The thermal band of a satellite platform enables the measurement of land surface temperature (LST), which captures the spatial-temporal distribution of energy exchange between the Earth and the atmosphere. LST plays a critical role in simulation mode...
The increasing severity of global wildfires requires advanced suppression strategies to mitigate impacts on the environment and human life. This work investigates the applicability of Multi-Agent Reinforcement Learning (MARL) to aerial wildfire suppr...
Multimodal sentiment analysis (MSA) has emerged as a prominent research frontier, enabling a comprehensive understanding of complex human emotions through the synergistic integration of heterogeneous multimodal signals. However, most existing approac...
In science, technology, engineering, arts, and mathematics (STEAM) education, artificial intelligence (AI) analytics are useful as educational scaffolds to educe (draw out) the students’ AI-Thinking skills in the form of AI-assisted human-centr...
It has been proven in research literature that the analysis of encrypted traffic with statistical analysis and machine learning can reveal the type of activities performed by a user accessing the network, thus leading to privacy risks. In particular,...
The black box nature of artificial neural networks limits the understanding of internal mechanisms and processes that happen inside hidden layers. The introduction of deep neural networks and efficient layer-wise training methods has enabled research...
Intelligent fault diagnosis for rotary machinery often suffers performance degradation under domain shifts between training and deployment environments. To address this, we propose a robust fault diagnosis framework incorporating three key components...
Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecular analysis, yet the recognition of mixed spectra remains challenging because severe peak overlap makes mixture-specific data expensive to acquire and difficult to cover exhaust...
This work was conducted within the framework of French university reforms undertaken since 2022. Regardless of learning level and target audience, project-based learning has proved its effectiveness as a teaching strategy for many years. The novelty...
Additive manufacturing processes such as the material extrusion of metals (MEX/M) enable the production of complex and functional parts that are not feasible to create through traditional manufacturing methods. However, achieving high-quality MEX/M p...
Achieving multi-million-hectare commitments from countries around the world to restore degraded lands in resilient and sustainable ways requires, among other things, huge volumes of tree planting material. Seed systems encompassing all forest reprodu...
Path planning for robots based on reinforcement learning encounters challenges in integrating semantic information about environments into the training process. In unseen or complex environmental information, agents often perform sub-optimally and re...
Internet users are benefiting from technologies of abstractive summarization enabling them to view articles on the internet by reading article summaries only instead of an entire article. However, there are disadvantages to technologies for analyzing...
Continuous learning is fundamental to professional and personal growth. Therefore, robust digital solutions are required to support adaptable and scalable educational demands. The Qualifications-Based Learning Model (QBLM) is a framework for qualific...
Many “Industry 4.0” applications rely on data-driven methodologies such as Machine Learning and Deep Learning to enable automatic tasks and implement smart factories. Among these applications, the automatic quality control of manufacturin...
Novel techniques in evolutionary optimization, simulation, and machine learning enable a broad analysis of domains like fluid dynamics, in which computation is expensive and flow behavior is complex. This paper introduces the concept of full domain a...
This paper proposes a methodological architecture to integrate Q-learning into economic modelling systematically. It addresses a common gap: the lack of a shared framework linking economic foundations to Reinforcement Learning components. Rather than...
With the rapid development of AI technologies, digital software, and teaching methods, flipped-learning approaches are becoming increasingly popular for academic education. Next-generation teaching modules will potentially enhance the learning experi...
As the coronavirus disease 2019 (COVID-19) trajectory remains unknown, online learning (OL) has replaced face-to-face teaching strategies in education institutions. Research and training focus on harnessing educators, but less is understood at the st...
Federated learning (FL) utilizes distributed data processing to enable collaborative machine learning model development while safeguarding user privacy. However, the decentralized nature of FL, combined with data heterogeneity, substantially expands...
The rapid integration of Generative Artificial Intelligence (GenAI) technologies into educational environments has generated new opportunities for developing intelligent systems capable of monitoring learner interactions, modeling learning behaviors,...
Structural health monitoring (SHM) plays a pivotal role in ensuring the integrity and safety of critical infrastructure and mechanical components. While traditional non-destructive testing (NDT) methods offer high-resolution data, they typically requ...
Reactive structural adhesives—epoxies, polyurethanes, and acrylics—are essential in high-performance applications, yet their development remains complex due to multiscale adhesion mechanisms, combinatorial formulation spaces, and stringen...
This paper develops a rigorous operator-theoretic framework for learning-augmented quasi-gradient methods in constrained optimization. We consider the minimization of an objective function over a closed convex feasible set, where feasibility is enfor...
In this paper, we present a novel blockchain-enabled approach to opportunistic federated learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates blockchain with OppCL to streamline the learning of autonomous vehicle mod...
