deep learning.
method Developed an open-access 3D dataset, IntrA, for intracranial aneurysms.
result Demonstrated the challenges and performance of 3D medical datasets.
Editorial discusses nine challenges in modern algorithmic trading.
problem Challenges in modern algorithmic trading and controls.
method Discussion of challenges without proposing solutions.
result No specific new results or findings.
ATOL vectorizes measures for topological learning, separating clusters of persistence diagrams.
problem Challenges in applying topological information to machine learning frameworks.
method A fast, unsupervised vectorization method for measures in Euclidean spaces.
result Successfully discriminates important space regions in persistence diagrams.
Terrorism has become one of the most tedious problems to deal with and a prominent threat to mankind. To enhance counter-terrorism, several research works are developing efficient and precise systems, data mining is not an exception. Immense data is floating in our lives, though the scarce availability of authentic ter…
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to design an effective system that, given a patient's medical history, is able to predict probable future conditions. Previous works, mostly c…
Hydrodynamics principles applied to finance, solving complex market models.
problem Understanding financial market dynamics using physics principles.
method Unified mathematical framework using Kelvin waves to solve differential and pseudo-differential equations.
result Solved various financial models including volatility and variance swaps.
Develops a fair post-processing method for student success predictions.
problem Ensuring fairness in predictive student models for educational applications.
method Uses the MADD metric to improve model fairness while maintaining accuracy.
result Successfully improved fairness of predictive models for student success.
Enhanced word embedding creates new consumer-friendly health terms.
problem Laymen's health terms are often jargon and hard to understand.
method Developed an enhanced GloVe word embedding technique to generate new consumer-friendly terms.
result New CHV terms generated from consumer-generated text.
Deep learning applied to biological data mining.
problem Mining complex biological data from diverse sources.
method Artificial neural networks, deep learning architectures.
result Deep learning techniques improve pattern recognition in biological data.
Method predicts rarity of image features to support research integrity investigations.
problem Difficulty in determining if image reuse is by chance or intentional.
method Statistical estimation of ORB features' chance occurrence across PubMed Open Access Subset dataset.
result The method produces decreasingly smaller p-values for more complex imagery, supporting null hypothesis.
Study provides long-term EMG data for multi-day biometric authentication.
problem Limited long-term EMG data for multi-day biometric authentication.
method Collected EMG data from 43 participants over three days.
result Multi-day biometric authentication results in low error rates.
The paper compares ML models for credit scoring and investment decisions using explainable AI.
problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.
The paper evaluates and benchmarks electricity price forecasting models.
problem Lack of rigorous evaluation methods and open datasets.
method Literature review, cross-market comparison, open datasets, and python toolbox.
result Best practices for electricity price forecasting are proposed.
Researchers develop a new method for analyzing shape changes in medical data.
problem Analyzing continuous transformations like shape changes in medical datasets.
method Geodesic regression using an affine connection setting on Lie groups.
result Efficient fixed point algorithm for computing geodesic relationships.
Magnetic particle imaging (MPI) data is commonly reconstructed using a system matrix acquired in a time-consuming calibration measurement. The calibration approach has the important advantage over model-based reconstruction that it takes the complex particle physics as well as system imperfections into account. This be…
Widely-used public benchmarks are of huge importance to computer vision and machine learning research, especially with the computational resources required to reproduce state of the art results quickly becoming untenable. In medical image computing, the wide variety of image modalities and problem formulations yields a…
Develops a new method for optimizing with uncertain data.
problem Uncertainty in real-world optimization problems.
method Combines chance constraints and constraint learning for mixed-integer linear optimization.
result Data-driven solution for setting probabilistic bounds on learned constraints.
Study combines intra-risk and contagion risk for SME bankruptcy prediction.
problem Predicting bankruptcy risk of SMEs considering both intra-risk and contagion risk.
method Proposes a novel model using Graph Neural Networks to combine intra-risk and contagion risk.
result Model outperforms state-of-the-art methods in bankruptcy prediction.
