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Reliable beat-level arrhythmia detection from electrocardiogram (ECG) signals is essential for continuous patient monitoring. However, many existing deep learning models remain computationally expensive and are sensitive...

With the rapid expansion of the digital music industry, streaming platforms are required to efficiently and accurately classify massive music collections to support personalized recommendation, content organization, and ...

Rich in multiple strategic rare metals, deep-sea manganese nodules constitute an essential mineral resource for balancing terrestrial resource supply and demand and facilitating the global energy transition. Accurate pre...

Manual monitoring of intravenous (IV) therapy is labor-intensive and susceptible to human error, posing risks to patient safety. Existing computer vision-based approaches have addressed either drip rate estimation or flu...

Abstract For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusio...

Abstract In an elastic network paradigm, where the transceiver is able to control several characteristics of the transmitted signal according to the optical link quality and capacity demand, receivers able to automatical...

Abstract An automatic markerless knee tracking and registration algorithm has been proposed in the literature to avoid the marker insertion required by conventional computer-assisted knee surgery, resulting in a shorter ...

The Global Flood Awareness System (GloFAS) is a promising flood-modelling tool available for most river basins worldwide. However, it contains inherent biases, notably linked to its spatial resolution. This study propose...

This paper introduces CONV_GBERT, a hybrid deep learning framework for robust sentiment classification. The model integrates the bidirectional contextual understanding of BERT, the generative capabilities of GPT, and the...

User populations in modern recommendation systems consist of diverse communities with distinct and often non-overlapping preferences. However, most existing models, such as traditional Variational Autoencoders (VAEs), op...

This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quant...

Brain–computer interfaces (BCIs) based on motor imagery (MI) provide a transformative direct communication pathway for patients with severe motor disabilities. However, the high inter-subject variability and non-statio...

Customer churn prediction is essential across data-driven industries such as insurance, digital banking, e-commerce, and subscription platforms, where retaining existing customers is typically more cost-effective than ac...

Infrastructure construction and maintenance is very critical in supporting a robust and sustainable transportation system. The primary goal of transportation infrastructure is to ensure safe and efficient movement. Howev...

The present study focuses on proposing various statistical models, such as linear regression (LR), nonlinear regression (NLR), and artificial neural network (ANN), to forecast the compressive strength of environmentally ...

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...

- In this study, we investigate the potential of using state-of-the-art neural network architectures to increase the accuracy in classification of Quranic recitations per verse. Using a dataset of more than 4000 audio re...

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 ...

Abstract The primary objective of this study is to develop and validate robust data-driven models for accurately predicting bacterial growth inhibition induced by cerium oxide nanoparticles across different bacterial str...

Introduction. Reliable and accurate state estimation plays a central role in mobile robotics, ensuring effective localization, navigation, and control in uncertain and dynamic environments. Traditional estimation methods...

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...

Measurements from global navigation satellite system reflectometry (GNSS-R) have proven effective for applications in sea surface height (SSH). Deep learning-based methods for spaceborne GNSS-R SSH retrieval can effectiv...

Abstract This model is highly helpful in thermal management and complex energy systems, particularly where precise control over heat and mass transfer is required. Solarized nanofluids can be used to improve heat absorpt...