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The management and operation of the power system would be significantly enhanced if accurate prior knowledge of power exchanges were provided, which could provide valuable insights into the system and help predict the re...

Abstract Marine habitat maps are essential tools for marine spatial planning, providing information for decision‐making in conservation and resource management. Accurate classification of benthic habitats supports thei...

IntroductionReliable classification of lung nodules from computed tomography (CT) images remains a challenging problem due to variations in image intensity, noise, and unstable feature representations during deep network...

BackgroundRecently, plant disease detection and classification have become major concerns in agriculture. Early detection of plant diseases supports farmers to take precautionary actions to prevent the spread of infectio...

Background For those with hearing loss, sign language (SL) is an essential communication tool. The most common and basic type of SL is American SL (ASL). ASL plays a vital role in various communicative contexts, includin...

In this research, we address a critical problem for strawberry growers in Lima: the management of phytosanitary diseases. Traditionally, these farmers relied on time-consuming, imprecise, and error-prone visual observati...

IntroductionEarly and accurate diagnosis of skin cancer has a huge impact on the survival rate of patients and deep learning-based CNN and dermatologists' intelligence support can fasten the diagnosis not only within the...

Abstract Advances in artificial intelligence (AI) and multi-omics integration are reshaping precision oncology by enabling deeper mechanistic understanding, improved characterization of tumor heterogeneity, and the accel...

Stellar population measurements in integral field unit surveys are often limited by low signal-to-noise ratios (S/Ns) in low-surface-brightness spaxels. Using controlled synthetic experiments, we investigate whether a de...

Abstract Underwater imaging suffers from various challenges such as color distortion, low contrast, and reduced visibility caused by light attenuation. Addressing these issues is crucial for improving the performance of ...

With the rapid advancement of online education, students’ sentiment feedback serves as a pivotal factor in enhancing course quality and refining pedagogical strategies. However, conventional sentiment analysis approach...

Abstract The current work on emotion recognition considering EEG signals has attracted significant attention due to its advantages in fields like mental health, neural computing, and human-computer interaction. Nonethele...

Biotic and abiotic stresses reduce crop yields, yet existing image‑based diagnostic models are often crop‑specific, computationally intensive, and poorly interpretable. This study present: (i) a multi-crop diagnostic...

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...

Abstract In the world of the Internet of Things (IoT), wireless sensor networks (WSNs) play a vital role in enabling seamless communication between connected devices. In a WSN data prediction system based, clustering and...

Downscaling is a critical step to bridge the gap between large-scale climate information and local-scale impact assessments. However, steep and complex mountainous terrain induces highly heterogeneous local-scale meteoro...

The effect of imbalanced data on the optical character recognition of Braille text is investigated by applying two techniques to a set of convolutional neural network image classification models. A multilabel classificat...

The rapid growth and increasing complexity of Internet of Things (IoT) networks require efficient real-time monitoring and anomaly detection mechanisms. Traditional machine learning approaches often struggle to handle th...

Abstract Graph Neural Networks have emerged as a powerful paradigm for artificial intelligence driven drug discovery, offering molecular representation learning that surpasses many conventional approaches. Traditional ex...

Abstract Training deep learning models requires substantial financial and human resources, so once deployed in untrusted environments, these models immediately attract the attention of attackers who seek to steal and mis...

ABSTRACT Deep learning profoundly impacts various areas, such as face recognition and language translation. Owing to the increasingly high computational costs of training neural architectures, it is intractable to manual...

As a key element of human settlements and fundamental features in maps, buildings play a critical role in urban planning and environmental monitoring. Automatically extracting buildings from remote sensing images is of s...

Remote sensing enables urban planners to gain macroscopic insights into urban structures and issues. Pan-sharpening plays a key role by merging multispectral and panchromatic images, combining high spatial resolution wit...

To achieve an optimal trade-off between processing efficiency and dimensional precision in the assisted electrode electrochemical discharge machining (AE-ECDM) of thermal barrier coating (TBC)-coated superalloys, a 1D co...