Paper compares ML methods for credit scoring, highlighting feature selection and scaling impacts.
problem Determining default risk in credit scoring models.
method Eight ML methods (SVM, Naive Bayes, DT, RF, XGBoost, KNN, MLP, LR) with feature selection and scaling.
result Feature selection and scaling improve model performance in credit scoring.
This paper enhances credit risk management using explainable AI techniques.
problem Lack of transparency and explainability in AI models for credit risk management.
method Implement LIME and SHAP for explaining ML-based credit scoring models.
result Demonstrates practical challenges and solutions for XAI methods in finance.
Model predicts sepsis early with high accuracy.
problem Early detection of sepsis to reduce mortality.
method MGP-AttTCN: Gaussian Process and attention-based deep learning.
result Model outperforms state-of-the-art with AUROC 0.660 and AUPR 0.483.
LU-Net improves cardiac segmentation accuracy and robustness.
problem Robustness and accuracy of deep learning cardiac segmentation.
method Multi-task end-to-end network designed to improve cardiac segmentation.
result Outperforms current best deep learning solution, reducing outliers and improving clinical indices.
This paper represents a preliminary (pre-reviewing) version of a sublinear variational algorithm for isotropic Gaussian mixture models (GMMs). Further developments of the algorithm for GMMs with diagonal covariance matrices (instead of isotropic clusters) and their corresponding benchmarking results have been published…
Study investigates OOD generalization methods for mechanics problems.
problem Real-world mechanics problems with unknown test environments and data distribution shifts.
method Investigates OOD generalization methods for regression problems in mechanics.
result OOD generalization methods perform better than traditional ML methods on mechanics-specific regression problems.
Topolow embeds dissimilarity data into Euclidean space robustly against non-metricity and sparsity.
problem Embedding dissimilarity data into Euclidean space when dissimilarities are non-metric or sparse.
method Topolow uses a physics-inspired, gradient-free optimization framework to maximize likelihood under a Laplace error model.
result Topolow outperforms standard MDS methods in reconstructing sparse and non-Euclidean data.
New statistical guarantee improves conformal predictors for small datasets.
problem Uncertainty quantification for small datasets in surrogate models.
method Proposed a new statistical guarantee for conformal predictors, converging to standard CP for large datasets.
result The new guarantee offers relevant information about coverage for small data sizes, improving applicability.
Neural network improves breast cancer diagnosis with high accuracy.
problem Improving accuracy in breast cancer diagnosis.
method Higher-order probabilistic perceptron (HOPP) model.
result HOPP model achieves up to 97% accuracy in classifying breast cancer tumors.
GAS-Norm improves deep learning time series forecasting in non-stationary settings.
problem Deep learning models struggle with non-stationary time series data.
method Combines GAS model for adaptive normalization with deep neural networks.
result Improves deep learning performance in 21 out of 25 settings.
GANs help create realistic synthetic health data, boosting medical research.
problem Challenges in creating realistic synthetic health data due to private patient data.
method Generative Adversarial Networks (GANs) to learn and produce synthetic health data.
result GANs can produce realistic synthetic health data, overcoming challenges in OHD.
A new system recommends specific knowledge concepts in MOOCs based on student interests.
problem MOOCs recommend courses but ignore specific knowledge concepts interests.
method End-to-end graph neural network (ACKRec) that combines content and context information.
result ACKRec effectively recommends knowledge concepts to MOOC students.
We considered observational data available from the MIMIC-III open-access ICU database and collected within a study period between year 2002 up to 2011. If a patient had multiple admissions to the ICU during the 30 days before death, only the first stay was analyzed, leading to a final set of 6,436 unique ICU admission…
Study uses satellite and lidar data to map forest height and biomass in France.
problem Mapping forest resources and carbon in large areas.
method Machine learning approach using Sentinel-1, Sentinel-2, ALOS-2, and GEDI Lidar data.
result High-resolution maps of forest height and biomass produced with good accuracy.