Multi-view (MV) data are increasingly collected from various fields, like IoT. The surge in MV data demands clustering algorithms capable of handling heterogeneous features and high dimensionality. Existing feature-weighted MV k-means (MVKM) algorith...
Machine learning models are built to predict the strain values for which edge cracking occurs in hole expansion tests. The samples from this test play the role of sheet metal components to be manufactured, in which edge cracking often occurs associat...
The knowledge of the forces acting on a structure enables, among many other factors, assessments of whether the component’s useful life is compromised by the current machine condition. In many cases, a direct measurement of those forces becomes...
Induction motors are a common component of electromechanical equipment in mining operations, yet they are susceptible to failures resulting from frequent start–stops, overloading, wear and tear, and component failure. It is evident that such fa...
During the last decades, intensive efforts have been devoted to the extraction of useful knowledge from large volumes of medical data employing advanced machine learning and data mining techniques. Advances in digital chest radiography have enabled r...
Semi-Supervised Continual Learning (SSCL) aims to enable deep learning models to learn incrementally from label-scarce streaming data without catastrophic forgetting. Existing SSCL methods face two core issues: severe overfitting to limited labeled s...
A significant trend in education is the increasing recognition of the need to shift from transmissive teaching to incorporating reflexive practices associated with real-life issues in learning, curriculum, and teaching. Merging Information and Commun...
Annotation tools are an essential component in the creation of datasets for machine learning purposes. Annotation tools have evolved greatly since the turn of the century, and now commonly include collaborative features to divide labor efficiently, a...
Aerosol chemical components are critical parameters that influence the atmospheric environment, climate effects, and human health. Retrieving global columnar atmospheric aerosol components from satellite observations provides foundational data and pr...
The recently released DigComp 3.0 framework represents the latest evolution of the European reference model for digital competences, expanding its descriptors, proficiency levels, and learning outcomes. This study applies text-mining and natural lang...
This study presents an advanced methodology for improving electronic assembly quality through real-time, inline inspection utilizing state-of-the-art artificial intelligence (AI) and deep learning technologies. The primary goal is to ensure complianc...
Reinforcement learning (RL) is a subdomain of machine learning concerned with achieving optimal behavior by interacting with an unknown and potentially stochastic environment. The exploration strategy for choosing actions is an important component fo...
1 December 2021
Implementation science (IS) has emerged as an integral component for evidence-based whole system improvement. IS studies the best methods to promote the systematic uptake of evidence-based interventions into routine practice to improve the quality an...
This review highlights recent advances in the application of Bayesian optimization to chemical synthesis. In the era of artificial intelligence, Bayesian optimization has emerged as a powerful machine learning approach that transforms reaction engine...
In a nuclear power plant (NPP), the used tools are visually inspected to ensure their integrity before and after their use in the nuclear reactor. The manual inspection is usually performed by qualified technicians and takes a large amount of time (w...
Machine learning model sharing markets have emerged as a popular platform for individuals and companies to share and access machine learning models. These markets enable more people to benefit from the field of artificial intelligence and to leverage...
The digitalization of shipbuilding processes has become an important trend in modern naval construction, enabling more efficient design, assembly, and maintenance operations. A key aspect of this digital transformation is traceability, which ensures...
Bipartite networks are common in real-world applications, where link prediction helps understand network evolution and make recommendations. Traditional methods have two major limitations; they often ignore edge weight information by treating links a...
The capability of efficiently processing the data streams emitted by nowadays ubiquitous sensing devices enables the development of new intelligent services. Data Stream Processing (DSP) applications allow for processing huge volumes of data in near...
Content Delivery Networks (CDNs) have evolved to meet surging data demands and stringent low-latency requirements driven by emerging applications like high-definition video streaming, virtual reality, and IoT. This paper proposes a hybrid CDN archite...
Concentrating solar thermal (CST) systems are unique among renewable energy options for the ease of integration with thermal energy storage (TES). This enables dispatchable (or continuous) production of electricity, process heat, solar fuels, or othe...
The Internet of Things (IoT) is rapidly advancing toward increased autonomy; however, the inherent dynamism, environmental uncertainty, device heterogeneity, and diverse data modalities pose serious challenges to its reliability and security. This pa...
Testing and application of suitable learning tools and methods can facilitate learning environments conducive to skill acquisition for the target group. This also applies to sports education at school level, including extracurricular activities. On t...
Process automation utilizes specialized technology and equipment to automate and enhance production processes, leading to higher manufacturing efficiency, higher productivity, and cost savings. The aluminum die casting industry has significantly gain...
Hyperspectral image (HSI) classification benefits from rich spectral information; however, high dimensionality of HSI data increases computational cost, noise sensitivity, and the risk of overfitting when labeled samples are limited. Most pretrained...
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