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Electrochemical migration (ECM) is a major concern in modern electronics because miniaturization has reduced insulation distances and electronics are now operated in harsher environments than before. Previous studies on ...

Spiking neural networks (SNNs) offer a biologically inspired, energy-efficient alternative to conventional artificial neural networks (ANNs). However, deep SNNs struggle to process complex EEG signals because their spike...

Efficient current control in boost converters is critical for electric vehicle (EV) battery charging applications, where dynamic operating conditions challenge conventional control strategies. This paper presents a novel...

Driven by satellite remote sensing observations, deep learning models provide a promising solution for the rapid estimation of subsurface thermohaline structures. By learning from large-scale paired surface–subsur...

Ising machines, including quantum annealing machines, are promising next-generation computers for combinatorial optimization problems. However, due to hardware limitations, most Ising-type hardware can only solve objecti...

Timely prediction of Intensive Care Unit (ICU) admission at hospital arrival is critical for clinical decision-making, resource allocation, and patient management, particularly during large-scale health crises such as th...

This study focuses on the explainability of AI operations for diagnosing degradations in cloud applications. Log data can provide essential contextual information on misbehaving applications in monitoring, evaluated agai...

Cybersecurity education often struggles to engage learners while effectively conveying complex web vulnerabilities. This challenge is particularly evident in topics such as cross-site scripting (XSS), where understanding...

Terrestrial evaporation ($E$) plays a crucial role in the water, energy, and carbon cycles and modulates climate change through multiple feedback mechanisms. While process-based models estimate $E$ using satellite-derive...

Zernike moments are well-suited for unmanned aerial vehicle scene matching because of their rotation invariance and robustness to noise. However, their high computational complexity poses challenges for meeting the real-...

This research describes a deep learning-based artificial neural network (ANN) approach for the detection, classification, and localization of series faults (open conductor faults) in power distribution networks. The ANN ...

Botnet communications often rely on Domain Generation Algorithms (DGAs) to establish channels among attack orchestrators and compromised hosts (bots). Our approach leverages on Artificial Intelligence (AI) to differentia...

Mobile Edge Computing (MEC) brings computation close to end-users to support latency-sensitive applications, such as tele-manipulation and autonomous driving. While Network Function Virtualization (NFV) facilitates the d...

Use cases for emerging quantum computing platforms become economically relevant as the efficiency of processing and availability of quantum computers increase. We assess the performance of Restricted Boltzmann Machines (...

Mobility-as-a-Service (MaaS) orchestration faces critical challenges in managing the Service Quality Index (SQI), Fleet Utilization Ratio (FUR), and Dynamic Pricing Index (DPI) under uncertain and time-varying urban dema...

Modern edge–cloud deployments integrate devices, edge servers, private networks, and cloud platforms into heterogeneous, multi-layer systems. Each layer exposes distinct metrics, measurement granularities, and tim...

Federated Prompt Learning (FPL) enables efficient collaborative adaptation of large-scale models, but it remains vulnerable to data heterogeneity: locally optimized prompts may become biased, thereby weakening the aggreg...

During the golden-time phase of urban disasters, emergency response systems must issue timely directives that remain compliant with jurisdictional policies and regulations, even when early human reports precede reliable ...

Accurate velocity estimation plays a key role in ensuring precise motion control and safe navigation of ground robots. Ground-view event cameras provide a reliable sensing modality for this task. They exploit the static ...

Unsupervised anomaly detection identifying both structural and logical anomalies is essential for modern industrial manufacturing. Although multi-score frameworks have been proposed, existing methods tend to reuse identi...

Adaptive learning systems require effective mechanisms to model students’ evolving learning states and provide personalized learning path recommendations. Traditional rule-based approaches are limited in capturing...

The remarkable success of large language models (LLMs) in code generation has transformed software development, with systems like Codex, CodeGen, and StarCoder demonstrating strong capabilities in synthesizing functional...

The growing demand for intensive artificial intelligence applications, such as deep neural networks and Large Language Models (LLMs), has driven the creation of architecture extensions and accelerators for matrix multipl...

Artistic style transfer has long been possible with the advancements of convolution- and transformer-based neural networks. Most algorithms apply the artistic style transfer to the whole image, but individual users may o...