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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...

Power transformers are critical components of power systems, and their safe and reliable operation is essential for both industrial and residential applications. Real-time condition monitoring and early fault detection a...

Coronal mass ejections (CMEs) are eruptions originating from the solar corona, usually carrying a large amount of fast-flowing plasma into interplanetary space, accompanied by an enhanced magnetic field. CMEs can form sh...

Wildfires are among the most destructive natural disasters and pose significant risks to ecosystems, infrastructure, and human lives. This study focuses on wildfire susceptibility mapping in Los Angeles County, a region ...

Language classification systems are used to classify spoken language from a particular phoneme sample and are usually the first step of many spoken language processing tasks, such as automatic speech recognition (ASR) sy...

Abstract Due to the complexity of substation systems and the diversity of data sources, potential inaccuracies or inconsistencies in data may compromise the accuracy of intelligent detection. To address this issue, an in...

To increase the storage capacity in holographic data storage (HDS), the information to be stored is encoded into a complex amplitude. Fast and accurate retrieval of amplitude and phase from the reconstructed beam is nece...

INTRODUCTION: Accurate power quality disturbances (PQDs) classification is critical for maintaining grid stability and reliability in modern power systems. However, existing deep learning methods predominantly rely on si...

Abstract Single-image dehazing is essential for safety-critical vision systems, yet existing CNN-based methods are often too heavy for edge devices or fail to adapt to spatially varying haze density. This paper introduce...

Abstract Accurate classification of phonocardiogram (PCG) signals is vital for the early detection of cardiovascular diseases (CVDs), a leading cause of global mortality. However, conventional machine learning and deep l...

Abstract Background Musculoskeletal diseases, such as Juvenile Idiopathic Arthritis (JIA), can lead to altered joint mobility and changes in gait pattern. While marker-based motion capture systems are considered the gold...

ABSTRACT: In recent years, the use of computing has increased along with medical skills, and this had impressive results in terms of classification and treatment, in addition to facilitating the matter of medical personn...

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...

Abstract Vision Transformers have demonstrated strong performance across diverse visual tasks; however, their high computational cost due to long token sequences remains a critical challenge. Sequence reduction via pooli...

"We propose the use of convolutional neural networks (CNN) for counting and positioning people in visible and infrared images. Our data set is made of semi-artificial images created from real photographs taken from a dro...

Recent advances in computer vision and deep learning, particularly Convolutional Neural Networks (CNNs), have significantly increased road safety. CNNs were used in this work to automatically detect and categorise traffi...

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...

Buildings serve as a key indicator of human land use, and building change detection plays a crucial role in urban growth and shrinkage monitoring, land use planning, and urban development assessment and management. Curre...

Conventional adversarial perturbations on traffic sign images often induce noticeable color shifts and abnormal contrast compared to natural images, rendering them susceptible to detection, particularly by Vision Transfo...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...

Abstract Upper-limb prosthesis users usually lack comprehensive feedback or touch. This significantly hinders their ability to interact with their environment and lead normal lives. This research involves the development...

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 Accurate monitoring and prediction of Chlorophyll-a (Chl-a) concentrations are critical for protecting desalination systems from algal blooms. This study presents an advanced framework employing the Graph Neural...

The need for early and precise identification of abnormalities has made the detection and classification of brain tumours essential components of medical diagnosis. Because brain tumours are naturally complex and can hav...