Forest wildfires are one of the most catastrophic natural disasters, which poses a severe threat to both the ecosystem and human life. Therefore, it is imperative to implement technology to prevent and control forest wildfires. The combination of ...

Subatomic particle track reconstruction (tracking) is a vital task in High-Energy Physics experiments. Tracking is exceptionally computationally challenging and fielded solutions, relying on traditional algorithms, do not scale linearly. Machine ...

This paper focuses on the hypothesis of optimizing time series predictions using fractal interpolation techniques. In general, the accuracy of machine learning model predictions is closely related to the quality and quantitative aspects of the ...

Deep-learning (DL) performs well in computer-vision and medical-imaging automated decision-making applications. A bottleneck of DL stems from the large amount of labelled data required to train accurate models that generalise well. Data scarcity and ...

This work uses machine learning to produce synthetic data of wave energy converters from time-expensive 3D simulations based on computational fluid dynamics models. The simulations to analyse the response of these systems to incoming waves are ...

• We propose a surrogate model for Wave Energy Converters Computational Fluid Dynamics.
• Generative models are used to create unseen scenarios of velocity and dynamic viscosity responses.
• Two different generative models are employed ...

Environmental conservation and biodiversity monitoring efforts have been greatly enhanced by the use of deep learning and computer vision technologies, particularly in protected areas such as parks and wildlife reserves. However, the development ...

Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, ...

Training machine learning models to identify human behavior is a difficult yet essential task to develop autonomous and adaptive systems such as smart homes. These models require large and diversified amounts of labeled data to be trained effectively. Due ...

This paper focuses on the creation and evaluation of synthetic data to address the challenges of imbalanced datasets in machine learning applications (ML), using fake news detection as a case study. We conducted a thorough literature review on generative ...

Data sharing among different institutions represents one of the major challenges in developing distributed machine learning approaches, especially when data is sensitive, such as in medical applications. Federated learning is a possible solution, ...

• Survival analysis prediction with a distributed learning framework.
• Synthetic time-to-event data generation through generative Bayesian Network.
• Privacy-preserving distributed learning for patients’ clinical data.
• Alternative ...

Terrain traversability estimation is a fundamental task for supporting robot navigation on uneven surfaces. Recent learning-based approaches for predicting traversability from RGB images have shown promising results, but require manual annotation ...

The 3D comprehension ability of indoor environments is critical for robots. While deep learning-based methods have improved performance, they require significant amounts of annotated training data. Nevertheless, the cost of scanning and ...

• Three synthetic point cloud datasets are created, each 10x larger than real data.
• Learning from synthetic data reduces reliance on annotated real data.
• Domain discrepancies affect the transfer of knowledge from synthetic to real ...

Synthetic data generation has been a growing area of research in recent years. However, its potential applications in serious games have yet to be thoroughly explored. Advances in this field could anticipate data modeling and analysis, as well as ...

• Simulator tailored for generating synthetic data in decision-based serious games.
• Modular architecture to easily fit other serious games and decision making scenarios.
• We use Bayesian networks to combine expert knowledge and data ...

Person re-identification (Re-ID) has long been a pressing challenge in the field of computer vision, with researchers primarily focusing on issues such as occlusion, clothing changes, and cross-modality scenarios. However, there has been a lack of ...

Recognition of human actions using machine learning requires extensive datasets to develop robust models. Nevertheless, obtaining real-world data presents challenges due to the costly and time-consuming process involved. Additionally, existing ...

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• NOVAction engine automatically generates massively diverse human action data.
• NOVAction23, created by NOVAction, novel dataset of human action data.
• Features 25,415 unique synthetic human action sequences with poses and labels.

Training robots in real-world environments can be challenging due to time and cost constraints. To overcome these limitations, robots can be trained in virtual environments using Reinforcement Learning (RL). However, this approach ...

• The increasing usage of robots cause the increase of programming time to adapt the arbitrary shapes of new products.

The objective of this paper is to study the impact of limited datasets on deep learning techniques and conventional methods in semantic image segmentation and to conduct a comparative analysis in order to determine the optimal scenario for ...

Machine Learning methods require a huge amount of data to train. Real world constraints and missing labels hinder the assimilation of large data sets and therefore limit these methods. A common applied solution is the generation of synthetic ...

• Novel conditional residual autoencoder method to extract functions and patterns.
• Synthetic time series data generation by manipulating latent features.
• Time series GAN method to generate interpretable features for synthetic data.

Increasing amount of cyber-attacks in past few years have necessitated the development of automatic threat detection systems for ensuring network and data security. Such automated threat detection models for handling cyber threats are subjected to ...

The challenge of mixed-integer temporal data, which is particularly prominent for medication use in the critically ill, limits the performance of predictive models. The purpose of this evaluation was to pilot test integrating synthetic ...

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• Addresses gap in modeling mixed-integer temporal data for ICU medication.
• Novel application of synthetic data integration to ICU medication data.
• Uses synthetic data to enhance the model's performance of fluid overload prediction.